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Vulnerability and Adaptation to Climate
Variability and Change in Western
China
A Final Report Submitted to Assessments of Impacts and
Adaptations to Climate Change (AIACC), Project No. AS 25
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Vulnerability and Adaptation to Climate
Variability and Change in Western
China
A Final Report Submitted to Assessments of Impacts and
Adaptations to Climate Change (AIACC), Project No. AS 25
Submitted by Yongyuan Yin
International Earth System Sciences Institute (ESSI), Nanjing University,
People’s Republic of China
2006
Published by
The International START Secretariat
2000 Florida Avenue, NW
Washington, DC 20009 USA
www.start.org
Contents
About AIACC………………………………………………………………………….page viii
Summary Project Information…………………………………………………………page ix
Executive Summary…………………………………………………………………….page xi
1
Introduction
1
1.1 AN INTEGRATED ASSESSMENT (IA) A PPROACH ................................................................................................... 1
1.1.1 Climate change and socio-economic scenarios................................................................................................ 2
1.1.2 Identifying present-day climate impacts and vulnerability ............................................................................. 3
1.1.3 Multi-criteria evaluation .................................................................................................................................... 3
1.2 THE H EIHE RIVER BASIN ....................................................................................................................................... 3
2
Characterization of Current Climate and Scenarios of Future Climate Change
5
2.1 ACTIVITIES CONDUCTED ........................................................................................................................................ 5
2.2 DESCRIPTION OF SCIENTIFIC METHODS AND DATA IN SPECIFYING CLIMATE SCENARIOS ................................. 5
2.2.1 Characteristics of climate changes in Northwest China in recent 50 years.................................................... 5
2.2.2 Projections of future climate change over West China in the 21st century .................................................. 13
2.2.3 Projection of climate change over Northwest China by using the regional climate model (RegCM_NCC).
.......................................................................................................................................................................... 16
3
Socio-Economic Futures
26
3.1 ACTIVITIES CONDUCTED ...................................................................................................................................... 26
3.2 DESCRIPTION OF SCIENTIFIC METHODS AND DATA............................................................................................ 26
3.2.1 Population and urbanization scenario ............................................................................................................. 26
3.2.2 Economic growth scenarios ............................................................................................................................ 26
3.3 DATA AND RESULTS ............................................................................................................................................. 27
3.4 CONCLUSIONS ....................................................................................................................................................... 31
3.5 MAIN REFERENCES ............................................................................................................................................... 32
4
Impacts and Vulnerability
33
4.1 ACTIVITIES CONDUCTED ...................................................................................................................................... 33
4.2 DESCRIPTION OF SCIENTIFIC METHODS AND DATA............................................................................................ 33
4.2.1 Resource system vulnerability to climate stresses in the Heihe Basin of China.......................................... 34
4.2.2 Vulnerability assessment of water resource system in Heihe River Basin under climate variation and
change .............................................................................................................................................................. 50
4.2.3 Ecosystem vulnerability assessment under climate variation and change ................................................... 63
4.2.4 Agricultural vulnerability assessment in the Heihe River Basin .................................................................. 73
4.2.5 Land Degradation in the Heihe River Basin in Relation to Climate Conditions ......................................... 75
5
Adaptation
82
5.1 ACTIVITIES CONDUCTED ...................................................................................................................................... 82
5.1.1 Adaptation activity one.................................................................................................................................... 82
5.1.2 Adaptation activity two ................................................................................................................................... 82
5.2 DESCRIPTION OF SCIENTIFIC METHODS AND DATA, RESULTS, CONCLUSIONS ................................................ 83
5.2.1 Adaptation measures evaluation to reduce water system vulnerability to climate change in Heihe River
Basin ................................................................................................................................................................ 83
5.2.2 Activity two: adaptive options under climate change in Zhangye City region............................................ 94
6
Capacity Building Outcomes and Remaining Needs
97
6.1 INTRODUCTION ..................................................................................................................................................... 97
6.2 MAIN ACTIVITIES IN RELATION TO CAPACITY BUILDING .................................................................................. 97
6.2.1 AS25 participated in the AIACC Kick-Off Workshop, United Nations Environment Programme
Headquarters, Nairobi, Kenya, 11-15 February 2002................................................................................... 97
6.2.2 Participation in the Scientific and Technical Advisory Panel (STAP) of the Global Environment Facility
(GEF) Expert Group Workshop on Adaptation and Vulnerability, 18-20 Feb. 2002, UNEP Headquarters,
Nairobi, Kenya ................................................................................................................................................ 98
6.2.3 AIACC training workshop on Development and Application of Integrated Scenarios in Climate Change
Impacts, Adaptation and Vulnerability Assessments ................................................................................... 98
6.2.4 Multi-Stakeholder Workshop and Training, 26th ~29th August 2002, Lanzhou, China .............................. 98
6.2.5 The AIACC Asia and the Pacific Regional Workshop ................................................................................. 99
6.2.6 International Adaptation Science Conference, May 2004, Lijiang, China ................................................100
6.2.7 Dr. Yin was invited as a key note speaker to the C5 Training Workshop .................................................100
6.2.8 AS25 project progress review meeting at the Cold and Arid Region Environmental and Engineering
Research Institute (CAREERI) of Chinese Academy of Science (CAS) in Lanzhou City, 22-23 Feb.
2005 ...............................................................................................................................................................100
6.2.9 Dr. Yin participated in the AIACC Vulnerability Synthesis Workshop, 7-13 March 2005, Bellagio, Italy .
........................................................................................................................................................................101
6.2.10The AS25 session at the International Symposium on Arid Climate Change and Sustainable
Developments (ISACS) Conference............................................................................................................101
6.2.11The
Third
AS25
Project
Research
Team
Workshop
........................................................................................................................................................................101
7
National Communications, Science-Policy Linkages and Stakeholder Engagement
7.1
7.2
7.3
7.4
8
INTRODUCTION ...................................................................................................................................................102
THE FIRST AS25 PROJECT STEERING COMMITTEE, EXPERT COMMITTEE AND RESEARCH TEAM WORKSHOP,
25 NOVEMBER 2003, BEIJING ............................................................................................................................102
THE SECOND AS25 PROJECT COMMITTEES AND RESEARCH TEAM WORKSHOP, BEIJING, 28-29 NOVEMBER
2004 ....................................................................................................................................................................103
POLICY I MPLICATIONS AND FUTURE DIRECTIONS ............................................................................................103
Outputs of the Project
8.1
8.2
8.3
8.4
8.5
102
105
INVITED PRESENTATIONS & WORKSHOPS – INTERNATIONAL OUTREACH......................................................105
PUBLICATIONS – REFEREED ...............................................................................................................................105
PUBLICATIONS – BOOKS AND CHAPTERS ..........................................................................................................106
PUBLICATIONS – CONFERENCE PROCEEDINGS (REFEREED) .............................................................................106
OTHER PUBLICATIONS .......................................................................................................................................106
List of Tables
Table 2.1: Linear trends of the temperature and rainfalls of the Heihe River Basin in each season from
1961 to 2004 ............................................................................................................................................................. 12
Table 2.2: Brief descriptions of the models and designs of experiments ............................................................. 13
Table 2.3: Annual mean temperature changes as projected by the AOGCMs ensembles with SRES A2 and
B2 for each decade along the line of Qing-Zang Railway for the first 50 years of the 21st century
(relative to 1961-1990) (unit: °C) ......................................................................................................................... 15
Table 2.4: Annual mean precipitation changes as projected by the AOGCMs ensembles with SRES A2
and B2 for each decade along the line of Qing-Zang Railway for the first 50 years of the 21st century
(relative to 1961-1990) (unit: %) .......................................................................................................................... 15
Table 2.5: Seasonal mean Pr variability in 2020 (units: %)...................................................................................... 19
Table 2.6: Seasonal mean Pr variability in 2030 (units: %)...................................................................................... 19
Table 2.7 Seasonal mean Ts change over NW China in 2030 (units: ºC).............................................................. 21
Table 2.8: Seasonal mean Ts change over SW China in 2030 (units: ºC) .............................................................. 21
Table 3.1a: Socio-economic data for the Heihe River Basin (2000) (Economic unit: US$ ’000) ....................... 27
Table 3.1b: Agricultural land use (unit: x 10,000) ..................................................................................................... 27
Table 3.2: Heihe region economic structure and labor structure in 2000 ............................................................ 28
Table 3.3: The results of population and urbanization projects for three cities in Heihe River Basin (Unit:
Population: 10,000 people; Urbanization %) .................................................................................................... 29
Table 3.4: Heihe River Basin GDP and three economic sector productivity scenarios..................................... 30
Table 4.1: Potential climate and other variables and resource vulnerability indicators................................... 36
Table 4.2: Current water withdrawal ratio in the Heihe River Basin (1991-2000) ............................................. 39
Table 4.3: Sensitivity matrix of water resource system to climate change in Heihe Basin .............................. 51
Table 4.4: Adaptive capacity of water resource system to climate in Heihe River Basin................................. 53
Table 4.5: Classification of vulnerability to climate variation ................................................................................ 53
Table 4.6: Average runoff composing of the stimulated result of Yingluoxia Hydrometric Station in future
40 years..................................................................................................................................................................... 57
Table 4.7: Water availability of various regions in the Heihe River Basin under climate change in the
future 40 years (108m3) .......................................................................................................................................... 58
Table 4.8: Water demands in Heihe River Basin under climate change scenario in the future 40 years
(108m3)....................................................................................................................................................................... 60
Table 4.9: Water supply and demand balance in Heihe River Basin under climate change in the future 40
years (108m3)............................................................................................................................................................ 61
Table 4.10: Water scarcity ratios in Heihe River Basin under climate change in the future 40 years (%) .... 62
Table 4.11: Sensitivity and adaptability indicators, weights and data sources .................................................. 64
Table 4.12: Standardized ecosystem vulnerability classes...................................................................................... 65
Table 4.13: Ecosystem vulnerability in Heihe River Basin in 2000........................................................................ 66
Table 4.14: NPP in Heihe river basin when climate change in future 40 year.................................................... 69
Table 4.15: NPP and HANPP of the middle reaches in Heihe River Basin, 2000 ............................................. 71
Table 4.16: HANPP of Heihe River Basin under climate change in future 40 year........................................... 72
Table 4.17: Agricultural sensitivity and adaptive capacity indicators and weights ......................................... 74
Table 4.18: Agricultural sensitivity classes of sensitivity levels ............................................................................ 74
Table 4.19: Agricultural adaptive capacity levels or classes................................................................................... 75
Table 4.20: Climate vulnerability classes for agricultural production ................................................................. 75
Table 4.21: Current climate vulnerability classes for agricultural production in Zhangye ............................. 75
Table 5.1: Decision tools applicable to multiple sectors .......................................................................................... 86
Table 5.2: Methods reviewed......................................................................................................................................... 87
Table 5.3: Identified adaptation options to reduce water vulnerabilities to climate stress ............................. 90
Table 5.4: Indicators used for evaluating adaptation options in the case study ................................................ 91
Table 5.5: AHP comparison table: water system....................................................................................................... 92
Table 5.6: Overall rank and score of adaptation options in the Heihe region .................................................... 93
Table 5.7: Adaptive options assessment results in Zhangye City ......................................................................... 96
List of Figures
Fig. 1.1: Flow-chart showing the research structure of the project ......................................................................... 2
Fig. 1.2: Map of the Heihe River Basin with approximate population distribution shown in shades of grey
(black is higher population density) .................................................................................................................... 4
Fig. 2.1: Distributions of linear trend coefficients of the daily mean temperature (Unit: °C/yr) in
Northwest China from 1951 to 2004. The shaded areas denote exceeding the 95% significant level ... 6
Fig. 2.2: Distributions of linear trend coefficients of extreme warm events (2a, Unit: day/yr) and extreme
cold events (2b, Unit: day/yr) in Northwest China from 1951 to 2004. The shaded areas denote
exceeding the 95% significant level...................................................................................................................... 7
Fig. 2.3: Distributions of linear trend coefficients of rainfall percentage (Unit: %/yr) in Northwest China
from 1951 to 2004 (Unit: time/yr). The shaded areas denote exceeding the 95% significant level........ 7
Fig. 2.4: Distributions of linear trend coefficients of rainfall intensity (4a, Unit: mm/yr) and rain day
number (4b, Unit: day/yr) in Northwest China from 1951 to 2004. The shaded areas denote
exceeding the 95% significant level...................................................................................................................... 8
Fig. 2.5: Distributions of linear trend coefficients of extreme rainfall events in Northwest China from 1951
to 2004 (Unit: time/yr). The shaded areas denote exceeding the 95% significant level ........................... 8
Fig. 2.6: Distributions of temperature and rainfall anomalies in West China in three periods ...................... 10
Fig. 2.7: Numbers of stations in the Heihe River Basin in each year from 1951 to 2004................................... 11
Fig. 2.8: Temperature anomaly series of the Heihe River Basin from 1961 to 2004 (Unit: °C). The real line is
the linear trend and the formula is the linear regression equation............................................................. 11
Fig. 2.9: Rainfall anomaly series of the Heihe River Basin from 1961 to 2004 (Unit: mm). The real line is the
linear trend and the formula is the linear regression equation.................................................................... 11
Fig. 2.10: Geographical distributions of the annual mean temperature anomalies for about 2050 relative to
1961-1990 as projected by the AOGCMs ensemble with SRES A2 (left) and B2 (right) over Northwest
China (Unit: °C)...................................................................................................................................................... 14
Fig. 2.11: The simulation and observation of Pr over NW China (a) and SW China (b) (units: mm/d) ...... 17
Fig. 2.12: The simulation and observation of Ts over NW China (a) and SW China (b) (units: ºC) .............. 17
Fig. 2.13: Annual mean Pr change over western China in the future(relative to 1961-1990) for the years of
2010 (a), 2020 (b) and 2030 (c) (units: mm/d) .................................................................................................. 18
Fig. 2.14: Annual mean Ts change over western China in the future(relative to 1961-1990)for the years of
2010 (a), 2020 (b) and 2030 (c) (units: ºC) .......................................................................................................... 20
Fig. 2.15: daily mean temperature change in NW China in 2030 (units: ºC)....................................................... 20
Fig. 2.16: Annual mean Pr difference between reforestation experiment and control run (RE-CTRL) (units:
mm) ........................................................................................................................................................................... 23
Fig. 2.17: Mean Ts difference between reforestation experiment and control run (RE-CTRL) (units: ºC) (a)
winter (DJF) (b) summer (JJA)............................................................................................................................. 24
Fig. 2.18: Annual mean wind difference near the ground between reforestation experiment and control
run (RE-CTRL) (units: m/s)................................................................................................................................. 24
Fig. 4.1: Flow-chart showing the general research approach ................................................................................. 35
Fig. 4.2a: Trend in growing season Palmer drought severity index (PDSI) for lower reach of the study
basin .......................................................................................................................................................................... 40
Fig. 4.2b: Trend in growing season PDSI for middle reach-lower part of the study basin.............................. 40
Fig. 4.2c: Trend in growing season PDSI for middle reach-upper part of the study basin ............................. 40
Fig. 4.2d: Trend in growing season PDSI for upper reach of the study basin .................................................... 40
Fig. 4.3: Trend of the events of water-use conflicts (number of violent events fighting for water) in the
study basin .............................................................................................................................................................. 41
Fig. 4.4: Areas with high-irrigation water demand (The negative units are in millimeters of deficit) ......... 42
Fig. 4.5: Per capita water resources (in cubic meters per capita, annually). Areas of low value (dark colors)
indicate a high demand for water resources not available through local supply. The break points are
taken from values in Feitelson and Chenoweth (2002).................................................................................. 43
Fig. 4.6: Map showing areas of relatively high vulnerability to adverse weather conditions ........................ 46
Fig. 4.7: Histogram of composite water use vulnerability levels in the Heihe River Basin............................ 47
Vertical axis is frequency of grid cell values.............................................................................................................. 47
Fig. 4.8: Schematic structure of one subwatershed in the HBV-96 model (Lindström et al., 1997) with
routines for snow (top), soil (middle) and response (bottom) ..................................................................... 55
Fig. 4.9: Water shortage ratios for various municipalities in Heihe River Basin in the future 40 years ....... 62
Fig. 4.10: Distribution of ecosystem vulnerability in Heihe River Basin (2000) ................................................. 66
Fig. 4.11: General research scheme of agricultural vulnerability assessment..................................................... 73
Fig. 4.12: The distribution of the stations in the Basin ............................................................................................. 77
Fig. 4.13: The land degradation map of Heihe River Basin in 1985 on the left, and 2000 on the right.......... 78
Fig. 4.14: The results of the NPP model in 1985 on the left, and 2000 on the right............................................ 79
Fig. 5.1: Multi-criteria adaptation options evaluation system ............................................................................... 89
About AIACC
Assessments of Impacts and Adaptations to Climate Change (AIACC) enhances capabilities in the
developing world for responding to climate change by building scientific and technical capacity,
advancing scientific knowledge, and linking scientific and policy communities. These activities are
supporting the work of the United Nations Framework Convention on Climate Change (UNFCCC) by
adding to the knowledge and expertise that are needed for national communications of parties to the
Convention.
Twenty-four regional assessments have been conducted under AIACC in Africa, Asia, Latin America and
small island states of the Caribbean, Indian and Pacific Oceans. The regional assessments include
investigations of climate change risks and adaptation options for agriculture, grazing lands, water
resources, ecological systems, biodiversity, coastal settlements, food security, livelihoods, and human
health.
The regional assessments were executed over the period 2002-2005 by multidisciplinary, multiinstitutional regional teams of investigators. The teams, selected through merit review of submitted
proposals, were supported by the AIACC project with funding, technical assistance, mentoring and
training. The network of AIACC regional teams also assisted each other through collaborations to share
methods, data, climate change scenarios and expertise. More than 340 scientists, experts and students
from 150 institutions in 50 developing and 12 developed countries participated in the project.
The findings, methods and recommendations of the regional assessments are documented in the AIACC
Final Reports series, as well as in numerous peer-reviewed and other publications. This report is one
report in the series.
AIACC, a project of the Global Environment Facility (GEF), is implemented by the United Nations
Environment Programme (UNEP) and managed by the Global Change SysTem for Analysis, Research
and Training (START) and the Third World Academy of Sciences (TWAS). The project concept and
proposal was developed in collaboration with the Intergovernmental Panel on Climate Change (IPCC),
which chairs the project steering committee. The primary funding for the project is provided by a grant
from the GEF. In addition, AIACC receives funding from the Canadian International Development
Agency, the U.S. Agency for International Development, the U.S. Environmental Protection Agency, and
the Rockefeller Foundation. The developing country institutions that executed the regional assessments
provided substantial in-kind support.
For more information about the AIACC project, and to obtain electronic copies of AIACC Final Reports
and other AIACC publications, please visit our website at www.aiaccproject.org.
viii
Summary Project Information
Regional Assessment Project Title and AIACC Project No.
Vulnerability and Adaptation to Climate Variability and Change in Western China (AS 25)
Abstract
With barriers such as extremely fragile ecological conditions, severe water shortage, few financial
resources, poor infrastructure, low levels of education, and little access to technology and
markets, Northwestern China has been suffering from climate variations and will experience
severe impacts of climate change on food production, water resources, and ecosystem health. In
this project, an integrated approach was developed for identifying regional vulnerabilities to
climate variations and change, and for prioritizing adaptation options to deal with climate
change vulnerability. Different methods were employed to form the integrated approach,
including surveys, workshops, multi-stakeholder consultation, ecological simulation modeling,
geographical information system (GIS), remote sensing, and multi-criteria decision-making. The
project addresses the following questions: 1) How vulnerable is Western China to current climate
variations and future climate change in key sectors? 2) What can the vulnerabilities of these key
sectors to present climate variations teach us about future vulnerability? and 3) What are the
desirable adaptation options to deal effectively with future climate change? The project has
engaged key Chinese stakeholders at national and local levels. It has also improved the scientific
capacity in the region and suggested practical adaptation options and/or policies to effectively
handle climate change impacts and to enhance regional sustainable development.
Project website: http://essi.nju.edu.cn/AIACC/website/index.htm
Administering Institution
International Earth System Sciences Institute (ESSI), Nanjing University, Nanjing, People’s
Republic of China 210093
Participating Stakeholder Institutions
Beijing, China: The Office of National Climate Change Coordination Committee (NCCCC) of
National Development Reform Commission (NDRC/NDRC), Ministry of Finance, China GEF
Office, China Meteorology Administration (CMA), Ministry of Science and Technology (MOST),
Ministry of Water Resources, State Environmental Protection Administration (SEPA) and Chinese
Academy of Agricultural Sciences.
Gansu Province, China (The study region): Bureau of Gansu Environmental Protection, Bureau of
Water Conservancy, Bureau of Zhangye Environmental Protection, Forest Bureau of Zhangye
Prefecture, CAREERI/CAS, Lanzhou University, and Northwest Normal University.
Countries of Primary Focus
People’s Republic of China
Case Study Areas
Heihe River Basin of northwestern China
Sectors Studied
Agriculture (crop production), water resources, and ecosystem.
Systems Studied
Food security, water shortage, land use conflicts, and ecosystem health.
Groups Studied
Groups: water resource managers, planners, farmers, women, minority people.
Involved stakeholders: state government agencies, provincial government, and communities
ix
Sources of Stress and Change
a) Primary sources: Climate change, climate variability, population growth and economic growth
b) Secondary sources: Land use change, water shortage, land degradation, desertification
Project Funding and In-kind Support
AIACC: US$185,000 grant plus funding for attending international training courses and
workshops; Contributions from the U.S., Canadian, and Chinese partner institutions: US$238,000;
USAID through START: US$15,000.
Investigators
Principal Investigator: Dr. Yongyuan Yin, International Earth System Sciences Institute (ESSI),
Nanjing University. Contact information: Adaptation and Impacts Research Division (AIRD),
Environment Canada, and Institute for Resources, Environment and Sustainability (IRES),
University of British Columbia (UBC), Room 442, 2202 Main Mall, Vancouver, B.C. Canada V6T
1Z4; Telephone: (604)822-1620; Fax: (604)822-9250; Email: [email protected]
Other Investigators: Professor Cheng, Guodong, Co-Principal Investigator, Cold and Arid
Regions Environmental and Engineering Research Institute (CAREERI), Chinese Academy of
Science (CAS), Lanzhou, PR China 730000; Professor Ding, Yihui, Project Coordinator, National
Climate Center (NCC), China Meteorological Administration (CMA). NO.46, Zhongguancun Nan
Dajie, Beijing 100081; Xu Ying, NCC; Li Qiaoping, NCC; Liu Yiming, NCC; Wang, Zenya, NCC;
Ersi Kang, CAREERI; Xu Zhongmin, CAREERI; Long Aihua, CAREERI; Peng Gong, ESSI;
Shuming Bao, ESSI; Jiaguo Qi, ESSI; Zhang Jin, Project Manager, CMA; and Wang Qin, Project
Manager, ESSI.
x
Executive Summary
Research problem and objectives
The AS25 project is a part of the Assessments of Impacts of and Adaptation to Climate Change in
Multiple Regions and Sectors (AIACC) project. The AS25 project developed various methods and/or
tools for land and water system vulnerability assessment.
“Who can feed China’s 1.5 billion people in the near future?” has been a major concern for world leaders.
For example, from late 2003, the price of soybeans on the world food market has doubled as a result of
China’s purchase of 13 percent of the world’s soybeans supply. For similar reasons, the price of rice has
gone up by 80 percent and that of wheat has increased as well. China is a major player in the world grain
market and large part of the grain demand comes from China. There is therefore an increasing concern
globally about the impacts of climate change on food security. In this connection, the purpose of the
project is to develop an integrated approach (IA) for identifying regional vulnerabilities to climate
variations and change, and for prioritizing adaptation options to deal with climate change vulnerability.
The study region is the Heihe River Basin in northwestern China and the key sectors included in the
research project are: food supply, water resources, land use conflicts, and ecosystem deterioration. Heihe
River Basin includes predominantly arid and semi-arid areas in the north and is dominated by mountains
in the south. With barriers such as extremely fragile ecological conditions, fewer financial resources, poor
infrastructure, lower levels of education, and lesser access to technology and markets, the region has been
suffering from the effects of climate variations and may experience severe impacts of climate change on
food production, water resources, and ecosystem health. Moreover, the region’s adaptive capacity is
lower than in the coastal region of China. People in this region face substantial and multiple stresses,
including rapidly growing demands for food and water, large populations at risk of poverty and
infectious diseases, degradation of land and water quality, and other issues that may be amplified by
climate change.
Approach
The research project adopts an integrated assessment (IA) approach, which provides a research
framework to integrate climate change scenarios, socio-economic scenarios, current climate vulnerability
identification, climate change impact assessment, sustainability indicator specification, adaptation option
evaluation, and multi-stakeholder participation. The IA provides an effective means for the synthetic
assessment of climate vulnerabilities and evaluation of the general performance levels of a set of
adaptation options through a multi-criteria and multi-stakeholder decision making process. The research
project contributes to science on regional vulnerability assessment and adaptation evaluation. Different
computer modeling and non computer-based methods were adopted to form the integrated approach.
These include survey, workshops, community engagement, multi-stakeholder consultation, general
circulation models (GCMs) and regional climate model (RCM), ecological simulation modeling, GIS,
remote sensing, and multi-criteria decision making (MCDM).
Scientific findings
Since its official beginning in August 2003 (the GEF Focal Point for China endorsed the AS25 project on
August 25, 2003), the research project has been examining, the extent to which three key sectors in the
Heihe River Basin are vulnerable to climate variations and change; the potential environmental and socioeconomic impacts of climate variations and change; and adaptation options desirable to deal with climate
vulnerabilities. In particular, the study has accomplished the following:
•
The project has successfully developed an integrated assessment (IA) approach to identify the
societal vulnerabilities to climate change scenarios. The approach as we have applied it,
integrates climate and socio-economic scenario setting, climate change impact assessment,
vulnerability identification, adaptation option evaluation, multi-criteria decision-making, and
multi-stakeholder participation.
xi
•
The IA was applied in the study region to assess current and future climate vulnerability and
risks, and to prioritize a number of adaptation options, which could be undertaken to reduce
vulnerabilities associated with climate variation and change.
•
The IA approach was also applied in the Heihe River Basin to evaluate a number of adaptation
options that could be undertaken to reduce vulnerabilities associated with climate change in the
arid and semi-arid region and communities of the study area.
•
The research effort has included a series of workshops and policy surveys with participation by a
broad range of public and private stakeholders, to identify sustainability indicator priorities, as
well as a series of desirable adaptation policies. The IA framework facilitated the participation of
regional stakeholders in climate change impact and adaptation option evaluation.
•
The study has improved our understanding of the interactions between regional sustainability
and climate change impacts.
•
The findings of the project have suggested desirable and practical adaptation options and/or
plans to effectively handle climate change impacts and to ensure sustainable development.
•
When conducting the research, thirteen graduate students and five local scientists from the study
region got train to design and apply IA methods in a real world context. And
•
The product of the research project is this final report submitted to the AIACC. Additional efforts
have been placed to publish peer reviewed many journal articles to provide scientific information
to other parts of the world.
Major findings of the project are summarized below
Climate change trend and scenarios
Recent climate change trend in northwest (NW) China for the past 50 years was investigated by analyzing
the temperature and rainfall from 1951 to 2004. The main results are:
1.
The daily mean temperature in northwest China increased significantly, with its linear trends in
most areas ranging from 0.2ºC/10year (yr) to 0.4ºC/10year, especially in Xinjiang, Qinghai and
Inner Mongolia.
2.
The change trends of both the daily maximum temperature and the daily minimum temperature
are similar to that of the daily mean temperature in the last 50 years. The increase of the
minimum temperature was most significant, while that of the maximum temperature was least
significant.
3.
The extreme warm events increased considerably in the last 50 years.
4.
Rainfall in areas west of 102.5E in NW China increased significantly, with the maximum linear
trends reaching 15%/10yr, while in areas east of 102.5E decreased. Both, rain days and the
rainfall intensity affected the change of rainfall amounts, with the latter being the main factor.
Rainfall increased most in the summer. In addition, extreme rainfall events increased
considerably in areas west of 102.5E.
5.
Around 1965, NW China showed a weak decreasing trend in temperature and a significant
increasing trend in rainfall. Then it changed abruptly in 1987 or so from a cold-dry state to a
warm-wet state in areas west of 102.5E including Xinjiang, Qinghai and the northern Gansu
province, while it changed from a cold-wet state to a warm-dry state in areas east of 102.5E
including the eastern Inner Mongolia, Shanxi and southern Gansu.
6.
From 1961 to 2003, the temperature in the Heihe River Basin increased significantly. The linear
trends were greater than the average for both China and NW China. Rainfalls in this study region
showed an increasing trend weaker than that of NW China.
Based on eight coupled global atmospheric and oceanic circulation models (AOGCMs), the climate
change projection over West China for the 21st century was calculated by the NCC/IAP T63 (National
Climate Center/Institute of Atmospheric Physics). The emission scenarios took account of the
anthropogenic greenhouse gases (GHG) emission and GHG plus sulfate aerosol (GS) increases, as well as
xii
IPCC SRES A2 (high emission) and B2 (medium emission). The anomalies of both temperature and
precipitation were relative to the 30 year mean of 1961~1990. Major projections are:
•
An obvious warming over West China in the 21st century would very likely occur, especially over
NW China. As an example, the temperature anomalies for SRES scenarios over West China might
increase by 1.0~2.5ºC by about 2050. The warming over West China might be higher than the
mean warming for the whole country.
•
The annual mean temperature change around 2050 relative to 1961~1990 would be 3.5~6.5ºC for
A2 and 2.5~4.5ºC for B2 over NW China.
•
The precipitation over most parts of West China might increase in the first 50 years of the 21st
century, especially in NW China. The precipitation might increase by about 5~30% for A2 and
5~25% for B2 by about 2050 relative to 1961~1990.
In addition, a nested regional climate model (RCM) was used to investigate climate change over western
China for the future 30 years with SRES A2 emission scenario. The change trends and features of
precipitation (P) and temperature (T) are:
•
Precipitation will slightly decrease over NW China in 2020. The distribution of P will reverse in
2030. Precipitation is expected to increase over NW China and parts of southwest (SW) China
with weaker annual change.
•
Air surface temperature will keep on increasing for the entire western region (especially in
summer), with annual mean increase value of 0.4ºC, which is lower than eastern China.
•
Along with the increase of the daily mean T, Tmax and Tmin will increase consistently. Due to
the larger Tmin increases than Tmax, the daily range of Ts is expected to decrease in the future.
Climate vulnerability assessment
By using vulnerability indicators, the climate vulnerability of the study region under current climate
conditions was investigated. The methods for the compilation of indicators, geographic allocation, and
synthesis of resource system vulnerability were carried out. The application results indicate the relative
vulnerability levels of land and water systems in different areas exposed to current climate stimuli. Key
climate vulnerabilities in the region include:
•
One important water vulnerability indicator is water withdrawal ratio defined as the ratio of
average annual water withdrawal to water availability. The water withdrawal ratios in the Heihe
River Basin under current climate conditions are extremely high (83%~ 125%), far exceeding the
critical threshold levels set by both WMO and Chinese government.
•
The Palmer drought severity index (PDSI) trends in growing season for lower and middle
reaches of the Heihe River Basin showed that the study areas have become drier in the past
decade. This trend would continue under the changing climate.
•
The trend of water use conflict in the study basin has been increasing in the past decade. The
trend of this social indicator suggests that water shortage in growing season becomes more and
more serious because of decreased water supply and increasing population and per capita water
use.
•
The water shortage vulnerability in Heihe River Basin ranked from the most vulnerable to the
least invulnerable for nine counties in the region. Climate change will have different impacts on
water system vulnerability in nine counties of the basin.
•
Resource system vulnerability was also assessed through the application of geographic
information system (GIS) for mapping the indicators. It provides information on the geographical
distribution of current climate vulnerability levels in different parts of the study region. Various
vulnerability maps show the relative vulnerability levels of water and land resources in different
areas exposed to current climate stimuli. A map of composite indicators representing the
vulnerability of both agricultural and domestic water users to climate stresses in the form of long
hot and dry spells was also generated to identify areas of high vulnerability in the region as a
whole.
xiii
•
A PhD thesis focusing on climate change impacts on the agricultural sector was completed as
part of the project. Under climate change conditions, periods of drought are likely to become
more frequent and severe. Land degradation and arable land loss problems, as well as limited
water supplies restrict present agricultural production and threaten future food security of the
region. Agricultural production vulnerability to climate change also showed a spatial disparity
among the nine counties in the region.
•
Another PhD student at Nanjing University used NOAA’s Advanced Very High Resolution
Radiometer (AVHRR) to measure the Normalized Difference Vegetation Index (NDVI), which
showed the density of green vegetation over the region. Results of the NDVI and land
degradation map indicate that the most severely affected region is the northern part of the basin,
while conditions are better in the south. A large amount of forest land was converted to
croplands from 1985 to 2000 in Qilian Mountain and Hexi Corridor, while the area of grasslands
decreased. Urban and built-up areas and barren areas increased in the north.
•
A third PhD thesis deals with climate change and arid and semi-arid ecosystem health in the
Heihe River Basin. The results indicated that the ecosystem of Heihe River Basin is very
vulnerable, with various degrees of vulnerability in different parts of the region. The most
vulnerable ecosystem is located in the lower reach of the basin with unmanaged grassland under
extreme arid conditions, which is extremely sensitive to climate change. For the middle and
upper reach regions, the rank of ecosystem vulnerability degree improves gradually.
•
Compared to the year of 2000, the pressure on ecosystems in the Heihe River Basin will increase
significantly in 40 years. The rate of the Human Appropriation of Net Primary Productivity
(HANPP) will surpass 50% (dangerous level) even under the best social economy scenario.
Moreover, in some areas of the study region, the HANPP rate will overshoot the system collapse
limit.
Adaptation Options Evaluation
Following a method designed by Yohe and Tol (2002), the AS25 project assessed the potential
contributions of various adaptation options in improving adaptive capacities of water resource systems.
The method uses adaptive capacity determinants to evaluate alternative adaptation options.
The results of the adaptation policy evaluation indicate that the feasibility of adopting technical and
engineering adaptation practices is relatively low. These options include expanding sprinkle, trickle,
pipeline irrigation, building reservoir upstream and increasing exploitation of groundwater. These option
are hard to adopt due to difficulties in obtaining considerable capital support. And farmers and water
resource managers are reluctant to invest in these engineering solutions that present high financial risks.
On the contrary, water-saving practices such as cropping and cultivation structure adjustments are more
feasible because of relatively small capital requirements.
The rank ordering of all water adaptation options evaluated by using the Yohe & Tol (2002) method is as
follows (from the most desirable and effective to less effective): adjusting crop structure; adopting more
effective water-saving irrigation plans; preventing water leakage from irrigation channels; reducing land
surface evaporation using plastic film and crop straw coverage; conserving soil moisture by deep plow
method; adopting drought-tolerant crop varieties; expanding more advanced irrigation techniques
including sprinkle, drip irrigation, and low-pressure irrigation pipe lines; building new reservoirs in up
reach area to regulate flow distribution; and increasing groundwater exploitation.
The AS25 researchers also applied an analytic hierarchy process (AHP) method, a multi-criteria decision
making (MCDM) technique, as an adaptation evaluation tool to identify the priorities of evaluation
criteria and to rank desirability of alternative adaptation measures. The results indicated that reforming
the economic structure was ranked the most desirable adaptation option for the Heihe River Basin. The
option of establishing farm water users’ society also scored fairly high. The moderate performance levels
for improved water allocation policies, establishing water permits and trade, and increasing awareness
and education options were due to the fact that these were relatively new measures in water resource
management in the study region. The scores for applying water saving equipment and technologies and
implementing water price system options were ranked near the bottom of the list by most participants
(especially from an economic perspective) and were not considered to be desirable adaptation options. It
appears that regional stakeholders consider these two options as expensive alternatives for dealing with
watershed management and farmers do not want experience higher water prices. Constructing water
xiv
works option was judged to be the most inefficient option from an economic perspective, and it was
ranked at the bottom overall among regional respondents.
Capacity building outcomes and remaining needs
One main objective of the AS25 project was to enhance the regional capacity of conducting climate
vulnerability and adaptation assessment. Building scientific capacity building was a primary concern of
the project. The project provided training to enable local decision makers and multi-stakeholders to
understand the linkages between climate change and sustainability. The regional climate change impact
and adaptation study was undertaken by local scientists in partnership with U.S. and Canadian experts.
This improved local scientific capacity and provided expertise available in Canada and the U.S.
Specifically, the project contributed to enhancing scientific capacity in the following ways:
•
Improved understanding of the interactions between regional sustainability and climate change
(All the activities of AS25 project).
•
Trained young scientists and graduate students to design and apply IA methods in a real world
context (About twenty young scientists and PhD students participated in the activities of the
project). Young scientists were trained during the course of implementing the project in
conducting research activities, organizing and attending workshops, applying various models,
and conducting householder surveys for adaptation options evaluation.
•
The project also involved multi-stakeholders and local experts in many project activities (A
training workshop was held in Lanzhou, in August 2002).
•
Many farmers were interviewed individually and asked to complete a survey in a one-on-one
interview or in a small group workshop-type setting in Heihe region.
•
Thirteen PhD and Master students worked on the project and among them three theses were
mainly derived from research activities of the project.
•
The AS25 project partnered with the Canada-China Cooperation in Climate Change (C5) Project
funded by the Canadian International Development Agency (CIDA). The China’s Office of the
National Climate Change Coordination Committee (NCCCC) of the National Development and
Reform Commission (NDRC), and the Chinese Academy of Agricultural Sciences (CAAS) held an
International Adaptation Conference entitled Climate Change: Building the Adaptive Capacity: An
International Conference on Adaptation Science, Management and Policy Options, May 17-19, 2004,
Lijiang, Yunnan Province, China.
•
A training course was arranged in CAREERI, Lanzhou, October 2004 to improve graduates’ skill
in conducting vulnerability and adaptation assessment.
•
The AS25 project helped CMA in organizing an international symposium on arid climate change
and sustainable development (ISACS), which was held in Lanzhou, May 23-24, 2005. An AS25
project session was included in the ISACS Conference.
National Communications, science-policy linkages and stakeholder
engagement
The AS25 project was an interdisciplinary study and took an approach that required multi-stakeholder
participation. The project activities included workshops, survey, and community engagement methods,
which were employed to involve multiple stakeholders, policymakers, and experts in the study process.
During the course of implementing the project, the research team built committed partnerships with
multi-stakeholders at national, provincial and local level. An essential part of the stakeholder engagement
strategy of this project was the establishment and participation of the Chinese Steering Committee and
Technical Committee consisting of key government agencies and experts responsible for China’s
international cooperation on climate change issues and national communications.
The Steering Committee (SC) included the following government agencies: National Climate Change
Coordinating Office of National Development and Reform Commission (NCCCC/NDRC), Chinese
Meteorology Administration (CMA), China GEF Office, State Environmental Protection Administration
(SEPA) and the two PIs of AS25 project. The SC also facilitated the integration of AS25 study participants
and results into China’s national communications. The Expert Committee (EC) consisted of experienced
xv
experts who had experience from previous GEF climate change projects and provided their skill and data
to the AS25 project.
•
The main interaction between AS25 project and China’s National Communications was in
involving Chinese government officials and experts responsible for preparing China’s NC.
•
There have been many opportunities for the AS25 project to contribute to the national
communications. Since the executing agencies and key experts responsible for China’s NC are
partners of the project (AS25 project Steering and Expert Committee leaders and members), the
AS25 project results can make an useful contribution to China’s NC.
•
The AS25 project held three project and committee workshops. All key members of the Steering
and Expert Committees participated in the workshops and provided their suggestions and
advice. The NCCCC is the Chinese agency responsible for leading China’s National
Communication Report.
•
Ms. Sun Cuihua and Prof. Lin Erda, two key persons responsible for the Chinese National
Communication Report preparation, were invited to attend the AIACC Asia-Pacific Regional
Workshop in Manila, Nov. 2004 to present China’s climate change policies and National
Communication report.
•
In addition, Dr. Yin was involved in the Canada-China Cooperation on Climate Change (C5)
project, which consisted of a “National Communication” component.
Policy implications and future directions
Working in partnership with local, provincial and national governments and other key stakeholders
(water use professionals, farmers, and other organizations), the study identified alternative effective
adaptation measures that could become practical options to deal with water vulnerabilities which would
likely become more severe in the study region due to the impacts of climate change. A properly
developed and implemented adaptation action plan consisting of various effective measures could have
positive benefits for the well-being and productivity of all people living in the region.
These effective adaptation measures can help reduce water resource vulnerability and water use conflicts.
Since water is the key determinant which influences all the economic activities and livelihoods of the
region, a reduction in water resource vulnerability will mitigate the impacts of climate change on the
agricultural sector and protect the livelihood of farmers. Water system sustainability can also improve
ecosystem health and reduce sandstorms, which have created a global environmental impact. The study
has generated the information for decision-makers to improve the adaptive capacity of resource systems
to cope with climate risks in Heihe River Basin. The project also:
•
Conducted policy surveys of alternative adaptation options or measures that were expected to
reduce water resource risks from climate change in the study region;
•
Prioritized alternative adaptation measures and identified desirable adaptation options that
could help the water infrastructure in the study region to cope with climate stresses;
•
Improved local capacity for climate risk assessment and adaptation evaluation; and
•
The AS25 research team has also published journal papers and a textbook that introduces a wide
range of research approaches, methods, and tools for assessing climate-change impacts,
vulnerabilities, and adaptation.
As a reasonable follow up, the AS25 project team prepared a concept paper entitled “ Adaptation
Actions to Reduce Water System Vulnerability to Climate Change in Heihe River Basin” which
will be submitted to ACCCA for consideration. A successful pilot adaptation action plan could become a
useful model for communities across the study region to reduce climate risks and rural poverty, and thus
to improve livelihood in poor regions. The follow up study will recommend steps in implementing
effective adaptation measures in the region to enhance regional sustainability.
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1 Introduction
The AS25 project, being carried out in northwestern China with financial support from the Global
Environmental Facility (GEF), is one of 24 such projects taking place in developing countries around the
world under the global project “Assessments of Impacts and Adaptations to Climate Change (AIACC)”.
The purpose of this study is to provide decision-makers with the information needed to reduce resource
vulnerability and to improve the adaptive capacity of the region in order to cope with climate change in
the Heihe River Basin of northwestern China.
The Heihe Basin located in northwestern China includes predominantly arid and semi-arid areas with
extremely fragile ecological conditions, few financial resources, poor infrastructure, low levels of
education, and restricted access to technology and markets. The region suffers from climate variations
and may experience more severe impacts of climate change on water resources, food production, and
ecosystem health in the future. Moreover, the region’s adaptive capacity is lower than that in the coastal
region of China. People in the region are facing substantial and multiple stresses, including rapidly
growing demands for food and water, poverty, land degradation, and other issues that may be amplified
by climate change.
While the demands for resources increase as populations and economies grow, the availability and the
inherent functions of natural resources are being reduced by water pollution, land degradation, and
climate stresses. For example, the fight over access to water resources in the Heihe River Basin has led to
disputes, confrontation, and many cases of violent clashes. The growing resources vulnerability has
posed a big challenge for government agencies to implement more effective resource management
policies.
Under climate change conditions, periods of drought are likely to become more frequent and severe, and
water shortages might increase water use conflicts. Land degradation problems and limited water
supplies have already restricted present agricultural production and threatened the food security of the
region. Climate change might cause negative impacts on food production in the region.
As the poorest region in a developing country, northwestern China is not well adapted to climate and
there is abundant evidence in terms of the crop and livestock losses from climate variations and extreme
weather events. Many areas of the region have been experiencing droughts, poverty and economic losses
associated with these droughts. Northwestern China needs the development and application of
environmental risk assessment and adaptation evaluation methods to deal with issues related to climate
change vulnerabilities, adaptation, and sustainable development. However, lack of knowledge and
scientific capacity in the region has become barriers to conducting such studies. In this respect, the AS25
project has enable the region by improving science capacity and assessment tools and information aimed
at the most vulnerable sectors. The study focused assessment of climate change impacts on economic
sectors and ecological systems sensitive to climate. These include water, agriculture and fragile grassland
ecosystems.
The project also enhanced the regional capacity of integrated assessment (IA). Science capacity building
was a primary concern of the project. The regional climate change impact and adaptation study was
undertaken by local scientists in partnership with the U.S. and Canadian experts. This improved local
scientific capacity and provided expertise available in Canada and the U.S.
1.1 An Integrated Assessment (IA) Approach
The AS25 project adopted an integrated assessment (IA) approach which combines computer modeling
and non-model based methods including a series of training workshops, policy survey, expert judgment,
community engagement, multi-stakeholder consultation, ecological simulation modeling, geographic
information system (GIS), remote sensing, and multi-criteria decision making.
The project addressed the following questions:
1.
2.
How vulnerable is the Heihe River Basin in north-western China to current and future climate stresses in
some sensitive sectors?
What can the vulnerabilities of these key sectors teach us about future vulnerability? and
1
3.
What are the desirable adaptation options to deal effectively with future climate changes?
The integrated assessment framework facilitated the participation of regional stakeholders in the whole
process. Training workshops were undertaken by as many local scientists as possible. Figure 1.1 shows
the general approach of the study.
Climate Scenarios
Socio-Economic Scenarios
1
2
Climate change impact and vulnerability assessment
3
Existing and potential
adaptation measures
Sustainability indicators
Desirable adaptation options
Multiple stakeholders, planners, analysts, and public
Domain of the multi-criteria adaptation options evaluation system
4
Fig. 1.1: Flow-chart showing the research structure of the project
1.1.1 Climate change and socio-economic scenarios
The project begins with a careful study of present-day climate conditions, impacts and stresses to provide
a baseline for assessing societal vulnerabilities to climate change. Three types of scenarios have been
specified: climate change, future socio-economic conditions, and adaptation options. The climate change
scenario represents possible future climatic conditions with various assumptions. Changes in population,
income, technology, consumption rates, and China’s new Western Region Development Strategy, have
been taken into account in developing the socio-economic scenarios.
2
1.1.2 Identifying present-day climate impacts and vulnerability
Component three of the project (Figure 1.1) can be divided into two parts. While part one examines
present-day climate impacts of various key sectors in the region, part two identifies societal
vulnerabilities to future climate change scenarios. Results of part one establish a base that can be used to
measure progress toward reducing vulnerability to future climate change. Once these measurements are
identified for each economic, ecological, and social vulnerability indicator, they can be applied to project
vulnerabilities of the sensitive sectors to future climate change scenarios.
1.1.3 Multi-criteria evaluation
There is a need for new research approaches and tools that can evaluate alternative adaptation strategies
or policies, which many impact assessment methods cannot do. IPCC (2001) suggests a list of high
priorities for narrowing gaps in vulnerability and adaptation research. Among them is the integration of
scientific information on impacts, vulnerability, and adaptation in decision making processes, risk
management, and sustainable development initiatives. In this respect, component 4 of the framework
focuses on methodology development to link impact assessment with sustainability evaluation assisted
by multi-criteria policy analysis and multi-stakeholder consultation.
1.2 The Heihe River Basin
The Heihe River Basin is located in a region with the latitude of 35.4–43.5°N and the longitude of 96.45–
102.8°E. Figure 2 is a map of the study region. The study area is the second largest inland river basin in
the arid region of northwestern China. The basin includes parts of two provinces (Qinghai and Gansu)
and Inner Mongolia Autonomous Region. With an area of 128,000 square kilometers, the basin
accommodates about 1.8 populations living in 11 counties, three small size cities, and five prefectures.
The region is composed of diverse ecosystems including mountain, oasis, forest, grassland, and desert.
Heihe River flows from a headwater on Qilian Mountain area to an alluvial plain with oasis agriculture,
and then enters deserts in Inner Mongolia, representing the upper, middle, and lower reaches of the
basin, respectively. The total distance of the Heihe River is 821 kilometers.
Heihe River Basin has a typical arid and semi-arid continental climate, characterized by low and irregular
rainfall, high evaporation and eminent drought periods. The basin can be divided into three typical
climate zones following the altitudinal gradient. In the southern slope of the Qilian Mountain, the climate
is wet and cold with a mean annual precipitation ranging from 300-500 mm. In the middle reach, the
climate becomes much dryer and the mean annual precipitation is only 100-200 mm. In the lower reach,
the average annual precipitation is less than 60 mm, which is one of the driest areas at the same latitude
on Earth (Digital Heihe River Basin URL: http://heihe.westgis.ac.cn/heihe_en/hhHeiHeiAll.htm).
Great temporal variations in temperature and precipitation also exist over the Heihe River Basin with
mean annual rainfall ranging from 30 to 860 mm between winter and summer. About 50-70%
precipitation is recorded during the summer. Mean daily temperature ranges from -14oC to 3oC in
January and from 11oC to 27oC in July (Gansu Meteorological Bureau, 2000).
Heihe River Basin is a poor region in China with harsh environment and fragile ecological systems. The
region is critically short of water and arable land, deficient in educated, technical and scientific personnel,
and far from even domestic markets. The major economic sector in the region is agriculture, and
irrigation is crucial for crop production. The leading crops are wheat, potatoes, and corn. The oasis
agriculture relies on irrigation by the Heihe River and its tributaries. While the Basin has fostered the
development of much oasis agriculture in the middle reach, rangeland farming in the upper reach and
herdsmen in the lower reach, towns, small hydropower plants, a large number of rural communities, and
government agencies, climate stresses have imposed considerable economic, social, and environmental
impacts.
With a resource based economy, the study region is very sensitive to climate. People in the basin are
facing substantial and multiple stresses, including rapidly growing demands for food and water, large
populations at risk to poverty, degradation of land and water quality, and other issues that may be
amplified by climate change.
3
Drought is one of the main climate disasters in the basin, with characteristics of high frequency and
significant damage. For example, droughts occurred in the middle reach about 50% of the time since 1951.
During drought years, while the precipitation volume of May and June is remarkably lower than the
mean, the annual evaporation remains from 2,000 to 2,650 mm (Chen and Qu, 1992). Under climate
change conditions, periods of drought are likely to become more frequent and severe, and water
shortages may increase water use conflicts. Land degradation problems and limited water supplies
restrict present agricultural production and threaten the food security of the region. Climate change may
cause negative impacts on food and fibre production in the region (Shi, 1995). In addition, decreases in
water availability and food production would lead to indirect impacts on human health. Kang et al.
(1999) suggested that spring out flow at the mountain outlet would be increased while summer flow
would be declined in 2030 under climate change scenarios. Irrigation demand in the summer accounts for
more than 70% of the total agricultural water consumption in the region. This seasonal shift of water
supply will affect agricultural production considerably.
There is already some evidence of changes in the trend of the past 50-year observed temperature, with a
more significant rise in Qilian Mountain area. During this period, annual average temperature has
increased more than 1°C in Sunan County and 0.9°C in upper mountain areas. The Qilian Mountain
glaciers have already been undergoing a rapid retreat with a rate of about one meter annually. The region
depends on the glaciers as important natural reservoirs for water supply. The water supply mainly comes
from the spring melting of glaciers (Cheng, 1997). Water supply declining has already affected land
resources with large areas of farmland undergoing desertification (Gansu Meteorological Bureau, 2000;
Digital Heihe River Basin URL).
Fig. 1.2: Map of the Heihe River Basin with approximate population distribution shown in shades of grey
(black is higher population density)
4
2 Characterization of Current Climate and Scenarios of
Future Climate Change
2.1 Activities Conducted
Report activities conducted during the study to characterize current climate and to construct scenarios of
future climate change for your study area.
This component was led by Professor Yihui Ding of National Climate Center (NCC), China
Meteorological Administration (CMA). Drs. Wang Zunya, Xu Ying and Li Qiaoping carried out each of
the following first three research activities respectively. The forth activity was conducted by Dr. Li and
others.
1.
Characteristics of climate change in Northwest China in recent 50 years.
2.
Projection of the climate change over Northwest China by using Coupled Atmosphere-Ocean
models.
3.
Projection of the climate change over Northwest China by using the regional climate model
(RegCM_NCC).
4.
Numerical simulation of impacts of vegetation on regional climate in Northwest China.
2.2 Description of Scientific Methods and Data in Specifying Climate
Scenarios
This section was prepared by: Ding Yihui, Li Qiaoping, Xu Ying, and Wang Zunya of the
National Climate Center, China Meteorological Administration.
2.2.1 Characteristics of climate changes in Northwest China in recent 50
years
2.2.1.1 Introduction
The surface temperature has being increased continuously since 1861 when meteorological records began
being recorded (IPCC, 1990). The temperature increased 0.5-0.8°C in China, which was consistent with
that of the global mean of 0.6°C. Northwest China is located in inland of East Asia where the warm and
wet air streams can rarely reach and is mainly controlled by arid and semi-arid climate. Global warming
has important impacts on the regional climate and the environment in Northwest China. It has been
witnessed increases of surface temperature, rainfalls and the runoff, accelerating of glacier melting, rises
of lake water levels, decreases of the frequency of gales and dust storms, improvement of vegetations and
so on. Considering all these changes, Shi et al. (2003) suggested that climate in Northwest China had
changed abruptly since around 1987 from a warm-dry state to a warm-wet state. Changes in temperature
and rainfalls in Northwest China in recent 50 years are discussed in following parts.
2.2.1.2 Data and method
The datasets of daily temperature and rainfall volume from 1951 to 2003 were used. These datasets were
obtained from 740 observation stations in China and were compiled by the Chinese National
Meteorological Center with the quality control being processed primarily. Due to different rebuilding
time of each of these stations, the record lengths of these datasets have some distinctions and there are
also many missing records. In this respect, further procedures have been taken before analyzing. First,
scattered missing records were filled in. The analysis excluded those years with long consecutive missing
5
data (>8 days) or too much missing data in total (>30 days). Some observation stations with short records
were also omitted. Finally, there were 591 stations with high quality datasets being selected for the study.
All the datasets at least included full time series data from 1971 to 2003. Then, all station datasets were
interpolated to grids of 2.5×2.5 and the area of 35-50N，70-110E was considered as Northwest China. The
method of least squares was used mainly to study the linear trends.
2.2.1.3 Long-term trends of temperature
Figure 2.1 shows the linear trends of daily mean temperature in Northwest China in recent 50 years. The
increases were obvious and exceeded the 95% significant level, especially in Qinghai and the northern
part of Xinjiang. Most linear trends ranged from 0.2°C/10yr to 0.4°C/10yr, which were higher than
China’s mean temperature of 0.22°C/10yr. Besides the daily mean temperature, the daily maximum
temperature and the daily minimum temperature increased obviously (Figure not shown). Of the three
elements, the daily minimum temperature increased most significantly, with linear trends almost ranging
from 0.3°C/10yr to 0.6°C/10yr, even being over 0.7°C/10yr in the northern part of Xinjiang, central
Qinghai and the eastern part of Inner Mongolia. The increasing trends of the daily maximum temperature
were relatively weaker, between 0.1°C/10yr and 0.3°C/10yr, and didn’t exceed the significant level in the
southern part of Qinghai and the northern part of Sichuan.
Fig. 2.1: Distributions of linear trend coefficients of the daily mean temperature (Unit: °C/yr) in Northwest
China from 1951 to 2004. The shaded areas denote exceeding the 95% significant level
Long-term changes in daily mean temperature of four seasons showed different patterns. Not all regions
in Northwest China showed increasing trends uniformly. In spring and summer, it decreased in most
areas except the northern part of Northwest China. And changes were weak and didn’t exceed the
significant level. The temperature increased in most areas in autumn and in whole Northwest China for
winter. The increasing trends were relatively more significant in autumn and the most significant in
winter, exceeding the 95% significant level in areas north of 35N. Changes of the daily maximum
temperature in every season were similar to that of the daily mean temperature. And the increasing
trends of the daily minimum temperature in every season were quite significant that they exceeded the
95% significant level in all regions.
Similar to the obvious increase of the mean temperature, frequencies of extreme cold or warm events also
changed. There are all kinds of indices to study extreme events. Detail information can be found from the
European Climate Assessment & Dataset (EC&D) Indices List (www.knmi.nl/samenw/eca). Days with
temperature higher than 90 percentile of the daily mean temperature were defined as extreme warm
events, while days with temperature lower than 10 percentile of the daily mean were defined as extreme
cold events. It can be observed from Figure 2.2 that extreme warm events increased obviously in
Northwest China and exceeded the significant level, with most linear trends ranging from 4days/10yr to
6days/10yr. The increasing trend was more significant in the northern part of Northwest China. The
changes in extreme cold events were similar with that of extreme warm events.
6
a
b
Fig. 2.2: Distributions of linear trend coefficients of extreme warm events (2a, Unit: day/yr) and extreme
cold events (2b, Unit: day/yr) in Northwest China from 1951 to 2004. The shaded areas denote exceeding
the 95% significant level
2.2.1.4 Long-term trends of the rainfall amount
In recent 53 years, the rainfall amount increased obviously in West China (Figure 2.3). Positive trends
distributed to the west of 102.5E and exceeded the 95% significant level, with two centers being located in
central Xinjiang and the eastern part of Tibet Plateau. The linear trend was only 10mm/10yr in central
Xinjiang, but this was equal to 15%/10yr which was the maximum amount in whole China and was great
enough to affect significantly the climate of Northwest China. Rainfalls to the east of 102.5E decreased
weakly.
Fig. 2.3: Distributions of linear trend coefficients of rainfall percentage (Unit: %/yr) in Northwest China
from 1951 to 2004 (Unit: time/yr). The shaded areas denote exceeding the 95% significant level
Rain day numbers and the rainfall intensity were accounted for changes in rainfall amounts in Northwest
China together (Figure 2.4). The distribution of linear trends of rain day numbers was highly similar to
that of rainfall amounts. Positive trends were for areas located to the east of 100E and they exceeded the
significant level in central Xinjiang and the eastern part of Tibet Plateau, of which the latter was most
significant, being over 7dyas/10yr. Rain day numbers decreased in areas to the east of 102.5E in recent 53
years, especially in the eastern part of Sichuan, Gansu and Shanxi. The rainfall intensity increased all over
Northwest China. But most linear trends didn’t exceed the significant level. To the east of 100E, both rain
day numbers and the rainfall intensity increased, thus the rainfall amounts increased, too. But to the west,
7
rain day numbers decreased and the rainfall intensity increased, while the rainfall amount decreased. The
study suggested that rain day number be the main factor effecting changes in rainfall amounts.
a
b
Fig. 2.4: Distributions of linear trend coefficients of rainfall intensity (4a, Unit: mm/yr) and rain day
number (4b, Unit: day/yr) in Northwest China from 1951 to 2004. The shaded areas denote exceeding the
95% significant level
Considerable differences were also observed in changes of rainfall amounts between seasons (Figure not
shown). Distributions of their linear trends were consistent in spring and summer. Positive trends were
mainly in Xinjiang, the eastern part of Tibet Plateau and Qianghai, with a few areas exceeding the 95%
significant level. Extent of changes in summer rainfalls was greatest in four seasons. Rainfall amounts
increased weakly in the northern part of Northwest China in autumn. In winter, rainfalls increased in
whole Northwest China, especially in the northern part of Xinjiang and the eastern part of Tibet Plateau,
where the linear trends exceeded the significant level.
Days with the value greater than 95% rainfall amounts were defined as extreme rainfall events. Shown in
Figure 2.5, 102.5E was a dividing line of the positive and negative trends. Positive trends were more
significant than negative trends and the increases of extreme rainfall events exceeded the 95% significant
level in central Xinjiang, the northern part of both Qinghai and Gansu Province. In addition, changes in
days with daily rainfalls over 10mm were analyzed (Figure not shown). Its distribution was similar to
Figure 2.5. The increasing of extreme rainfall events was an important factor resulting in the increasing
trends of rainfall amounts in the western part of Northwest China.
Fig. 2.5: Distributions of linear trend coefficients of extreme rainfall events in Northwest China from 1951
to 2004 (Unit: time/yr). The shaded areas denote exceeding the 95% significant level
8
2.2.1.5 Climate shift in Northwest China
It was observed that the temperature in Northwest China began to increase from 1951 and decreased
slightly from 1965 to mid-1970s. Then it continued to increase and changed abruptly in about 1987 by the
Mann-Kendall test (Figure not shown). As for rainfalls, the abrupt changes in the eastern part and the
western part of Northwest China, being divided by 102.5E, were analyzed separately for their opposite
linear trends. In the western part of Northwest China, rainfalls decreased from 1951 to 1967. Then it
began to increase, but there was no any significant abrupt change. In the eastern part, rainfalls decreased
from 1951 to 1972 and then started to increase from 1972 to 1985. There was an abrupt change around
1985 and rainfalls decreased significantly. In general, rainfalls changed much consistently both in the
eastern part and the western part of Northwest China before 1987 or so. But after 1987, rainfalls in the
western part increased continuously, while the eastern part experienced decreasing rainfalls. Considering
the process of changes in temperature and rainfalls comprehensively, 1965 and 1987 were two turning
points for climate trends in Northwest China. Based on the observation, the whole time series data from
1951 to 2003 can be divided into three parts: 1951-1965; 1965-1987 and 1987-2003 (Figure 2.6).
During the first period, it was relatively dry in Xinjiang and Qinghai but relatively wet in the southern
part of Gansu and Shanxi. And it was relatively cold in whole Northwest China, with cold centers being
located in Qianghai and the northern part of Xinjiang. During the second period, rainfall anomalies to the
east of 102.5E were positive in Northwest China, being out-of-phase with that to the west. It was
relatively cold in the whole West China. During the third period, most areas of West China were wet and
the negative anomalies mainly located to the south of 35N and to the east of 102.5E. And it was warmer in
West China, where most positive anomalies ranged between 0.3°C and 0.5°C, especially in Xinjiang，
Gansu and the northern part of Inner Mongolia. It can be concluded that the climate shifted from the
cold-dry state to the warm-wet state in Xinjiang, Qianghai and the northern part of Gansu, while it
shifted from the cold-wet state to the warm-dry state in Shanxi, the eastern part of Inner Mongolia and
the southern part of Gansu. Shi et al. (2003) indicated that climate in Northwest China shifted from the
warm-dry state to the warm-wet state around 1987. The term “warm-wet state” was relative to the warmdry state after the Little Ice Age was terminated. And the analysis above verified the abrupt change
around 1987. In fact, both the increasing trends and the abrupt change occurring in about 1987 were most
significant in summer rainfalls.
9
Temperature anomalies (Unit:
°C)
Rainfall anomalies(Unit:mm)
1951-1965
1966-1987
1988-2003
Fig. 2.6: Distributions of temperature and rainfall anomalies in West China in three periods
2.2.1.6 Climate changes in the Heihe River Basin
In the Heihe River Basin there were 13 climate stations which were included in the study. Different
lengths of the data records in these stations are illustrated in Figure 2.7. There were only 2 stations in
1951. But the numbers of stations increased fast and reached to a stable condition of 12 after 1960s.
Datasets from 1961 to 2003 were used for analysis, as fewer stations in 1950s. Similarly, the temperature
in the Heihe River Basin showed a significant increasing trend from 1961 to 2003, especially after 1986
when the positive anomalies domain. The linear trend of the temperature in the basin was 0.32°C/10yr,
being greater than those both in China and in Northwest China (Figure 2.8). Figure 2.9 showed the longterm change of rainfalls in the basin for recent 43 years. The rainfall amount increased obviously in the
period from 1961 to 1975 and there was no significant trend after 1970s. The linear trend of the whole
series was 2.6 mm/10a and didn’t exceed the 95% significant level, being weaker than the increasing
trend in Northwest China. The changes of the temperature and rainfalls of the Heihe River Basin in four
seasons can be seen in Table 2.1. The temperature showed obvious increasing trend in each season, with
winter ranking the first and the linear trend being over 0.5°C/10yr. The increasing trends in autumn,
10
summer and spring decreased orderly. Rainfalls had positive trends in summer and winter but had
negative trends in spring and autumn. Actually, they changed little except in summer.
Fig. 2.7: Numbers of stations in the Heihe River Basin in each year from 1951 to 2004
Fig. 2.8: Temperature anomaly series of the Heihe River Basin from 1961 to 2004 (Unit: °C). The real line
is the linear trend and the formula is the linear regression equation
Fig. 2.9: Rainfall anomaly series of the Heihe River Basin from 1961 to 2004 (Unit: mm). The real line is
the linear trend and the formula is the linear regression equation
11
Spring
Surface temperature (°C/10yr)
0.19*
Summer
Autumn
0.24*
0.30*
Winter
0.55*
Rainfall amount (mm/10yr)
- 0.5
2.4
- 0.03
0.23
*denoting to exceed the 95% significant level
Table 2.1: Linear trends of the temperature and rainfalls of the Heihe River Basin in each season from 1961 to 2004
2.2.1.7 Summary
Climate changes in Northwest China in recent 50 years have been discussed by analyzing the
temperature and rainfalls from 1951 to 2004. The main results are as follows:
1.
The daily mean temperature in Northwest China have increased significantly, with linear trends
in most areas ranging from 0.2°C/10yr to 0.4°C/10yr, especially in Xinjiang, Qinghai and Inner
Mongolia. In spring and summer, the temperature decreased weakly, while it increased
significantly in winter.
2.
Both the daily maximum temperature and the daily minimum temperature changed similarly to
the daily mean temperature in recent 50 years. The increase of the minimum temperature was
most significant, while change of the maximum temperature was least significant.
3.
The extreme warm events have increased obviously in recent 50 years.
4.
Rainfalls to the west of 102.5E in Northwest China have increased significantly, with the
maximum linear trends reaching 15%/10yr, while those to the east of 102.5E have decreased
obviously. Both rain day numbers and the rainfall intensity effected the change of rainfall
amounts, with the latter being the main factor. Rainfalls increased most in summer. And, the
extreme rainfall events have increased a lot to the west of 102.5E, too.
5.
Around 1965, Northwest China showed a weak decreasing trend in temperature and a significant
increasing trend in rainfalls. Then it changed abruptly in 1987 from the cold-dry state to the
warm-wet state in areas west of 102.5E including Xinjiang, Qinghai and the northern Gansu
provinces, while it changed from the cold-wet state to the warm-dry state in areas east of 102.5E
including the eastern Inner Mongolia, Shanxi and the southern Gansu.
6.
From 1961 to 2003, the temperature in the Heihe River Basin increased significantly. The linear
trends of warming were greater than those of China and Northwest China. Rainfalls in the basin
showed an increasing trend weaker than that of the Northwest China.
2.2.1.8 References
IPCC, 1990:Climate Change: The IPCC scientific assessment. J.T.Houguton,
J.Ephraums,(eds.).Cambrige University Press, Cambrige, UK, 365 pp
G.J.Jenkins and
Chen Longxun, Shao Yongning, Zhang Qingfen, Ren Zhenghai, Tian Guangsheng, Primary analysis
about the climate change in China in recent 40 years, Quarterly Journal of Applied Meteorology, 1991
，2(2):164-173
Li Dongliang, A study of the climatic characters and anomalies of the mean annual temperature in
Northwest China, Analysis and prediction of the climate change in Northwest China, ed by Xie
Jingnan. Beijing: Meteorological Press. 2000, 43-48.
Tu Qipu, Deng Ziwang, Zhou Xiaolan, Studies on the regional characteristics of air temperature
abnormal in China, Acta Meteorological Sinica, 2000, 58(3): 288-290.
12
Wei Zhigang, Dong Wenjie, Hui Xiaoyin, Evolution of trend and interannual oscillatory variablities of
precipitation over Northwest China, Acta Meteorological Sinica, 2000, 58(2): 234-243.
Ding Yihui, Wang Shourong, Panorama of climate and environment of Northwest China, Beijing:
Meteorological Press, 2001.
Shi Yafeng, Shen Yongping, Li Dongliang, Assessment of the climate shift from the warm-dry state to the
warm-wet state in Northwest China, Beijing: Meteorological Press, 2003.
Weihong Qian, Xiang Lin, Regional trends in recent temperature indices in China, Cliamte Research,
2004, 27:119-134.
st
2.2.2 Projections of future climate change over West China in the 21
century
The climate change over West China during the 21st century has been projected by using eight coupled
global atmospheric and oceanic circulation models (AOGCMs) which were provided by both the IPCC
scientific assessment report in 2001 and the results run by the NCC/IAP T63 (National Climate
Center/Institute of Atmospheric Physics). The emission scenarios took account of the anthropogenic
greenhouse gases (GG) emission and GG plus sulfate aerosol (GS) increasing, as well as IPCC SRES A2
(high emission) and B2 (medium emission). Table 2.2 indicates the model descriptions and designs of the
numerical experiments.
The model ensembles have been calculated. The anomalies of both temperature and precipitation were
relative to the 30 years mean of 1961-1990.
NCC/IAPT63
Hadley
GFDL
DKRZ
CSIRO
CCSR
CCC
NCAR
Author
XU Ying(2002)
Mitchell&Joh
ns(1997)
Haywood et
al.(1997)
Roeckner
et al.(1998)
Gordon&O’
Farrell(1997)
Emori et
al.(1999)
Boer et
al.(1999a,b)
Washingto
n .Meehl
(1999)
AOGCM
1.875"1.875
3.75"2.5/L19
R15/L9
T42/L19
R21/L9
T21/L20
T32/L10
OGCM
1.875"1.875
3.75"2.5/L20
4.5"3.75/L
12
2.8"2.8/L
17
R21/L21
2.8"2.8/L1
7
1.8"1.8/L
29
control
#year!
170
400
1000
1000
GG
#year!
1890-1999
(history
data!
2000-2030
(SRES-A2;B2)
1860-1989
(history
data!
1990-2099
#1%/yr,0.5%
/yr! (SRESA2;B2)
1958-2057
IS92a
19612100(SRESA2;B2)
1860-1989
(history
data !
1990-2099
#1%/yr)
1880-1990
(history data)
1990-2099
(IS92a)
(SRES-A2,B2)
18902099(1%/yr)
18902100(SRESA2,B2)
1850-2100
(1%/yr)
(SRESA2,B2)
1870-2084
GG+Aerso
l#year!
130
1860-1990
IS92a
1990-2099
(1%/yr,0.5%/
yr)
1765-2065
IS92a
1860-1990
($%
&' )
1990-2099
(IS92a)
1880-1990
($%
&' )
1990-2099
(IS92a)
1%/yr,1890
-2099
1850-2100
(1%/yr)
1870-2049
#1%+DS
A!
1890-2099
1870-2100
Table 2.2: Brief descriptions of the models and designs of experiments
Eight AOGCMs with the various emission scenarios project that an obvious warming over West China in
the 21st century would very likely occur, especially over Northwest China. As an example, the
temperature anomalies for SRES scenarios over West China might increase by 1.0-2.5°C in about 2050.
The warming over West China might be higher than the mean of the whole China.
Figure 2.10 shows the geographical distributions of the annual mean temperature change for about 2050
relative to 1961-1990 as projected by the AOGCMs ensembles with SRES A2 and B2. The obvious
warming of 3.5-6.5°C for A2 and 2.5-4.5°C for B2 over Northwest China is noticed, respectively. The
13
warming of 1.0-1.5°C over Southwest China is less than it over Northwest China. For four seasons, there
is the obvious warming of 2.5°C or more in winter and a less warming of 1.0-2.0°C in summer (figures are
not shown).
Fig. 2.10: Geographical distributions of the annual mean temperature anomalies for about 2050 relative to
1961-1990 as projected by the AOGCMs ensemble with SRES A2 (left) and B2 (right) over Northwest
China (Unit: °C)
The precipitation over the most parts of West China might increase in the first 50 years of the 21st century,
especially in Northwest China. The precipitation might increase by about 5-30% for A2 and 5-25% for B2
in about 2050 relative to 1961-1990, respectively. The precipitation might increase clearly in winter and
slightly in summer and autumn (figures are not shown).
2.2.2.1 Projections of climate change along the line of the Qing-Zang Railway
Qing-Zang Railway traversed the western parts of China. We paid more attention to the projections of the
climate change in the 21st century along the line of the Qing-Zang Railway.
The AOGCMs ensembles with the SRES A2 and B2 point out the warmer climate over the northern and
eastern parts of Tibetan Plateau than other regions. The warming of 1.6°C along the line of Qing-Zang
Railway for 2011-2040 is projected and 2.4-3.4°C for 2041-2070. The linear trends of the annual mean
temperature for the first 50 years of the 21st century are 2.0-2.5°C/50a for A2 and 1.5-2.0°C/50a for B2,
respectively. For the seasonal changes, an obvious warming in winter is noted to compare with other
seasons.
Table 2.3 gives the annual mean temperature changes for each decade of the first 50 years of the 21st
century along the line of Qing-Zang Railway as projected by the AOGCMs ensembles with SRES A2 and
B2 in detail. For example, there is a warming of 0.63-0.90°C for A2 and 0.97-1.15°C for B2 in 2010s and
2.56-2.96°C for A2 and 2.37-2.65°C for B2 in 2050s, respectively.
14
City
decade
2010
2020
2030
2040
2050
2010
2020
2030
2040
2050
Geermu
Wudaoliang
Anduo
0.83
1.29
1.66
1.80
2.56
0.90
1.31
1.72
1.94
2.72
0.87
1.48
1.68
2.02
2.96
1.01
1.27
1.84
2.11
2.64
0.98
1.27
1.88
2.22
2.65
1.15
1.31
1.94
2.11
2.50
Naqu
SRES-A2
0.90
1.54
1.67
2.05
2.96
SRES-B2
1.10
1.33
1.95
2.07
2.50
Dangxiong
Lasa
Totohe
0.87
1.54
1.66
2.00
2.93
0.63
1.42
1.55
1.89
2.73
0.88
1.33
1.69
1.99
2.81
1.14
1.31
1.94
2.08
2.48
1.10
1.24
1.82
1.98
2.37
0.97
1.28
1.88
2.17
2.57
Table 2.3: Annual mean temperature changes as projected by the AOGCMs ensembles with SRES A2 and B2 for
each decade along the line of Qing-Zang Railway for the first 50 years of the 21st century (relative to 1961-1990)
(unit: °C)
The projections of the precipitation changes might be more complicated to compare with temperature
changes. The AOGCMs ensembles with SRES A2 and B2 project that the annual mean precipitation along
the line of Qing-Zang Railway might increase by about 5% for A2 and 25-30% for B2 in 2011-2040, and 1015% for A2 and 30-35% for B2 in 2041-2070, respectively.
Table 2.3 indicates the projections of the annual mean precipitation changes as projected by the AOGCMs
ensembles with SRES A2 and B2 for each decade along the line of Qing-Zang Railway for the first 50
years of the 21st century (relative to 1961-1990). For example, there is an increasing precipitation of 1-4%
for A2 and 2-10% for B2 in 2010s and 5-15% for A2 and 6-15% for B2 in 2050s, respectively.
City
decade
2010
2020
2030
2040
2050
2010
2020
2030
2040
2050
Geermu
Wudaoliang
Anduo
3.29
3.74
6.36
6.20
12.33
3.15
6.91
9.32
8.99
15.52
3.89
5.43
6.58
9.48
13.56
7.56
8.22
12.22
8.24
15.04
9.28
10.34
12.52
10.78
14.86
8.36
9.87
13.64
14.80
14.16
Naqu
SRES-A2
3.57
5.20
4.69
8.60
11.69
SRES-B2
6.98
8.82
11.35
12.94
12.29
Dangxiong
Lasa
Totohe
1.43
3.0
2.18
4.70
7.53
0.14
1.95
0.98
3.13
5.06
3.63
6.58
8.78
9.44
15.34
3.56
6.33
9.54
9.74
7.65
2.07
4.49
7.45
7.02
5.57
9.51
10.29
12.55
11.94
14.68
Table 2.4: Annual mean precipitation changes as projected by the AOGCMs ensembles with SRES A2 and B2 for
each decade along the line of Qing-Zang Railway for the first 50 years of the 21st century (relative to 1961-1990)
(unit: %)
The further research will narrow the uncertainties of the model projections.
15
2.2.2.2 References
Houghton, J.T., G.J.Jenkins and J.J.Ephraums (eds.), Climate Change, The IPCC Scientific Assessment,
Cambridge University Press, Cambridge, 1990, 364pp.
Houghton, J.T., B.A.Callander and S.K.Varney (eds.), Climate Change 1992, The Supplementary Report to
the IPCC Scientific Assessment, Cambridge University Press, Cambridge, UK, pp200.
Houghton, J.T., L.G.Meira Filho, B.A.Callander, N.Harris, A.Kattenberg and K.Maskell (eds.), Climate
Change 1995: The Science of Climate Change, Cambridge University Press, 1996, Cambridge, UK,
pp572.
Houghton, J.T., The IPCC Special Report on Emissions Scenarios (SRES), Cambridge University Press,
Cambridge, UK, 2000, pp120.
Houghton, J.T., Ding, Y., eds.,, Climate Change 2000, The Scientific Basis, Cambridge University Press,
Cambridge, UK, 2001，pp 770.
Xu Y., Ding Y. and Li D., Climate change over Qing-Zang regions for the next century, Plateau
Meteorology, 2003, 22(5)，451-457 (in Chinese).
Gao X., Li D., Zhao Z.-C. and FillippoGiorigi，Numerical simulations of climate change along the
line of Qing-Zang Railway due to the greenhouse effects, Plateau Meteorology, 2003，22(5)，
458-463 (in Chinese).
Boer, G.J., G.Flato, M.C.Reader, and D.Ramsden, A transient climate change simulation with greenhouse
gas and aerosol forcing: experimental design and comparison with the instrumental record for the
20th century, Clim. Dyn., 2000，16，405-425
Boer, G.J., G.Flato, M.C.Reader, and D.Ramsden, A transient climate change simulation with greenhouse
gas and aerosol forcing: projected climate for the 21st century, Clim.Dyn., 2000，16，427-450
Emori, S., T.Nozawa, A.Abe-Ouchi, A.Numaguti, M.Kimoto, and T.Nakajima, Coupled oceanatmosphere model experiments of future climate change with an explicit representation of surface
aerosol scatting, J. Met. Soc. Jap., 1999，77，1299-1307
Gordon, H.B. and S.P.O’Farrell, Transient climate change in the CSIRO oupled model with dynamic sea
ice, Mon.Wea.Rev., 1997,125, 875-907.
Haywood, J.M., R.J.Stouffer, R.T.Wetherald, S.Manabe, and V.Ramaswamy, Transient response of a
coupled model to estimated changes in greenhouse gas and sulfate concentrations, Geophys.res.Lett.,
1997,24, 1335-1338.
Mitchell, J.F.B., T.J.Johns, J.M.Gregory, S.B.F.Tett, Climate response to increasing level of greenhouse
gases and sulphate aerosols, Nature, 1995,376, 501-504.
Zhao Z.-C., Ding Y., Xu Y. and Zhang Jin, Detection and projection of climate change over
Northwest China for the 20th and 21st century due to the human activity, Climate and
Environment Research, 2003，8(1)，26-34 (in Chinese).
Xu Y., Zhao Z-C., and Li D., Simulations of climate change for the next 50 years over Tibetan Plateau and
along the line of Qing-Zang Railway, Plateau Meteorology, 2005，24(5)，698-707 (in Chinese).
2.2.3 Projection of climate change over Northwest China by using the
regional climate model (RegCM_NCC)
2.2.3.1 Introduction
From the projection results get by CGCM one can see that, the precipitation (Pr) and air surface
temperature (Ts) over western China have remarkable change in the future decades (Xu, 2003). Because of
16
the lower resolution, the CGCM shows some deficiency in capturing some regional features. It’s
necessary to use dynamical down-scaling to get regional climate simulation results with higher resolution
and longer time scale by using the nested regional climate model. The Regional climate model
(RegCM_NCC) is forced at its lateral boundaries by the output of the global ocean-atmosphere coupled
model (NCC/IAP T63). The present-day simulation (control run) with the nested RCM has been
undertaken for 30 years (1961-1990). Based on the climate integration, a 30-year (2001-2030) climate
change projection (under IPCC SRES A2 emission scenario) has been conducted. In the results analysis,
annual and seasonal change of Pr and Ts over western China has been discussed.
2.2.3.2 Model description and experimental designs
The regional climate model (RegCM_NCC; Ding et al., 2000) used here has been developed by the
National Climate Center of China based on RegCM2 /NCAR (Giorgi et al., 1993a, b) by modifying and
assembling various physical parameterization schemes, such as the mass flux cumulus parameterization
scheme, the TKE planetary boundary layer scheme, the improved land process model (LPM). Due to its
good capability in reproducing climate features over East Asian regions, the model has been used widely
in weather and climate simulations and short-term climate prediction over China (Ding et al., 2002; Liu
and Ding, 2002).
By using the regional climate model, 5-year(1998-2002)simulation over East China was undertaken to
evaluate the capability of the model (Li et al., 2004). The model domain encompasses a large area
including East Asian and whole Chinese continent at a horizontal resolution of 60km with the domain
center in 110°E and 35°N. Results show that the model can well reproduce the basic circulation over East
Asia and the seasonal variation of Pr and Ts. Figure 2.11 and Figure 2.12 show the simulated and
observed Pr and Ts over Northwest (NW) China (80-110ºE，35-45ºN) and Southwest (SW) China (80110ºE，25-35ºN), respectively. It can be seen that the simulated Pr and Ts over western China are
consistent with the observation, especially in NW China. However, the model overestimates Pr in SW
China. That mainly because there has large terrain (Tibetan plateau) in this area and the model produces
much Pr on the leeward side of terrain. This kind of systematic error can also be found in many other
models’ results. The simulated Ts over western China is well agreement with the observation. The annual
mean and seasonal variation features of the wind field and other variables can also be well indicated in
the model.
%
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Fig. 2.11: The simulation and observation of Pr over NW China (a) and SW China (b) (units: mm/d)
%$
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,-./0
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Fig. 2.12: The simulation and observation of Ts over NW China (a) and SW China (b) (units: ºC)
The used global ocean-atmosphere coupled model has been established by Chinese scientists. The
circulation model has been developed by China National Climate Center (NCC) and the Ocean model by
Institute of Atmosphere Physics of Chinese Academy of Science (Xu, 2002).
17
Driven by the output of GCM, the climate simulation has been undertaken for 30 years (1961-1990) as the
climate background field. Based on the NCEP reanalysis data, the evaluation of the simulation results
show that the model has a certain ability to simulate past climate change. The model can reproduce the
characteristics of mean circulation over East Asia, as well as the seasonal variation and spatial
distribution of Pr and air Ts over the main regions of China. There still have some systematic errors in the
simulated Pr. Based on the control run, the projection of the future climate change (under IPCC SRES A2
emission scenario) has been conducted for 30 years (2001-2030).
2.2.3.3 Climate change over NW China for future 30 years
Difference of Pr between the sensitive experiment and present time experiment is considered as Pr
change due to the increasing of the CO2 concentration. Fig 2.13 shows the distribution of annual mean Pr
change over western China in the years of 2010, 2020 and 2030(relative to 1961-1990), respectively. It is
indicated that Pr in western China has remarkable change in the future. Pr will increase slightly in
Sinkiang and south of Hetao in 2010. Pr is expected to increase in Inner Mongolia and SW China in 2020.
The area with Pr increasing mainly located in Inner Mongolia and Sichuan basin in 2030.
Fig. 2.13: Annual mean Pr change over western China in the future(relative to 1961-1990) for the years of
2010 (a), 2020 (b) and 2030 (c) (units: mm/d)
Tables 2.5 and 2.6 show the seasonal mean of Pr change in percentage over western China in 2020 and
2030, respectively. Pr over NW China in 2020 presents slightly decrease in spring, summer and fall, but
18
increase in winter. Pr over SW China decreases in spring and summer, but increase remarkable in fall and
winter, especially in fall. The annual mean Pr presents an increase trend. The distribution of Pr changed
obviously in 2030. Pr over NW China presents increase trend consistently in all seasons of the year. The
largest increase percentage will happen in fall by about 18%. There will be less increase in summer and
winter. Total increase in the whole area is 11%. But Pr over SW China shows decreasing in both summer
and fall and the annual mean Pr has little change. Pr over the whole western region presents weaker
increase trend in the future.
Season
Spring(MAM)
Summer(JJA)
Fall(SON)
Winter(DJF)
Annual
NW
SW
-0.09
-0.06
-0.19
-0.19
-0.25
0.72
0.31
0.40
-0.06
0.22
Table 2.5: Seasonal mean Pr variability in 2020 (units: %)
Season
Spring(MAM)
Summer(JJA)
Fall(SON)
Winter(DJF)
Annual
NW
SW
0.14
0.04
0.03
-0.19
0.18
-0.30
0.10
0.40
0.11
-0.01
Table 2.6: Seasonal mean Pr variability in 2030 (units: %)
The direct impacts of CO2 increase is the change of Ts. Figure 2.14 shows the annual mean Ts change over
western China in 2010, 2020 and 2030. Ts over western China is expected to increase in all the 3 periods,
which is consistent with that in whole China and the globe. Compared with eastern China, the increase
extend of Ts in western China is weaker by the range of 0-0.5ºC, with maximum value of 1ºC. Ts in
western of Sinkiang increase more significant than the other regions in 2020. The obvious increasing area
happens in Yunnan and Guizhou Plateau in 2030. Seasonal mean Ts in western China is expected to
increase in all the 4 seasons, with a larger increase in summer in northwest part of Sinkiang and eastern
plateau by the maximum value of 1.5ºC.
19
Fig. 2.14: Annual mean Ts change over western China in the future(relative to 1961-1990)for the years of
2010 (a), 2020 (b) and 2030 (c) (units: ºC)
Figure 2.15 shows the area mean daily maximum temperature (Tmax), minimum temperature (Tmin) and
daily range of Ts in 2030. Along with the increase of the daily mean Ts, Tmax and Tmin increase
consistently (except for October), with the highest valure found in July. Due to the Tmin increasing larger
than Tmax, the daily range of Ts expects to decrease in the future (except for April).
"#$
-./0
"
-1/0
234
%#$
%
"
&
'
(
$
)
*
+
,
"%
!%#$
!"
!"#$
Fig. 2.15: daily mean temperature change in NW China in 2030 (units: ºC)
20
""
"&
Table 2.7 shows the seasonal mean Ts over NW China in 2030. Ts, Tmax and Tmin increases more in
spring and summer than in the other seasons. Furthermore, Ts in fall shows decrease slightly, which
mainly presents Ts decreasing in October. The annual mean warming in NW China is 0.2ºC, which being
lower than eastern China. The daily range of Ts decreases in the all seasons.
Season
Ts
Tmax
Tmin
daily range
Spring(MAM)
0.44
0.35
0.38
-0.03
Summer(JJA)
0.47
0.27
0.52
-0.25
Fall(SON)
-0.12
-0.34
-0.07
-0.27
Winter(DJF)
0.07
-0.09
0.11
-0.20
Annual
0.22
0.05
0.24
-0.19
Table 2.7 Seasonal mean Ts change over NW China in 2030 (units: ºC)
There are some differences in Ts change in SW China (Table 2.8). The warming will be more obvious in
fall and winter than that in spring. The annual mean Ts increases by 0.4 ºC. The daily range of Ts
increases in summer and fall but decreases in the other seasons. Ts in fall also presents remarkable
increasing, which is different when comparing with that in 2020.
Season
Spring(MAM)
Summer(JJA)
Fall(SON)
Winter(DJF)
Annual
Ts
Tmax
Tmin
daily range
0.21
-0.02
0.26
-0.27
0.32
0.30
0.04
0.26
0.48
0.68
0.26
0.42
0.65
0.45
0.70
-0.25
0.42
0.35
0.31
0.04
Table 2.8: Seasonal mean Ts change over SW China in 2030 (units: ºC)
The other variables also shows significant change in 2030, that including in the wind speed increasing
near the ground, evaporation decreasing, as well as sea surface pressure ascending. But these
characteristics change with different season.
2.2.3.4 Summary
Based on IPCC SRES A2 emission scenario, impacts of greenhouse effects on climate change over western
China by using a nested regional climate model have been investigated. The change trends and features
of Pr and Ts over western China in future 30 years have been analyzed and the following conclusions can
be drawn:
1.
Due to the effect of greenhouse gas increase, precipitation will slightly decrease (increase) over
NW (SW) China in 2020. The distribution of Pr will reverse in 2030. Pr is expected to increase
over NW China and partial areas in SW China with weaker annual change.
2.
Air surface temperature will keep on increasing in whole western region (especially in summer),
with annual mean increase value of 0.4ºC, which is lower than the eastern China.
3.
Along with the increase of the daily mean Ts, Tmax and Tmin will increase consistently. Due to
the Tmin increases larger than Tmax, the daily range of Ts is expected to decrease in the future.
The results indicate some significant change of Pr and Ts in western China due to increasing of CO2
concentration. However, there still exists some difference when comparing with the other simulation
results. Thinking the much uncertainties that come from models and projections in the present studies,
further research is needed to get more reliable projection for the future climate change by more modeling
simulation and comparison analysis.
21
2.2.3.5 References
Ding Y.H., Y.F. Qian, H. Yan, K. Gao, T.L. Shen, M.Q. Miao, W.L. Li, Y.M. Liu, N. Zhao, L. Yi, and X.L.
Shi, 2000: Improvement of high-resolution regional climate model and its application to numerical
simulation of prolonged heavy rainfalls in East Asia. In Development of Operational Short-term
Climate Model series.2. Ding YH (eds). China Meteorological Press, Beijing, 217-231. (in Chinese)
Ding Y.H., Y.M. Liu, Y.J. Song and Q.Q. Li, 2002: Research and Experiments of the Dynamical Model
System for Short-Term Climate Prediction. Climatic and Environmental Research, 7,236-246(in Chinese)
Gao X.J., Y.H Lin, Z.C Zhao and Filippo Giorgi. 2003: Impacts of greenhouse effect on typhoon over
China as simulated by a regional climate model. Acta Meteor Sinica，61(1):29−37 (in Chinese)
Giorgi F., M. arinucci, G.T. Bates, 1993a: Development of a second-generation regional climate model
(RegCM2), PartⅠ: boundary-layer and radiative transfer processes. Mon. Wea. Rev., 121, 2794-2813
Giorgi F., M. arinucci, G.T. Bates, 1993b: Development of a second-generation regional climate model
(RegCM2), PartⅡ: convective processes and assimilation of lateral boundary conditions. Mon. Wea.
Rev., 121, 2814-2832
Li Q.P., Y.H. Ding. Multi-year simulation of the East Asian monsoon and precipitation in China using a
regional climate model and evaluation. Acta. Meteo. Sinica,2004,62(2):140-153 (in Chinese)
Liu Y.M., Y.H. Ding, 2002: Simulation of heavy rainfall in the summer of 1998 over China with regional
climate model. Acta Meteo. Sinica, 16, 348-362 (in Chinese)
Qin D.H. (eds). 2005. Climate and Environment Changes in China. Volume I: Climate and Environment
changes in China and there projections. China Science Press, Beijing, 507-555 (in Chinese)
Xu Y., Y.H. Ding and Z.C. Zhao2003: Scenario of temperature and precipitation changes in Northwest
China due to human activity in the 21st century. Journal of glaciology and geocryology, 25(3):327-330
(in Chinese)
Xu Y., Y.H. Ding, Z.C. Zhao and J. Zhang. 2003: A scenario of seasonal climate change of the 21st century
in Northwest China. Climatic and Environmental Research. 8(1):19-25 (in Chinese)
Xu Y. 2002. A study of numerical simulation of impacts of human activities on climate chnage. Ph.D.
paper, Chinese Academy of Meteorological Sciences.244pp (in Chinese)
2.2.4 A numerical simulation study on the impacts of vegetation change on regional climate
in Northwest China
2.2.3.6 Introduction
The change of vegetation cover has impacts on regional environment and climate through changes of
energy and water vapor flux between land and atmosphere, which has been approved by more and more
scientific study. In recent years, a numbers of numerical studies in China found that heavy vegetation
changes in large area have significant effects on regional Pr and Ts, so much as on the intensity of East
Asian summer monsoon. However, there are some spatial-temporal differences in the results about the
effects process and sensitive regions. In this study, the effects of Reforestation in NW China on regional
climate have been investigated by using the regional climate model.
2.2.3.7 Experimental designs
The experiments have been done by using the RegCM_NCC. Two experiments were carried out, one is a
controll run with current vegetation cover in China (hereafter CTRL), the other is a sensitivity experiment
with changed land cover in NW China. In the reforestation experiment (refer as “RE” test), the vegetation
classes in the area between 85º-110ºE and 35º-43ºN is replaced with various forest. It is consistent with the
project of tree planting over NW China. Each of the experiments is integrated for 15 months (1997.121999.02), and the analysis is started from March, 1998, with the fore 3 month being regarded as the
model’s “spin-up” time.
22
2.2.3.8 Experimental results analysis
The effects of land cover change on climate are investigated based on difference of sensitivity experiment
and controll simulation. Figure 2.16 just shows annual mean Pr change. One can see that reforestation in
NW China tends to increase local Pr in all seasons of the year, especially in summer and fall. The annual
mean Pr increases, which expected to lighten drought over there in some degree though Pr amount being
small. Furthermore, Pr in Huanghe River basins increases significantly. Thus, Reforestation in NW China
expected to increase Pr in local region, parts of the area in North China, lighten the drought condition
over there, consequently. On the other hand, Pr decreases in the area of the Yangtze River basins and
south China in summer.
Fig. 2.16: Annual mean Pr difference between reforestation experiment and control run (RE-CTRL) (units:
mm)
Reforestation in NW China also has heavy impacts on Ts (Shows in Figure 2.17). Ts in the re-greening
area becomes warmer in winter with the maximum value of 1.8ºC in north of Inner Mongolia. However,
Ts becomes cooler in the other 3 seasons, especially in summer with the center value of 3.5ºC. The annual
mean Ts decreases too. On the other hand, increase of Ts in winter could weaken the occurrences of the
disaster weather such as cold events, while which may be lead to growth season of the crops prolonging.
23
Fig. 2.17: Mean Ts difference between reforestation experiment and control run (RE-CTRL) (units: ºC) (a)
winter (DJF) (b) summer (JJA)
Reforestation in NW China also has significant impacts on the stream field of mid-lower troposphere. In
the wind difference field between DE and CTRL at 850hPa, southerly wind flows to the west of test area,
while an anti-cyclonic circulation existing in northern area. As a result, the easterly wind strengthens in
the test area. It can be notice that the southwesterly monsoon flow becomes strong in summer. However,
the winter monsoon becomes weaker in winter with the two branch airflow from East China Sea and
South China Sea, which reduce the intensity of the northeasterly wind flow from west of Siberian to
Chinese continent.
Wind speed also decreases significantly in the test region and around area, especially in winter, with the
maximum value of 3.5m/s. As a result of re-greening project, the occurrences of the dust storm in NW
China may be decrease in some degree.
Fig. 2.18: Annual mean wind difference near the ground between reforestation experiment and control run
(RE-CTRL) (units: m/s)
24
2.2.3.9 Summary and discussion
1.
Reforestation in NW China tends to increase local Pr in spring and summer, hence, reducing
drought over there. Pr also increases in North China, but decrease in Yangtze River basins.
2.
After re-greening in NW China, the local Ts becomes warmer in winter but cooler in summer,
while wind speed near the ground will decrease, thus maybe reduce the occurrences of dust
storm weather in some regions.
3.
Reforestation in NW China also has obvious effects on the intensity of East Asian winter and
summer.
The investigation provides us some important clues on how land cover change affects Chinese climate,
while there also are some limitations, such as the vegetation changes are exaggerated and the
experiments are performed only on slightly longer than one year, which is relatively short for climate
response, therefore, the results should be verified through further study.
2.2.3.10 References
Ding Y.H., Y.M. Liu, Y.J. Song and Q.Q. Li, 2002: Research and Experiments of the Dynamical Model
System for Short-Term Climate Prediction. Climatic and Environmental Research, 7,236-246(in Chinese)
Fu C.B., and H.L.Yuan. A virtual numerical experiment to understand the impacts of recovering natural
vegetation on the summer climate and environmental conditions in East Asia. Chinese Science
Bulletin, 2001,46(8):691-695 (in Chinese)
Xue Y. The impact of desertification in the Mongolian and the Inner Mongolian Grassland on the regional
climate. J Climate, 1996b, 9(9): 2173-2189
Zheng Y.Q., Y.F.Qian, M.Q.Miao, G.Yu, Y.S.Kong, D.H. Zhang. The effects of vegetation change on
regional climate I: simulation results. Acta. Meteo. Sinica, 2002, 60(1):1-16 (in Chinese)
Ding,Y.H., Q.P. Li, W.J. Dong, 2005:A numerical simulation study of the impacts of vegetation changes on
regional climate in China. Acta Meteorological Sinica， 63(5):604-621(in Chinese)
25
3 Socio-Economic Futures
3.1 Activities Conducted
Changes in socio-economic conditions, such as population, urbanization, and economic growth were
taken into consideration in developing socio-economic scenarios. Base on the statistics and the economic
development plan of Gansu Provincial government, and the National Government Western Development
Strategy, the economic and social development scenarios of the Heihe River Basin were specified for the
AS25 study. Additional data required for the identification of socio-economic scenarios came mainly from
government documents and reports including population development strategy, industrial and
agriculture modernization strategy, industrial structure and productive forces distribution strategy, and
urbanization plan (Xu, 1999).
3.2 Description of Scientific Methods and Data
3.2.1 Population and urbanization scenario
The major driving forces such as population trends, economic development and urbanization will
determine future vulnerability and adaptive capacity levels. These major factors thus provide the basis
for setting socio-economic scenarios in the assessment of climate vulnerability, impacts and adaptation
policies to deal with climate change. In addition, specifying these socio-economic scenarios is prerequisite
in climate vulnerability assessment and adaptation evaluation.
In this connection, the socio-economic scenario activity focused on research in projecting future trends of
these main factors. In designing socio-economic scenarios for vulnerability and adaptation assessment,
population projections need to be carried out. Different methods were used to project population trends
to set scenarios to reflect future demographic uncertainties based on population projections. The
population projections were used as input to models used to assess future vulnerabilities and adaptive
capacity.
With a large proportion of its population still living in rural areas, the study region is accelerating its
urbanization process through economic growth. The term urbanization rate is defined here as the ratio of
non-agricultural population to the total population in a specific city or region.
Several methods were adopted for population and urbanization projections. The Auto-Regressive
Integrated Moving Average method, ARIMA (2, 1, 1) model with belt-interference, was used for the
Zhangye City which had long time-series data. Considering the fact that both Jiuquan City and Jiayuguan
City had relatively short time-series data, a GM (1, 1) model was used (Xu, 1999).
Zhangye City is a typical agricultural area，and thus the urbanization level is low comparing with
coastal region of China. In 1995, urban population in Zhangye City was only 18.45%. Jiayuguan City is a
relatively new industrial city with the big Jiuquan Steel and Iron complex. Urbanization rate of Jiayuguan
was the highest among the three cities. Jiuquan City had an urbanization rate of 31.14%.
3.2.2 Economic growth scenarios
The need for long-term economic growth scenarios was primarily in connection with future demands for
water, food and ecosystem functions. The Input-Output Analysis (IOA) method was used to project
future (2000-2050) GDP trends and economic structure change. Every industrialized country now
possesses a national-level input-output accounting framework. IOA has also achieved popularity in
recent times as a useful tool in climate change related studies (Parry et al., 1987; Riebsame, 1988; Malone
and Yohe, 1992; Cohen, 1993). The main contribution of IOA is that it provides explicit supply and
demand characteristics of individual economic sectors in different regions, and illustrates the nature of
interrelationships among these economic sectors and between regions.
26
3.3 Data and Results
Data for socio-economic scenario setting were mainly derived from municipal government statistics
(Zhangye City Government, 1978-2000; Jiayuguan City Government, 1993-2000; Suzhou Prefecture and
Jinta County, 1990-2000). Table -1 illustrates data of urban population, urbanization ratio, GDP, and other
economic activities and sectors in different counties of the three cities for the year 2000.
City/County
Shandan
Minle
Zhangye
Lingze
Gaotai
Sunan
Zhangye
City
Jiuquan City
Jiayuguan
City
Jiuquan
Jinta
Jiayuguan
Population Urbanization
(%)
(person)
GDP
Government
revenue
Agriculture
production
value
Industry
production
value
13154.25
11387.50
32750.00
9635.88
9487.25
2806.25
4126.50
5600.25
11687.50
4310.88
5723.88
1207.00
131542.50
113875.00
327500.00
96358.75
94872.50
28062.50
6127.50
10515.00
25268.75
6101.25
5248.75
5522.50
41660.00
80533.75
194946.25
63042.50
85300.00
18063.75
73490.00
59503.75
132941.25
28075.00
53823.75
21795.00
22551.88
10087.50
8190.38
4347.13
225518.75
100875.00
18521.25
5835.00
85193.75
66050.00
101270.00
43532.50
22413.38
936.00
224133.75
27962.50
13181.25
505851.25
Table 3.1a: Socio-economic data for the Heihe River Basin (2000) (Economic unit: US$ ’000)
Arable
Grain
Cotton
Oil seed Vegetable land Irrigation Forest Rangeland
production production production production
area
area
area
area
City/County
(t)
(t)
(t)
(t)
(mou) (mou) (mou)
(mou)
Shandan
9.23
NA
2.32
0.47
59.71
28.40
73.20
4.89
Minle
Zhangye
Zhangye Lingze
City
Gaotai
Sunan
Jiuquan
City
19.85
28.66
14.20
15.43
1.28
NA
NA
0.03
0.18
0.0081
3.93
1.26
0.36
0.38
0.075
0.40
44.00
4.21
7.56
0.0354
91.37
68.62
23.68
31.50
6.14
48.23
68.45
23.65
27.90
4.06
2.46
60.22
2.21
51.24
2.74
1.35
NA
NA
NA
181.25
Jiuquan
20.65
0.031
0.45
31.03
60.73
61.25
10.34
5.87
Jinta
6.35
1.88
0.21
6.54
28.33
28.32
28.13
41．61
1.05
NA
0.025
7.20
5.25
4.25
0.67
NA
Jiayuguan
Jiayuguan
City
Note: t: tonne; mou: Chinese land area unit; 1 mou = 1/15 ha.
Table 3.1b: Agricultural land use (unit: x 10,000)
27
Primary Sector
Incremental Employment
(%)
Secondary Sector
Tertiary Sector
Employment
Incremental
Employment
(%)
Incremental
Employment
(%)
(10,000 people)
Zhangye
City
0.4180
13.99
0.2918
28.42
0.2902
57.59
70.54
Jiayuguan
City
Jiuquan City*
0.0418
12.60
0.7530
59.59
0.2052
27.81
8.81
0.2853
5.03
0.38
8.45
0.3347
86.52
3.86
*In Jiuquan City, while incremental rate only included Suzhou and Jinta, employment covered the whole city.
Table 3.2: Heihe region economic structure and labor structure in 2000
City/County
2000
2010
2020
2030
2040
2050
Zhangye City (population)
124.99
129.30
135.08
140.35
140.35
140.35
Urbanization
17.05
19.12
22.31
25
28.2
30
Urban population
21.31
24.72
30.13
35.09
39.58
42.11
Shandan County
19.53
20.20
21.11
21.93
21.93
21.93
Urbanization
14.31
16.05
18.72
20.99
23.67
25.19
Urban population
2.79
3.24
3.95
4.60
5.19
5.52
Minle County
23.54
24.35
25.44
26.43
26.43
26.43
Urbanization
6.69
7.50
8.75
9.81
11.07
11.77
Urban population
1.57
1.83
2.23
2.59
2.93
3.11
Ganzhou
47.93
49.58
51.80
53.82
53.82
53.82
Urbanization
25.42
28.51
33.26
37.28
42.05
44.74
Urban population
12.18
14.13
17.23
20.06
22.63
24.08
Lingze Coungty
14.61
15.11
15.79
16.41
16.41
16.41
Urbanization
13.29
14.90
17.39
19.49
21.98
23.39
Urban population
1.94
2.25
2.75
3.20
3.61
3.84
Gaotai County
15.79
16.33
17.06
17.73
17.73
17.73
Urbanization
11.73
13.15
15.35
17.20
19.40
20.64
Urban population
1.85
2.15
2.62
3.05
3.44
3.66
Sunan County
3.59
3.71
3.88
4.03
4.03
4.03
Urbanization
26.76
30.01
35.01
39.25
44.27
47.10
Urban population
0.96
1.11
1.36
1.58
1.78
1.90
Jiayuguan City
(population)
15.97
19.37
25.00
30.13
30.13
30.13
Urbanization
75.39
85.23
87.35
89.07
91.46
93.52
Urban population
12.04
16.50
21.84
26.83
27.54
28.16
Jiuquan City (population)
46.96
56.95
62.75
68.49
68.49
68.49
Urbanization
27.83
30.25
33.46
36.50
39.24
41.58
28
Urban population
13.07
17.23
20.99
24.99
26.87
28.47
Suzhou District
33.20
40.26
44.36
48.42
48.42
48.42
Urbanization
33.97
34.97
38.66
42.18
45.35
48.05
Urban population
11.28
14.08
17.15
20.42
21.96
23.27
Jinta County
13.76
16.69
18.39
20.07
20.07
20.07
Urbanization
18.33
18.87
20.87
22.76
24.48
25.93
Urban population
2.52
3.15
3.84
4.57
4.91
5.20
Table 3.3: The results of population and urbanization projects for three cities in Heihe River Basin (Unit:
Population: 10,000 people; Urbanization %)
District/
County
Suzhou
District
Jinta County
Shandan
County
Minle
County
GDP (unit: million US$)
Year
Economic Structure (Share)
Total
Primary
Sector
Secondary
Sector
Tertiary
Sector
Primary
Sector
Secondary
Sector
Tertiary
Sector
2000
2005
225.50
308.75
81.88
101.13
65.75
107.75
77.88
102.13
0.3632
0.3274
0.2917
0.3490
0.3451
0.3307
2010
2015
436.38
634.38
137.88
192.00
149.63
222.63
152.00
225.00
0.3159
0.3028
0.3428
0.3509
0.3483
0.3547
2020
2030
843.25
1450.75
238.13
328.00
285.25
580.00
326.75
566.88
0.2824
0.2260
0.3383
0.3998
0.3875
0.3907
2040
2050
2383.63
3524.38
460.75
615.00
964.50
1532.38
1002.50
1455.38
0.1933
0.1745
0.4046
0.4348
0.4206
0.4130
2000
2005
100.88
138.13
43.50
53.63
21.38
35.00
36.00
47.25
0.4310
0.3885
0.2117
0.2533
0.3573
0.3423
2010
2015
2020
195.13
283.75
377.25
73.13
102.00
126.38
48.50
72.25
92.63
70.38
104.25
151.38
0.3749
0.3592
0.3350
0.2488
0.2547
0.2455
0.3606
0.3673
0.4012
2030
2040
649.00
1066.13
174.00
244.63
188.38
313.13
262.50
464.25
0.2682
0.2294
0.2902
0.2937
0.4045
0.4354
2050
2000
1576.50
131.50
326.50
41.25
497.63
45.38
674.00
44.88
0.2071
0.3137
0.3156
0.3450
0.4275
0.3413
2005
2010
199.75
274.88
60.38
75.25
68.00
96.88
72.38
108.13
0.3022
0.2737
0.3405
0.3526
0.3624
0.3932
2015
2020
351.00
440.50
91.13
97.38
118.50
152.25
150.25
210.38
0.2597
0.2211
0.3375
0.3455
0.4279
0.4775
2030
2040
682.88
1168.38
139.50
183.38
258.50
518.38
322.25
562.50
0.2043
0.1570
0.3785
0.4436
0.4718
0.4815
2050
2000
2005
1517.38
113.88
172.88
240.63
56.00
81.88
651.38
38.63
57.75
747.13
41.25
66.50
0.1586
0.4918
0.4738
0.4293
0.3387
0.3342
0.4924
0.3626
0.3850
2010
2015
238.00
303.88
102.13
123.75
82.38
100.63
99.38
138.13
0.4292
0.4071
0.3461
0.3313
0.4178
0.4547
29
Ganzhou
District
Lingze
County
Gaota
County
Sunan
County
2020
2030
381.38
591.25
132.13
189.38
129.38
219.63
193.50
296.38
0.3466
0.3202
0.3391
0.3715
0.5073
0.5013
2040
2050
1011.50
1313.50
248.88
326.50
440.50
553.50
517.38
687.13
0.2461
0.2486
0.4355
0.4214
0.5115
0.5231
2000
2005
327.50
497.13
116.88
171.00
89.63
134.25
121.00
195.00
0.3568
0.3438
0.2737
0.2701
0.3694
0.3923
2010
2015
684.38
873.88
213.13
258.13
191.38
234.00
291.38
404.88
0.3114
0.2954
0.2797
0.2677
0.4257
0.4632
2020
2030
1096.75
1700.25
275.88
395.13
300.63
510.50
567.00
868.38
0.2515
0.2324
0.2740
0.3002
0.5169
0.5108
2040
2050
2908.88
3777.75
519.38
681.38
1023.75
1286.38
1516.00
2013.50
0.1786
0.1804
0.3519
0.3405
0.5212
0.5330
2000
2005
2010
96.38
146.25
201.38
43.13
63.00
78.63
30.75
46.13
65.63
22.50
36.25
54.13
0.4473
0.4310
0.3904
0.3192
0.3149
0.3261
0.2335
0.2479
0.2690
2015
2020
257.13
322.75
95.25
101.75
80.25
103.13
75.25
105.38
0.3703
0.3153
0.3122
0.3196
0.2928
0.3267
2030
2040
500.25
855.88
145.75
191.63
175.13
351.25
161.50
281.88
0.2913
0.2238
0.3501
0.4104
0.3228
0.3294
2050
2000
1111.50
94.88
251.38
57.25
441.38
22.88
374.38
14.75
0.2261
0.6033
0.3971
0.2412
0.3368
0.1556
2005
2010
144.00
198.25
83.75
104.38
34.25
48.88
23.75
35.50
0.5813
0.5265
0.2379
0.2464
0.1652
0.1792
2015
2020
253.13
317.75
126.50
135.13
59.75
76.75
49.38
69.13
0.4994
0.4252
0.2359
0.2415
0.1950
0.2177
2030
2040
2050
492.50
842.63
1094.38
193.50
254.38
333.75
130.25
261.25
328.38
105.88
184.88
245.63
0.3929
0.3019
0.3049
0.2645
0.3101
0.3000
0.2151
0.2194
0.2244
2000
2005
28.00
42.63
12.13
17.63
9.00
13.50
7.00
11.25
0.4301
0.4144
0.3201
0.3159
0.2498
0.2652
2010
2015
58.63
74.88
22.00
26.63
19.13
23.50
16.88
23.50
0.3753
0.3561
0.3271
0.3131
0.2878
0.3132
2020
2030
94.00
145.75
28.50
40.75
30.13
51.13
32.88
50.25
0.3031
0.2801
0.3205
0.3512
0.3495
0.3453
2040
2050
249.25
323.75
53.63
70.38
102.63
128.88
87.88
116.63
0.2152
0.2174
0.4116
0.3983
0.3524
0.3604
Table 3.4: Heihe River Basin GDP and three economic sector productivity scenarios
30
GDP (unit: million US$)
City
Zhangye
City
Jiayuguan
City
Jiuquan
City
Year
Economic Structure (Share)
2000
792.25
Primary
Sector
326.50
2005
2010
1202.63
1655.50
477.63
595.50
336.88
480.25
388.13
579.75
0.3972
0.3597
0.2801
0.2901
0.3227
0.3502
2015
2020
2114.00
2653.13
721.38
770.75
587.00
754.13
805.63
1128.25
0.3412
0.2905
0.2777
0.2842
0.3811
0.4253
2030
2040
2050
4112.88
7036.50
9138.13
1104.00
1451.25
1903.88
1280.75
2568.38
3227.50
1728.13
3016.88
4006.75
0.2684
0.2062
0.2083
0.3114
0.3650
0.3532
0.4202
0.4287
0.4385
2000
2005
224.13
355.63
9.38
14.50
168.75
244.50
46.00
96.63
0.0418
0.0408
0.7529
0.6875
0.2052
0.2717
2010
2015
495.88
645.63
20.25
28.88
330.13
408.38
145.50
208.38
0.0408
0.0447
0.6657
0.6325
0.2934
0.3227
2020
2030
847.50
1335.63
36.75
53.25
507.63
746.13
303.13
536.25
0.0434
0.0399
0.5990
0.5586
0.3577
0.4015
2040
2050
1913.75
2671.63
76.50
111.25
1023.75
1347.75
813.50
1212.63
0.0400
0.0416
0.5349
0.5045
0.4251
0.4539
2000
2005
326.38
446.88
125.38
154.75
87.13
142.75
113.88
149.38
0.3841
0.3463
0.2669
0.3194
0.3489
0.3343
2010
2015
2020
631.50
918.13
1220.50
211.00
294.00
364.50
198.13
294.88
377.88
222.38
329.25
478.13
0.3341
0.3202
0.2986
0.3137
0.3212
0.3096
0.3521
0.3586
0.3917
2030
2040
2099.75
3449.75
502.00
705.38
768.38
1277.63
829.38
1466.75
0.2391
0.2045
0.3659
0.3704
0.3950
0.4252
2050
5100.88
941.50
2030.00
2129.38
0.1846
0.3980
0.4175
Total
Secondary
Sector
224.88
Tertiary
Sector
240.75
Primary
Sector
0.4122
Secondary
Sector
0.2838
Tertiary
Sector
0.3039
Note: Note: 1 US$ = 8 RMB
3.4 Conclusions
The socio-economic scenarios and climate change scenarios were used in vulnerability and adaptive
capacity assessment to represent future climate and socio-economic conditions.
Ehrlich (1996), presenting a simple equation with a simplified term, illustrated the relationship between
population growth and consumption level increase associated with economic growth, and the
environmental impacts. The equation is expressed as:
I=PxAxT
Where: I is the total societal impacts; P is the population size; A is the living standard (or per capita
consumption); and T is the technology used.
It is obvious that the total environmental impacts of a region will be proportional to both
population size and per capita consumption level. To date, most studies on population-resourcesenvironment indicate that population expansion is the main cause of ecological depletion and
unsustainability. Rapid population growth in developing countries has always been considered as a
threat to destroy the Earth’s life-support systems (Ehrlich, 1996). Population grow in China, associated
with rapid industrialization and urbanization, is driving consumption level higher and increasing the
31
demand for natural resources and the environment. These effects will be reflected in climate impact and
vulnerability assessment in the next section.
3.5 Main References
Ehrlich, A.H. 1996. “Toward a sustainable global population.” In: D.C. Pirages (ed.) Building Sustainable
Societies: A Blueprint for a Post-Industrial World. Armonk, New York: M.E. Sharpe, Inc.
Xu, Z.M. 1999. Evaluation of Sustainable Development and Water Resources Carrying Capacity. PhD
Thesis, Chinese Academy of Sciences, CAREERI, Lanzhou, China (in Chinese).
32
4 Impacts and Vulnerability
4.1 Activities Conducted
1.
Resource system vulnerability to climate stresses in the Heihe Basin of China;
By Yongyuan Yin, Nicholas Clinton, Bin Luo, and Liangchung Song
The main goal of this study is to develop effective methods to measure vulnerability and to assess
the environmental risks in dealing with climate stresses. The study reviewed and developed
methods for the formulation of indicators to assess water and land resource vulnerability to
climate variation and change. Indicators are discussed in relation to their specificity, descriptive
power, thresholds, and capacity for showing spatial distribution of vulnerabilities using existing
or modeled data. Resource system vulnerability is assessed though the use of applicable
indicators, relevant literature references, and application of a geographic information system
(GIS) for the mapping of the indicators.
2.
Vulnerability assessment of water resource system in Heihe River Basin under climate variation
and change;
By J.S. Zhang, Z.S. Cheng and M.Q. Wang
This study applied vulnerability assessment methods developed in activity 1 above to identify
the water resource system sensitivities and vulnerability under current climate and climate
change scenarios. A hydrological model was also used in projecting future flow changes under
climate change scenarios.
3.
Ecosystem vulnerability assessment under climate variation and change;
By L.D. Sun
This activity also followed the research approach developed by activity 1 to examine the
ecosystem sensitivity, vulnerability adaptability in the Heihe River Basin in year 2000 and 2040.
4.
Agricultural sector vulnerability assessment under climate variation and change;
By J.X. Xu
This activity also followed the research approach developed by activity 1 to examine the
agricultural sector sensitivity, vulnerability adaptability in the Heihe River Basin under current
climate and climate change scenarios.
5.
Land degradation in the Heihe River Basin in Relation to climate conditions;
By F.M. Hui, Peng Gong, and J.G. Qi
This activity developed a land degradation model based on the following factors: precipitation,
temperature, vegetation fraction cover, slope and aspect, soil type, and land use cover. The study
used remote sensing to identify the degree of land degradation, and to suggest potential policies
for preventing degradation.
4.2 Description of Scientific Methods and Data
A system’s vulnerability is related to a system’s resilience, defined as the capability of the system to
maintain its functionality in the face of a particular environmental change. The adaptive capacity of the
system is critical in determining the boundaries of coping ranges of the system. Coping ranges are
defined by the critical thresholds within which systems will have relatively benign experiences and
beyond which systems feel significant effects under climate variation and/or change. In this study, the
vulnerability of a system is defined as its propensity to undergo impacts that lead to disruptions in the
nominal functionality of the system as a result of climate variation or change.
This section presents methods, data, results and conclusions for each of the activities listed in section 4.1.
Please note that vulnerability assessment activities 2 to 4 all adopted an indicator approach described in
33
section 4.2.1. They all followed the environmental risk assessment approach because resource system
vulnerability is closely linked to environmental risk. Climate risk can be expressed in the following
simple formulas:
Environmental Risk (ER) = exposure (e) frequency (probability) × consequence
Consequence = f{intensity, sensitivity (s), adaptive capacity (a)}
Where, the frequency or probability of an environmental stress can be expressed as the likelihood of a
specific hazard (e.g., climate extreme). The consequence is the damage or adverse impacts of the
environmental stress, which is determined by the function of intensity of the stress, as well as the
sensitivity and adaptive capacity of the exposure system.
In many cases, however, we cannot obtain quantitative data of the probability distribution function for
these factors. Linguistic representations could be used, such as very frequent, reasonably probable,
unlikely, and extremely unlikely, to reflect the probability parameters. In fact, the assumption that
vulnerability can somehow be expressed in terms of various combinations of these three attributes is a
necessary simplification for the investigation of vulnerability in a variety of forms. Thus, in climate
vulnerability assessment, we need to identify the relevant intensity of climatic stresses and the frequency
of their occurrences. This will be followed by sensitivity and adaptive capacity assessment to identify the
potential climate risks.
The three vulnerability assessment activities (2, 3 and 4) were conducted separately from the assessment
described in section 4.2.1. These activities followed an approach developed by activity 1 but made some
modifications and also took consideration of future climate change. The three research activities
presented in sections 4.2.2, 4.2.3 and 4.2.4 dealt with vulnerability and adaptation assessment for water
resources, terrestrial ecosystem and agricultural sector respectively. They all set indicators to identify
sensitivities and adaptive capacities. Then, they calculated the system vulnerability by using a very
simple formula: V = f (S, A).
Where: V is vulnerability level; S is sensitivity level; and A is adaptive capacity level.
In the three sections, only additional methods are described, along with data sources, results and
conclusions.
4.2.1 Resource system vulnerability to climate stresses in the Heihe Basin
of China
Yongyuan Yin, Nicholas Clinton, Bin Luo, and Liangchung Song
4.2.1.1 A Vulnerability assessment approach developed for this study
The climate vulnerability assessment in this study integrates climate scenario setting, sensitivity analysis,
vulnerability indicator selection, vulnerability measurement and mapping. A general approach of the
vulnerability assessment is shown in Figure 4.1 in which there are four components: 1) climate scenarios; 2)
sensitivity analysis; 3) vulnerability indicators; and 4) system vulnerability to climate stimuli. The AS25
project has completed investigation on all these components and the AS25 project final report will present all
these activities. It is not, however, the intention of this chapter to discuss in detail all the components. Given
the fact that a large amount of research has been on climate change scenario development and climate
sensitivity analysis, the following section focuses mainly on vulnerability assessment.
34
Sensitivity analysis
Assessing present-day vulnerabilities of water and land resources to
climate stresses, and mapping climate vulnerabilities in the Heihe
River Basin
Climate vulnerability indicators
Multiple stakeholders, planners, analysts,
and public
Current climate variability
and climate change scenarios
Fig. 4.1: Flow-chart showing the general research approach
4.2.1.2 Applying the research approach in the Heihe River Basin
The Heihe River Basin case study of vulnerability assessments is presented below for illustration
purposes. This section provides information on the applications of vulnerability indicator methods and
GIS mapping to show the geographical distribution of current climate vulnerability levels of the region.
In the case study, several key vulnerability indicators are selected to measure resource vulnerability
under current climate conditions. GIS is used to identify the spatial distribution of water resource
vulnerability by combining the indicators of domestic water deficit (Srdjevic et al. 2003) and irrigation
deficit (Qi and Cheng, 1998) into a composite indicator. Maps, tables, and figures provide visual displays
of resource system vulnerability, which can help policy makers identify the most vulnerable subunits. It
should be indicated that there have been few practical applications of such an approach yet, particularly
in climate vulnerability research. In this sense, the methodology developed by this study can provide an
introduction to a useful computer technique for climate vulnerability assessment.
Results of vulnerability assessment to current climate variation can establish a baseline set of
measurements and observations that could be used to measure progress toward reducing vulnerability to
future climate change. Once these vulnerability measures are identified for various vulnerability
indicators, they can then be applied to project potential vulnerabilities of the resource systems to future
climate change scenarios. Thus, the research on present vulnerabilities of resource systems can provide
insights into potential impacts and vulnerabilities associated with future climate change.
4.2.1.3 Climate exposure and sensitivity analysis
In identifying present-day climate risks, existing climate variation patterns need to be specified (IPCC,
2001). Recent climate change trend in northwest (NW) China for the past 50 years was investigated by
analyzing the temperature and rainfall from 1951 to 2004 (See section 2.2.1). Climate change scenario
specification for this study represents the possible future climate conditions under various assumptions.
Based on eight coupled global atmospheric and oceanic circulation models (AOGCMs), the climate
change projection over west China for the 21st century was calculated by the NCC/IAP T63 (National
Climate Center/Institute of Atmospheric Physics) (See section 2.2.2). A regional climate model
(Ncc/RegCM2) nested with a coupled GCM (NCCT63L16/T63L30) and Hadley Center model (HadCM2)
was employed for designing climate change scenarios (See section 2.2.3).
35
The purpose of sensitivity analysis is to identify those climate variables possessing relative importance in
determining resource system vulnerability. In addition, sensitivity analysis can indicate those key aspects
of resource systems, which are sensitive to certain climate variables. Since the relations between climate
variables and various system aspects are based on historical statistics or experience, this kind of
information can be derived from experts or stakeholder consultation. In this connection, stakeholder
workshops and surveys were carried out to provide sensitivity information. The consultation with
stakeholders on climate sensitivity is also part of a capacity building process of the study. In the
consultation process, stakeholders identified key climate stresses, vulnerability indicators and critical
thresholds they use in resource management.
Climate sensitivity analysis through stakeholder consultation in vulnerability assessment of the AS25
project followed the Hunter Valley case study (Hennessy and Jones, 1999). A potential sensitivity matrix
was generated during a stakeholder workshop to identify climate variables with the greatest forcing and
activities with the broadest sensitivity in the study region. The sensitivity results indicate that water
shortage is the main concern of the study region. And rainfall variability and soil moisture levels have the
greatest impact, while temperature has a slightly moderate effect. Obviously, rural resource use activities
are very sensitive to climate events.
4.2.1.4 Identification of critical vulnerability indicators
The research procedure follows with an identification of indicators to measure resource vulnerability in
this study. To select critical indicators for vulnerability assessment, the first major source of information
used for the study was government reports, documents, and other published materials on resource
issues. Based on some existing key policy concerns in the region, indicators for measuring resource
vulnerability under climate stimuli were identified. Some operationally useful key indicators in
vulnerability and adaptive capacity assessment are listed in Table 4.1.
Climate and Other Related Factors
Rainfall variability
Maximum temperature
Soil moisture
Minimum temperature
Wind
Cold snap
Heat stress days
Accumulated degree days
Cropping area
Population growth
Economic growth
Technology
Consumption
Urban expansion
Resource management
Government policies
Resource Vulnerability Indicators
Food security
Farm income
Water scarcity (withdrawal ratio)
Drought hazards
Palmer drought severity index (PDSI)
Water use conflicts
Arable land loss
Groundwater stress
Salinity
Soil erosion
Grassland deterioration
Table 4.1: Potential climate and other variables and resource vulnerability indicators
The preceding discussion indicates that climate risks and vulnerabilities are determined by many factors
including climate stimuli, the system’s sensitivity to climate, the adaptive capacity, and other response
options to deal with risks. These factors (Table 4.1, left column) that influence the system exposure risk
can mainly be divided into climate stimuli (e.g., rainfall variability, temperature extremes, and so on in),
36
properties of the resource use systems (resource management), as well as economic and social forces
(economic and population growth). These factors affect the spatial distribution of climate impacts and
adaptive capacity which could result in significant differences in climate risks and vulnerabilities
geographically. It is thus instructive to examine various factors influencing climate vulnerability, and
classify the region into several distinct risk zones in order to facilitate the establishment of more effective
adaptation policies and plans.
Since resource system vulnerability is related to failures of the resource system to provide economic,
social and ecological functions to meet societal demands, indicators listed in the right column of Table 4.1
reflect some aspects of these functions, it is obvious that farm income is one of the most important
indicators for measuring vulnerability. Poor peasants in rural western China expect to generate more
economic profits from water and land use. Improvements in economic return will also reduce system
vulnerability (Yohe and Tol, 2002).
In China, providing enough food for the 1.3 billion people is always a big challenge. There has been an
increasing concern about China’s food security or its ability to feed itself. The provision of adequate food
on a continual basis is a major indicator of regional sustainability. The food security indicator reflects the
ability to achieve higher levels of self-sufficiency, and it can be used to check whether the resource base
can provide a sufficient food supply.
Early 2000, the Chinese central government launched a major, new initiative to develop China's poor,
backward western regions. China’s National West Development Strategy has opened a new chapter of
economic growth and expansion in China’s western regions. The motivations behind the West
Development Strategy are aimed at rapid changes in Western China over the next few decades and
easing the income disparities between coastal and interior China. Stimulated by this new development
strategy, many of the industrial and housing developments occur in productive farmlands, forestry lands,
and wildness areas. How to slow down the conversion of farmland to urban and industrial uses is critical
for regional sustainability in western China. Thus, a further indicator to protect and conserve arable land
reflects this concern.
It is now generally realized that an environmental concern should be incorporated in decision making in
an effort to achieve sustainable development (World Commission on Environment and Development,
1987). There are a large number of parameters that can be used as indicators of ecological vulnerability. In
Table 4.1, the environmental concern is reflected in the indicators of salinity, soil erosion, and grassland
deterioration.
There is an increasing concern about the implications of climate change for water management (Gleick,
1990) and water use conflicts in the semiarid region of western China (Kang et al., 1999). Dealing with
potential water use conflicts under changing climate is therefore considered as an important indicator.
The fight over access to water resources in the Heihe River Basin has led to disputes, confrontation, and
many cases of violent clashes. Changing water supply induced by climate warming may increase water
use conflicts in the region.
In order to improve the reliability of the information on indicators derived from existing sources,
workshops and surveys with stakeholders and decision-makers in the region were conducted to discuss
major policy issues related to climate change and environmental risks. Various government officials and
experts were invited to workshops in the Heihe River Basin. Stakeholder representatives were consulted
to identify key concerns related to resource use in the region and to prioritize these indicators. These
representatives included officials from various ministries of Gansu Province, bureaus of municipal
governments, research institutes, women groups, and universities. It was indicated during these
workshops that water shortage was the key problem relating to sustainable development in the region.
Almost all the ecological un-sustainability problems are caused by water shortage in the region.
4.2.1.5 Mapping vulnerability
Mapping the spatial distribution of system vulnerability is useful in helping policy makers identify the
most vulnerable subunits. To show geographic distribution of the vulnerability levels using indicators,
several spatial scales have been considered, ranging from square kilometers, county level, and sub-basin
to the whole basin based on data availability and other logistical reasons. For test purposes, this study
applied GIS mapping techniques to illustrate resource system vulnerabilities using existing and modeled
data at different scales.
37
The probabilistic concept of vulnerability is directly applicable to the geographic assessment of
vulnerability and facilitates interdisciplinary synthesis of geographic information. For example, outputs
from climate models in the form of projected mean temperatures and precipitation provide indication of
future exposure to potentially deleterious environmental conditions. These outputs are commonly
available as geographically referenced grids. These grids could be used to produce probability of
exposure based on the distributional assumptions of climate model accuracy. Similarly, social
information, by county, jurisdiction or other geographic entity, can be collected and assembled into
indicators of adaptive capacity (or lack thereof). Probability of adaptive capacity could be inferred from
[0, 1] scaled indices or percentage of some threshold level for the index (percent of threshold revenue, for
example). Assuming this setup, we have undertaken to map sensitivity as a geographic data set (layer) to
be integrated as above with layers that represent exposure probability and lack of adaptive capacity
probability. As discussed earlier, the requisite ingredients for vulnerability to a particular event include
exposure to the event, sensitivity to events of that magnitude and duration, and lack of adaptive capacity
to handle events of that type. Any lack of these key ingredients, means the system is not vulnerable. For
example, if P (exposure to event) = 0, then vulnerability = 0; likewise for the other terms. A map of
vulnerability is created by multiplying (using map algebra, a form of overlay analysis for rasters in which
the maps are multiplied cell by cell) the input maps of probability of exposure, sensitivity and lack of
adaptive capacity. Thus teams with different emphases can produce maps that are easily integrated
under this conceptual framework. While probability maps may be readily created (frequency of
observable or projectable events, for example), indicators may also be used as a proxy. The following
sections illustrate the use of geographic data to create maps of sensitivity to be integrated with the results
of climate simulations and social studies of adaptive capacity.
4.2.1.6 Some preliminary results of resource vulnerability in the Heihe River Basin
For illustration purposes, measurements of some key vulnerability indicators were carried out in the case
study. While all indicators listed in Table 4.1 were investigated by the AS25 research team, this study
only calculated resource vulnerability indicators of water withdrawal ratio, water use conflicts, and PDSI,
as well as conducted vulnerability mapping. It should be noted that calculations of these indicators were
neither comprehensive nor systematic. Based on data availability, PDSI was calculated at river reach
level, water withdrawal ratio and water conflict (events of disputes, confrontation, and violent clashes for
accessing water resources) were measured at the basin scale. Results presented here are mainly water
system vulnerability under current climate conditions. Vulnerability assessments for other indicators and
under climate change scenarios are presented in the following sections.
Water withdrawal ratio
One important water vulnerability indicator is water withdrawal ratio defined as the ratio of average
annual water withdrawal to water availability. The critical thresholds for indicators were set to compare
with indicator values of different areas to identify their vulnerability levels against these indicators. If
indicator values do not exceed the threshold level, it is assumed that the system will have a relatively
benign experience under climate stresses, but beyond the threshold level, the system will suffer
significant stress under climate variation and/or change. For example, a critical threshold level for the
drought indicator can be determined by the amount of rainfall required in a specific region. It also can be
set using a more complex method such as the accumulated deficit in irrigation allocations over a number
of seasons (Jones and Page, 2001).
For annual water withdrawal ratio indicator, the World Meteorological Organization (WMO) suggests
that the ratio exceeds 20% and 40% of annual water availability be considered as medium and high water
stress respectively (WMO, 1997). According to this definition, any regions where average annual water
withdrawals exceed 40% of annual water availability are considered as under “severe water stress”. In
Northern China, however, the threshold of this indicator is much higher. It suggests a high stress level at
60% for annual water withdrawal ratio indicator (Xie, 2000; Gansu Meteorological Bureau, 2000).
Table 4.2. lists water withdrawal ratios in the Heihe River Basin under current climate conditions. The
current water withdrawal ratios in the study region are extremely high (83%- 125%), far exceeding the
critical threshold levels set by both WMO and Chinese government.
38
Year
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Water Availability (108m3) 35.90 34.33 35.46 35.57 34.04 34.64 34.63 34.33 34.70 34.84
Total water withdrawal
(108m3)
29.02 27.38 35.40 28.81 29.55 35.76 28.01 41.35 35.45 32.33
Water withdrawal ratio
81% 80% 100%
81%
87% 103% 81% 120% 102% 93%
Table 4.2: Current water withdrawal ratio in the Heihe River Basin (1991-2000)
Water stress in the region might be intensifying under future climate change scenarios because of
growing water withdrawals (related to population and economic growth) and/or decreasing water
availability (related to climate change).
The Palmer Drought Severity Index
Drought hazards are based on rainfall amount, and if rainfall remains below the threshold levels for a
sustained period, drought hazards are declared (Alberta Agriculture, 2000). The drought index (PDSI)
was used as an indicator in measuring the frequency of drought hazards over time. PDSI simulates
monthly soil moisture content and is thus suitable to compare the severity of drought events among
regions with different climate conditions. The Palmer drought severity index (PDSI) was introduced by
Palmer (1965) for measurement of meteorological drought. It has been widely used in different regions of
the world to study severity of drought hazards (Briffa et al., 1994; Kothavala, 1999; Ntale and Gan, 2003).
Because PDSI can simulate monthly soil moisture content, it is thus suitable to compare the severity of
drought events among regions with different climate zones and seasons (Makra et al., 2002).
Time series of monthly temperature and monthly precipitation for 15 meteorological stations were
collected from the meteorological service of Gansu province. The computation of the PDSI begins with a
climatic water balance using historic records of monthly precipitation and temperature. Soil moisture
storage is considered by dividing the soil profile into two layers. The indicator operates on a monthly
time series of precipitation and temperature to produce a single numerical value between –4 and +4 that
represents the severity of wetness or aridity for a particular month. Any PDSI values above +4.00 or
below –4.00 fall into the "extreme" category of drought or wet spell, respectively.
Figure 4.2a illustrates the trend in growing season PDSI for lower reach of the study basin. It shows that
the lower reach area has been drier in the past decade. This trend would continue under the changing
climate (Kang et al., 1999). During dry years, while the precipitation volume of May and June is
remarkably lower than the mean, the annual evaporation remains very high (Chen and Qu, 1992). Figure
4.2b illustrates the trend in growing season PDSI for middle reach-lower part of the study basin. It also
shows that this area has a trend of becoming drier in the past decade. Figure 4.2c illustrates the trend in
growing season PDSI for middle reach-upper part of the study basin. The drought trend for this area in
the past decade is not significant because this area is much closer to the high mountains and the annual
average precipitation is relatively higher. Figure 4.2d illustrates the trend in growing season PDSI for
upper reach of the study basin. It shows that the drought trend in this area has little changes in the past
decade due to higher annual average precipitation. This has particular significance for the whole basin
because most of the water resources in the study basin are from the upper reach of the basin. These
figures suggest that even though the drought trend of upper part basin is not significant, drought will
still become more serious in the lower part of the study basin. Considering that most water consumption
in the basin is from the middle reach, the results of drought index can provide information for water
resources allocation and water use planning in the entire basin.
39
6
10
Growing season PDSI
8
Growing season PDSI
growing season PDSI
Poly. (growing season PDSI)
6
4
2
0
-2
4
growing season PDSI
Poly. (growing season PDSI)
2
0
-2
-4
19
61
19
64
19
67
19
70
19
73
19
76
19
79
19
82
19
85
19
88
19
91
19
94
19
97
20
00
-6
2000
1997
1994
1991
1988
1985
1982
1979
1976
1973
1970
1967
1964
1961
-4
Year
Year
Fig. 4.2b: Trend in growing season PDSI for middle
reach-lower part of the study basin
5
4
3
2
1
0
-1
-2
-3
-4
-5
5
growing season PDSI
4
Growing season PDSI
Poly. (growing season PDSI)
3
growing season PDSI
Poly. (growing season PDSI)
2
1
0
-1
-2
-3
-4
19
6
1
19
64
19
67
19
70
19
73
19
76
19
79
19
82
19
85
19
88
19
91
19
94
19
97
20
00
19
6
1
19
64
19
67
19
70
19
73
19
76
19
79
19
82
19
85
19
88
19
91
19
94
19
97
20
00
Growing season PDSI
Fig. 4.2a: Trend in growing season Palmer drought
severity index (PDSI) for lower reach of the study
basin
Year
Year
Fig. 4.2c: Trend in growing season PDSI for middle
reach-upper part of the study basin
Fig. 4.2d: Trend in growing season PDSI for upper
reach of the study basin
Note: Poly means fitted trend line of growing season PDSI
Water use conflict
Water use conflicts are events of disputes, confrontation, and violent clashes for accessing water
resources. The number of these events was traced to show a system's failure of supplying a certain
amount of water for multiple users. In the Heihe River region, various water use policies and plans have
been implemented or designed to limit or prohibit the utilization of certain amount of water by sectors or
regions. Controversies have occurred, of course, as a result of such policies. For the water diversion
policy in Heihe River Basin, farmers in upper and middle reaches of the river already argue that less
water for irrigation has led to detrimental consequences in the agricultural sector, whereas others have
indicated that the new policy has been able to revive a dried lake located in the downstream region.
Obviously, water policies or regulations may make some sectors or regions worse off and others better off
because of their redistributive nature. It is this redistributive nature of policies that often aggravate water
use conflicts.
Figure 4.3 illustrates the trend of water use conflict in the study basin. It shows that the trend of water use
conflict has been increasing in the past decade. The trend of this social indicator suggests that water
shortage in the growing season becomes more and more serious because of decreased water supply and
increasing population and per capita water use.
40
Fig. 4.3: Trend of the events of water-use conflicts (number of violent events fighting for water) in the
study basin
Mapping vulnerability in the Heihe River Basin
The following indicators were computed from a wide variety of ancillary data sets. These data will be
described for each indicator. The indicators focus on weather, agriculture, and water resources. Although
numerous assumptions are made in the analysis, it is important to keep in mind that the objective is to
elucidate geographic patterns. Thus, relative differences between areas are the important trend under
investigation.
The agricultural sector relies heavily on irrigation water, mainly from river flow and groundwater
sources, due to the aridity of the region. For this reason, vulnerability of this sector is largely a function
of water resources. To evaluate agricultural vulnerability, both crop water demand and water resource
availability were assessed.
Precipitation and temperature data were collected from meteorological stations distributed through the
Heihe River Basin. The data were averaged, on a monthly basis, over the period from 1995 to 2000 in
order to represent current conditions. Using a method similar to Tan et al. (2002), these data were
interpolated to one-kilometer grid cells and partitioned into infiltration and runoff fractions (in
millimeters) using the “rational” method, in which runoff is estimated from rainfall, catchment size, and
infiltration. Rational runoff coefficients were created from inputs of a Digital Elevation Model (DEM), soil
type, and land cover using the method described as follows. Unique combinations of soil type and land
cover were determined from spatial data sets over the project area. Each soil/landcover combination was
assigned a United States Department of Agriculture Natural Resources Conservation Service
(USDA/NRCS) “curve number” for characterizing soil infiltration and runoff (USDA/NRCS, 1999). The
curve numbers were converted to rational runoff coefficients (c in the equation below) using Tan et al.
(2002) equation 3 and the DEM on the project area. Runoff was computed based on the rational runoff
equation Q = cIA, where Q is discharge, I is rainfall (assumed at constant intensity) and A is catchement
area. The runoff was routed to a theoretical channel network (derived from the DEM) to assess monthly
values of discharge, geo-located at one-kilometer resolution. The difference between estimated runoff
and monthly rainfall was assumed to be infiltration.
41
The spatial distribution of cropland map of the Heihe River Basin was created from data of the
International Geosphere-Biosphere Program (IGBP). Crop types were also based on the data in the land
cover data set from IGBP. Estimated crop evapotranspiration was computed using the FAO method
described in FAO Drainage and Irrigation Paper 56 (Allen et al., 1999). The FAO Crop Water Requirement
(or CROPWAT) model can be used to estimate some critical values of crop growth and water
requirements. The computation of indicators of crop stress or yield index can be achieved by using (Allen
et al., 1999):
Yield Index = ETc-stressed/ETc-max
Weather conditions were based on a combination of the measured data and the CLIMWAT database,
which is a set of weather records from observation stations distributed globally. The simulated
evapotranspiration data were compared to the estimated rainfall, on a monthly basis. It is assumed that
all infiltrated rainfall is available for crop growth, while runoff is not. This analysis indicates areas of crop
water deficit, meaning that the infiltrated rainfall is insufficient to meet crop demand. Thus, the deficits
indicate the amount of irrigation water needed to maximize crop growth. Areas of high deficit will place
more pressure on irrigation infrastructure and neighboring areas of surplus. Figure 4.4 shows the
distribution of these high-demand areas.
Fig. 4.4: Areas with high-irrigation water demand (The negative units are in millimeters of deficit)
The map indicates that there are geographic differences in demand for irrigation. This is due to the
combination of crop type under cultivation (some crops require more water) and local variation in
climate conditions, particularly rainfall, temperature and humidity. The areas where high crop water
42
demand and low rainfall converge are areas with high irrigation demand. In the likely event of climate
change, areas with high water deficits, shown in Figure 4.4, will be more affected by fluctuations in the
supply of irrigation water. Thus, this deficit can serve as an indicator of vulnerability.
However, irrigation will compete with humans for available water. The Landscan data set was utilized in
the analysis of per capita water supply in the Heihe River region (Dobson et al., 2000). It was assumed
that none of the infiltrated water was available for humans and that human consumption would rely
entirely on runoff. Thus, the per capita water resources index was computed as the annual runoff
(supply) divided by the population (demand). Where this index is very low, it indicates that either there
are a lot of people, there is a low supply, or a combination of the two. Similar to the analysis of crop water
requirements, the areas of low per capita water yield indicate areas of high demand from external
sources. These areas may exert more pressure on neighboring regions with a surplus of water resources.
However, there may be cumulative impacts associated with high demand for water for both domestic
and agricultural use. Figure 4.5 indicates the areas of high demand for domestic water supplies. Units are
in cubic meters per year, per capita.
Fig. 4.5: Per capita water resources (in cubic meters per capita, annually). Areas of low value (dark colors)
indicate a high demand for water resources not available through local supply. The break points are taken
from values in Feitelson and Chenoweth (2002)
43
A simple comparison of available runoff and population—what Feitelson and Chenoweth (2002) call
“annual per capita internal renewable water resources”—indicates the geographic areas of high demand.
Figure 4.5 shows areas where the local demand for water is not met by a “readily available” local supply
(from local runoff). The “dependence ratio” indicator suggested by Lane et al. (1999) describes this deficit
as the fraction of the local demand that must be met through water transfers. Areas of supply deficit are
likely to be of varying degrees of vulnerability based on the extent of local resources available for
remedying this supply-demand imbalance (in other words, the adaptive capacity). For example, the
income per capita and the existing level of water supply infrastructure development will determine how
well these areas can import water from elsewhere or otherwise provide local people with a safe source of
water. This map should be interpreted as the sensitivity of the system, rather than the vulnerability which
is a function of sensitivity, exposure and lack of adaptive capacity. It is notable that some areas are barely
at the subsistence level in terms of access to water. Similar to the indicator for the agricultural sector, the
areas of high demand for water will be more affected by any fluctuations in supply that result from
changes in climate.
The above metrics are fairly indirect indicators of sensitivity and current trends in weather and climate
can illustrate the potential exposure to climate change. The following indicators of exposure are based on
the analysis of weather at seven observation stations between the years 1999 and 2003: the number of
months over the last 5 years during which rainfall was 20% lower than the long-term average for that
month, the number of days over the last 5 years during which the maximum temperature was more than
5°C higher than the mean monthly maximum. These indicators are computed based on the methods
proposed in Kaly and Pratt (2000). It is notable that the periods of dry weather are in the upper indices of
vulnerability, whereas the “heat wave” indicator is much higher than anything discussed in Kaly and
Pratt (2000). While Kaly and Pratt (2000) describe the indicators for “vulnerability”, here we are using
them for exposure. We also created a map to show the number of months over the past 5 years during
which rainfall was 20% lower than the long-term average for that month. It is an indicator of drought
stress and, as such, should be considered in conjunction with the per capita domestic water indicator and
the crop water deficit indicators as shown in Figures 4.4 and 4.5, respectively. Similarly, another map was
created to show the number of days over the past 5 years during which the maximum temperature was
more than 5°C higher than the mean monthly maximum. It is a “heat wave” map that should also be
considered in conjunction with the other indicators, in terms of illustrating the areas of likely weather
extremes. These exposure indices we have used are for historical and illustrative purposes. Ideally,
gridded GCM output regarding future exposure to climate extremes would be used to predict areas of
likely future vulnerability.
These indicators can be scaled and multiplied, to obtain a geographic amalgam of vulnerability using the
probabilistic framework. To do this, the weather indicators (dry periods and hot periods) were divided
by their relative maxima and multiplied to obtain a composite indicator of weather extremes on a scale of
[0, 1], with 1 representing areas with more frequently observed extreme weather. This composite was
used in the construction of two additional indicators: one representing the vulnerability of agriculture to
weather extremes (agricultural vulnerability indicator hereafter), and one representing the vulnerability
of domestic water availability to weather extremes (domestic vulnerability indicator hereafter). The per
capita water indicator and crop water deficit indicator were normalized to [0, 1] with 1 (most vulnerable)
corresponding to the lowest per capita water availability and the highest irrigation deficit, respectively.
Each normalized indicator was then multiplied by the composite weather indicator in the manner
described by the probabilistic equation of vulnerability (and assuming each area to have equivalent
adaptive capacity) to obtain two indices of vulnerability for the respective water uses. Here, we assume
adaptive capacity is constant across the project area and is therefore not included in the analysis.
However, this additional characteristic could be readily incorporated to the analysis given suitable
geographic data. By using the scaled indicators as proxies for probability estimates, the vulnerability
indices represent likelihood that there should be a confluence of extreme weather and “marginal”
conditions in the form of very low water availability or a high irrigation deficit on local cropland. This
interpretation is justified by the fact that these marginal conditions will be aggravated by hot weather
that increases evapotranspiration of crops and evaporation of water from channels or other
impoundments, and by a lack of rainfall that restricts replenishment of supply. As agricultural and
domestic water are competing uses, it is logical to combine these two vulnerability indictors into a
composite indicator that reveals in what areas there are likely to be shortages and conflicts when there are
adverse weather conditions. The composite indicator was created by adding the agricultural and
domestic vulnerability indicators, summing over a rectangular 3-pixel neighborhood, and scaling to [0, 1].
44
The rationale behind this manipulation is based on the assumption that in a 3 kilometer neighborhood,
competing water uses will compound each other and result in a localized area of higher vulnerability (of
both uses) to adverse weather conditions. This composite indicator is displayed in Figure 4.6.
In keeping with our probabilistic conceptual framework, Figure 4.6 should be interpreted as the
likelihood of water resource system vulnerability, a critical system in this arid region. The high
vulnerability areas (values close to one) are determined by the confluence of water resource system
sensitivity and exposure to environmental extremes. Since domestic and agricultural uses compound
each other (are additive effects) in this analysis, having a low vulnerability in either of these sectors does
not render the area invulnerable, but vulnerability is reduced by the absence of competing uses. Of
course, the vulnerability is relative to the scale of analysis, since the indicators were normalized based on
regional extremes. By expanding the scope of the analysis, it is likely that new extremes would be
introduced and the indices would automatically adjust themselves accordingly.
The obvious area of high vulnerability in the south-east portion of the region consists of a population
center, with high agricultural production, in an area that has historically experienced deleterious (not
enough rain and too hot) climate conditions. Areas of very low vulnerability may be due to either the
absence of extreme weather conditions, or the absence of an irrigation deficit and a high per capita water
supply. This is consistent with what we would expect for this region with one caveat. As noted earlier,
we did not incorporate a lack of adaptive capacity indicator in this analysis. Therefore, the vulnerability
of rural areas with no means of coping with climatic extremes and high sensitivity may be
underestimated. Similarly, the vulnerability of relatively urban areas with more extensive infrastructure,
monetary resources and/or political clout, may be over estimated.
It is notable that there is a large portion of Figure 4.4 (irrigation deficit) that shows the ‘No Data’ value
and this value has not made its way into Figure 4.6, the map for vulnerability. This is explainable by the
fact that the map sources we used do not show agriculture in the ‘No Data’ areas (at the one kilometer
resolution scale). We therefore assume that there is no demand for irrigation water in those areas. The
infiltrated water would likely be used by local vegetation or enter the groundwater, but is never the less
assumed to be unavailable for humans.
45
Fig. 4.6: Map showing areas of relatively high vulnerability to adverse weather conditions
The source of water for society is rainfall; variations in rainfall thus affect the supply of water for
competing uses (Krol et al., 2001). Reduced water availability resulting from low rainfall is compounded
by the decreased quality of the diminished supply (Qi and Cheng, 1998). While the demands for water
resources increase as populations and economies grow, the availability of water are being reduced by
climate variation (See Figure 4.2). The fight over access to water resources in the Heihe River Basin has
led to increasing disputes, confrontation, and many cases of violent clashes (See Figure 4.3). The growing
water use conflicts have posed a big challenge for local government agencies to implement some effective
water allocation policies.
This composite indicator represents the vulnerability of both agricultural and domestic water users to
unfavorable weather conditions in the form of long hot and dry spells. It should not be interpreted as an
absolute measure of vulnerability, rather as a way of identifying areas of high vulnerability in the region
46
as a whole. The relative distribution of such vulnerability levels is shown in Figure 4.7. The histogram
illustrates a bimodal distribution with values concentrated in the lower range and a fairly small number
in the upper range. This is appropriate given that the analysis was designed to identify areas of high
vulnerability for the purpose of building the adaptive capacity in those areas.
Fig. 4.7: Histogram of composite water use vulnerability levels in the Heihe River Basin
Vertical axis is frequency of grid cell values
4.2.1.7 Summary
The study seeks to provide answers to some important questions in relation to climate vulnerability
assessment. It provides information on the geographical distribution of current climate vulnerability
levels of the region. The results indicate the relative vulnerability levels of water and land resources in
different areas exposed to current climate stimuli. The vulnerability indicator measurements for resource
systems can be applied to project potential vulnerabilities of the resource systems to future climate
change scenarios. In addition, the chapter contributes to methodological development in vulnerability
assessment and mapping.
By using vulnerability indicators, the climate vulnerability of the study region under current climate
conditions has been investigated. The methods for the compilation of indicators, geographic allocation,
and synthesis are valid for other regions as well. By taking a probabilistic approach, the framework
automatically scales up due to the consistency of the [0, 1] scale. If true probability measures are
unavailable (though they frequently are available), the analysis is automatically normalized to regional
extremes. Thus, the method should be viewed as portable, but intra-region comparisons will not, in
general, be valid. The applications presented here are intended to benefit future studies that aim to assess
resource system vulnerability. The consideration of scale will be important in the determination of what
indicators are necessary and feasible for the inclusion in any potential climate vulnerability assessment.
In vulnerability and adaptive capacity measurement, many of the indicators can be expressed in
numerical terms, particularly for those climate and physical variables. It is also recognized in the case
study, however, that many indicators cannot be quantified, and many of the threshold levels can only be
qualitatively described. As a result, some data used in the case study are fairly abstract and not
47
particularly meaningful out of context. However, assuming food production to be an important element
of society and a logical starting point for vulnerability reduction, this composite indicator is informative.
Specifically, the areas of highest vulnerability, as evidenced by the indicators, have been narrowed down
to several square kilometers. Assuming constraints to the adaptive capacity of the entire region, these
places could be designated as high priority in terms of implementing effective adaptation strategies to
prevent long-term damage from climate change.
For examining system vulnerability to climate change, a natural resource system representing particular
future conditions needs to be proposed. For example, water resource system design, operation, and
management policy can be specified over time. The specification will include assumptions regarding
system design, operation, and hydrologic and other inputs and demands that are all key aspects of a
water system scenario representative of what could occur in the future. Incorporated into that scenario
are key indicators of resource vulnerability. The uncertainties arising in estimating future demand or
operational changes can be comparable to those associated with projecting climate change, and can be
equally complicated for vulnerability assessments.
As a pilot study, the methods used in this study are subject to critiques. When applying vulnerability
assessment methods in the case study, vulnerability indicator selection and vulnerability measurement
were not carried out in a comprehensive and systematic way. This is in large part a result of spatial data
availability over the study area. However, these methods are effective in vulnerability assessment and
mapping spatial distributions of resource system vulnerability. When future climate change and socioeconomic scenarios are available, these methods can also be applied to estimate indicator values in the
future. This will produce future vulnerability data for each indicator.
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Srdjevic, B., Medeiros, Y.D.P. and Faria, A.S. 2003. “An Objective Multi-Criteria Evaluation of Water
Management Scenarios”, Water Resources Management. Vol 18. 35–54
49
Tan, C. H., Melesse, A.M. and Yeh, S.S. 2002. “Remote sensing and GIS in runoff coefficient estimation in
China, Taipei”. Proceedings of the 23rd Asian Conference on Remote Sensing. Kathmandu, Nepal.
November, 2002. http://www.gisdevelopment.net/aars/acrs/2002/pos3/217.pdf
USAID, 1997. FEWS Project: Vulnerability Assessment, Published for USAID, Bureau for Africa, Disaster
Response Co-ordination, Arlington, VA.
USDA/NRCS. 1999.
“SCS Runoff Equation.”
Employee
http://www.wcc.nrcs.usda,gov/hydro/hydro-training-course.html.
Training
Module
205.
Wang, Z.Y. 2005. Climate Change Analysis for Western China: 1951-2004. PhD Thesis, China
Meteorological Administration, Beijing, China.
WMO, 1997. Comprehensive assessment of the freshwater resources of the world (overview document).
World Meteorological Organization, Geneva.
World Commission on Environment and Development. 1987. Our Common Future. Report of the World
Commission on Environment and Development. Oxford, UK: Oxford University Press.
Xie, Jingnan (ed.) 2000. Northwestern China Arid Climate Change Research and Projection, Volume II:
Drought and Flooding Indicators. China Meteorological Press, Beijing (in Chinese).
Xu Y., Y.H. Ding, Z.C. Zhao and J. Zhang. 2003: A scenario of seasonal climate change of the 21st century
in Northwest China. Climatic and Environmental Research. 8(1):19-25 (in Chinese)
Yin, Y.Y. et al. (forthcoming). AS25 Project Final Report. START/AIACC.
Yohe, G. and Tol, R.S.J. 2002. “Indicators for social and economic coping capacity – moving toward a
working definition of adaptive capacity” Global Environmental Change (12): 25-40.
4.2.2 Vulnerability assessment of water resource system in Heihe River
Basin under climate variation and change
By J.S. Zhang, Z.S. Cheng and M.Q. Wang
The overall goal of this activity is to better understand the current vulnerability, sensitivities and adaptive
capacity to climate variability and change of water resource system in the Heihe River Basin. Specific
issues addressed in this research activity are: i) develop sensitivity and vulnerability indicators of water
resource system in Heihe River Basin, ii) synthesize present-day overall climatic vulnerability, and iii)
assess the vulnerability of water resource system under climate change scenarios. Time horizons
considered are from the past 40 years to present, and through 2030-2050.
4.2.2.1 Present-day water resource system vulnerability assessment
The vulnerability assessment was divided into two parts: sensitivity analysis and adaptive capacity
calculation. Then vulnerability was calculated as:
Vulnerability = Sensitivity/Adaptive Capacity
Data for this study were from government statistics (at County level); observation records from weather
stations and hydrology stations located in the region; government reports on water consumption and
allocation plans; and existing research literature.
Sensitivity analysis
Sensitivity Indicator System
The IPCC Third Assessment Report cited the definition of vulnerability by Smit et al. (1999) as the
“degree to which a system is susceptible to injury, damage, or harm (one part—the problematic or
detrimental part—of sensitivity)”. Sensitivity is the degree to which a system is affected by or responsive
to climate stimuli. However, there are lots of different factors or variables in water resource system, such
as temperature, precipitation, evaporation, drought degree, water resource volume, ratio of groundwater
in the overall water resource, and so on. Based on the historical statistical information and local water
managers’ experience, the sensitivity indicators matrix with the weight of each indicator was constructed.
50
This was resulted from a series of consultation with local experts and multi-stakeholders with the
assistance of applying the AHP method. The AHP method is a multi-criteria decision making tool which
is described in detail in the adaptation sections. The sensitivity matrix is presented in Table 4.3.
The sensitivity matrix is grouped into climate indicators, water resource indicators and social-economic
factors, which include annual precipitation, variation ratio of precipitation in key months, drought index,
runoff of outlets of the mountains, variation ratio of runoff, ratio of population and economic (Table 4.3).
Assessment area
category
indicator
climate indicators
water resource
indicators
social-economic
factors
water resource
indicators
social-economic
factors
water resource
indicators
social-economic
factors
Jinta county
Value
Index of
sensitivity
48.9
Jiayuguan county
Value
Index of
sensitivity
83.5
0.526
0.109
0.2
variation ratio of
precipitation of key
months
0.15
0.109
Draught index
0.1
0.04
Runoff out of
mountain
0.2
8.54
variation ratio of
runoff
0.15
ratio of population
0.1
7.85
7.46
5.3
ratio of economic
0.1
9.8
12.1
8.47
weight
Shandan county
Value
Index of
sensitivity
Minle county
Value
Index of
sensitivity
Sunan county
Value
Index of
sensitivity
254.3
90.7
0.4317
0.026
2.877
1
1.0959
0.068
193.6
370.7
variation ratio of
precipitation of key
months
0.15
-0.111
0.049
Draught index
0.1
0.086
Runoff out of
mountain
0.2
0.5764
variation ratio of
runoff
0.15
ratio of population
0.1
ratio of economic
0.1
weight
ratio of economic
0.04
0.2
0.2
Annual precipitation
variation ratio of
precipitation of key
months
Draught index
Runoff out of
mountain
variation ratio of
runoff
ratio of population
0.6235
0.068
Annual
precipitation
Assessment area
category
indicator
climate indicators
Suzhou County
Value
Index of
sensitivity
Annual
precipitation
Assessment area
category
indicator
climate indicators
weight
2.8964
0.225
2.0175
0.071
1.7444
0.146
0.8137
1.776
-0.121
0.036
5.68
4.86
5.82
7.57
9.6
7.9
Ganzhou City
Value
Index of
sensitivity
Linze County
Value
Index of
sensitivity
Gaotai County
Value
Index of
sensitivity
125.2
-0.012
122.3
0.109
105.8
0.083
-0.202
0.061
0.9795
0.049
0.9692
0.055
8.19
5.15
-0.059
6.23
-0.022
6.22
5.68
8.66
8.41
11.98
Table 4.3: Sensitivity matrix of water resource system to climate change in Heihe Basin
51
3.58
-0.182
2.0096
Sensitivity Computation
To integrate all the indicators for the sensitivity analysis, the data collected were normalized to a scale of
[0, 1] (See section 4.2.1). For indicators that more means more vulnerable, their standardizations methods
were the ratio of their original value and their average value, and vice versa. The total sensitivity of water
system is sum of all the products of standardized value and their weights, the results is shown in Table
4.3. The results suggested that because their more remarkable variation ratios of the runoff, Shandan and
Gaotai counties are the most sensitive regions in Heihe river watershed, whose scores reached 2.8964 and
2.0089, respectively; and turn and turn about these two are Minle county, Jiayuguan city, Ganzhou
county, Linze gounty, Sunan county and Jinta county; the least sensitive is Suzhou county, whose index
of sensitivity is 0.4317.
Adaptive capacity assessment
Adaptive Capacity Indicators
Adaptive capacity is defined in IPCC TAR as “The ability of a system to adjust to climate change
(including climate variability and extremes), to moderate potential damages, to take advantage of
opportunities, or to cope with the consequences.” The methods to identify adaptive capacity indicators
were similar to those for designing sensitivity indicators. The adaptive capacity indicators were grouped
into two categories: social-economic indicators and water resource indicators. The indicator system is
presented by Table 4.4.
Calculation of Adaptive Capacity
Adopting the same normalization process, the study calculated adaptive capacity levels of indicators for
different counties in the region. Results are illustrated in Table 4.5.
Classification of Present-day vulnerability
The climate vulnerability of a resource system was measured using the following simple formula:
V =S/A
In order to show the relative vulnerability levels in different counties, the following formula was used to
get a standardized vulnerability value V*.
V * = [V ! min(V )] /[max(V ) ! min(V )] "100
The ranks of vulnerability classes were shown in Table 4.5.
Assessment area
category
indicator
GDP per capita
social-economic
factors
water resource
indicators
Net income per
capita of
countrymen
Suzhou County
Jinta county
Jiayuguan county
Weight
Value
0.2
0.1
0.543
0.586
1.123
3516
3348
3698
10349
3519
43400
Gross finance
income
0.1
ratio of non agriculture GDP
0.1
water
consumption of
each unit GDP
0.1
groundwater ratio
of gross water use
0.2
water available per
capita
0.1
index of reservoir
vulnerability
0.1
75.92
Index of
sensitivity
10.7459
Value
56.91
Index of
sensitivity
0.9518
Value
97.19
0.374
0.498
0.053
0.213
0.166
0.458
2032
2919
591
0.0008
0.1226
0.2761
52
Index of
sensitivity
1.8632
Assessment area
category
social-economic
factors
water resource
indicators
Shandan county
social-economic
factors
water resource
indicators
Sunan county
Weight
Value
GDP per capita
0.2
0.539
0.387
0.624
Net income per
capita of
countrymen
0.1
Gross finance
income
0.1
2810
4902
2710
5371
3390
2251
ratio of non agriculture GDP
0.1
water
consumption of
each unit GDP
0.1
groundwater ratio
of gross water use
0.2
water available per
capita
0.1
index of reservoir
vulnerability
0.1
indicator
Index of
sensitivity
Value
Index of
sensitivity
Value
68.63
0.9465
50.8
0.8814
57
0.138
0.345
0.179
0.251
0.19
0.43
745
1337
5125
0.1287
0.0569
0.0905
Ganzhou City
Linze County
Gaotai County
Weight
Value
GDP per capita
0.2
0.547
0.528
0.952
Net income per
capita of
countrymen
0.1
2951
Gross finance
income
0.1
2825
4881
2820
4199
ratio of non agriculture GDP
0.1
water
consumption of
each unit GDP
0.1
groundwater ratio
of gross water use
0.2
water available per
capita
0.1
index of reservoir
vulnerability
0.1
indicator
Index of
sensitivity
1.0288
Assessment area
category
Minle county
Index of
sensitivity
Value
Index of
sensitivity
Value
13100
1.5405
12.2485
62.5
Index of
sensitivity
55.27
1.8905
39.67
0.53
0.338
0.708
0.147
0.052
0.183
2022
3737
2742
0.0007
0.0197
0.0128
Table 4.4: Adaptive capacity of water resource system to climate in Heihe River Basin
Variation bound
Degree of vulnerability
0-10
The lest
vulnerable
10-30
Invulnerable
30-50
Average
vulnerable
50-70
Vulnerable
70-100
The most
vulnerable
Table 4.5: Classification of vulnerability to climate variation
Summary of current vulnerability to climate variation
The ranks indicated that under current climate situation, the water use systems in Suzhou County and
Ganzhou County are not vulnerable due to their lower sensitivity and relatively higher adaptive capacity;
the water use systems in Jinta County, Jiayuguan City, Sunan County and Linze County are relatively
53
invulnerable; Gaotai County has an average vulnerability; Minle County is relatively vulnerable and
Shandan County is the most vulnerable area in the Heihe River Basin.
4.2.2.2 Water resource system vulnerability assessment under future climate change
The water resource system operation and management can be specified over time. The specification in
this study included assumptions regarding hydrologic, water allocation and demands that are all key
aspects of a water system scenario representative of what could occur in the future. Incorporated into
future scenarios are key variables that stakeholders considered important and relevant to water resource
vulnerability. The uncertainties arising in estimating future demand or supply changes can be
comparable to those associated with projecting climate change, and equally complicated for vulnerability
assessments. Demand changes may compound climate-related uncertainties in dealing with water
resources systems, or may offer a source for adaptations to reduce water system vulnerabilities to climate
change. In this respect, development of a set of demand/supply change scenarios is a critical step in
water resource system vulnerability assessment.
Measure water vulnerabilities to future climate change
Following the indicator method presented in section 4.2.1, measures of water resource vulnerability
values can be defined by vulnerability indicators. In this assessment, water shortage ratio was used to
show water resource vulnerability under climate change conditions in the Heihe River Basin. If over time
these water shortage ratio measures are improving (the vulnerabilities are decreasing), the water system
being studied is getting increasingly sustainable. It was expected, however, that the results would show
vulnerability of water shortage ratio worsening.
Projecting future hydrological conditions under climate change scenarios
A hydrologic simulation model was employed to project the levels of river flow at the Qilian Mountain
outlet under climate change. The hydrological model was developed by Swedish Meteorological and
Hydrological Institute and modified by Kang to simulate the changes of mountain runoff under climatic
changes in the Heihe River Basin (Kang et al, 1999). The basic structure of the model was described in
some detail below.
The HBV Model
The HBV model (Bergström, 1976, 1992) is a rainfall-runoff model, which includes conceptual numerical
descriptions of hydrological processes at the catchment scale. In different model versions HBV has been
applied in more than 40 countries worldwide with such different climatic conditions. The model has been
applied for scales ranging from lysimeter plots (Lindström and Rodhe, 1992) to the entire Baltic Sea
drainage basin (Bergström and Carlson, 1994; Graham, 1999). HBV can be used as a semi-distributed
model by dividing the catchment into subwatersheds. Each subwatershed is then divided into zones
according to altitude, lake area and vegetation. The model is normally run on daily values of rainfall and
air temperature, and daily or monthly estimates of potential evaporation. The model has been used for
flood forecasting (Bergström et al., 1992), water resources planning (for example Jutman, 1992, Brandt et
al., 1994), and estimates of nonpoint source pollutants load (Arheimer, 1998).
The schematic diagram of the structure of HBV-96 is presented in Figure 4.8 (Lindström et al., 1997). The
figure only shows the most important characteristics of the model, and detail descriptions are given
below. The classes of land use are normally open areas, forests, lakes and glaciers. It is possible to use
different values of SFCF, SFDIST, CFMAX, ECORR and the interception storage capacity IC for different
vegetation zones, but the ratios between the values for forested and non forested areas are kept constant.
54
Fig. 4.8: Schematic structure of one subwatershed in the HBV-96 model (Lindström et al., 1997) with
routines for snow (top), soil (middle) and response (bottom)
Water balance equation: The general water balance equation in the HBV Model is:
55
P ! E !Q =
d
[ SP + SM + UZ + LZ + lakes ]
dt
where P is precipitation; E is evapotranspiration; Q is runoff; SP is snow pack; SM is soil moisture; UZ is
upper groundwater zone; LZ is lower groundwater zone; lakes is lake volume.
Optimal interpolation of precipitation and temperature: The standard version of HBV uses a rather crude
weighting routine and lapse rates for computation of areal precipitation and air temperatures. In HBV-96,
a geostatistical method, based on optimal interpolation (e.g., Daley, 1991) was employed. This method is
similar to kriging and has been frequently used in meteorological data processing. The method may be
based purely on data from meteorological stations and general knowledge of the
precipitation/temperature pattern. One may also add the information included in a meteorological
model and take into consideration, e.g., topography and prevailing winds (Johansson, 2002).
Evapotranspiration: The model is run with monthly data of long term mean potential evapotranspiration,
usually based on the Penman formula (Penman, 1948). These data are adjusted for temperature anomalies
(Lindström and Bergström, 1992). As an alternative, daily values can be calculated as being proportional
to air temperature, but with monthly coefficients of proportionality. Instead the potential
evapotranspiration from forested areas is often assumed to be 15 % higher than that from open areas,
based on the findings by Johansson (1993). Evaporation from lakes will occur only when there is no ice.
Ice conditions are modeled with a simple weighting subroutine on air temperature, which results in a lag
between air temperature and lake temperature. It is assumed that the lake is frozen when the weighted
temperature drops below zero.
Soil moisture: The soil moisture accounting of the model is based on a modification of the bucket theory
in that it assumes a statistical distribution of storage capacities in a basin. This is the main part controlling
runoff formation. This routine is based on the three parameters, namely BETA, LP and FC (see Figure 1).
BETA controls the contribution to the response function or the increase in soil moisture storage from each
millimeter of rainfall or snow melt. LP is a soil moisture value above which evapotranspiration reaches its
potential value, and FC is the maximum soil moisture storage in the model. The parameter LP is given as
a fraction of FC.
Snowmelt modelling: The standard snowmelt routine of the model is a degree-day approach, based on air
temperature, with a water holding capacity of snow which delays runoff. Melt is further distributed
according to the temperature lapse rate and is modeled differently in forests and open areas. A threshold
temperature, TT, is used to distinguish rainfall from snowfall. The snowpack is assumed to retain melt
water as long as the amount does not exceed a certain fraction of the snow. When temperature decreases
below the threshold temperature, this water refreezes gradually. Glacier melt will occur only in glacier
zones and follows the same type of formula as for snowmelt, but with another degree-day factor. No
glacier melt occurs as long as there is snow in the zone. A snow distribution can be made in each zone by
subdividing it into a number of subareas with different snow accumulation.
Response function and routing: The runoff generation routine is the response function, which transforms
excess water from the soil moisture zone to runoff. It also includes the effect of direct precipitation and
evaporation on a part which represents lakes, rivers and other wet areas. The function consists of one
upper, non-linear, and one lower, linear, reservoir. These are the origin of the quick (superficial channels)
and slow (base-flow) runoff components of the hydrograph. Level pool routing is performed in lakes
located at the outlet of a subwatershed. The division into submodels, defined by the outlets of major lakes
(not shown in the figure), is thus of great importance for determining the dynamics of the generated
runoff. The routing between subwatersheds is described by the Muskingum method (e.g., Shaw, 1988).
Lakes/Reservoirs: Precipitation on lakes is the same as for a non-forested zone at the same altitude and
will be added to the lake water regardless of ice conditions in the same way for both rain and snow.
Evaporation from lakes will equal the potential evaporation but can be modified by a parameter and will
occur only when there is no ice. Transformation of runoff is taking place after water routing through the
lake according to a rating curve. If no specific rating curve for the lake is given as input, the model will
assume a general rating curve.
56
Parameter estimation: Although the automatic calibration routine is not a part of the model itself, it is an
essential component in the practical work. The standard criterion (Lindström, 1997) is a compromise
between the traditional efficiency, R2 by Nash and Sutcliffe (1970).
R 2 = ( F02 ! F 2 ) / F02
(1)
!
F 2 = # [Qr (t ) " Qc(t )]2
(2)
t =0
!
F02 = # (Qr " Qr ) 2
(3)
t =0
where Qr(t) is the observed discharge；Qc(t) is the simulated discharge; and τ is period of simulation;
Qr is the averaged value of observed discharge;
In practice the optimization of only R2 often results in a remaining volume error. The criterion above
gives results with almost as high R2 values and practically no volume error. The best results are obtained
with w close to 0.1. The automatic calibration method for the HBV model developed by Harlin (1991)
used different criteria for different parameters. With the simplification to one single criterion, the search
method could be made more efficient. The optimization is made for one parameter at a time, while
keeping the others constant. The one-dimensional search is based on a modification of the Brent parabolic
interpolation (Press et al., 1992).
Data inputs to the model included precipitation and temperature at standard meteorological stations. The
model’s outputs included monthly evapotranspiration and runoff from the mountainous watersheds. The
mountainous watersheds were divided into two basic altitude zones, the high mountain ice, snow and
permafrost zone, and the mountain vegetation zone. The temperature and precipitation data for each
climate change scenario were used to drive the hydrologic model, producing estimates of runoff (hence,
stream flow) and evapotranspiration.
The hydrologic model used in the case study was a well-calibrated hydrologic model that was sufficient
to translate precipitation and temperature changes into runoff changes.
Under the climatic scenario, the response of the runoff out of the outlet of the mountainous watershed to
the climate change is simulated; the results can be seen in Table 4.6.
Period
1980s
(observed)
2010s
(stimulated)
2020s
(stimulated)
2030s
(stimulated)
2040s
(stimulated)
Runoff
/mm
173.3
34.8
167.6
30.3
170.2
29.3
155.5
27.7
158.4
30.5
Variation ratio
Compared to
1980s /%
100.0
Precipitation
/ mm
Evaporation
/ mm
463.2
287.6
Ratio of
glacier melt
water / %
5.7
-3.3
560.9
337.8
6.8
-1.8
540.1
334.0
6.6
-10.3
545.4
343.6
7.7
-8.6
555.9
344.7
7.2
Ratio of
snow melt
water / %
Table 4.6: Average runoff composing of the stimulated result of Yingluoxia Hydrometric Station in future 40 years
57
The simulation indicated that during 1990 to 2000, the precipitation of Heihe River Basin increased
associated with increasing of temperature. The snow melt runoff increasing, to certain extent,
compensated the reduction of runoff associated with increasing of temperature and evaporation. As a
result, the runoff increased by 3% in this period. From 2010 to 2040, however, while temperature will
increase dramatically, the rising of precipitation will be less than the runoff reduction due to increasing
evaporation caused by higher temperature. As a result, the projected annual gross runoff will decrease by
less than 10%. The runoff in spring will increase with more snow melt runoff in spring, but the increment
will be small. The total runoff at the outlet of the mountainous watershed will change in a range about
±10% compared to that in 1980s as a result of a warmer climate warming.
Water Availability in various Municipalities
The state government of China set some regulations to allocate water resources in the Heihe River Basin among the
three jurisdictions: from upper, middle reaches to downstream, Qinhai Province, Gansu Province and InnerMongolia. Various water use policies and plans have been implemented or designed to limit or prohibit the
utilization of certain amount water by different economic sectors or regions. For the water allocation policy in Heihe
River Basin, responsible government agencies have set up basically four rules for future water allocation schemes:
1) balance the ratio of water deficiency; 2) the region with a relatively higher economic efficiency will have the
priority to use water; 3) to equalize per capita water use gradually; and 4) based on current allocation policy and
make adjustment gradually. The calculation of water allocation was based on water balance. Four variables that
determine water allocation were: ratio of agriculture water deficiency, water use intensity per unit GDP, per capita
water use, and per unit area water use. The objective function of the water allocation model is:
F!f (min AX 1deficiency , min BX 2GDP , min CX 3 percapita , min CX 4unitfield )
where: AX1deficency is the difference of water deficiency ratios between different regions; BX2GDP is the
difference of water use intensity per unit GDP between regions; CX3per capita is the difference of per capita
water use between regions, and CX4 unit field is the difference of water use per unit area between regions.
The water resources allocated (supply) for different municipalities from 2000 to 2040 are shown in Table
4.7.
Suzhou
County
Jinta
County
Jiayuguan
County
Year
2000
6.7474
4.0171
0.9436
Year
2010
6.5721
3.9065
6.5094
Year
2020
Year
2030
Year
2040
Shandan
County
1.4544
Minle
County
Sunan
County
Ganzhou
City
Linze
County
Gaotai
County
3.1462
0.4
9.69
5.46
4.33
0.9282
1.4184
3.0423
0.4563
7.8703
4.3408
3.5681
3.8669
0.9227
1.4056
3.0052
0.4518
7.7988
4.2982
3.5388
6.4026
3.7996
0.9133
1.3836
2.9419
0.4442
7.8402
4.3229
3.5558
6.5721
3.9065
0.9282
1.4184
3.0423
0.4563
8.2468
4.3370
3.6806
Table 4.7: Water availability of various regions in the Heihe River Basin under climate change in the future 40 years
(108m3)
Projecting Water Resource Demands for Future 40 Years
There are several major factors, which influence water demand in the region. Water demand is a function
of factors including climate, water users, size of water use activities, water use efficiency, the price
elasticity of demand, environmental pollution, demand management options (economical, technical, or
policy), lifestyle associated with income increasing, and population growth. Demand on water resources
will likely increase as population and income increases, and the implications of these demand changes for
58
water resources system vulnerability as well as for regional hydrology (e.g., groundwater extraction) may
be at least as significant as the implications of potential climate change. Future water demands in the
region were projected using an IPAT model which was originally used to illustrate environmental
consequences of population growth. This model was a part of the socio-economic scenario setting
exercise of the AS25 project.
Introduction of IPAT: Ehrlich (1996), presenting a simple equation with a simplified term, illustrated the
relationship between population growth and consumption level increase associated with economic
growth, and the environmental impacts. The equation is expressed as:
I=PxAxT
where: I is the total societal impacts; P is the population size; A is the living standard (or per capita
consumption); and T is the technology used.
It is obvious that the total environmental impacts of a region will be proportional to both population size
and per capita consumption level. To date, most studies on population-resources-environment indicate
that population expansion is the main cause of ecological depletion and un-sustainability. Rapid
population growth in northwest China has always been considered as a threat to natural resources
sustainability. Water resource demand depends on the population, economic scale, and water use
intensity, ecosystem protection policy, and so on. In this case study, IPAT was used to build future water
demand scenarios.
Water resource demand of the future 40 years: In this assessment, we defined I as water resource
demand, P as population, A as per capita GDP, and T as water use intensity per GDP unit. The average
degressive rate of T was computed by a series of water use intensity per GDP unit from 1992 to 2001 in
Heihe River Basin. The degressive rate of T used in water demand scenario was -5.72%. The population
and economic scenarios for this study were discussed previously. Table 4.8 illustrates the water demand
scenario of the future 40 years. The result showed that water resource demand for each municipality in
future 40 years will have a small increase compare with the year 2000 excluding the ecosystem water
demand. So the result presented in Table 4.8 represents the lowest water resource demand; and water
scarcity in the future will be a nerve-racking problem for Heihe River Basin.
59
Categories
Year/regions
Year 2000
I
P
A
T
I
P
A
T
Water
demand
(108m3)
Population
(104
persons)
GDP
per
capita
(104yuan
per
capita)
Water
intensity
of unit
GDP
(m3 unit
GDP)
Water
demand
(108m3)
Population
(104
persons)
GDP
per
capita
(104yuan
per
capita)
Water
intensity
of unit
GDP
(m3 unit
GDP)
6.9499
Suzhou County
33.2
0.543
4.0821
Jinta County
13.76
0.586
0.3740
0.4978
Year 2010
Year 2020
Year 2030
7.2451
7.7685
7.4160
40.26
44.36
48.42
0.867
1.521
2.397
0.2075
0.1152
0.0639
4.3116
4.6254
4.4153
16.69
18.39
20.07
0.935
1.641
2.587
0.2762
0.1533
0.0850
Year 2040
Year/regions
6.7610
48.42
3.938
Jiayuguan County
0.0355
4.0246
20.07
4.250
Shandan County
0.0472
Year 2000
Year 2010
1.3076
1.1584
15.97
19.37
1.123
2.048
0.0526
0.0292
1.5700
1.6869
19.53
20.2
0.539
1.089
0.1383
0.0767
Year 2020
Year 2030
1.0986
0.9607
25
30.13
2.712
3.546
0.0162
0.0090
1.5000
1.2903
21.11
21.93
1.669
2.491
0.0426
0.0236
Year 2040
Year/regions
0.7638
30.13
5.081
Minle county
0.0050
1.2250
21.93
4.262
Sunan county
0.0131
Year 2000
Year 2010
3.2303
3.6486
23.54
24.35
0.387
0.782
0.3454
0.1916
0.4
0.4647
3.59
3.71
0.624
1.264
0.1786
0.0991
Year 2020
Year 2030
Year 2040
3.2442
2.7907
2.6492
25.44
26.43
26.43
1.199
1.790
3.062
0.1063
0.0590
0.0327
0.4134
0.3557
0.3375
3.88
4.03
4.03
1.938
2.893
4.948
0.0550
0.0305
0.0169
Year/regions
Year 2000
10.49
Ganzhou City
47.93
0.547
0.3376
6.08
Linze County
14.61
0.528
0.7082
Year 2010
Year 2020
10.257
9.1208
49.58
51.8
1.104
1.694
0.1873
0.1040
6.3304
5.6297
15.11
15.79
1.066
1.635
0.3929
0.2180
Year 2030
Year 2040
7.8458
7.4481
53.82
53.82
2.527
4.324
0.0577
0.0320
4.8418
4.5964
16.41
16.41
2.439
4.172
0.1210
0.0671
Gaotai County
15.79
0.952
0.5301
Year/regions
Year 2000
5.1
Year 2010
Year 2020
4.6649
4.1487
16.33
17.06
0.971
1.490
0.2941
0.1632
Year 2030
Year 2040
3.5680
3.3873
17.73
17.73
2.222
3.802
0.0906
0.0502
Table 4.8: Water demands in Heihe River Basin under climate change scenario in the future 40 years (108m3)
Water Scarcity Ratio under Climate Change
Balance of water supply and demand: In arid and semi-arid regions, water is the focus of sustainable
development. As most of the indicators chosen in the present-day vulnerability assessment were difficult
to estimate under future socio-economic and climate scenarios. In vulnerability assessment under future
60
scenario conditions, water scarcity ratio or shortage ratio was used as an vulnerability indicator.
Comparing the water supply stimulated by the modified HBV model with water demand predicted by
the identity IPAT, water balance information in the future 40 years indicates that there will be serious
water shortage until year 2030 in the whole Heihe River Basin except Sunan County. Water scarcity ratios
in various municipalities range from -1.27% to -16.33% (See Table 4.9).
Year
2000
Year
2010
Year
2020
Year
2030
Year
2040
Average
Suzhou
County
Jinta
County
Jiayuguan
County
Shandan
County
Minle
County
Sunan
County
Ganzhou
City
Linze
County
Gaotai
County
-0.2025
-0.065
-0.364
-0.1156
-0.0841
0
-0.8
-0.62
-0.77
-0.673
-0.4051
-0.2302
-0.2685
-0.6063
-0.0084
-2.3868
-1.9896
-1.0968
-1.2591
-0.7585
-0.1759
-0.0944
-0.239
0.0384
-1.322
-1.3315
-0.6099
-1.0134
-0.6157
-0.0474
0.0933
0.1512
0.0885
-0.0056
-0.5189
-0.0122
-0.1889
-0.6674
-0.1181
-0.3925
0.1644
-0.1306
0.1934
-0.0384
0.3931
-0.0770
0.1188
0.0475
0.7987
-0.7431
-0.2594
-0.9439
0.2933
-0.4391
Table 4.9: Water supply and demand balance in Heihe River Basin under climate change in the future 40 years
(108m3)
Spatial distribution of water scarcity ratio: The changes of the water scarcity ratio under climate change in
the future 40 years are shown in Table 4.10 and Figure 4.9. The water shortage ratio of each region will
further deteriorate comparing with that of the year 2000. Water supply will not be enough to meet the
demand from 2001 to 2010, which will be the most austere period of water shortage. Beginning from 2011,
there will be small surplus in Sunan County of water resources for utilization. But the situation will be
much worse for Suzhou County and Jinta County in the same period with rising water shortage ratios.
For other municipalities in the study region, the shortage conditions will stay serious, though the water
demand amount will start to decrease associated with improvement of water use technologies. From
2021, Minle County and Shandan County will have some surplus, and Ganzhou City and Gaotai County
will reach a relative balance. However, other municipalities will need additional water supply to sustain
the development in this period. From 2031 to 2040, except for Suzhou, Jinta and Linze Counties that will
still short of water, most of other municipalities will realize water balance, even with some surplus. In
general, in future 40 years, Linze County will experience the largest water shortage of nearly 100 million
m3 a year; the second will be Ganzhou City with a shortage of 74.31 million m3 a year; and followed by
Suzhou County, Gaotai County, Jinta County, Jiayuguan City, Minle County and Shandan County. Sunan
County will be in surplus. In terms of average water shortage ratio, Linze County will be the largest
(16.33%), Jiayuguan City will be the second largest (9.43%), and followed by Gaotai County, Suzhou
County, Jinta County, Ganzhou City, and Shandan County.
61
Year
2000
Year
2010
Year
2020
Year
2030
Year
2040
Average
Suzhou
County
Jinta
County
Jiayuguan
County
Shandan
County
Minle
County
Sunan
County
Ganzhou
City
Linze
County
Gaotai
County
-2.91
-1.59
-27.84
-7.36
-2.60
0.00
-7.63
-10.20
-15.10
-9.29
-9.40
-19.87
-15.92
-16.62
-1.81
-23.27
-31.43
-23.51
-16.21
-16.40
-16.01
-6.29
-7.37
9.29
-14.49
-23.65
-14.70
-13.67
-13.94
-4.93
7.23
5.42
24.88
-0.07
-10.72
-0.34
-2.79
-8.97
-2.93
-8.85
21.52
-9.43
15.79
-1.31
14.84
-1.27
35.20
13.51
10.72
-6.95
-5.64
-16.33
8.66
-9.00
Table 4.10: Water scarcity ratios in Heihe River Basin under climate change in the future 40 years (%)
Fig. 4.9: Water shortage ratios for various municipalities in Heihe River Basin in the future 40 years
62
4.2.2.3 References
Arheimer, B. (1998) Riverine Nitrogen - analysis and modelling under Nordic conditions.
Bergström, S. (1976) Development and application of a conceptual runoff model for Scandinavian
catchments. SMHI Reports RHO, No. 7, Norrköping.
Bergström, S. (1992) The HBV model - its structure and applications. SMHI Reports RH, No. 4,
Norrköping.
Bergström, B. and Carlsson, B. (1994) River runoff to the Baltic Sea: 1950-1990. Ambio (23):280-287).
Brandt, M., Jutman, T. & Alexandersson, H. (1994) Sveriges Vattenbalans. Årsmedelvärden 1961-1990 av
nederbörd, avdunstning och avrinnning. SMHI Hydrologi, nr 49, Norrköping.
Daley, R. (1991) Atmospheric Data Analysis. Cambridge University Press.
Harlin, J. (1991) Development of a process oriented calibration scheme for the HBV hydrological model.
Nordic Hydrology, Vol. 22, pp. 15-36.
Johansson, B. (1993) The relationship between catchment characteristics and the parameters of a
conceptual runoff model - A study in the south of Sweden. Contribution to the Second International
Conference on FRIEND, Oct. 1993, Braunschweig, IAHS Publication No. 221, 475-482.
Johansson, B. (2002) Estimation of areal precipitation for hydrological modelling in Sweden. Ph.D.thesis.
Earth Science Centre, Göteborg University, Report A76 2002.
Jutman, T. (1992) Production of a new runoff map of Sweden. Nordic hydrological Conference, Alta,
Norway, NHP report No. 30. pp 643-651. Kanaltryckeriet, Motala. pp. 200.
Lindström, G. & Harlin, J. (1992) Spillway design floods in Sweden. II: Applications and sensitivity
analysis. Hydrological Sciences Journal, Vol. 37, No. 5, pp. 521-539.
Lindström, G. (1997) A Simple Automatic Calibration Routine for the HBV Model. Nordic Hydrology,
Vol. 28, No. 3, pp. 153-168.
Lindström, G. and Bergström, S. (1992) Improving the HBV and PULSE-models by use of temperature
anomalies. Vannet i Norden, Vol. 25, No. 1, 16-23.
Nash, J.E., Sutcliffe, J.V. (1970) River flow forecasting through conceptual models. Part I - A discussion of
principles. Journal of Hydrology, Vol. 10(3), pp. 282-290.
Penman, H.L. (1948) Natural evapotranspiration from open water, bare soil and grass, Proc. R. Soc.
London, Ser. A, 193, pp. 120-145.
Press, W.H., Teukolsky, S.A., Vetterling, W.T. & Flannery. B.P. (1992) Numerical Recipes in FORTRAN.
The Art of Scientific Computing, Second Edition, Cambridge University Press.
Shaw, E.M. (1988) Hydrology in practice, second edition. Chapman and Hall, London, U.K.
4.2.3 Ecosystem vulnerability assessment under climate variation and
change
By L.D. Sun
This activity also followed the research approach developed by activity 1 to examine the ecosystem
sensitivity, vulnerability adaptability in the Heihe River Basin in year 2000 and 2040. The research steps
are the same as those in the case of water resource vulnerability assessment. Methods used for assessing
current vulnerabilities to climate variation were also based on sensitivity and adaptive capacity
indicators. The main methodological difference between this case and the water resource vulnerability
assessment is methods used for projecting future vulnerability. Thus, this section will focus on discussion
of methods used for projecting future ecological vulnerabilities under climate change scenarios.
63
4.2.3.1 Current climate vulnerability assessment for ecosystem
Ecosystem sensitive indicators and weights
The key method in assessing current ecosystem vulnerability is the establishment of an indicator system.
The selected sensitive indicators included climate indicators and other non-climate indicators. The
reasons for selecting these indicators are:
1.
In the Heihe River Basin, an arid and semi-arid region, the water is the key factor influencing
ecological environment vulnerability through precipitation and its stability. For simplification of
assessment, we choose the precipitation as the water resources supply indicator for vulnerable
ecosystem.
2.
The annual ≥0°C accumulated temperature is another important index to judge whether the
thermal is sufficient or not for an ecosystem. The thermal resources not only affect the ecological
environment directly, but also influence ecosystem through the match condition with water
resources (manifesting in humidity), the vegetation distribution pattern and density.
3.
The improper coordination between water and thermal, the hot and dry climate, will possibly
cause drought. Under low humid condition, the ecosystem will be more vulnerable, the humidity
and ecosystem vulnerability have a negative relationship. The formula of humidity K is:
K =R/0.1∑ ≥ 0°C
where: R represents annual precipitation (mm); ∑ ≥ 0°C is the temperature ≥ 0 °C annual
accumulated temperature.
4.
Wind influence.
5.
Other factors are population and land that are the primary factors affecting ecosystem
vulnerability.
sensitivity indices
weights
Data
source
Adaptability
indices
*
Climate factors
Other factors
Humidity
0.20
1
Annual average wind
(m/s)
Annual precipitation
(mm/a)
0.15
1
0.15
1
Annual accumulated
temperature (∑ ≥
0°C)
0.10
1
Percentage of
desertification (%)
Variability of
grassland area (%/a)
Population density
(person/km2)
0.20
2
0.10
2
0.10
3
Socioeconomic
factors
Natural
environment
factors
weights
Data
source
*
Net income of
peasant
Ratio of population
illiteracy (%)
Population growth
rate (‰ a-1)
0.20
3
0.20
3
0.10
3
Ratio of forest
cover (%)
0.20
3
Ratio of grassland
cover (%)
Arable land per
capita (ha)
0.20
3
0.10
3
Data sources：1. Meteorological Bureau of Gansu Province；2. Remote sensing and invested data on the spot in 2000；3. The
Statistical Yearbook (2000) of Zhangye, Jiuquan, Jiayuguan and Ejinaqi.
Table 4.11: Sensitivity and adaptability indicators, weights and data sources
Sensitivity, S, is obtained by the following formula:
64
S = " X i ! Wi
where, Wi is the weight of i index; Xi is the concrete value of i index.
Adaptability indices and their weights
The selection of adaptive capacity indices in Table 4.11 is based on the following considerations:
1.
Social economy factors: The ability of coping with climate stresses is determined by the average
net income of farmers and their level of education.
2.
Natural environment factors: For ecosystem in the study region, the ecosystem vulnerability has
a good correlation with the vegetation coverage. Vegetation coverage was used to measure
ecosystem vulnerability. For agriculture, the average arable land per person is the important
production base in local rural economy development. The per capita arable land was adopted to
reflect the potential adaptability of rural areas to climate stresses.
Adaptability, A, was obtained by using the following formula:
A = " # i ! Wi
Where: Wi is defined as previously (see Table 4.11), γi is the concrete value of i index.
Computation of ecosystem vulnerability to climate variation
Given sensitivity, S, and adaptability, A, the following simple formula was used to calculate the
vulnerability in the basin:
V =S/A
In order to be easy to compare the vulnerability in different municipalities, the following formula was
applied to normalize the vulnerability calculated.
V p = (V p* " min V p* ) /(max V p* " min V p* ) ! 100
Consulting with the related experts and local ecosystem managers, the ecosystem climate vulnerability
ranks in this research are listed in Table 4.12
V
class
0-10
1 least
vulnerability
10-30
2 less
vulnerability
30-50
3 middling
vulnerability
50-70
4 more
vulnerability
70-100
5 most
vulnerability
Table 4.12: Standardized ecosystem vulnerability classes
Results of ecosystem vulnerability assessment in Heihe River Basin
The results of the current ecosystem vulnerability assessment in the study region for the year 2000 are
presented in Table 4.13 including nine counties. The assessment did not include Jiayuguan City due to
data availability.
65
Name
Suzhou
Jinta
Ejiana
Shandan
Minle
Sunan
Ganzhou
Linze
Gaotai
Sensitivity
Adaptability
67
47
68
46
73
36
55
60
54
52
46
68
61
58
70
42
69
48
Vulnerability
Standard
vulnerability
1.43
1.48
2.03
0.92
1.04
0.68
1.05
1.67
1.44
59.32
60.32
100.00
17.83
27.81
10.00
26.78
73.28
57.84
4
4
5
2
2
1
2
4
4
Vulnerability
class
Table 4.13: Ecosystem vulnerability in Heihe River Basin in 2000
Table 4.13 indicates that climate sensitivities of Ejina and Linze are the highest in 2000 with scores of 73
and 70 respectively. Sensitivity scores of Sunan and Minle are relatively low, 46 and 54. The adaptability
scores for different municipalities are just in opposition direction to that of sensitivity. While adaptability
in Sunan is the highest, Ejina is the lowest. This reflects the exact situation in the study region. From
results shown in Table 4.13 and Figure 4.10, the most vulnerable ecosystem is located in the low reach of
the basin including Ejina which is mainly covered by wilderness prairie with low vegetation coverage
fraction. The low reach is characterized with a very dry climate with very high evaporation. It is the most
vulnerable area in entire basin. The next vulnerable area is Linze County in the middle reaches. The
Suzhou, Jinta County and Gaotai County in the middle reaches are also ranked as more vulnerable.
Ganzhou, Minle County and Shandan County are less vulnerable. Sunan County located under the foot
of Qilian Mountain where vegetation coverage is high, is ranked as the least vulnerable area in the entire
basin.
Fig. 4.10: Distribution of ecosystem vulnerability in Heihe River Basin (2000)
66
4.2.3.2 Assessing Ecosystem Vulnerability to Climate Change in future 40 Years
The Net Primary Productivity (NPP) and Miami Model
UNESCO implemented the International Biology Programme (IBP) in 1963-1972 estimating the net
primary productivity (NPP) in the world and generated many important results. Models were developed
to estimate the biological biomass indirectly based on certain environment parameters (mainly
meteorological data). The Miami model was adopted for this study based on the consideration that the
main input data for the Miami model were temperature and effective moisture content which had close
relations with the growth and distribution of plants. The two parameters in the Miami model are easy to
collect (Lieth, 1975; Lieth and Whittaker, 1975).
Biological data and meteorological data from 53 regional observation stations were used to derive the
empirical formula using the least squares method:
Y1 = 3000 / [1+ exp(1.315-0.119T)]
Y2 = 3000 × [1- exp(-0.000664P)]
Where: Y1 is the biological productivity according to annual mean temperature, g/(m2.a);Y2 is the
biological productivity according to annual precipitation, g/(m2.a); T is the average temperature (°C); P is
the average annual precipitation (mm).
Input correction and computed results of Miami model
The above methods for calculating the net primary productivity (NPP) are suitable for the estimation
under natural conditions. They are not appropriate for projecting NPP under the influence of humanity
activity. The calculation results indicated that NPP in middle and lower reaches of the Heihe River Basin
will likely be low because the middle and lower reaches areas are relatively developed. In order to
reasonably project the change of NPP caused by human activities, the input to the model was corrected in
calculation.
The main factor influencing land natural productivity by human activity is irrigation. Calculation of the
NPPs for middle and lower reaches of Heihe River Basin in future 40 years was based on the model input
of the corrected effective precipitation, which was the quantity of irrigation water plus the annual
precipitation in future 40 years and minus the evaporation increase caused by the rising temperature.
Table 4.14 presents the corrected temperature, precipitation, Y1, Y2 and Y in various counties from 1960
to 2040. It should be noted that the NPPs of ecosystem in Jiayuguan City and Suzhou County were not
included in this research. In order to display the extent with which climate change affects the ecosystem,
we also calculated the NPP change in future 40 year and 1960-1995.
The result indicated that the biological productivity of ecosystem in Heihe River Basin will change in
future 40 year. Sunan County will experience the highest change (average 8.19 t/hm2• a); followed by
Minle County (average 8.09 t/hm2• a); and then followed by Qilian County (average 7.12 t/hm2• a).
Qilian and Sunan County are located in upstream region where the temperature and precipitation might
increase under climate change scenario. It is likely the biological productivity in this region will increase.
While both temperature and precipitation will increase, the temperature is still the primary control factor
over ecosystem in Qilian County.
Minle, Shandan, Ganzhou, Linze, Gaotai and Jinta Counties located in the Middle reaches will likely
experience a decline tendency of NPP in future 40 year. Among the six counties, Shandan and Minle will
drop slightly with rates of 1.06% and 1.22% respectively. Shandan County’s NPP will start to increase
from 2012 to 2030. The results showed that NPP in Gan Zhou, Linze and Gaotai in future 40 year will
drop considerably comparing with those in the period from 1960 to 1995, with drop rates at 44.62%,
36.05% and 19.48% respectively. It is obvious that the ecosystem is the most vulnerable in Ganzhou
County followed by Linze County. Gaotai County is the least vulnerable area. The NPP will be 4.98 t/ha•
a，with a drop rate of 31.4% in Jinta County in future 40 years.
The mean temperature and precipitation rose from 1960 to 1995 about 1.1°C and 2 mm respectively in
Ejinaqi, a district located in the lower reaches. Even with irrigation water, the NPP still dropped by
42.97%. With an increase of evaporation from farmland due to warmer climate, the region will need more
water for irrigation to ensure crop production in the middle reaches of the Heihe River Basin. The NPP
67
reduction caused by globe warming in Ejinaqi might be more than the projected results. The ecosystem in
Ejinaqi is the most vulnerable.
The results indicated that ecosystem vulnerability in Ejinaqi was ranked as the most vulnerable under
climate change scenario, followed by the middle reaches. The ecosystem vulnerability to climate change
in upstream is the least.
68
69
451.84
475.23
484.95
517.54
1.37
1.21
1.16
1.17
1.23
2001-2010
2011-2020
2021-2030
2031-2040
Future 40 yr
average
242.06
248.07
298.67
234.04
477.06
226.66
230.18
253.37
218.64
6.51
6.59
6.52
6.7
6.58
6.2
7.44
7.48
7.42
7.55
7.47
2001-2010
2011-2020
2021-2030
2031-2040
Future 40 yr
average
1960-1995
2001-2010
2011-2020
2021-2030
2031-2040
Future 40 yr
average
t/ha•a
Y2
8.18
8.7
8.25
8.08
7.7
8.48
4.67
4.31
5.39
4.54
4.45
4.72
11.9
11.9
11.8
11.9
11.8
10.8
4.27
4.03
4.63
4.23
4.19
7.71
Ganzhou District
11.1
11.2
11.2
11.1
11.1
9.82
Shandan County
7.12
7.08
7.07
7.1
7.22
6.35
Qilian County
Y1
4.27
4.03
4.63
4.23
4.19
7.71
4.67
4.31
5.39
4.54
4.45
4.72
7.12
7.08
7.07
7.1
7.22
6.35
Y
-44.62
-47.73
-39.95
-45.14
-45.65
-1.06
-8.69
14.19
-3.81
-5.72
12.13
11.5
11.34
11.81
13.7
Variety
(%)
7.84
7.96
7.78
7.86
7.75
6.5
4.58
4.63
4.56
4.57
4.54
3
8.47
8.49
8.47
8.54
8.4
7.6
Mean
yearly
temp.
(°C)
323.93
304.85
346.52
315.35
329
542.37
473.96
486.85
478.57
473.75
456.66
479.82
273.7
269.28
272.36
263.41
289.73
417.37
Correct
ional
pptn.
(mm)
Table 4.14: NPP in Heihe river basin when climate change in future 40 year
232.21
255.71
257.89
5
1960-1995
482.39
500
Correc
tional
pptn.
(mm)
0
Mean
yearly
temp.
(°C)
1960-1995
year
areas
t/ha•a
Y2
4.98
4.9
4.96
4.81
5.25
7.26
8.09
8.27
8.16
8.08
7.84
8.19
12.2
12.3
12.1
12.2
12.1
11
5.8
5.48
6.16
5.66
5.88
9.07
Linze County
9.49
9.54
9.48
49.5
9.46
8.32
Minle County
12.7
12.7
12.7
12.8
12.7
12
Jinta County
Y1
5.8
5.48
6.16
5.66
5.88
9.07
8.09
8.27
8.16
8.08
7.84
8.19
4.98
4.9
4.96
4.81
5.25
7.26
Y
-36.05
-39.58
-32.08
-37.6
-35.17
-1.22
0.98
-0.37
-1.34
-4.27
-31.4
-32.51
-31.68
-33.75
-27.69
Variety
(%)
8.01
8.06
8
8.03
7.95
7.3
2.83
2.85
2.81
2.86
2.78
2
9.6
9.66
9.54
9.59
9.59
8.5
Mean
yearly
temp.
(°C)
391.55
387.33
391.94
387.17
399.76
503.46
486.24
499.12
479.6
488.75
477.5
450
231.57
218.03
242.43
233.26
232.57
431.42
Correct
ional
pptn.
(mm)
t/ha•a
Y2
4.26
4.02
4.46
4.3
4.27
7.47
8.26
8.44
8.17
8.3
8.14
7.75
12.3
12.4
12.3
12.3
12.3
11.7
6.86
6.79
6.87
6.8
6.99
8.52
Gaotai County
8.19
8.21
8.18
8.22
8.17
7.62
Sunan County
13.7
13.8
13.7
13.7
13.7
12.7
Ejinaqi
Y1
6.86
6.79
6.87
6.8
6.99
8.52
8.19
8.21
8.17
8.22
8.14
7.62
4.26
4.02
4.46
4.3
4.27
7.47
Y
-19.48
-20.31
-19.37
-20.19
-17.96
7.48
7.74
7.22
7.87
6.82
-42.97
-46.18
-40.29
-42.44
-42.84
Variety
(%)
Computation of human appropriation of net primary production (HANPP) in Heihe River Basin in
2000
The ways of Human appropriation net primary production mainly are through crop harvest, herds,
cultivation, logging, picking the firewood and so on. Currently, the calculation of HANPP employs a
simple method of dividing the quantity of biological products produced by a harvest coefficient or an
economy output coefficient (Krausmann, 2001).
Based on 2002 statistics data of Zhangye City and 2000 statistics data of Jiuquan City, we calculated
current economy-deliver coefficient of each crop. Then the economic production of each crop was divided
by its corresponding economy-deliver coefficient to get the total harvested biological production.
Summing up all the biological productions of various crops, we obtained the total biological production
for the region. The reason that we used 2002 statistics data instead of 2000 statistics for Zhangye City was
that Zhangye suffered a severe drought (once every 60 years) in 2000, which greatly affected the crop
production. The 2002 harvest was close to the normal.
We also calculated the consumption of biological products by livestock. All the numbers of big domestic
animals including pig, sheep, as well as poultry in all counties of Zhangye City, Suzhou and Jinta, were
counted and converted into the sheep-equivalent unit based on 2000 statistics. Each sheep-equivalent unit
needs 1,000 kilograms of fresh grass. With these data, we calculated the total biological products
consumed by the livestock sector.
Because of the poor vegetation cove in the Heihe River Basin, there has been very little firewood
collection activity in the study region. This study thus only calculated the forest harvest by logging
activities. We adopted a conversion rate that each cubic lumber equals 20 tons biomass in the calculation.
Based on above assumptions and methods, we calculated the total potential NPP (see Table 4.14) and the
HANPP (see Table 4.15) for each municipality. Due to lack of data for Ejinaqi, Jiayuguan and Qilian
County, these three municipalities were not included. In addition, due to lack of observed NPP data in
the basin, this research used the potential NPP calculated by the Miami model.
70
projects
areas
Harvested plant biomass
(104t/a)
Harvested animal biomass
(104t/a)
Suzhou
Jinta
Shandan
Minle
Ganzhou
2693305
1016996
1133003
1685169
2753903
391
142
108
237
676
General harvested biomass
(104t/a)
2693695
1017138
1133111
1685407
2754579
Usable biological production
area (ha)
General biomass (104t/a)
198700
1512107
149100
1082466
483502
2282129
337861
2767085
351708
2711672
HANPP per person (t/a)
HANPP rate (%)
81135
178.14
73920
93.96
58019
49.65
71598
60.91
57471
101.58
projects
areas
Harvested plant biomass
(104t/a)
Harvested animal biomass
(104t/a)
General harvested biomass
(104t/a)
Linze
Gaotai
Sunan
Total in
the middle
reaches
842891
1001603
106269
11233138
277
176
108
1583
843168
1001779
106377
11234721
General biomass (10 t/a)
HANPP per person (t/a)
236588
2145850
57712
278715
2374652
63444
1866446
14222321
29631
3552841
29098281
65337
HANPP rate (%)
39.29
42.19
0.75
38.61
Usable biological production
area (ha)
4
Table 4.15: NPP and HANPP of the middle reaches in Heihe River Basin, 2000
Table 4.15 shows that the current average HANPP is 38.61% in the basin, which is close to the upper limit
of global HANPP rates of 24-39% estimated by Vitousek (1986). The human pressure on land ecosystem
in the basin was too intensive. In terms of geographic distribution of HANPP, Suzhou District and
Ganzhou District have already surpassed 100%. The results are possibly attributed to the fact that both
districts are the most developed agricultural areas in Heihe River Basin (the most important districts of
commodity grain base in the region). There is no additional suitable land available for agricultural
production. The HANPP rate in Jinta County also reached 100%. Ecosystem vulnerability of Jinta County
could be even more dangerous than that of Ganzhou District. Moreover, the current HANPP in Minle,
Shandan, Gaotai and Linze County showed that ecosystem vulnerability in these municipalities were also
very high. Only the HANPP rate in Sunan County showed that some potential ecological capacity was
still available.
There is strong relevance between the average HANPP per person and HANPP rate (correlation
coefficient: 0.735). It indicated that the more average HANPP per person, the higher pressure on
ecosystem. In addition, the higher efficiency land use, such as converting the natural oasis ecosystem into
the artificial oasis agriculture ecosystem in the basin, has greatly reduced the oasis ecosystem biodiversity
and made the ecosystem extremely vulnerable although the NPP increased.
The ecosystem vulnerability assessment under climate change in future 40 year
The human land utilization is one of the most important reasons which cause biodiversity loses. With
population and economy growth, HANPP may continue to increase in the region. This research analyzed
71
the change of HANPP in the middle reaches of Heihe River Basin in future 40 year. The computed results
are presented in Table 4.16.
indices
NPP
(104t)
counties
year
Need of
HANPP
(104t)
Ratio of
HANPP
(%)
NPP
(104t)
Jinta County
Need of
HANPP
(104t)
Ratio of
HANPP
(%)
NPP
(104t)
Shandan County
Need of
HANPP
(104t)
Ratio of
HANPP
(%)
Minle County
2000
1082466
1017138
93.96
2282129
1133111
49.65
2767085
1685407
60.91
2001-2010
782775
1233725
157.61
2151583
1171984
54.47
2648833
1743411
65.82
2011-2020
717171
1359389
189.55
2195098
1224781
55.80
2729920
1821453
66.72
2021-2030
739536
1483574
200.61
2606075
1272357
48.82
2756949
1892335
68.64
2031-2040
730590
1483574
203.07
2083893
1272357
61.06
2794114
1892335
67.73
742518
1390066
Ganzhou District
187.21
2259162
1235370
Linze County
54.68
2732454
1837384
Gaotai County
67.24
2000
2711672
2754579
101.58
2145850
843168
39.29
2374652
1001779
42.19
2001-2010
1473658
2849412
193.36
1391135
872028
62.68
1948218
1036041
53.18
2011-2020
1487727
2976998
200.10
1339086
911272
68.05
1895262
1082355
57.11
2021-2030
1628410
3093089
189.95
1457380
947054
64.98
1914772
1124862
58.75
2031-2040
1417385
3093089
218.23
1296500
947054
73.05
1892475
1124862
59.44
1501795
3003147
Sunan County
199.97
1371025
919352
67.06
Total of the middle reaches
1912681
1092030
57.09
2000
14222321
106377
0.75
29098281
11234721
38.61
2001-2010
15192873
109931
0.72
25589076
12283027
48.00
2011-2020
15342189
114968
0.75
25706452
13090365
50.92
2021-2030
15248866
119413
0.78
26351988
13861241
52.60
2031-2040
15323524
119413
0.78
25538481
13861241
54.28
Average in
40 year
15276863
115931
0.76
25796499
13273969
51.46
Average in
40 year
Average in
40 year
Table 4.16: HANPP of Heihe River Basin under climate change in future 40 year
The computed results in Table 4.16 illustrated the ecosystem vulnerability changes under climate change
and population growth scenario in future 40 year. On the one hand, except the increasing trend of NPP in
Sunan County and Minle County, the NPP will decline in other five counties. On the other hand, along
with the increasing population and economic growth, the HANPP rate will increase rapidly in middle
reaches counties in future 40 year (except Sunan County) and will rise from 38.61% in 2000 to 54.28% in
2031-2040, the average HANPP rate will be 12.85%.
Among the seven counties calculated, HANPP rates in Ganzhou District and Jinta County will rise from
101.58% and 93.96% in 2000 to 218.23% and 203.07% in 2031-2040 respectively. Given the fact that water
resources and other social economic conditions in Jinta County are relatively poorer than those in
Ganzhou District, the ecosystem pressure in Jinta County would be higher than that in Ganzhou District
in future 40 year. The HANPP rate will rise greatly in Linze County in future 40 year from 39.29% to
72
73.05% in 2031-2040. The growth rates of HANPP in Gaotai County, Minle County and Linze County will
be 14.9%, 6.33% and 5.03% in future 40 year respectively comparing with those in 2000. The only
exceptional case is Sunan County where the HANPP rate will only grow 0.03% in future 40 year, and
grow 0.01% comparing with that of in 2000.
From the analysis results in Table 4.16, the ecosystem vulnerability ranks from high to low in Heihe River
Basin in future 40 year in middle reaches are: Suzhou District, Jinta County, Ganzhou District, Minle
County, Linze, Gaotai County, Shandan County and Sunan County.
4.2.3.3 References
Krausmann, F. 2001. “Land-use and industrial modernization: An empirical analysis of human influence
on the functioning of ecosystems in Austria 1830–1995” J. of Land Use Policy 18: 17-26.
Lieth, H. 1975. “Modelling the primary productivity of the world”. pp. 237-263. In H. Lieth and R. H.
Whittaker (eds.), Primary Productivity of the Biosphere. Springer-Verlag, Berlin.
Lieth, H. and Whittaker, R.H., (eds.) 1975. The Primary Productivity of The Biosphere. New York, Springer
Verlag.
Municipal Governments in Heihe River Basin, 2002. The Statistical Yearbook of Zhangye, Jiuquan,
Jiayuguan and Ejinaqi. Government Documents.
Vitousek, P., Ehrlich, P., Ehrlich, A.H. and Matson, P. 1986. “Human appropriation of the products of
photosynthesis” J. BioScience, 36: 363–373.
4.2.4 Agricultural vulnerability assessment in the Heihe River Basin
Xu, Jinxiang
This research activity assesses the vulnerability of agriculture production to climatic variation using 2000
as the baseline year. The research approach is the same as in the previous two sections. The general
research components are presented by Figure 4.2.4.1. As described early, the main agricultural
production is located in the middle reaches of the Heihe River Basin. The region has a unique agricultural
system, the oasis agriculture occupied in the boundary of Zhangye City. In this connection, agricultural
vulnerability assessment to climate stresses focused on Zhangye City.
Sensitive
indicator
Indicator method
Current Vulnerability
Adaptive
indicator
AEZ model
Potential grain
production (2000)
Potential grain production
(Future climate scenarios)
Ricardian
model
Land net revenue
(2000)
Land net revenue
(Future climate scenarios)
Vulnerability
(Future climate scenarios)
Fig. 4.11: General research scheme of agricultural vulnerability assessment
73
4.2.4.1 Current agricultural vulnerability assessment
Sensitivity
indicators
Adaptive
capacity
indicators
Climate sensitive
indicators
Rainfall variation ratio in
key months
Climate disaster ratio
Drought index
Rainstorm days
Variation of accumulated
degree days in key crop
growing season
Socio-economic factors
Weight
Average net revenue per
farmer
Non-agriculture
production ratio in GDP
Agriculture population
ratio
Average grain yield
0.1129
0.2687
Other sensitivity
factors
Forest coverage
Weight
Agricultural
production factors
Irrigated lands
ratio
Farmland index
Weight
Average per farmer
arable land
0.0982
0.1686
0.1590
0.0801
0.1087
0.1491
Weight
0.2213
0.0253
0.1619
0.0699
0.1805
Table 4.17: Agricultural sensitivity and adaptive capacity indicators and weights
Sensitivity
level (%)
Rainfall
variation ratio
in key months
(%)
Climate
disaster
ratio (%)
Drought
index
Rainstorm
days
Forest
coverage
(%)
<1
1.0-3.0
Variation of
accumulated
degree days
in key crop
growing
season (ºC)
<50
50-100
20
40
<10
10-20
<15
15-30
<1
1.0-1.5
60
20-40
30-50
1.5-2.0
3.0-5.0
100-200
30-10
80
100
40-60
>60
50-75
>75
2.0-4.0
>4.0
5.0-7.0
>7.0
200-300
>300
10-5
<5
Table 4.18: Agricultural sensitivity classes of sensitivity levels
74
>70
70-30
Adaptive
capacity
level (%)
20
40
60
80
100
Farmland
index (%)
Irrigated
lands
ratio (%)
Average
grain
yield
(kg)
Average
per farmer
arable
land (ha)
<60
60-70
70-80
80-90
>90
<20
20-40
40-60
60-80
>80
<200
200-300
300-400
400-500
>500
<0.05
0.05-0.08
0.08-0.11
0.11-0.18
>0.18
Average
net
revenue
per
farmer
(RMB)
<150
150-300
300-700
700-1000
>1000
Nonagriculture
production
ratio in
GDP (%)
Agriculture
population
ratio (%)
<5
5-20
20-40
40-70
>70
>90
90-80
80-70
70-60
<60
Note: Adaptive capacity level 100% represents the optimal socio-economic and ecological conditions for agricultural production
Table 4.19: Agricultural adaptive capacity levels or classes
Vp
Class
0-20
1
Least
vulnerable
20-40
2
Less vulnerable
40-60
3
Average
vulnerable
60-80
4
Vulnerable
80-100
5
Most
vulnerable
Table 4.20: Climate vulnerability classes for agricultural production
Municipality
Sensitivity
value (S)
Adaptive
capacity (A)
Vp* = S/A
Vulnerabili
ty
Vulnerabili
ty class
Ganzhou
Gaotai
59.458
59.458
70.91
73.5
0.8385
0.8090
44.80
27.26
3
2
Linze
Shandan
59.458
51.926
77.926
67.36
0.7630
0.7709
0.00
4.67
1
1
Minle
54.12
67.296
0.8042
24.45
2
Sunan
54.12
58.098
0.9315
100.00
5
Table 4.21: Current climate vulnerability classes for agricultural production in Zhangye
Table 4.21 indicates that Sunan County is the most vulnerable agricultural region in the Heihe River
Basin. This is followed by Ganzhou District. While Gaotai and Minle County are less vulnerable, Linze
and Shandan are the least vulnerable agricultural region in the basin. In Ganzhou, Gaotai and Minle,
relatively low rainfall and high evaporation are the main factors constraining agricultural activities.
Although Sunan has a small sensitivity score, its adaptive capacity is the lowest. As a result, Sunan has
the highest vulnerability score.
4.2.5 Land Degradation in the Heihe River Basin in Relation to Climate
Conditions
By F.M. Hui, Peng Gong, and J.G. Qi
75
4.2.5.1 Introduction
Soil degradation is a serious environmental problem and an important aspect of global change (Dregne et
al., 1991; UNCED, 1992). Soil degradation not only threatens two thirds of the world’s countries and
regions, one quarter of the land areas, and over 1 billion people, but it also aggravates serious problems,
such as food security, economic development, and resource protection (Desertification Information
Network of China; UNCCD, 1994; Wang, 1997). China is afflicted with some of the worst soil degradation
in the developing world. In 1999, the total area of degraded soil in China is 2.674 million km2,
approximately 27.9% of China’s land mass (Lu and Wu, 2002).
Soil degradation is the sudden decrease of land productivity or of land value, resulting from natural
forces or improper land use practices (Johnson and Lewis, 1995; Jing, 1999). Degradation can occur in
various manners, such as through desertification, meadow degradation, water and soil loss (i.e. soil
erosion), decreases in soil fertility, soil salinization, and soil pollution (Liu, 1995; Cai and Meng, 1999; Xu,
1998). Soil degradation directly affects vegetation, as it causes decreases in plant area and coverage, and
also impacts plant physical characteristics. In addition, soil degradation can also cause changes in the
composition of plant diversity (e.g. changing from forestland to shrubland), ultimately leading to
decreases in productivity.
Previous studies on soil erosion and soil desertification in the 80s and the 90s developed methods for
monitoring soil degradation using remotely sensed data. The methods developed by these studies can be
summarized as follows: 1. processing remotely sensed data using supervised and unsupervised
classification methods, following which the type and degree of soil erosion and desertification is
identified; 2. selecting indexes for measuring soil erosion and desertification, based on remote sensing,
following which each of these indexes are assigned a different weight, and integrated to derive a result
(Bastin et al., 1995; Tripathy et al., 1996; Del Valle et al., 1998; Wessels et al., 2004; Geerken and Ilaiwi,
2004; Wang and Sun, 1996; Gao et al., 1998; Sun and Li, 2000). These methods can determine the status of
soil degradation, and can provide control reference values. However, these methods do not consider the
influence of climate, topography, and human activities.
While there has been much research using image classification and vegetation index based on remotely
sensed data, studies on soil degradation models that integrate environmental, social, and economic
factors at the landscape level are still rare. In addition, net primary productivity (NPP) is an important
index of ecological productivity (Bjorklund et al., 1999), but at present, quantitative studies of plant NPP
in areas threatened by desertification are very scarce, especially studies using NPP to determine
ecological security. We therefore developed a model which integrates the main factors that influence soil
degradation, and we validated it using an NPP model.
4.2.5.2 Methods
We developed a land degradation model based on the following factors: precipitation, temperature,
vegetation fraction cover, slope and aspect, soil type, and land use cover. The model monitors the degree
of land degradation, and determines potential policies for preventing degradation.
Precipitation, vegetation fraction cover, and temperature can slow the land degradation process in Heihe
River Basin. Since Heihe River Basin is distant from the ocean, precipitation, vegetation fraction cover,
and temperature heavily influence productivity. However, different slopes and aspects have different
influences on land degradation, while soil type has a large influence, as the content of organic matter of a
given soil type will differ from area to area. For instance, the productivity in oases is higher, due to
human activity modifying the course of land degradation. Thus, based on our analysis of factors
influencing soil degradation, our land degradation model, LDI, can be described as
LDI =
SA ! S ! L
!X
V ! P !T
(1)
where: LDI is the land degradation index; P is the amount of precipitation; L is the land use cover; V is the
vegetation fraction cover; SA is the slope and aspect; S is the soil type; and, T is the average temperature
during the plant growth season. Here, we considered only the temperatures of months suitable for plant
growth (i.e., 0-30°C), and took the average temperature during the plant growth season. X represents
other factors that can influence land degradation.
76
4.2.5.3 Data processing
We used the following data: climate data, DEM data, land use data, and NDVI of AVHRR. The climate
data include monthly precipitation and temperature, taken from a total of 22 metrological stations; Figure
4.12 illustrates the distribution of the stations in the Basin. The land use cover data is a shape file, and
describes the land use of two years, 1985 and 2000. The spatial resolution of DEM and NDVI is 1 km. The
available soil data describes soil type; however, the physical and chemical properties of a given soil type
differed greatly in different places, especially in terms of soil organic matter, which contributes greatly to
vegetation productivity. Soil type data could therefore not represent the spatial influence of soil on
vegetation, and was thus not used in the model. Slope and aspect data was obtained from EROS of USGS,
where a detailed algorithm for pixel calculation is available on their website [28].
Fig. 4.12: The distribution of the stations in the Basin
All the data was re-projected and made to conform to the same spatial resolution (i.e., 1 km), before the
data was processed. The projection is UTM-47N, where the datum is WGS-84. Different data have
different units, so all data used in the model was normalized. The data normalization is consistent with
the different land degradation classes. In this study, the land degradation classes will be arranged into 6
classes, such that the normalized data is also arranged into 6 classes. The yearly data from every station
was put into a file with the station name, its latitude, longitude, and elevation. The file was then changed
into a shape file, and was interpolated using the IDW method.
Land use cover data was in vector format. Data from 1985 and 2000 were standardized using the same
scheme. There are 6 classes of land cover: 1. cropland; 2. forest; 3. grassland; 4. water; 5. urban and builtup areas; and, 6. barren areas. Cropland is greatly influenced by human activity, which is considered in
the model. Mountains meadows are also productive, and were thus classified as grasslands.
We used the monthly NDVI data to calculate the vegetation fraction cover (Graetz et al.1988; Dymond et
al. 1992; Kittich et al. 1995; Gutman and Ignatov 1998). For a given pixel, we first selected its highest
monthly NDVI value, with which the vegetation fraction cover can be calculated using the following
equation:
f v = ( NDVI ! NDVI soil ) /( NDVI veg - NDVI soil )
(2)
77
where: fv is the vegetation fraction cover; NDVI is the value of every pixel; NDVIsoil is the highest value in
barren areas; and, NDVIveg is the lowest value of the pixels completely covered by forest or grass.
(Erroneous: Please see end)
4.2.5.4 Results and validation
We produced the land degradation map of Heihe River Basin using the normalized data and the LDI
model (Figure 4.13). The most severely affected region is in the North of the basin, while conditions are
better in the South. Figure 4.14 also shows that croplands and forests are located in Qilian Mountain and
Hexi Corridor. In this region, the growth conditions are better, and the influence of human activity is also
large, especially around oases.
Fig. 4.13: The land degradation map of Heihe River Basin in 1985 on the left, and 2000 on the right
We validated the LDI model using an NPP model. The NPP model was developed by Zhou and Zhang
(1995), and incorporates eco-physiological features and regional evapo-transpiration models. The NPP
model relates two well-known balance equations on the earth’s surface, the water balance equation and
the heat balance equation. Research has suggested that the NPP model can simulate the NPP of natural
vegetation in arid and semi-arid regions. Figure 4.14 illustrates the results of the NPP model.
78
Note: the inaccurate “cow eye” pattern in both years.
Fig. 4.14: The results of the NPP model in 1985 on the left, and 2000 on the right
The results of the NPP model show that the NPP of natural vegetation increased greatly in 2000,
especially in the middle and southern regions of the basin. During the same period, there was also a small
increase in the north. However, the NPP model produced some inaccurate results, particularly in the
northern region. This inaccuracy was due to the limited number of climate stations across the large area
of Heihe River Basin, 140,000 km2, producing the “cow-eye” results when station files that included
errors were interpolated. However, despite the errors, the land degradation result from the LDI model is
consistent with the changes in NPP in the past 15 years.
4.2.5.5 Discussion
Since the LDI model considered the main factors that influence soil degradation, it successfully predicted
land degradation in Heihe River Basin. The NPP model validated the ability of the LDI model to predict
the degradation of the land.
However, soil data and other data related to soil properties was not used in this study, meaning the
model did not integrate all the possible factors causing soil degradation. In addition, the number of
climate stations in the basin was insufficient. Therefore, the interpolation results are not entirely accurate,
which can lead to inaccuracies in the model predictions.
In this study, all the data was normalized into 6 classes. However, for future studies, the normalization
could be re-classified according to the requirements of the given study. Since the normalization was the
key process for the model, the normalization method will be further studied.
4.2.5.6 Reference
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UNCED. 1992. Managing Fragile Ecosystems: Combating Desertification and Drought United
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Drought and/or Desertification, particularly in Africa. [R] Paris.
Wang J.H., Sun S.H.1996.Type classification and quantitative evaluation system of desertification. Arid
Environment Monitoring, 10(3): 129-137 (in Chinese)
Wang L.X. 1997. The situation and countermeasure of landdegradation in global scale: summarization on
the 9th International Soil and Water Conservation Conference.Soil and Water Conservation in
China,5: 8-10 (in Chinese)
Wessels, K.J., Prince, S.D., Frost, P.E.,and van Zyl, D. 2004.Assessing the effects of human-induced land
degradation in the former homelands of northern South Africa with a 1km AVHRR NDVI timeseries. Remote Sensing of Environment,91(1):47-67
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problems in China. Hefei:University of Science and Technology of China Press.16-23(in Chinese)
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Chinese)
Zhou G.S., Zhang X.S.1996. Study on NPP of Natural Vegetation in China under Global Climate Change.
Acta Phytoecologica Sinica, 20(1):11-19(in Chinese)
Zhou G.S., Zheng Y.R., Luo T.X., Chen S.Q.1998.NPP Model of Natural Vegetation and Its Application in
China. Scientia Silvae Sinicae, 34(5):2-11(in Chinese)
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http://edc.usgs.gov/products/elevation/gtopo30/hydro/asia.html
81
5 Adaptation
In response to a growing understanding of adaptation as an important response to climate variation and
change, the AS25 project team carried out some activities in identifying, assessing, and in some cases
evaluating measures to adapt to climate variation and change. The Heihe River Basin is not well adapted
to climate and there is abundant evidence in terms of the crop losses and ecosystem deterioration from
climate variations and extreme weather events.
Many areas of the region are experiencing droughts, poverty and economic losses associated with these
droughts. The growing water use conflicts have posed a big challenge for Chinese government agencies
to implement some effective water allocation policies. The fight over access to water resources in the
Heihe Basin has led to disputes, confrontation, and many cases of violent clashes. In this respect, the
AS25 project adopted a participatory approach for evaluating adaptation measures to deal with water
resource vulnerability.
5.1 Activities Conducted
Three activities were conducted during the AS25 project implementation to investigate adaptation to
climate change. One activity was to incorporate adaptive capacity in assessing climate vulnerabilities for
water use system, ecosystem and agricultural production. This activity has been discussed in sections
4.2.2, 4.2.3 and 4.2.4. The other two activities applied different approaches to prioritizing adaptation
options or practices.
5.1.1 Adaptation activity one
This activity focused on methodology development for water resource adaptation options evaluation to
provide decision-makers with the information needed to improve the adaptive capacity of water resource
system to cope with climate change in Heihe region of Northwestern China. By focusing on adaptation
evaluation tools or methods, this activity first reviewed some background of adaptation option
evaluation. This was followed by a literature review of two broad categories of adaptation evaluation:
adaptations with impact assessment and the evaluation of adaptations in policy analysis.
The literature review helped to understand the current status of various adaptation evaluation tools.
Various methods or tools for evaluating adaptation options were reviewed and discussed. The review of
adaptation evaluation tools led to the conclusion that we need to pursue new directions that are essential
if the climate change impacts and adaptation studies are to remain relevant in sustainability decision
making.
An integrated approach, based on a multi-criteria decision making technique, was applied in the Heihe
River Basin study for illustration. The following section will discusse how the integrated approach was
applied in the Heihe Basin for identifying desirable adaptation options to reduce climate change
vulnerabilities. The analytic hierarchy process (AHP), an MCDM technique, was adopted to develop an
adaptation evaluation tool to identify the priorities of evaluation criteria and to rank desirability of
adaptation measures. The case study provided some articulation on how the integrated approach could
provide an effective means for the synthetic evaluation of the general desirability levels of a set of
adaptation measures through a multi-criteria and multi-stakeholder decision making process. Thus, the
activity contributed to the science on adaptation option evaluation.
5.1.2 Adaptation activity two
This activity adopted the method proposed by Yohe and Tol (2002) for evaluating systems’ adaptive
capacity to deal with climate stress. The Yohe and Tol method was applied in the Heihe River Basin to
evaluate alternative adaptation options to improve water resources’ coping capacities by focusing on
several key determinants of adaptive capacity. The case study in the Heihe River Basin using this method
assisted by a policy survey illustrated how the method was applied. The case study provided some
results showing how effective of different adaptation options that could reduce water resource
vulnerability in the study region.
82
5.2 Description of Scientific Methods and Data, Results, Conclusions
Note: The following sections will describe all these aspects based on each of the two activities.
5.2.1 Adaptation
measures
evaluation
to
reduce
water
system
vulnerability to climate change in Heihe River Basin
Yongyuan Yin
5.2.1.1 Introduction: Problem identification and questions addressed
In the Heihe region, various water use policies and measures have been implemented or designed to limit
or prohibit the utilization of certain amount water by sectors or regions. Controversies have occurred, of
course, as a result of such policies. Obviously, water policies or regulations make some sectors or regions
worse off and others better off because of their re-distributive nature. It is this re-distributive nature of
policies that often aggravates water use conflicts.
Some adaptation measures were introduced recently to the region including overall water supply
control, water permits, water right certificates, farmer water use association, water pricing adjustment,
and better water allocation policy. What seems to be missing, however, is an overarching strategy that
brings the climate change concern into water use decision making process. For the most part, the impact
of climate change on water system has received scant attention from government agencies and others
responsible for water resource management and planning. A partial explanation for the limited response
to take consideration of climate change in water use management might be due to the lack of knowledge
or awareness of the issue by policy makers and general public.
In this respect, multi-criteria options evaluation of adaptation measures was conducted in the study to
provide decision makers with information on desirable climate change adaptation measures. The purpose
of this activity was to provide decision-makers with the information needed to improve the adaptive
capacity of water resource system to cope with climate change in the region. In particular, the activity
addressed the following questions:
•
What are the existing and potential adaptation measures that can be used to reduce water
resource risks from climate change in the Heihe River Basin?
•
How effective will each of the alternative adaptation options be in dealing with risks?
•
What is the current status of adaptation evaluation methods or tools? and
•
How can we prioritize alternative adaptation measures and identify key adaptive options that are
desired to help the current water resource infrastructure to cope with climate change?
5.2.1.2 Current status of adaptation science and evaluation tools
Research on developing well designed adaptation strategies and/or options will provide the information
and understanding necessary for establishing efficient adaptation options or policies to deal with climate
vulnerability. By focusing on adaptation evaluation tools or methods, this activity first evaluated tools
associated with two broad categories of adaptation evaluation: adaptations with impact assessment and
the evaluation of adaptations in policy analysis. This was followed by a critical review of a range of tools
that have been used across and within different natural resource and socioeconomic sectors. The review
provided researchers with key information about available tools, special features of each tool, and the
strengths and weakness of different tools. This review of adaptation evaluation tools led to the conclusion
that we needed to discover and pursue new directions that were essential if the climate change impacts
and adaptation studies were to remain relevant in sustainability decision making.
The adaptation assessment associated with conventional impact studies has been developed by IPCC
(Carter et al., 1994). An alternative adaptation policy analysis approach aimed at improving adaptability
83
and resilience of various systems sensitive to climate change, and strengthening system sustainability
was late developed by UNDP/GEF (Lim et al., 2005). Various methods or tools which could be employed
for evaluating adaptation options were reviewed and discussed.
An integrated approach assisted by the multi-criteria decision making, was designed and applied to the
case study. The IA approach, bringing together stakeholders from water managers, farmers, other water
users, government agencies, and the academic research community, demonstrated how to apply the IA in
a real case.
The most comprehensive list of adaptation evaluation tools is provided in the compendium of decision
tools to evaluate climate change adaptation strategies or options published by the Secretariat and made
available to the participants at the tenth session of the Subsidiary Body for Scientific and Technological
Advice (SBSTA) (FCCC/SBSTA/2000/INF.4) (Stratus Consulting Inc., 1999). This compendium
illustrated the current state of knowledge by compiling and describing various decision tools from other
disciplines, such as management science, economics, and systems engineering. It contributed to a series of
documents which decision makers can turn to for guidance in developing adaptation strategies. The
compendium focuses on specific adaptation option evaluation tools that can be applied to examining
adaptation options. As an ongoing process, tools described in the compendium have been applied in
many case studies for purposes other than climate change assessment. These tools will be steadily
improved and their applications to climate change adaptation evaluation will be modified to make them
more relevant and useful.
While the resource compendium is useful as a reference document to select available decision tools for
evaluating specific adaptation strategies, it is not a manual handbook describing how to implement each
tool. It is rather a survey of possible tools that can be applied to a broad spectrum of situations. The
compendium is intended for use by either assessment managers or technical researchers and does not
require extensive technical knowledge of modeling or specific decision-making techniques to use. Some
of the tools described in the compendium may require particular expertise, but these requirements are
described in the summary table.
It is obvious that the documents and reports provided by the Secretariat are useful for assisting
researchers and decision makers to conduct adaptation option evaluation, even the information is limited
and not comprehensive. It indicates that there is a lack of capacity and knowledge of designing and
applying adaptation evaluation tools in the climate change impact research community. To improve our
capacity of adaptation options evaluation, more efforts should be considered to identify appropriate
experts to evaluate methods and provide more qualified information. The ability to design and apply
adaptation evaluation methods is linked to the issue of capacity-building.
5.2.1.3 Adaptation option evaluation tools in relation to impact assessment
The science of adaptation usually applies two approaches in evaluation of adaptations. The
distinguishing of the two types of adaptation evaluation approaches is important in the evaluation and
selection of effective or desirable options to deal with climate change. The first approach examines the
effectiveness of alternative short term or autonomous adaptation options by using impact assessment
modeling (Rosenzweig and Parry, 1994; Carter et al., 1994). Another approach deals with anticipatory or
planned adaptation strategies and government policies. And thus the tools used for the second approach
are related to policy evaluation or analysis (Stratus Consulting Inc. 1999; Lim et al., 2005).
Carter and others (1994) provided guidelines for climate change impact assessment and option
evaluation, and introduces a variety of methods which, however, are reviewed rather general and lack
specific guidelines or prescriptions. In this respect, two concurrent research programs, the U.S. Country
Studies Program and the United Nations Environmental Programme (UNEP) Handbook as part of
UNEP's Guidelines for Climate Change Country Studies were developed to fill the gap. The former
program provided assistance to more than 50 developing countries and countries with economies in
transition to develop capacity to assess vulnerability to climate change and adaptation options
evaluation. In the U.S. Country Studies, a guidebook of a set of specific methods and approaches for
impact assessment and adaptation evaluation was provided (Benioff et al., 1996). In the latter program,
UNEP sponsored the writing of a handbook on assessing climate change impacts and adaptation, to serve
as a supporting resource for its country studies program. The Handbook presents an overview of
different
methodologies
and
covers
several
sectors
(Feenstra
et
al.,
1998).
While these approaches have been successful in providing a research framework and guidance for
84
climate change impacts research, all of them focus mainly on the impacts of climate change than on
adaptation option evaluation. It has been a common experience in applying these research approaches in
many climate change impact studies at country, regional and sectoral scales, that the overwhelming part
of the time, efforts, and resources were devoted to the selection and application of climate scenarios, and
impact assessments. It has been invariably noted that insufficient time and efforts were left to develop the
adaptation component.
Various applications of climate change impact assessment methodologies have been discussed in the
literature. The majority of the applications focus on negative effects of climate scenarios on different
aspects of human and ecological systems. Applications which also took considerations of adaptation
evaluation were relatively rare.
There are some shortcomings associated with these scenario-driven approaches from the point of view of
the need to improve our understanding and evaluation of adaptation in a policy analysis context. The
problem is more fundamental than simply a matter of available project time and financial resources.
There are many important reasons why a range of applications of the scenario-based approaches have not
yielded useful results for the purposes of adaptation option evaluation and policy analysis (Lim et al.,
2005).
First, while the concept of adaptation has a long history of use in ecological and resource management, it
is relatively new to many researchers who have been coordinating research projects on climate change
impact studies. In research design process, there is a lack of consideration for the evaluation of adaptation
measures and policies. Climate scientists have been more interested in results that show the potential
impacts of future climate conditions upon ecosystems, economy and society.
Secondly, sometimes consideration of adaptation evaluation was given after the impact assessment was
completed. It is often at that stage there were time and resource limits to conduct detailed evaluation.
Many climate scientists do not recognize that a major part of impact assessment is to determine whether
alternative adaptation measures or options could lead to a reduction in damages associated with different
climate change scenarios. Thus, the impact assessment should be conducted with different climate change
scenarios coupled with or without certain adaptation options. Given a set of alternative options to deal
with climate change are available and their various implications for biophysical and socio-economic
systems, the task of identifying the more effective and desirable adaptation options is not easy.
Thirdly, in order for new adaptation policies to be effective, they must be based on sufficient knowledge of
the ecological, economic, and social effects of climate change. However, climate change impact researchers
have been working without sufficient knowledge of the quantitative effects of climate change scenarios on
human and ecological systems. Most of the impact researchers are physical scientists by training with little
knowledge in social science and policy analysis. As a result, knowledge of the diversity and complexity of
ecological and socio-economic processes is inadequately developed, which makes it extremely difficult to
understand the conditions necessary for ensuring ecological stability and resilience (Holling, 1978).
5.2.1.4 The adaptation evaluation tools for policy analysis
There is an evident need therefore for new research approaches and tools that can be used specifically in
the adaptation option evaluation, selection, and decision making. The compendium discussed previously
described a range of general decision tools that are applicable to evaluate adaptation policies in multiple
sectors (Stratus Consulting Inc., 1999). The compendium groups the tools into three broad categories:
initial survey, economic analysis, and general modeling (Table 5.1).
85
Expert judgement
Screening of adaptation options
Adaptation decision matrix
Initial Survey Tools
Economic Analysis
General Modeling
Uncertainty and Risk Analysis TEAM
Benefit-cost analysis
CC: TRAIN/VANDACLIM
Cost-effectiveness analysis
Source: Stratus Consulting Inc., 1999
Table 5.1: Decision tools applicable to multiple sectors
The initial survey tools include expert judgment, screening of adaptation options, and the adaptation
decision matrix, and are useful for identifying potential adaptation strategies or narrowing down the list
of appropriate options. They are generally relatively informal, inexpensive, and utilize qualitative
judgment rather than quantitative data. General tools for economic analysis include financial analysis,
benefit-cost analysis, cost-effectiveness analysis, and risk-benefit/uncertainty analysis. These are typically
used to determine which options are most economically efficient, and to assist the user in deciding which
adaptation option is the most appropriate once a final list of adaptation options has been compiled.
General modeling tools include TEAM and CC: TRAIN. These address different adaptation strategies
across a number of sectors, and are used to evaluate several sectors of concern in a particular region.
More detailed information on these methods is available from Stratus Consulting Inc. (1999).
The Multi-Criteria evaluation tools
A range of methods/tools developed in decision theory, multi-criteria evaluation, and systems analysis
can be adopted for adaptation option evaluation. These methods can be used effectively to link climate
change impact assessment with regional sustainability. Some of the advanced analytical techniques
including goal programming (GP), fuzzy pattern recognition (FPR), neural network (NN) and analytical
hierarchy process (AHP), are introduced here to show how they can be used to identify the desirable
adaptation options in dealing with climate change vulnerabilities (Table 5.2).
The Goal Programming (GP)
Yin et al. (2000) applied an integrated land assessment framework (ILAF) to evaluate alternative
adaptation options against multiple sustainability indicators. The ILAF established linkages between
climate change impact assessment and decision making, and between climate change and regional
sustainable development (RSD). Any adaptation options need to be evaluated according to the
goals/indicators of RSD, such as economic viability, environmental quality, and social acceptability.
The goal programming (GP) model of the ILAF was used for policy analysis to estimate the likely
consequence of a potential adaptation policy on regional sustainability goal achievement. This type of
information provides a basis for planners or decision-makers to determine the adequacy and
effectiveness of the policy before it is implemented.
In the ILAF policy evaluation, alternative policies can be evaluated by relating their various effects to a
number of relevant goals/indicators. In order to assess the effectiveness of different adaptation policies in
achieving regional sustainability, a base scenario reflecting 'business as usual' conditions of the region is
usually created for comparison. A comparison of the results between the policy scenario and the baseline
scenario would show the different goal achievements under the two scenarios. If the goal achievements
are improved significantly under the policy scenario, then this policy is assumed to be effective. The
model would be run iteratively with a list of alternative policy scenarios. By proceeding in this manner
through a series of scenarios, it is possible to evaluate whether the policies are in keeping with the stated
goals or indicators, and the desirable policy options can be identified.
Fuzzy Pattern Recognition
The fuzzy pattern recognition technique based on fuzzy set theory (Zadeh, 1965) can be applied to group
alternative adaptation measures into several classes reflecting the effectiveness of these measures. The
impact assessment results are essential inputs for the fuzzy pattern recognition exercise. It is expected
86
that the fuzzy method will provide an effective means for the synthetic evaluation of the general pattern
of the judgements undertaken through multiple stakeholders decision making.
Yin (2001a) has illustrated an approach that was employed to link flood impact analysis and sustainable
watershed policy evaluation. The research framework incorporated a multi-stakeholder consultation, a
fuzzy pattern recognition method, and other technologies in examining the implications of flood
management for regional sustainable development. The method provides a viable means for the synthetic
analysis of the general performance levels of a set of options based on a multitude of sustainability
indicators.
Evaluative Criteria
Ecological
simulation
modeling
Biophysical
impact
assessment
Biophysical
Objectives
No
Yes
Quantitative
Goal
Tradeoffs
No
Goal
programming
APF
Yes
Yes
Yes
Quantitative
Yes
Yes
Fuzzy set
APF
Yes
Possible
Yes
Quantitative
and
qualitative
No
Yes
Neural net
APF
Yes
Possible
Yes
No
Yes
AHP
APF
Yes
Yes
Yes
Quantitative
and
qualitative
Quantitative
and
qualitative
No
Yes
Method
Approach
Multiple
Objectives
Multiple
Sector
Systematic
Data
required
Alternative
adaptations
No
Note: APF: Adaptation Policy Framework
Table 5.2: Methods reviewed
The Neural Network
Another method which might be employed for identifying desirable options is the neural network (NN).
NN technology attempting to mimic the computational architecture of the human brain by computers can
provide intelligent functions such as learning and pattern recognition. A neural network is composed of
many non-linear processing units (neurons or nodes) operating in a parallel manner. These nodes are
connected with weights that are adjustable during the learning process which takes place to improve the
performance of the neural network. The basic structure of a general neural network consists essentially of
a set of relationships between various processing nodes. These relationships are expressed in
mathematical formulations (Yin and Xu, 1991).
The procedure for operating the neural net model for adaptation option evaluation begins by training the
net with the training algorithm. Input data files are called upon from the database in parallel to the
network. The net is trained by selecting some random weights and internal thresholds and then
presenting all training data sequentially. The training algorithm repeatedly adjusts the weights after
every trial by using side information specifying the correct adaptation option until the weights converge.
The net is considered as converged when outputs no longer change on successive iterations, and one
87
Purpose
Mainly
for
impact
analysis
Multicriteria
policy
analysis
Multicriteria
policy
analysis
Varies
Multicriteria
policy
analysis
output of the last iteration corresponding to the most likely option is high, the other outputs are low.
Then the output is used directly as restored memory.
Adaptation Option Evaluation by AHP
The analytical hierarchy process (AHP) method can also provide an effective way to identify desirable
adaptation options involving multiple criteria. AHP has been employed to evaluate alternative policies,
allocate resources, conduct sensitivity analysis for resource use planning, and select desirable project
locations for both developed and developing countries (Saaty, 1980; 1982). When applying for adaptation
option evaluation, the AHP requires the decision makers to provide judgements on the relative
importance of each option relative to each criterion. The result of the AHP is a prioritized ranking
indicating the overall preference for each of the adaptation options.
The approach AHP takes is to ask the decision makers (DMs) to determine his/her preference between
two options of how it contributes to each criterion given certain impacts of the options. In this exercise, a
decision maker compares options two at a time (pairwise comparison). Then DMs will specify their
judgements about the relative importance of each option in terms of its contribution to the achievement of
the overall goal. That is, in our case, to alleviate the adverse consequence of climate change (Yin and
Cohen, 1994).
Yin (2001b) developed an IA approach, assisted by AHP, to evaluate a number of adaptation options that
could be undertaken to reduce vulnerabilities associated with climate change in the coastal region and
communities of Georgia Basin (GB) in Canada. The AHP application in the GB study included a series of
workshops and internet based surveys with participation of a broad range of public and private
stakeholders, and policymakers from different affected sectors to identify sustainability indicator
priorities, as well as a series of desirable adaptation policies. The AHP application can improve our
understanding of the interactions between regional sustainability and climate change impacts.
In the GB case study, alternative adaptation options to deal with various vulnerabilities were evaluated
against a set of sustainability indicators. The study results provided a prioritized ranking indicating the
overall preference for each of the adaptation options in coastal regions sectors of the study area. The AHP
facilitated the participation of regional stakeholders in climate change impact and adaptation option
evaluation. Results of the AHP analysis were presented in the project final report (Yin, 2001b).
5.2.1.5 An integrated assessment of adaptation evaluation in the AS25 Project
In this activity, an integrated approach, was developed for the case study. The approach which was
applied in the Heihe River Basin, bringing together partners from the private sector, the public sector
policy community, and the academic research community, demonstrates how to meet the challenge of
linking climate change adaptation and sustainable development. The analytic hierarchical process (AHP)
was employed to assist the evaluation.
Figure 5.1 illustrates the IA framework for adaptation option evaluation linking impact assessment with
local sustainability assisted by multi-criteria policy analysis and multi-stakeholder consultation in Heihe
region. In the following discussion, not all the components shown in Figure 5.1 are covered with the same
detail. Rather, focus is on the two main concerns of the component: the sustainability indicators/goals
identification and adaptation policy evaluation. The case study presents the importance of indicator/goal
setting in regional sustainability research and the approach to identify indicators/goals. Then, the IA
system assisted by analytic hierarchy process (AHP) is introduced to illustrate how sustainability
indicators/goals and climate change vulnerabilities can be represented in the analytical system to link
climate change impacts, adaptation, and regional sustainability evaluation.
Description of potential response options
A set of existing and possible adaptation options to deal with vulnerabilities of climate variation and
change was identified for multi-stakeholder consultation and multi-criteria evaluation. An inventory of
existing and potential adaptation options was developed. The options inventory included descriptions of
the options and relevant information. Numerous potential adaptation options were available for dealing
with water vulnerabilities to climate change. An initial screening process was conducted to reduce the
number of options for further detailed evaluation. The multi-stakeholder consultation helped to arrive at
a collective group recommendation on the selection of adaptation options for further multi-criteria
evaluation.
88
There have been a growing number of local, provincial and national initiatives and programs underway
that address various aspects of the water shortage problem, such as comprehensive water use planning,
pay for water systems, and water pricing to limit water consumption. Meanwhile, government
established policies for water recycling, pollution control and water-efficient technology to improve
water use efficiencies in industries and farming. For example, the Ministry of Water Resources
introduced the first pilot project to build a water-saving society in Zhangye City located in the Heihe
region in 2001. Some adaptation measures were introduced to the region including overall water supply
control, water permits, water right certificates, farmer irrigation association, water pricing adjustment,
and better water allocation policy. Preliminary results of the pilot project have shown positive effects in
dealing with water shortage problem.
What seems to be missing, however, is an overarching strategy that brings the climate change concern
into water decision making process. For the most part, the impact of climate change on water system has
received scant attention from government agencies and others responsible for water resource
management and planning. A partial explanation for the limited response to take consideration of climate
change in water use management might be due to the lack of knowledge or awareness of the issue by
policy makers and general water users.
Water System Vulnerabilities in the Heihe River
Region
Sustainable development
indicators or
multiple evaluation criteria
Identification and inventory of
existing and potential adaptation
measures
Desirable adaptation options
Multiple stakeholders, planners, analysts, and public
Fig. 5.1: Multi-criteria adaptation options evaluation system
Design indicators/criteria to evaluate alternative adaptation measures
The research also included an identification of evaluation indicators. In this study, indicators were
evaluation criteria or standards by which the efficiency of alternative adaptation options could be
measured. Indicators of the economic, social, and environmental dimensions of regional sustainable
development were identified.
Multi-Stakeholder consultation and multi-criteria evaluation of adaptation options
Multi-criteria options evaluation (MCOE) of adaptation measures was one of the major components of
the study. The MCOE approach was used to identify desirable adaptation measures by which decision
89
makers could alleviate the vulnerabilities and to take advantage of positive impacts associated with
climate change in the region.
In particular, multi-stakeholder consultation (MSC) and MCOE were used to relate impact information to
decision making requiring subjective judgement and interpretation. In the evaluation process, alternative
options were evaluated by relating their various impacts to a number of relevant indicators. The results of
impacts generated in various impact assessments were used as references for ranking the performance of
each adaptation option against each sustainability indicator. The AHP was adopted to develop the
adaptation evaluation tool to rank the priorities and desirability of adaptation measures (Yin, 2001b).
5.2.1.6
Applications and results of the Heihe River Basin case study
The IA was applied to the Heihe River Basin for illustration purposes. While the IA is a tool that can be
used to help identify the desirable adaptation measures, the multi-criteria evaluation is only a part of an
extensive discourse with stakeholders across the study region.
Water system adaptation options for evaluation
In general, alternative adaptation options can be grouped into two categories: engineering and nonengineering measures. The formers are involved in construction works that attempt to supply more water
resources for various users. These structural measures include reservoirs, irrigation systems and more
wells. Options in the latter category are essentially those measures that are not of an engineering nature,
or do not involve construction. These non-engineering measures include demand management, water use
policy, pricing, water trade and permits, and other institutional and governance measures.
A primary screening process was conducted by the research team to select among a large number of
alternative adaptation options those of more possible measures for further evaluation with the multicriteria evaluation process. The following list of adaptation options was identified through the initial
screening process to reduce the key climate change impacts and vulnerabilities presented above (see
Table 5.3). These potential options were evaluated and compared by experts and stakeholders in the
Heihe River Basin.
No.
Adaptation
options
1
B1
2
B2
3
4
B3
B4
5
B5
6
7
8
B6
B7
B8
Note
Reform economic structure so that large water consumption
sectors will be reduced
Through water use regulation, establish water use permit and
water trade system
Construct water works (e.g. irrigation system)
Establish farm water users’ society or committee to improve
institutional arrangement
Upgrade and/or adopt advanced water use technologies
including water use save irrigation methods
Governments set water price to control water demand
Improve water allocation policies
Increase water save awareness and education
Table 5.3: Identified adaptation options to reduce water vulnerabilities to climate stress
Water System Sustainability Indicators/Multi-Criteria Setting
To link climate change impact analysis, adaptation option evaluation and sustainability evaluation, water
system sustainability indicators must be set and performance of adaptation options must be measured in
a manner that integrates social, environmental, and economic parameters that may be influenced by
climate. Based on information gathered from stakeholders through householder surveys and consultation
meetings, indicators for evaluating adaptation options to reduce water system vulnerability were
90
specified for this study (Table 5.1.5). Four broad water use sustainability goals were chosen to act as
criteria in the AHP evaluation.
Stakeholder meetings and surveys were conducted to involve experts and stakeholders in the evaluation.
Completing a copy of the questionnaire during each survey among a wide range of individuals,
researchers assisted stakeholders to respond to the survey questions during their convenient time. The
survey was mainly administered in one-on-one interviews and in small group/workshop settings.
Reduce per unit production water use A1
Increase economic return of water/m3 A2
Ecological and environmental benefit A3
Costs of adaptation options A4
Maximize water use efficiency
Maximize economic return to society
Minimize harm to the natural environment
Minimize economic costs to society
Table 5.4: Indicators used for evaluating adaptation options in the case study
The Expert Choice (EC) 2000 software package was used to facilitate the application of AHP in this study.
Survey questions were designed according to the principles of AHP so that the responses could be input
into the software program for compilation and analysis. It provides an overall score for each alternative
option by distributing the importance of the indicators among the adaptation options, thereby dividing
each indicator’s priority into proportions relative to the percentage of alternative.
With the four indicators listed in Table 5.4, and a set of adaptation options (Table 5.3) to compare, a
decision hierarchy model was created. This decision hierarchy was quite simple because it included a
single overall goal, with two levels below it in the hierarchy: a set of criteria/indicators, and a list of
alternative adaptation options. Once the relative importance of individual criteria was determined,
decision-makers were asked to think about the preference of each alternative adaptation option in terms
of achieving a single criterion.
The survey was designed as a series of tables. Respondents were given a pair of indicators or a pair of
options, and asked to compare them using a numerical sliding scale. The comparison scale ranged from 1
to 5, with 1 representing options that are equally effective (or indicators that are equally important), and 5
representing options where one is extremely more important than another (see Table 5.5). The purpose of
providing these comparison tables is to facilitate the AHP pairwise comparison process. Respondents
select the relative effectiveness scale based on his/her preference given certain impacts information of the
adaptation options. In this exercise, a stakeholder compares two options (first column and the far right
column) at a time (pairwise comparison). Respondents specify their judgements about the relative
importance of each option by checking the appropriate cells. In Table 5.5, for example, the respondent
considers “Reform economic structure” equally important comparing with “Adopt advanced water use
technologies” (row 5), and a check is placed under scale “1”. Comparing “Reform economic structure”
with “water use permit and water trade system” in row 3, a check was put on the right side of scale “4” to
indicate that the option in the far right column was “strongly more effective than the option in the first
column. While scale “1” means option in the first column and the far right column are equally important,
scales on the right side of scale “1” indicate that the options in the far right column are more important
than the option in the first column, vice versa.
91
Adaptation Option
5
Reform economic
structure
Reform economic
structure
Reform economic
structure
Reform economic
structure
Reform economic
structure
Reform economic
structure
Reform economic
structure
Relative Effectiveness Scale
4
3
2
1
2
3
4
x
x
Adaptation Option
5
water use permit and water
trade system
Construct water works
x
x
Establish farm water users’
society or committee
Adopt advanced water use
technologies
Governments set water price
x
x
x
Improve water allocation
policies
Increase water save awareness
and education
Note the relative effectiveness scale: 1 – equally effective; 2 – marginally more effective; 3 – moderately more effective; 4 – strongly
more effective; 5 – very strongly more effective.
Table 5.5: AHP comparison table: water system
Preliminary results of the AHP analysis
Responses were received for the Heihe River Basin from respondents of water experts, managers, and
various levels of government. All were stakeholders in the study region and were responsible for water
resource management and planning.
Reform economic structure (B1) was ranked the most desirable adaptation option for the Heihe region,
with farm water user society (B4) option scoring fairly high as well (see Table 5.6). The moderate
performance levels for improve water allocation policies (B7), establish water permits and trade (B2), and
increase awareness and education (B8) options were due to the fact that these were relatively new
measures in water resource management in the study region. The scores for apply water save equipment
and technologies (B5) and implement water price system (B6) options were near the bottom of the list by
most participants (especially from an economic perspective) and were not considered to be desirable
adaptation options. It appears that regional stakeholders consider the two options are expensive
alternatives for dealing with watershed management and farmers do not want experience higher water
prices. Construct water works (B3) option was judged to be the most inefficient option from an economic
perspective, and it was ranked at the bottom overall among regional respondents.
The above information is useful for decision making in selecting efficient options by governments,
especially when considering the goal of Heihe River Basin sustainability. The results suggest that
institutional options (reform economic structure, water user society, and water permits) would be
considered as desirable against the four evaluation indicators. Implementation of these water use
adaptation options in the Heihe River Basin might be able to reduce water vulnerabilities associated with
climate change in the context of regional sustainability. These adaptation options can be incorporated into
a comprehensive basin sustainable development plan.
92
Water resource adaptation options
AHP result
Rank order
Reform economic structure B1
0.26
1
Form farm water user society B4
0.18
2
Improve water allocation policies B7
0.14
3
Establish water permits and trade B2
0.13
4
Increase awareness and education B8
0.12
5
Apply water save equipment and tech B5
0.08
6
Implement water price system B6
0.05
7
Construct water works B3
0.04
8
Table 5.6: Overall rank and score of adaptation options in the Heihe region
5.2.1.7 Summary
Working in partnership with local, provincial and national governments and other key stakeholders
(water use professionals, farmers, and other organizations), the activity identified alternative effective
adaptation measures which could become practical options to deal with water vulnerabilities likely to
become more severe in the Heihe River Basin due to the impacts of climate change. A properly developed
and implemented adaptation action plan consisting of various effective measures could have positive
benefits to the well-being and productivity of all people living in the Heihe region.
These effective adaptation measures can help reduce water system vulnerability and water use conflicts.
In an indirect term, a reduction in water system vulnerability will mitigate the impacts of climate change
on agricultural system and protect the livelihood of farmers. Water system sustainability can also
improve ecosystem health and reduce sandstorms which have created a global environmental impact. In
addition, a successful adaptation action plan could become a useful model for communities across China
and around the world.
5.2.1.8 References
Benioff, R., Guill, S. and Lee, J. (eds.). 1996. Vulnerability and Adaptation Assessments: An International
Guidebook. Dordrecht, The Netherlands: Kluwer Academic Publishers.
Carter, T.R., Parry, M.L., Harasawa, H. and Nishioka, S. (eds.). 1994. IPCC Technical Guidelines for
Assessing Climate Change Impacts and Adaptations. Department of Geography, University College,
London.
Feenstra, J., Burton, I., Smith, J. and Tol, R. (eds.). 1998. Handbook on Methods for Climate Change
Impact Assessment and Adaptation Strategies, version 2.0. United Nations Environment Programme,
Nairobi, and Institute for Environmental Studies, Vrije Universiteit, Amsterdam.
Holling, C.S. (ed.) 1978. Adaptive Environmental Assessment and Management. Chichester: John Wiley.
IPCC, 2001. Climate Change 2001: Impacts, Adaptation, and Vulnerability. Summary for Policymakers. A
Report of Working Group II of the Intergovernmental Panel on Climate Change. Geneva,
Switzerland.
Lim, B., Spanger-Siegfried, E., Burton, I., Malone, E. and Huq, S. (eds.) 2005. Adaptation Policy
Frameworks for Climate Change: Developing Strategies, Policies and Measures, Cambridge
University Press, Cambridge, U.K.
Rosenzweig, C. and Parry, M. 1994. “Potential impact of climate change on world food supply”, Nature,
367: 133-138.
Saaty, T.L. 1980. The Analytic Hierarchy Process. McGraw-Hall, New York.
93
Saaty, T.L. 1982.Decision Making for Leaders: The Analytical Hierarchy Process for Decisions in A
Complex World. McGraw-Hall, New York.
Stratus Consulting Inc., 1999. Compendium of Decision Tools to Evaluate Strategies for Adaptation to
Climate Change Final Report, UNFCCC Secretariat, Bonn, Germany, FCCC/SBSTA/2000/MISC.5.
Yin, Y., Cohen, S., and Huang, G. 2000. “Global climate change and regional sustainable development: the
case of Mackenzie Basin in Canada”, Integrated Assessment 1: 21-36.
Yin, Y. 2001a. "Flood management and water resource sustainable development: the case of Great Lakes
Basin" Water International 26(2): 197-205.
Yin, Y. 2001b. Designing an Integrated Approach for Evaluating Adaptation Options to Reduce Climate
Change Vulnerability in the Georgia Basin. Final Report Submitted to Adaptation Liaison Office,
Climate Change Action Fund, Ottawa, Canada.
Yin, Y. and Cohen, S. 1994. "Identifying regional policy concerns associated with global climate change",
Global Environmental Change 4 (3): 245-260.
Yin, Y. and Xu, X. 1991. "Applying neural net technology for multi-objective land use planning" J. of
Environmental Management. 32: 349-356.
Zadeh, L.A. 1965. “Fuzzy sets”, Information and Control, 8: 338-353.
5.2.2 Activity two: adaptive options under climate change in Zhangye City
region
By Wang, M.H., Xu, Z.M. and Long, A.H.
5.2.2.1 The method employed to assess adaptation options
The Yohe and Tol method and eight determinants selected
This activity adopted the method proposed by Yohe and Tol (2002) for evaluating water resources’
adaptation options to deal with climate stress. The Yohe and Tol method was applied in Zhangye City
located in the middle reaches of the Heihe River Basin. Several several key determinants of adaptive
capacity were selected for evaluating alternative adaptation options assisted by questionnaire. The
activity provided some results showing how effective of different adaptation options that could reduce
water resource vulnerability in the study region.
The eight determinants of adaptive capacity selected for evaluation reflected a variety of system, sector,
and location specific characteristics:
1.
The range of available technological options for adaptation;
2.
The availability of resources and their distribution across the population;
3.
The institutional structure, the decision-making authority, and the decision criteria that would be
employed;
4.
The stock of human capital including education and personal security;
5.
The stock of social capital including the definition of property rights;
6.
The system’s access to risk spreading processes;
7.
The ability of decision-makers to manage information, the processes by which these decisionmakers determine which information is credible, and the credibility of the decision-makers,
themselves; and
8.
The public’s perceived attribution of the source of stress and the significance of exposure to its
local manifestations.
The detailed method developed by Yohe and Tol is not described here and interested readers should refer
to Yohe and Tol (2002). In general, alternative adaptation options were scored subjectively against the
94
eight determinants by water managers or experts from a range from 0 to 5 for feasibility degree and from
0 to 1 for efficacy level. The judgment is based on a systematic consideration of the feasibility and efficacy
degree to which each adaptation option would help or impede the adoptive capacity against a specific
determinant. A low feasibility score (close to 0) for an adaptation option means a significant shortcoming
for implementing this option. A high feasibility score (close to 5) for an adaptation option indicates a high
feasibility of adopting the option. The overall feasibility score for one specific adaptation option is the
minimum feasibility score among the eight determinants. Similarly, the high efficacy level suggests the
adaptation option is considered more efficient in reducing climate exposure and/or sensitivity to stress.
The potential contribution of any adaptation option to water resource adaptive capacity is defined as the
simple product of its overall feasibility and efficacy score.
Steps for adaptive options evaluation
The method discussed above includes four steps:
1.
Defining and assessing current climate impact and stress for providing the basis of adaptive
options evaluation. This step has been described in section 4.
2.
Identifying the current water resource vulnerability, which may become more serious under
future climate condition. This step has also been presented in section 4.
3.
Evaluating adaptive policy and action to current climate variation. This step is the focus of this
activity.
4.
Conducting surveys and workshops to select adaptive policy.
5.2.2.2 Assessing current adaptation options in Zhangye City Region
Zhangye City is located in the middle reaches of Heihe River Basin, which accounts for 95% of the basin
cultivated land, 91% of population and 89% gross domestic product of the basin. In this area, water is the
most important resource for economic development and ecological build and farmer income. Where there
is water, there is oasis agriculture. In February 2001, the state government decided to enact a water
division policy to leave certain amount of river flow for the lower reaches. The policy requested the
middle reaches leave river flow for discharge 0.95 billion m3 to the lower reaches while the upper and
middle reaches maintain 1.58 billion m3 of water. As a result, Zhangye should increase 0.255 billion m3
discharge, which means that Zhangye must reduce 0.58 billion m3 extraction volume, which equals to
water used for irrigation of 600,000 ha cropland. Identifying effective adaptive options to reduce water
use vulnerability will in fact improve agriculture production and ecosystem health.
After consulting with water experts and managers, four engineering options and five non-engineering
options were selected for assessment to cope with climate variation and change. These options include:
(1) preventing water leakage from irrigation channels; (2) expanding more advanced irrigation
techniques including sprinkle, drip irrigation, and low-pressure irrigation pipe lines; (3) building new
reservoirs in upper reach area to regulate flow distribution; (4) increasing groundwater exploitation; (5)
conserving soil moisture by deep plow method; (6) reducing land surface evaporation using plastic film
and crop straw coverage; (7) adopting drought-tolerant crop varieties; (8) adjusting crop structure (such
as reducing crop area which consume large amount of water); and (9) adopting more effective watersaving irrigation plans extending the irrigation system which most effective saving water.
In the application, a survey questionnaire was designed and distributed to 100 water planners, experts
and managers who had engaged in water management for long time period. Ninety two questionnaires
were returned. In order to remove biases, we used a mean value as the result. Table 5.7 shows the experts
judgement and results of coping capacity.
The results of the adaptation policy evaluation indicate that the feasibility of adopting technical and
engineering adaptation practices is relative low. These options include expanding sprinkle, trickle,
pipeline irrigation, building reservoir in upstream and increasing exploitation groundwater. This is due
to difficulties of obtaining considerable capital support. And farmers and water resource managers are
reluctant to invest these engineering works with high financial risks. On contrary, water-saving practices
such as cropping and cultivation structure adjustments are as more feasible because of relatively small
capital requirements.
95
The rank ordering of all water adaptation options evaluated is as follows (from the most desirable and
effective to less effective): adjusting crop structure; adopting more effective water-saving irrigation plans;
preventing water leakage from irrigation channels; reducing land surface evaporation using plastic film
and crop straw coverage; conserving soil moisture by deep plow method; adopting drought-tolerant crop
varieties; expanding more advanced irrigation techniques including sprinkle, drip irrigation, and lowpressure irrigation pipe lines; building new reservoirs in up reach area to regulate flow distribution; and
increasing groundwater exploitation.
Determinant Build
Exploitation Lining Sprinkle Cultivation Covering Using
Adjust Water
reservoir groundwater channel and drip water
with
drought crop
saving
irrigation saving
plastic tolerance structure institution
sheets
breed
Resource
2.5
2.5
3.2
2.5
3.6
3.3
3.0
3.7
3.4
Institution
3.9
1.8
3.5
3.9
3.8
3.7
3.8
4.1
4.1
Human
capital
3.2
3.4
4.2
3.6
4.2
4.1
3.8
4.2
4.0
Social capital
3.7
2.1
4.0
4.4
3.9
4.0
3.8
4.1
4.2
Risk spread
3.2
3.0
3.9
3.7
3.8
3.9
3.9
4.0
4.0
Information
management
3.7
3.2
4.1
3.9
3.8
3.7
3.6
3.8
3.7
Public aware
3.7
3.2
4.2
3.9
4.1
4.0
4.0
4.2
4.3
Feasibility
score
2.5
1.8
3.2
2.5
3.6
3.3
3.0
3.7
3.4
Efficiency
score
0.7
0.5
0.8
0.7
0.6
0.7
0.7
0.8
0.7
Coping
capacity
1.8
1.0
2.4
1.9
2.0
2.4
2.0
2.9
2.5
Score
Table 5.7: Adaptive options assessment results in Zhangye City
5.2.2.3 Adaptive options to future climate change
The method was not applied in the Heihe River Basin for the purpose of evaluating adaptation options to
reduce future climate change vulnerability. The activity only provided some general suggestions of
potential adaptation options to reduce vulnerabilities in several key sectors under climate change
scenarios.
For water, agricultural, ecosystem sectors, as well as the basin as a whole, a list of adaptation options was
suggested for each of the sector and the basin for reducing climate change vulnerabilities discussed in
section 4. These potential adaptation options were also recommended by experts and stakeholders in the
basin. The discussion on adaptive options to future climate change was rather general and descriptive in
nature, and thus is not presented here.
5.2.2.4 Reference
Yohe, Gary and Tol, Richard S.J. 2002. “Indicators for social and economic coping capacity-moving
toward a working definition of adaptive capacity” Global Environmental Change, 12: 25-40.
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6 Capacity Building Outcomes and Remaining Needs
6.1 Introduction
One main objective of the AS25 project was to enhance the regional capacity of conducting climate
vulnerability and adaptation assessment. Science capacity building was be a primary concern of the
project. The project has been providing training to enable local decision makers and multi-stakeholders to
understand the linkage between climate change and sustainability. The regional climate change impact
and adaptation study was undertaken by local scientists in partnership with the U.S. and Canadian
experts. This improved local scientific capacity and provided expertise available in Canada and the U.S.
In particular, the project achieved the following aspects in capacity building.
•
Improved understanding of the interactions between regional sustainability and climate change
(All the activities of AS25 project).
•
Trained young scientists and graduate students to design and apply IA methods in a real world
context (More than ten young scientists and PhD students were participating in the activities of
the project). Young scientists were trained during the course of implementing the project:
carrying out research activities, organizing and attending workshops, applying various models,
and conducting householder surveys for adaptation options evaluation.
•
The project also involved multi-stakeholders and local experts in many project activities (A
training workshop was held in Lanzhou, in August 2002).
•
Many farmers were interviewed individually and asked to complete a survey in a one-on-one
interview or in a small group workshop-type setting in Heihe region.
•
Eight PhD graduates were working on the project and among them three theses were mainly
derived from research activities of the project.
•
The AS25 project partnered with the Canada-China Cooperation in Climate Change (C5) Project
funded by the Canadian International Development Agency (CIDA), the China’s Office of
National Climate Change Coordination Committee (NCCCC) of National Development and
Reform Commission (NDRC), and the Chinese Academy of Agricultural Sciences (CAAS) held an
International Adaptation Conference entitled Climate Change: Building the Adaptive Capacity: An
International Conference on Adaptation Science, Management and Policy Options, May 17-19, 2004,
Lijiang, Yunnan Province, China.
•
A training course was arranged in CAREERI, Lanzhou, October 2004 to improve PhD graduates’
skill in conducting vulnerability and adaptation assessment.
•
The AS25 project helped CMA in organizing an international symposium on arid climate change
and sustainable development (ISACS) which was held in Lanzhou, May 23-24, 2005. An AS25
project session was included in the ISACS Conference.
6.2 Main Activities in Relation to Capacity Building
6.2.1 AS25 participated in the AIACC Kick-Off Workshop, United Nations
Environment Programme Headquarters, Nairobi, Kenya, 11-15
February 2002
Three key investigators of the AS25 project were invited to attend the AIACC Kick-off Workshop. Several
international experts in vulnerability and adaptation research fields also participated in the workshop as
mentors. The objectives of the workshop included: launching AIACC projects, discussing objectives of
AIACC and individual projects, assisting project teams in project design to achieve objectives, feedback
on training courses and needs, encouraging inter-project networks and sharing of skills, and sorting out
97
administrative issues. Assessment methods for selected sectors were presented in greater detail and
project needs for related training were discussed.
6.2.2 Participation in the Scientific and Technical Advisory Panel (STAP)
of the Global Environment Facility (GEF) Expert Group Workshop on
Adaptation and Vulnerability, 18-20 Feb. 2002, UNEP Headquarters,
Nairobi, Kenya
Dr. Yin, PI of the AS25 project, was invited to participate in the Scientific and Technical Advisory Panel
(STAP) of the GEF Expert Group Workshop on Adaptation and Vulnerability at UNEP Headquarters,
Nairobi, Kenya. This Workshop provided a great opportunity for Dr. Yin to discuss with many renowned
experts on adaptation and vulnerability issues. The STAP Expert Group Workshop on Adaptation and
Vulnerability was held to provide strategic advice to GEF, particularly on the scientific and technical
issues underpinning adaptation strategies and responses.
The workshop brought together about forty experts with experience and expertise in vulnerability
assessment and adaptation processes and responses, particularly as they relate to the integration of
adaptation concerns in mainstream development. In this context, a number of case studies on adaptation
in different countries or regions were presented. The case study presentations were followed by working
group sessions. Four working groups were convened to discuss in details key issues identified in the
background paper and those case studies. Each working group focused on a specific sector: agriculture,
water, human settlement and health, and biodiversity. The working groups discussed key issues or
relevance to the integration of adaptation measures into mainstream development.
6.2.3 AIACC training workshop on Development and Application of
Integrated Scenarios in Climate Change Impacts, Adaptation and
Vulnerability Assessments
The AS 25 project sent Ms. Yingxia Pu, a lecturer in Department of Geography at Nanjing University to
attend the Scenario Workshop sponsored by AIACC in UK, which is part of the capacity building efforts
within the AIACC program. The intensive workshop on climate scenarios was entitled "Development and
Application of Scenarios in Impacts, Adaptation and Vulnerability Assessments." and was being held at
the Tyndall Centre for Climate Change Research at the University of East Anglia in Norwich, UK on
April 15-26, 2002.
6.2.4 Multi-Stakeholder Workshop and Training, 26th ~29th August 2002,
Lanzhou, China
The Workshop and Training Course brought together researchers and stakeholders on climate change
and sustainability issues related to the resources sectors in the Heihe River Basin. The workshop built a
team of interested parties to develop a conceptual integrated assessment framework. Through
presentations by researchers and stakeholder representatives and group discussion, the workshop
reached a common understanding regarding the methods among the investigating partners, and the
stakeholders. The four-day workshop-training course enabled local scientists and stakeholders to have a
better understanding of integrated assessment and policy evaluation. Main contents covered in the
training course included climate change, resource use conflicts, IA methodologies, economic analysis
methods, multi-criteria decision making techniques, multi-stakeholder consultation, sustainable
development, and gender and equality issues.
In particular, the workshop accomplished the follows:
98
•
Discuss research approaches to link climate change, vulnerability, resource use conflicts and regional
sustainability;
•
Identify major problems and key concerns related to climate in the region;
•
Enable local scientists to conduct research on integrated assessment and adaptation policy
evaluation;
•
Review data availability for integrated assessment;
•
Highlight various methods for environmental and economic impact analyses;
•
Improve Chinese decision makers’ knowledge on the environmental, social and economic risks
and impacts associated with climate change; and
•
Enhance stakeholder participation in the study.
About forty five people attended the workshop, among them eleven were women. The Chinese
participants included representatives from the Chinese research partners and across the Heihe region.
Key researchers and stakeholders from CAREERI and ESSI, provincial government departments,
municipalities, and communities with expertise in the areas of resource uses, and people with a vested
interest in natural resources and the impacts that climate change may have on the region attended the
workshop. The diverse Chinese participation indicated a strong interest in the research project.
Through presentations, the workshop gave an overview of the project and highlighted the broad issues of
climate change, the adaptation options and regional sustainability issues. Dr. Yin provided a brief picture
of the research project. Dr. Souquan Zhou of ESSI/Nanjing University reported the IA activities
conducted previously by Dr. Yin and himself in the Yangtze Delta to provide some guidelines of the IA
research. The workshop then had breakout group discussion aimed to improve understanding of the
project objectives. It was followed by stakeholder presentations outlining major problems and key
concerns related to resource use in the region.
This was followed by presentations of stakeholder representatives to identify environmental and socioeconomic problems and concerns in natural resource management in the region. The third day was
covered by course work on concepts and methods of environmental and economic assessment. Day 4
focused on hands-on training of step-by-step methodological design for the IA component.
Key researchers from CAREERI and ESSI participated in the training course. Instead of giving all the
lectures or presenting all the methods by one person (Dr. Yin), several key investigators of the project
presented various methods. Topics covered in the training included identifying potential policy options
or plans, specifying regional sustainability indicators, engaging communities, establishing databases,
examining the social, economic, and environmental impacts of alternative scenarios, and building the
integrated assessment tool for policy evaluation.
6.2.5 The AIACC Asia and the Pacific Regional Workshop
The first AIACC Asia-Pacific Region Workshop was held from 25-27 March, in Bangkok, Thailand. The
AS25 project sent three key investigators and one key Chinese scientist, Prof. Lin, Erda as a stakeholder
representative to participate the workshop. The second AIACC Asia-Pacific Region Workshop was
hosted by the College of Forestry and Natural Resources of the University of the Philippines Los Baños.
The AS25 project sent a delegation of four researchers and two government representatives to the
workshop.
The two regional workshops brought together participants in AIACC assessments in the region, members
of the climate change research and policy communities of Asia and the Pacific Region, and
representatives of international organizations. The purposes of the two workshops were to share
information about the objectives, interim results and future plans of AIACC and other assessment
activities in the region, discuss how these efforts can contribute to National Communications to the
UNFCCC, adaptation actions and development policy, and strengthen networks that support assessment
and action to reduce climate risks.
At the two workshops, the AS25 project team presented their research updates and chaired sections.
Topics presented included Chinese climate change policies and National Communications, agricultural
99
impacts of climate change in China, GCM results in China, and resource system vulnerability in the
Heihe River Basin.
6.2.6 International Adaptation Science Conference, May 2004, Lijiang,
China
Cooperated with the CIDA C5 project, the AS25 project held an International Adaptation Conference
entitled Climate Change: Building the Adaptive Capacity: An International Conference on Adaptation
Science, Management and Policy Options in May 2004 in Lijiang, China. All members of the Office of
National Climate Change Coordination Committee (NCCCC) of National Development and Reform
Commission (NDRC), and Mr. Gao Feng, Chief Negotiator of China at UNFCCC, participated the
conference and delivered their key note speeches at the opening session. The NCCCC is the Chinese
agency responsible for preparing China’s National Communication Report.
About 80 scientists, experts, officials participated in the conference to share their global knowledge about
adaptation to climate change. Dr. Ian Burton gave a presentation on behalf of Dr. Neil Leary about the
AIACC. Dr. Ian Nobel of World Bank also gave a key note speech. Four journalists of Chinese major
media were also invited to the conference. They wrote climate change reports for the Chinese press
agency and the English-language news paper - China Daily.
Sponsors of the conference included the Canadian International Development Agency (CIDA),
Assessments of Impacts and Adaptations to Climate Change (AIACC), United Nations Environment
Program (UNEP), System for Analysis, Research and Training (START), Third World Academy of
Sciences (TWAS), the Meteorological Service of Canada, China Meteorological Administration, China
National Development and Reform Commission (NDRC), Chinese Academy of Agricultural Sciences
(CAAS), The Canada-China Cooperation in Climate Change (C5) Project, and the Integrated Assessments
of Vulnerabilities and Adaptation to Climate Variability and Change in the Western Region of China
(AS25) Project.
6.2.7 Dr. Yin was invited as a key note speaker to the C5 Training
Workshop
Dr. Yin gave a key note speech at the Canada – China Workshop on Building Regional Capacity in
Climate Change Adaptive Capacity and Adaptation Options in Agriculture and Ecosystem sponsored by
the Canada-China Cooperation in Climate Change Project (C5), February 25-27, 2005 in Yinchuan,
Ningxia, China. Yin’s presentation title was “Assessing Resource System Vulnerability to Climate
Change”.
The workshop was attended by 50 representatives from Chinese governmental agency, regional expert
and multilateral agencies. The workshop objective was to help key Chinese institutions to identify and
assess the sensitivities and vulnerabilities associated with climate change in agriculture. It focused on
strengthening Chinese research on analyzing adaptive capacity and providing useful recommendations
on regional development plan, and assisting China to reduce their vulnerability and adapt to the adverse
effect of climate change.
Key Chinese decision-makers on climate change policy delivered the Inaugural Address. Presentations
were also made by C5 research team and representatives from Canada, UK and Australia.
6.2.8 AS25 project progress review meeting at the Cold and Arid Region
Environmental and Engineering Research Institute (CAREERI) of
Chinese Academy of Science (CAS) in Lanzhou City, 22-23 Feb. 2005
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On 22-23 Feb. 2005, on his way to Yinchuan (see above), Dr. Yin stopped in Lanzhou to have meetings at
CAREERI to evaluate research progress on VIA by CAREERI researchers. Ms. Sun Landong, Mr. Xu
Jingxiang and Dr. Zhang Jishi presented their updated research on VIA in grassland, agricultural and
water systems respectively. All the three system VIA studies had completed for current climate
conditions. It was indicated that the RCM data provided by CMA team had some errors which could not
be used for VIA assessment for future climate change scenarios. Dr. Yin suggested that the three
researchers to provide more detailed explanation on vulnerability indicator setting and threshold values
determination. Ms. Sun was asked to contact with Dr. Li of CMA to correct the RCM data as soon as
possible. The meeting also discussed other issues including the AS25 Session in the ISACS late May 2005
in Lanzhou.
6.2.9 Dr. Yin participated in the AIACC Vulnerability Synthesis Workshop,
7-13 March 2005, Bellagio, Italy
Yin was invited to participate in the AIACC conference Vulnerability to Climate Change in the Developing
World at the Bellagio Study and Conference Center in Bellagio, Italy. The conference was funded by
START from a grant provided by the Rockefeller Foundation. At the conference, Yin presented a poster
paper on Assessing Resource System Vulnerability to Climate Change: Methodology and participated in a series
of intensive working meetings during which Yin was requested to contribute to writing a collective
synthesis paper, as well as revise and refine the paper that Yin and et al. submitted for the conference.
6.2.10 The AS25 session at the International Symposium on Arid Climate
Change and Sustainable Developments (ISACS) Conference
Dr. Yin helped China Meteorological Administration (CMA) in organizing an international symposium
on arid climate change and sustainable development (ISACS) held in Lanzhou, May 23-24, 2005. The
conference was sponsored by CMA, Gansu Province, Chinese NSF, Environment Canada and US NOAA.
Prof. Ding Yihui (AS25 project coordinator) was the Chair of the Scientific Committee. Dr. Yongyuan Yin
was invited by the Conference Organizer to be a co-sponsor of the conference when he visited the Gansu
Meteorological Bureau in late June 2004. An AS25 session was set in the ISACS. The conference provided
a venue for AS25 researchers to present their results.
More than ten AS25 project researchers and experts attended the ISACS. Two members of the Expert
Committee of AS25 project, Professors Ding Yihui and Liu Chunzheng played important roles for the
symposium. Prof. Ding was the Chair of the Science and Technical Committee of ISACS and Prof. Liu
Chaired sessions at the symposium. Dr. Yin was a member of the organization committee.
6.2.11 The Third AS25 Project Research Team Workshop
The Third AS25 project team workshop was held in Beijing, 10-11 November 2005. The purpose of the
Third AS25 project workshop was to provide a forum for project investigators to present their research
results and to divide labor for preparing the Final Report which would be submitted to AIACC next
spring. At the AS25 Third Workshop, project investigators presented results on climate change scenarios,
impacts and vulnerability assessment, adaptation option evaluation. Dr. Yin provided an overall review
of the project, its achievements and outstanding issues. He also provided comments on scenarios and VIA
studies, and suggestions of further improvements.
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7 National Communications, Science-Policy Linkages and
Stakeholder Engagement
7.1 Introduction
The AS25 project was an interdisciplinary study and took an approach that required multi-stakeholder
participation. The project activities included workshops, survey, and community engagement methods
which were employed to involve multiple stakeholders, policymakers, and experts in the study process.
During the course of implementing the project, the research team had built committed partnerships with
multi-stakeholders at national, provincial and local level. An essential part of the stakeholder engagement
strategy of this project was the establishment and participation of the Chinese Steering Committee and
Technical Committee consisting of key government agencies and experts responsible for China’s
international cooperation on climate change issues and national communications.
The Steering Committee (SC) included the following government agencies: National Climate Change
Coordinating Office of National Development and Reform Commission (NCCCC/NDRC), Ministry of
Science and Technology (MOST), Chinese Meteorology Administration (CMA), China GEF Office, State
Environmental Protection Administration (SEPA) and the two PIs of AS25 project. The SC also facilitated
the integration of AS25 study participants and results into China’s national communications. The Expert
Committee (EC) consisted of experienced experts who had experience from previous GEF climate change
projects and provided their skill and data to the AS25 project.
•
The main interaction between AS25 project and China’s National Communications was to
involving Chinese government officials and experts who are responsible for preparing China’s
NC.
•
There had been many opportunities for AS25 project to contribute to the national
communications. Since the executing agencies and key experts responsible to China’s NC were
partners of the project (steering and expert committee leaders and members), the AS25 project
could make useful contribution to China’s NC.
•
The AS25 project held two project and committee workshops. All key members of the Steering
and Expert Committees participated in the workshops and provided their suggestions and
advice. The NCCCC is the Chinese agency responsible for leading China’s National
Communication Report.
•
Ms. Sun Cuihua and Prof. Lin Erda, two key persons responsible for Chinese National
Communication Report preparation, were invited to attend the AIACC Asia-Pacific Regional
Workshop in Manila, Nov. 2004 to present China’s climate change policies and National
Communication report.
•
In addition, Dr. Yin was involved in the Canada-China Cooperation on Climate Change (C5)
project which consisted in a “National Communication” component. Dr. Yin gave a presentation
to C5 Chinese participants at a workshop held from March 29 to April 7, 2004 in Vancouver, BC,
Canada.
7.2 The First AS25 Project Steering Committee, Expert Committee
and Research Team Workshop, 25 November 2003, Beijing
The first AS25 Project AS25 Project Steering Committee, Expert Committee and Research Team workshop
was held on Nov. 25, 2003 at China National Climate Center in Beijing, China. Members of the Project
Steering Committee (SC) Sun Cuihua (NCCCO), Wang Bangzhong (CMA), Lu Xuedu (MOST), Wen Gang
(China GEF Sectary) and members of Experts Committeee (EC), Ding Yihui (CMA), Yongyuan Yin (PI),
Ren Zhenhai (SEPA), Lin Er’da, Long Ai’hau (CAREERI, on behalf of Xu Zhongmin), Li Manchun (ESSI,
on behalf of Peng Gong) participated in the meeting. Some additional researchers, Xu Yin, Gao Xianqin,
Shi Xueli, Li Qiaopin and Project Manager, Zhang Jing also attended the workshop.
Wang Bangzhong, Director General of CMA, presided the meeting in the morning. SC members were
briefed the report by Professor Ding Yihui, coordinator of the project regarding project progress. Dr.
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Yongyuan Yin, PI of the project, reported the status of the project, work plans, and research activities for
each groups. And representative of each sub-research group presented research progress. SC members
made their comments and suggestions to the project.
SC members regarded this project as an innovative and special project with clear thoughts of research
and its prospective delivery might support Chinese government international activities in global climate
changes and provide strong scientific support for Chinese government in the 4th IPCC Assessment
Report.
7.3 The Second AS25 Project Committees and Research Team
Workshop, Beijing, 28-29 November 2004
The purpose of the Second AS25 project workshop was to provide a forum for project investigators to
present their research updates and to evaluate the progress of the project by the Steering and Expert
Committees. At the Second project meeting, project investigators presented papers on climate scenarios,
impacts and vulnerability assessment, adaptation option evaluation. Members of the two committees
provided their comments on progress of the research and suggestions of future work.
The Second project meeting was attended by several Steering and Expert Committee members including
Ms. Sun Cuihua, Director of Office of National Climate Change Coordination, ONCCC/NDRC, Dr. Wen
Gang of China’s GEF Office, Professor Liu Chunzheng of the Ministry of Water Resources and Prof. Ren
Zenhai and Dr. Gao Qinxian of Chinese State Environmental Protection Administration (SPEA).
Project committee members provided some important comments and suggestions. Dr. Gao Qinxian of
SPEA expressed his concern about how to integrate research activities presented by individual
investigators of the project. Dr. Wen Gang of Chinese GEF Secretary Office also gave some crucial
suggestions: 1) As an applied research, the work should aimed at providing information to meet decision
makers’ requirements; 2) Given limited funding, the research should be focused on quantitative
assessment; 3) It is important to cooperate among research components; 4) Project should identify some
key points; and 5) It should try display results with more graphs, tables, and charts and less words to
decision makers.
Ms. Sun of ONCCC gave some instructions at the conclusion session as follows:
•
Those young scientists working on the project had made good progress, which was a good sign
of this project.
•
Each research group should follow the deadline set in the sub-contractor to provide required
data. This could not be further delayed. Cooperation among groups should be improved.
•
Before the completion of the project, a final project workshop should be held for decision makers,
stakeholders, and policy makers from difference government agencies to evaluate the research
findings and final reports.
•
It would be nice if AIACC Phase II would be available to provide some technical assistance to
China’s GEF adaptation project (proposal submitted by ONCCC).
7.4 Policy Implications and Future Directions
Worked in partnership with local, provincial and national governments and other key stakeholders
(water use professionals, farmers, and other organizations), the study identified alternative effective
adaptation measures which could become practical options to deal with water vulnerabilities which
would likely become more severe in the study region due to the impacts of climate change. A properly
developed and implemented adaptation action plan consisting of various effective measures could have
positive benefits to the well-being and productivity of all people living in the region.
These effective adaptation measures can help reduce water resource vulnerability and water use conflicts.
Since water is the key determinant which influences all the economic activities and livelihood of the
region, a reduction in water resource vulnerability will mitigate the impacts of climate change on
agricultural sector and protect the livelihood of farmers. Water system sustainability can also improve
ecosystem health and reduce sandstorms which have created a global environmental impact. The study
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has generated the information for decision-makers to improve the adaptive capacity of resource system to
cope with climate risks in Heihe River Basin. The project also:
•
Conducted policy surveys of alternative adaptation options or measures that were expected to
reduce water resource risks from climate change in the study region;
•
Prioritized alternative adaptation measures and identified desirable adaptation options that
could help the water infrastructure in the study region to cope with climate stresses;
•
Improved local capacity for climate risk assessment and adaptation evaluation; and
•
The AS25 research team has also published journal papers and a textbook that introduces a wide
range of research approaches, methods, and tools for assessing climate-change impacts,
vulnerabilities, and adaptation.
•
As a reasonable follow up, the AS25 project team prepared a concept paper entitled “ Adaptation
Actions to Reduce Water System Vulnerability to Climate Change in Heihe River Basin”
which will be submitted to ACCCA for consideration. A successful pilot adaptation action plan
could become a useful model for communities across the study region to reduce climate risks and
rural poverty, and thus to improve livelihood in poor regions. The follow up study will
recommend steps in implementing effective adaptation measures in the region to enhance
regional sustainability.
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8 Outputs of the Project
8.1 Invited Presentations & Workshops – International Outreach
Yin, Y.Y. 2005. “Climate change and resource system sustainability” paper presented at the WMO
Technical Conference on Climate as a Resource. 1-2 November 2005, Beijing, China.
Li, Q.P. and Ding,Y.H. 2005. “A numerical study of the impacts of vegetation changes on regional climate
in China” Paper presented at The International Symposium on Arid Climate Change and Sustainable
Developments (ISACS), May 23-24, Lanzhou, China.
Yin, Y.Y. 2005. Resource System Vulnerability to Climate Change: Assessment Methodology. Paper
presented, Adapting to Climate Change in Canada 2005 Workshop, May 4-7, 2005, Montreal, Quebec.
Yin, Y.Y. 2005. Climate Change Impact, Vulnerability and Adaptation Assessment. Invited speaker,
Canada – China Workshop on Building Regional Capacity in Climate Change: Adaptive capacity and
Adaptation options in agriculture and ecosystem, February 28, 2005 – State Environmental Protection
Administration, Beijing, China.
Yin, Y.Y. 2005. Resource System Vulnerability to Climate Change: Assessment Methodology. invited key
note speaker at the Canada – China Workshop on Building Regional Capacity in Climate Change
Adaptive Capacity and Adaptation Options in Agriculture and Ecosystem, February 25-27, in
Yinchuan, Ningxia, China.
Yin, Y.Y. 2004. Overview of Climate Change Vulnerability, Impacts and Adaptation Research in Canada
and China. Invited Speaker. Journalists’ Workshop and School Twinning Initiatives, Canada-China
Cooperation on Climate Change (C5), March 27 to April 3, 2004, Vancouver, B.C.
Yin, Y.Y. 2004. Designing Evaluation Tools to Identify the Implications of Climate Change and Economic
Development for Sustainability in Lijiang and Yulong Mountain Region, China. Invited Speaker.
Bridging Scales and Epistemologies: Linking Local Knowledge with Global Science in Multi-Scale
Assessments, Millennium Ecosystem Assessment, 17 ~ 20 March 2004, Alexandria, Egypt.
He, Y.Q., Yin, Y.Y. and Zhang, D. 2004. Changing Features of the Climate and Glaciers in China’s
Monsoonal Temperate-glacier Region. Invited Speaker. Bridging Scales and Epistemologies: Linking
Local Knowledge with Global Science in Multi-Scale Assessments, 17 ~ 20 March 2004, Alexandria,
Egypt.
Yin, Y.Y. 2003. Vulnerability Assessment Methods. Invited Speaker. The Canada-China Cooperation on
Climate Change (C5) Training Workshop, 12 ~ 14 Sept. 2003, Huangshan, China.
Yin, Y.Y. 2003. Designing Adaptation Evaluation Tools to Reduce Climate Change Vulnerability in
Western China. Invited Speaker. The International Symposium on Climate Change (ISCC), 31 March
~ 3 April 2003, Beijing, China.
Yin, Y.Y. 2002. Adaptation Evaluation Tools and Analysis Methods for Climate Change. Keynote Speaker,
Proceedings of the Annual Meeting of the Chinese Ecological Economics Society and Western China
Ecological Sustainability. Lanzhou, China.
8.2 Publications – Refereed
Li, Q.P., Ding,Y.H. and Dong, W.J. 2006. “A numerical simulation study of the impacts of current land
use change on regional climate in China” Acta Meteorological Sinica, (accepted) (in Chinese).
Li, Q.P., Ding,Y.H. and Dong, W.J. 2006. “A numerical simulation study of the short-term impacts of soil
moisture anomalies on regional climate in China” Acta Applied Meteorological Sinica, (accepted) (in
Chinese).
Ding,Y.H., Li, Q.P. and Dong, W.J. 2005. “A numerical simulation study of the impacts of vegetation
changes on regional climate in China” Acta Meteorological Sinica, 63(5): 604-621(in Chinese).
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Yin, Y.Y. 2004. “Methods to link climate impacts and regional sustainability” J. of
Environmental Informatics 2(1): 1-10.
8.3 Publications – Books and Chapters
Yin, Y.Y., Xu, Z.M. and Long, A.H. 2006. “Adaptation Measures Evaluation to Reduce Water System
Vulnerability to Climate Change in Heihe River Basin” In: Neil Leary et al., (eds.) Adaptation to
Climate Change in the Developing World. (Accepted)
Yin, Y.Y., Clinton, N., Luo, B. and Song, L.C. 2006. “Assessing Resource System Vulnerability to Climate
Change in northwest China” In: Neil Leary and Sara Beresford (eds.) Vulnerability to Climate Change in
the Developing World. (Accepted)
Yin, Y.Y., Gong, Peng and Ding, Y.H. 2006. “Chapter 23, Assessment of Vulnerability and Adaptation to
Climate Change in Western China” In: Congbin Fu (ed.) Changes in the Human-Monsoon System of East
Asia in the Context of Global Change. Island Press (in press).
Yin, Y.Y. and Wang, Guixin, 2004. Global Climate Change Impact Assessment: Methods and Applications.
High Education Press, Beijing, China. (in Chinese)
8.4 Publications – Conference Proceedings (refereed)
Yin, Y.Y. 2005. “The AS25 Project: Methodologies and Research Activities” In: A. Fenech, D. MacIver, H.
Auld, B. Rong, and Y.Y. Yin, (eds.) Climate Change: Building the Adaptive Capacity. Environment
Canada, Toronto, Ontario.
He, Y.Q., Yin, Y.Y., Zhang, D., Yao, T.D., Yang, M.X., Zhang, Z.L., Pang, H.X. Gu, J. and Lu, A.G. “Studies
on Climate and Glacial System: Mt. Yulong, China” In: A. Fenech, D. MacIver, H. Auld, B. Rong, and
Y.Y. Yin, (eds.) Climate Change: Building the Adaptive Capacity. Environment Canada, Toronto,
Ontario. 426p.
Clinton, N., Yin, Y.Y. and Gong, P. 2005. “Geographic Allocation of Vulnerability Indicators in the Heihe
Basin: Methodology” In: A. Fenech, D. MacIver, H. Auld, B. Rong, and Y.Y. Yin, (eds.) Climate
Change: Building the Adaptive Capacity. Environment Canada, Toronto, Ontario. 426p.
Rong, B., Ma, S.M., Lin, E.D. and Yin, Y.Y. “Adaptive Capacity to Climate Change in the Agriculture of
Northeastern China” In: A. Fenech, D. MacIver, H. Auld, B. Rong, and Y.Y. Yin, (eds.) Climate
Change: Building the Adaptive Capacity. Environment Canada, Toronto, Ontario. 426p.
Fenech, A., Rong, B., MacIver, D., Yin, Y.Y., and Auld, H. “Chapter 1 Climate Change: Building the
Adaptive Capacity” In: A. Fenech, D. MacIver, H. Auld, B. Rong, and Y.Y. Yin, (eds.) Climate
Change: Building the Adaptive Capacity. Environment Canada, Toronto, Ontario. 426p.
Fenech, A., MacIver, D., Auld, H., Rong, B. and Yin, Y.Y. (eds.) Climate Change: Building the Adaptive
Capacity. Environment Canada. Toronto, Ontario. 426p.
Xu, Z.M., Cheng, G.D. and Qiu, Guoyu 2005. “ImPACTS identity of sustainability assessment. Acta
Geographica Sinica 60(2): pp 198-208. (in Chinese with English Abstract)
Xu, Z.M. Cheng, G.D., Long, A.H., Loomis, J., Zhang, Z.Q. and Hamamura, K. 2005. “Evaluating the
performance of different willingness to pay question formats for valuing environmental restoration in
rural China” Environment and Development Economics (in press).
Kang, E.S., Cheng, G.D., Song, K.C., Jing, B.W., Liu, X.D. and Wang, J.Y. 2004. “Simulation study of
energy and water balance in soil-vegetation-atmospheric system in the Heihe mountainous area of
Hexi Corridor” Science in China D: Earth Sciences 34(6): 544-551.
8.5 Other Publications
Yin, Y.Y. 2004. “A comprehensive assessment of vulnerability and adaptation to climate change in
western areas of China (AS25 Project). World Environment 3: 23-25 (in Chinese).
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For copies of final reports from the AIACC project and other information about the project,
please contact:
AIACC Project Office
The International START Secretariat
2000 Florida Avenue, NW, Suite 200
Washington, DC 20009 USA
Tel. +1 202 462 2213
Fax. +1 202 457 5859
Email: [email protected]
Or visit the AIACC website at:
www.aiaccproject.org