Download Introduction of Chinese Participants

Introduction of Chinese Participants
Knowledge Exchange on Climate Change and Glaciers October 11-­‐15, 2016, Switzerland WANG, Yanjun
Nanjing University of Information Science and Technology
(NUIST)
ZHAI, Song
China Meteorological Administration (CMA)
GAO, Chao
Department of Geography, Anhui Normal University
WANG, Run
School of Resources and Environmental Science,
Hubei University
JIANG, Tong
National Centre on Climate Change (NCCC)
China Meteorological Administration (CMA)
2
Chinese Map
3
Vulnerability and Risk
Assessment of Climatic
Disasters in China
Wang Yanjun
Nanjing University of Information Science and
Technology(NUIST)
WANG, Yanjun
Nanjing University of Information Science and Technology
(NUIST)
EDUCATIONS:
Sept.2003 --- June 2006 Nanjing Institute of Geography and
Limnology, Chinese Academy of Sciences (CAS)
Ph.D., Physical geography
Sept.2000 --- June 2003 Nanjing Normal University
M.Sc, Physical geography
Sept.1996 --- June 2000 Hunan Normal University
B.Sc., Geography
5
WANG, Yanjun
REPRESENTATIVE PUBLICATIONS:
1、Wang Yanjun, Wen Shanshan, Li Xiucang, Thomas Fischer,Su Buda, Wang Run, Jiang
Tong.
Spatiotemporal distributions of influential tropical cyclones and associated economic
losses in China in 1984–2015, Natural Hazards, 2016, DOI 10.1007/s11069-016-2531-6
2、Wang Yanjun, Gao Chao, Zhai Jianqing, et al.,. Spatio-temporal changes of exposure
and vulnerability to floods in China. Advances in Climate Change Research. 2015, doi:
10.1016/j.accre.2015.03.002
3、Gao Chao,Yao Mengting, Wang Yanjun (CA), et al., Assessment of three hydrological
models: a case study for different catchment size and input temporal resolution.
Hydrological Sciences Journal. 2015
4、Buda Su，Xiaofan Zeng，Jianqing Zhai，Yanjun Wang，Xiucang Li. Projected
precipitation and streamflow under SRES and RCP emission scenarios in the
Songhuajiang River basin, China. Quaternary International，2014. 1-11.http://dx.doi.org/
10.1016/j.quaint.2014.03.049
5、Wang Yanjun, Gao Chao，Wang Anqian, et al.Temporal and spatial variation of
exposure and vulnerability of flood disaster in China. Progressus Inquisitiones De
Mutatione Climatis, 2014，10（6）：391-398.(in Chinese)
6
Time series of population and economic vulnerability to floods in China
Population
vulnerability（%）
16
y = 0,2516x + 3,1243
R² = 0,4578
12
8
4
0
1984
1987
1990
1993
1996
1999
2002
2005
2008
2011
Year
Economic
vulnerability（%）
4
y = -0,0359x + 1,4312
R² = 0,1698
3
2
1
0
1984
1987
1990
1993
1996
1999
Year
2002
2005
2008
2011
Spatial variation of population and economic vulnerability to floods
Risk mapping of floods in China
Floods
Disaster
hazardaffected body
Element
Hazard
Exposure
Vulnerability
Risk
Population
Economy
Crop
Risk Mapping of rainstorm waterlogging in Nanjing
Hazard mapping
vulnerability
Exposure
Risk mapping
Data Engineer!
Administrative Officer
ZHAI Song
China Meteorological Administration (CMA)
ZHAI, Song
China Meteorological Administration (CMA)
EDUCATIONS:
Sept.1995 --- June 1999 China Agricultural University
B.Sc., Water Conservancy and Irrigation
!
Climate Change and Impact on Water
Resources in the Huai River Basin
(Hydrological model; ANN, HBV and
SWIM)
Dr. Gao Chao,
Department of Geography,
Anhui Normal University
GAO, Chao
Department of Geography, Anhui Normal University
EDUCATIONS:
09/1997-07/2001 B.Sc., at Department of Geography,
Anhui NormalUniv.
09/2003-07/2006 M.Sc., at Department of Geography,
Anhui NormalUniv.
09/2007-07/2010 Ph.D., at Nanjing institute of
Geography&Limnology, CAS,China
GAO, Chao
REPRESENTATIVE PUBLICATIONS:
!
1、C. Gao, M.T. Yao, Y.J. Wang, J.Q. Zhai, S. Buda, T. Fischer, X. F. Zeng & W.P.
Wang (2015):Hydrological model comparison and assessment: criteria from catchment
scales and temporal resolution, Hydrological Sciences Journal. DOI:
10.1080/02626667.2015.1057141
2、Chao Gao, Zhengtao Zhang, Jianqing Zhai. Research on meteorological thresholds
of drought and flood disaster: a case study in the Huai River Basin, China.Stochastic
Environmental Research & Risk Assessment.2015, 29:157-167
3、GAO Chao，CHEN Shi，ZHAI Jianqing，ZHANG Zhengtao，LIU Qing. 2014.
On threshold of drought and flood disasters in Huaihe River basin.Advances in Water
Science, 25(1):36-44.
4、Chao Gao, Shi Chen, Jian Yu. River islands change and impacting factors in the
lower reaches of the Yangtze River based on remote sensing. Quaternary International,
2013(304):13-21.
5．Chao Gao, Marco Germmer, Xiaofan Zeng, et al. Projected Streamflow in the
Huaihe River Basin (2010-2100) using Artificial Neural Network (ANN)，Stochastic
Environmental Research & Risk Assessment 2010,24:685-697
Climate change and hydrological model application
in the Huai River basin
Shandong Province
The Yellow River
Henan Province Anhui Province
The Yangtze River
The study area is the Huai River basin.
Jiangsu Province
Bengbu Hydr.stat.
Being the 6th largest river in China, the Huai River is an important river
in central China. It flows from the west to the east.
Its waterway is about 1,000 km long and catchment area is about
1.9×105km2.
The Huai River valley is located at China’s transition between northern
climate and southern climate.
Bengbu hydrological gauging station is the important control station of
upper and middle reaches of the Huai River
Seasonal and annual variation of runoff is notable.
Tempo-spatial characteristics of Precipitation and runoff
Precipitation
Runoff
MK trend of precipitation and runoff in the east China in 1958-2007
•Precipitation had decrease trend,but no trend on runoff in the Huai R.
Precipitation
1100
Runoff depth
Precipitation
900
800
600
500
(mm/a)
1000
(c)
400
300
Runoff depth
(mm/a)
1200
700
200
600
100
500
0
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Year
Annual precipitation and runoff depth in Huai R.
Hydrological model
ANN model
DEM
HBV model
Landuse
SWIM model
Sub-basins
Soil
Meteorological threshold of flood disasters
Steps:
1 ) Select typical years which have suffered severe flood
2) To determine the flood-causing meteorological thresholds
3) Flood disaster is divided into five grades
Table 1 The Classified grade table of flood-causing meterological threshold
Grade
normal
light flood
flood
heavy flood
Threshold
1.00~1.19
1.20~1.79
1.80~2.49
2.50~3.00
extreme
flood
more than
3.00
The distribution map of flood-causing meteorological threshold in
1968, 1975, 1991 and 2007 in Huai River Basin
Meteorological threshold of drought disasters
Steps:
1 ) Select typical years which have suffered severe drought by
SPI: the SPI value of 1966, 1978, 1988, 2001 are all larger than
-1.5
!
2 ) The drought-causing meteorological threshold is calculated
based on the daily precipitation
!
3) Drought disasters are divided into five grades
Table 1 The Classified grade table of drought-causing climate threshold
Grade
normal
light drought
drought
heavy drought
Threshold
1.00~1.19
0.85~0.99
0.70~0.84
0.60~0.69
extreme
drought
less than 0.60
The distribution map of drought-causing climate threshold in
1968,1975,1991,2007 in Huai River Basin
The relationship between the disaster-causing
threshold and damage area of crops
The relational map between threshold of extreme flood disaster and damage
area of crops over Huai River Basin
The prediction model of drought area of crops
The prediction model can reflect the relationship between actual
damage area of crops and disaster-causing threshold very well.
The relationship between drought-causing threshold and damage area of
crops in Huai River Basin
26
Adaptation to Climate Change for Water
Sustainability via Smart Land-Use
Management Plans
Policy and Strategy for Environmental
Protection for the Yangtze River Economic
Belt
Dr. Run Wang!
Hubei University
WANG, Run
School of Resources and Environmental Science,
Hubei University
EDUCATIONS and Working Experience:
!
1990, degree in economic geography, Northwestern University;
2001, PhD with the support of DAAD (German Academic Exchanges Services),
University of Giessen;
2008-2013, German development aid expert at Institute of Urban Environment
(IUE), Chinese Academy of Sciences in Xiamen, China;
!
since 2014, distinguished professor, Hubei University;
!
28
Wasserverknappung,
Wassernutzungskonflikte
und Wassermanagement in
Trockengebieten
Zentralasiens
(Usbekistan, Kasachstan, Kirgisistan,
Xinjiang/VR China)
2001-­‐2006
Snow Cover Changes in the Bosten-Lake Valley in the past 10 Years
29
WANG, Run
!
REPRESENTATIVE
PUBLICATIONS During the working in IUE, Xiamen:
!
!
Jian Liu, Run Wang*, Yanwei Sun et al.: A barrier Analysis for the Development of Distributed Energy
in China: A Case Study in Fujian Province. Energy Policy, Volume 60, September 2013, 262-271(SCI/
SSCI)
!
Yanwei Sun, Run Wang*, Jian Liu et al.: GIS-based approach for potential analysis of solar PV
generation at the regional scale- A case study of Fujian Province. Energy Policy, Volume 58, July
2013, Pages 248-259 (SCI/SSCI)
!
Yanwei Sun, Run Wang*, Jian Liu et al. Spatial planning framework for biomass resources for power
production at regional level: a case study for Fujian Province, China. Applied Energy, Volume 106,
June 2013, Pages 391–406 (SCI)
!
Lishan Xiao, Xinhu Li, Run Wang*. Integrating climate change adaptation and mitigation into
sustainable development planning for Lijiang City. International Journal of Sustainable Development
and World Ecology.2011,18(6), 515-522. (SCI)
!
Run Wang*, Wenjuan Liu, Lishan Xiao, Jian Liu and William Kao (2011): Path towards achieving of
China's 2020 carbon emission reduction target-A discussion of low-carbon energy policies at
province level. Energy Policy, Volume 39, Issue 5, May 2011, Pages 2740-2747 (SCI/SSCI)
30
Water Sustainability the Province Hubei
• Water shortage
• Uneven natural water distribution
• Grand investment in dams and reservoirs
• Water pollution
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Watershed Management
Water Resources Management
Adaptation to Climate Change: Water-Food-Energy Nexus
Topic1: Climate Model and GIS: Basic data, future trend of climate
change, Landuse changes
Topic 2: Watershed Management and Planning: water-food-energy
nexus, environmental protection policy, watershed planning
Climate model (REGCM4)
in response to hydrological
cycle in the watershed
34
Databank on hand
!
Period
!
!
!
Resolution
RCP
Data（days）
!
!
!
CCLM
01.02.2006-
31.12.2050
0. 5° ×0. 5°
RCP2.6,
RCP4.5,
RCP8.5
Ave. Temp.#
Max. Temp.#
Min. Temp.#
Precipitation
!
!
!
RegCM.4
01.02.2006-
31.12.2050
0. 5° ×0. 5°
RCP4.5
RCP8.5
Max. Temp.#
Min. Temp.#
Precipitation
!
!
!
CN05.1
01.01.1961-
31.12.2014
0. 5° ×0. 5°
Ave. Temp.#
Max. Temp.#
Min. Temp.#
Precipitation
Water-Food-Energy Nexus
The first inland Nuclear Power Station
Electricity from TGP and its distribution
Water and agriculture
Water and urbanisation
Water in regions (innov. mechanism)
Why there are problem on water-energy nexus? Or the speciality of them in Hubei?
1. Yangtze River Economic Belt - high investment and active economic activity
2. There is little sources for energy in Hubei.
3. Selection and structure of future energy very important (for economy, environment
and social society)
4. How about the effect on water regime is the key question!
36
water shortage/uneven distribution - value (eco), water resources
management
grand investment - infrastructure (green and grey)
pollution - innovation mechanism, coordination
what we learnt from the Rhine often was its past (from polluted to clean)
what we should learn from the Rhein is its present, more important is its future
(new problem, innovative solution, in EU frame)
How does Germany evaluate the ecological functions of water bodies?
how does Germany plan to build the infrastructure for water supply and water treatment?
What is the evaluation system for a healthy watershed in Germany?
How does Germany manage the natural hazards related to water?
How does Germany face to waste water (Multi-sources of water supply)?
How does Germany plan for adaptation to water problem in its
high exposure areas?
37
An Overview of India’s Energy and Climate Policy under
the Background of "Make in India”
!
!
Run Wang1,2, AiLing Cai1, BingJie Sun1,Tong Jiang3, Run Liu1
1 College of Resources and Environment, Hubei University, 430062 Wuhan, China;
2 Hubei Key Laboratory of Regional Development and Environmental Response, Wuhan 430062 China;
3 National Climate Center, China Meteorological Administration, Beijing 100081, China
Abstract：India's energy and climate policy will have a significant impact both on the
situation of the global greenhouse gas emission and the success of the global climate
cooperation afterwards Paris Climate Conference in the future. To have a clear understanding
about the dynamics and the targets of the energy and climate policy of India, this paper firstly
introduces the current situation of the energy system and identifies challenges towards
achieving the country’s energy objectives as well as the economic and social development
targets. The second part deals with India’s energy policy context under the “New Policy
Scenario”, its UDAY plan for a healthy financial system in the energy distribution, and the
climate policy before and after the Paris Climate Conference. Finally, it explores the two
policies briefly, which are helpful for making relevant policies in our country, forming further
strategy for “One Belt and One Road” plan and multilateral cooperation strategy between
China and India.
38
Team Leader and Project Designer from Chinese Side
JIANG Tong
National Centre on Climate Change (NCCC)
China Meteorological Administration (CMA)
Prof. Dr. Jiang Tong
!
Associate editor for Atmospheric Research (AR) Associate editor for Climate Services (CLISER) Associate editor for Hydrological Sciences Journal (HSJ) Guest Editor for Special Issues on Asian larger Rivers in Quaternary
International (QI) Vol. 1 Interactions with estuaries and coasts, Vol. 186, 2008 Vol. 2 Climate change, river flow and sediment, Vol. 208, 2009 Vol. 3 Climate change, river flow and watershed management, Vol.226,
2010 Vol. 4 Climate, hydrology and ecosystems, Vol. 244, 2011 Vol. 5 Climate, water discharge, water and sediment quality, Vol. 282,
2012 Vol. 6 Changes in hydro-climate and water environments, Vol. 304, 2013 Vol.7 Shorter/longer hydro-climate changes in humid and arid
environments, Vol.336, 2014 Vol. 8 Impacts from human activities and climate change, Vol.380-381,
2015
40
Projected changes in climate,
runoff and flood/drought over Indus River Basin
Data sets
Data
CRU
(Climatic
Research
Unit)
Observation
Simulation
and
Projection
APHRODITE
(Asian
PrecipitationHighlyResolved
Observational
Data
Integration
Towards
Evaluation)
Data
integrated by
CMIP 5
models
Monthly average temperature Time: 1901-2012
Resolution: 0.5°*0.5°
provided by University of East Anglia
Daily precipitation
Time: 1951-2007
Resolution: 0.5°*0.5°
Monthly average temperature, Time: 1901-2100
Monthly total precipitation
Simulation period：1901-2005
Projection period：2006-2100
Scenarios: RCP2.6、RCP4.5、RCP8.5
Resolution: 1°*1°
Daily average temperature,
Daily precipitation
CCLM
Details
Time: 1901-2100
Simulation period：1901-2005
Projection period：2006-2100
Scenarios: RCP2.6、RCP4.5、RCP8.5
Resolution: 0.46°*0.46°
Methods
Improvement of climate model: to get accurately climate variables
Bias correction EDCDF (Equidistant Cumulative Distribution Functions) adjusts the CDF of the datasets for
projection period on the bias between the model and observation cumulative distribution
functions (CDFs)
Methods
(1)
GCM multi-model ensemble
(resolution: 1°*1°)
Bias correction and
statistical downscaling
Downscaled GCM
(resolution: 0.5°*0.5°)
1 bias correction
2 calculate the climate state (30 years), resolution: 0.5°*0.5°
3 interpolate the climate state to GCM grid resolution (1°×1°), x denotes the results
4 calculate the weight coefficient r (temperature: GCM – x, precipitation: GCM/x)
5 interpolate the weight coefficient to a high spatial resolution (0.5°×0.5°)
6 add r to observed grid temperature data or multiply r by observed grid precipitation data
(2)
CCLM
(Regional Climate Model,
resolution: 0.46°*0.46°)
Bias corrected CCLM
Bias correction
Result 1: Future changes in climate
Near term: 2016-2035
Downscaled
GCM
CCLM
RCP 2.6
RCP 4.5
RCP 8.5
Changes of annual mean temperature (a~f) and precipitation (g~i) during 2016-2035 under 2.6, 4.5, 8.5
scenarios relative to 1986-2005 in the Indus River Basin（Bold line denotes 0 isoline）
Mid-21st century: 2046-2065
Downscaled
GCM
CCLM
RCP 2.6
RCP 4.5
RCP 8.5
Changes of annual mean temperature (a~f) and precipitation (g~i) during 2046-2065 under 2.6, 4.5, 8.5
scenarios relative to 1986-2005 in the Indus River Basin（Bold line denotes 0 isoline）
Late-21st century: 2081-2100
Downscaled
GCM
CCLM
RCP 2.6
RCP 4.5
RCP 8.5
Changes of annual mean temperature (a~f) and precipitation (g~i) during 2081-2100 under 2.6, 4.5, 8.5
scenarios relative to 1986-2005 in the Indus River Basin（Bold line denotes 0 isoline）
Result 2: Future changes in runoff
Near term: 2016-2035
%
Downscaled
GCM
CCLM
RCP 2.6
RCP 4.5
RCP 8.5
Spatial distribution of depth of runoff during 2016-2035 scenarios relative to 1986-2005
under 2.6, 4.5 and 8.5 scenario (Bold line denotes 0 isoline)
Mid-21st century: 2046-2065
%
Downscaled
GCM
CCLM
RCP 2.6
RCP 4.5
RCP 8.5
Spatial distribution of depth of runoff during 2046-2065 scenarios relative to 1986-2005
under 2.6, 4.5 and 8.5 scenario (Bold line denotes 0 isoline)
Late-21st century: 2081-2100
%
Downscaled
GCM
CCLM
RCP 2.6
RCP 4.5
RCP 8.5
Spatial distribution of depth of runoff during 2081-2100 scenarios relative to 1986-2005
under 2.6, 4.5 and 8.5 scenario (Bold line denotes 0 isoline)
Result 3: Future changes in flood/drought
Near term: 2016-2035
Downscaled
GCM
CCLM
RCP 2.6
RCP 4.5
RCP 8.5
Mean SPI12 distribution over Indus River Basin for the period of 2016-2035 compared
with 1986 to 2005 mean under 2.6, 4.5 and 8.5 scenario
Mid-21st century: 2046-2065
Downscaled
GCM
CCLM
RCP 2.6
RCP 4.5
RCP 8.5
Mean SPI12 distribution over Indus River Basin for the period of 2046-2065 compared
with 1986 to 2005 mean under 2.6, 4.5 and 8.5 scenario
Late-21st century: 2081-2100
Downscaled
GCM
CCLM
RCP 2.6
RCP 4.5
RCP 8.5
Mean SPI12 distribution over Indus River Basin for the period of 2081-2100 compared
with 1986 to 2005 mean under 2.6, 4.5 and 8.5 scenario
Indus River Basin
Climate change impacts on glaciers and streamflow
in headwater catchments of the Tarim River
Sari Djaz
Kakshaal
Glacier melt
Streamflow
Sari Djaz
Kakshaal
South Asia Water Initiative: Glacier Monitoring Study Tour -­‐ Quite, Ecuador, January 26-­‐31,2014
Asian Larger Rivers and Cryosphere (Glacier, Snow Cover and Permafrost ) response to
Climate Change
- China , Chinese Tibet and Asian Highlands -
Ni Guangheng
Qinghua University, China
Jiang Tong, Zhai Jianqing
National Climate Center
China Meteorological Administration
Challenging Climate change and Cryosphere in Asian Highlands and providing water resources for Asian Rivers
Outlines
➢ Overview for Asian Larger Rivers (NGH) ➢ The outputs from IPCC AR5 WGI and WGII (JT) ➢ Climate Change in China (JT): Observation and Projection ➢ Climate Change in Tibet and Asian High lands (ZJQ) Observation and Projection ➢ Solutions for monitoring climate change (ZJQ) ➢ Case study in Glacier River Basin in China (NGH)
Challenging Climate change and Cryosphere in Asian Highlands and Rivers
It provides 70% to
95% of
downstream
freshwater in
semi-arid and
arid areas
It provides 30% to
60% of downstream
freshwater in humid
areas
Outlines
➢ Overview for Asian Larger Rivers (NGH) ➢ The outputs from IPCC AR5 WGI and WGII (JT) ➢ Climate Change in China (JT): Observation and Projection ➢ Climate Change in Tibet and Asian High lands (ZJQ) Observation and Projection ➢ Solutions for monitoring climate change (ZJQ) ➢ Case study in Glacier River Basin in China (NGH)
National Climate Center: Climate Services for Asia and the World
Soil Moisture Monitoring by FY3 satellite
National Climate Center: Climate Services for Asia and the World
Vegetation water Monitoring by FY-­‐3 satellite
Publications: Asian Larger Rivers
➢ Quaternary International (IF:1.7) on larger Asian rivers Volume 1 Interactions with estuaries and coasts, vol. 186, 2008 Volume 2 Climate change, river flow and sediment, vol. 208, 2009. Volume 3 Climate change, river flow and watershed management, vol.226, 2010. Volume 4: Climate, Hydrology and Ecology, vol. 244, 2011 Volume 5: Climate, Water discharge, water and sediment quality, vol. 282. 2012 Volume 6: Larger Asian rivers: Changes in hydro-­‐climate and water environments. 2013,304:1-­‐4
The outputs of IPCC AR5 WGI and WGII Climate change 2013: the Physical Science Basis Climate change 2014: impact, adaptation and vulnerability
Observation: Global Mean Temperature Changes
（relative to 1961–1990）
From 1880 to 2012, Global Mean Temperature increased
0.85℃。Comparing 1850−1900 with 2003−2012, Global mean
temperature increased 0.78℃
Observation: decadal change
Each of the last three decades has been successively warmer at the Earth’s surface than any preceding decade since 1850.
In the Northern Hemisphere, 1983–2012 was likely the warmest 30-­‐
year period of the last 1400 years (medium confidence).
Climate Change in China
Observation and Projection
Data Meteorological parameters
instructions
Observed Data
monthly mean temperature
monthly precipitation
Period: 1961-2012
Meteorological stations:635
Data Resources：NCC
Simulation and
projection
monthly mean temperature
Daily precipitation
Period：1961-2050
baseline Period：1986-2005
Projection Period：2014-2050
Resolution
Data Resources：NCC
!
（GCMs）
Global Climate Models
模式名称
模式中心
分辨率
BCC-CSM1-1
BNU-ESM
CanESM2
BCC, CMA, China
GCESS, BNU, China
Canadian Centre for Climate Modeling and Analysis, Canada
128×64
128×64
128 × 64
CCSM4
CNRM-CM5
National Center for Atmospheric Research, USA
CNRM/Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique, France
288 × 192
256 × 128
CSIRO-Mk3-6-0
CSIRO in collaboration with Queensland Climate Change Centre of Excellence, Australia
192 × 96
FGOALS-g2
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid 128 × 60
Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, and Tsinghua University,
China
FIO-ESM
GFDL-CM3
FIO, SOA, Qingdao, China
GFDL, National Oceanic and Atmospheric Administration, USA
128 × 64
114 × 90
GFDL-ESM2G
GFDL, National Oceanic and Atmospheric Administration, USA
114 × 90
GFDL-ESM2M
GFDL, National Oceanic and Atmospheric Administration, USA
114 × 90
GISS-E2-H
GISS, National Aeronautics and Space Administration, USA
114 × 90
GISS-E2-R
GISS, National Aeronautics and Space Administration, USA
114 × 90
HadGEM2-AO
Jointly with Met Office Hadley Centre and NIMR, Korea Meteorological
192 × 145
IPSL-CM5A-LR
MIROC5
MIROC-ESM
MIROC-ESM-CHEM
MPI-ESM-LR
MRI-CGCM3
NorESM1-M
IPSL, France
AORI, NIES, JAMSTEC, Japan
JAMSTEC, AORI and NIES, Japan
JAMSTEC, AORI and NIES, Japan
MPI for Meteorology, Germany
MRI, Japan
Norwegian Climate Centre, Norway
96 × 96
256 × 128
128 × 64
128 × 64
192 × 96
320 × 160
144 × 96
Climate Change in Tibet and Asian Highland Observation and Projection
Water Sources for Asian Large Rivers
Location: the Southwest of China
Area: above 1.2 million KM2
Population: above 3.0 million (2010)
GDP: above 20 billion RMB (2010)
Observed and GCM Data
40 Observed Weather Stations
112 GCMs grids (1°×1°) Observation: Temperature change Annual Temperature since 1960 Have increased significantly Upward trends of Temperature since 1960 are mainly happened in the southern part
Case study in Glacier River Basis
Hydrological model calibration in an alpine area based on dominant runoff generation mechanisms
Study Area (I)
p The Tailan alpine watershed is
located in Xinjiang
Autonomous Region,
Northwest China.
!
p It has a drainage area of about
1324 km2. Elevation ranges
from 1600m a.s.l. to 7100m
a.s.l. Glacier coverage occupies
about 33% to the total basin
area according to China Glacier
Inventory (CGI) .
Study Area (II)
p The Tailan basin hydrology is characterized as[Shen Y.P. et al.]:
p 1. Climate condition, especially for temperature and precipitation,
show significant spatial variability.
p 2. Melting water and rainfall are the main sources for local
streamflow. Subsurface flow is relatively small in wet period, and
dominates the streamsflow in winter (January, February and
December.
p 3. Rive system in Tailan basin is a simple fan system. Given the
large topography drop and small drainage area, river confluence
time is as short as about 1day. Melt water and rainfall can quickly
flow into the main channel and arrive at the basin outlet.
Data (I)
p Two automatic weather stations(AWS) were set up in upstream
mountain area gauging precipitation (P) and temperature(T).
Spatial distribution of P, T were estimated by lapse rates:
!
p where Co is the variable value at low altitude, and Cp is the lapse
rate, H and h is the high and low elevation respectively.
10
7
6
6,5
5,25
0
3
12
-6
obs.tem
sim.tem
-12
2015/8/2
2015/8/17
10
7,5
5
2,5
0
-2,5
-5
-7,5
-10
2015/9/1
obs.tem
sim.tem
3,5
1,75
-0,5
-4
2015/7/2
2015/5/17
2015/7/17
0
obs.tem
sim.tem
-1,75
2015/8/1-3,5
02015/6/2
8
4
0
-4
-8
-12
-16
2015/6/17
-5
-10
obs.tem
sim.tem
2015/4/2
2015/5/2
obs.tem
sim.tem
2015/4/17
-15
obs.tem
sim.tem
-20
2015/3/2
2015/6/1
Validation of estimated temperature lapse rate
2015/3/17
2015/4/1
Data (II)
p Glacier cover area was determined according to China Glacier
Inventory (CGI) data. And snow cover extent was obtained from
MODIS snow cover area (SCA) products deducting the glacier
coverage.
p Three successive steps were taken to filter the MODIS products to
remove cloud coverage error:
p Step 1: Combined the snow cover products of two satellites, Terra
(MOD10A2) and Aqua (MYD10A2). As long as the value of a
pixel is marked as snow in either satellite, the pixel value is then
marked as snow.
p Step 2: Spatial combine: Checking the values of the nearest four
pixels around one pixel, if at least three of the four surrounding
pixels were marked as snow, then the center pixel is snow also.
53
Climate change impacts on glaciers and streamflow
in headwater catchments of the Tarim River Duethmann et al. Environ. Res. Lett. 11 (2016) 054024
Study area
➢ Aksu River is the most important tributary to the Tarim
!
➢ Water from the mountain areas is highly relevant due to the very
arid climate in the lowlands
Duethmann et al. Environ. Res. Lett. 11 (2016) 054024
Ensemble of climate change projections
▪ 3 different emission scenarios (RCP2.5, RCP4.5, RCP 8.5) !
▪ 9 Global climate models from the latest IPCC report and 2 regional climate models !
▪ Different hydrological model parameters !
=> Range of projections gives an idea of the order of magnitude of the uncertainties
Foto: D. Düthmann
Duethmann et al. Environ. Res. Lett. 11 (2016) 054024
The hydrological model WASA
▪ Includes simulation of glacier geometry changes !
▪ Comprehensively calibrated to daily discharge, long term discharge trends, geodetic glacier mass balance, glacier mass balance time series !
▪ Previous applications in catchments
in Central Asia: Duethmann et al. (2015): WRR, 51(6)
Duethmann et al. (2014) WRR, 50(3) Duethmann et al. (2013), HESS, 17(7)
Foto: D. Düthmann
Duethmann et al. Environ. Res. Lett. 11 (2016) 054024
Projected changes in temperature and precipitation
Sari Djaz
Kakshaal
Duethmann et al. Environ. Res. Lett. 11 (2016) 054024