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Resource System Vulnerabilities to Climate Variation and Change
(Presentation at 2004 Asia-Pacific Region Workshop
November 2-5, 2004, Manila, Philippines)
By Yongyuan Yin1, Nicholas Clinton2, and Bin Luo3
1 Adaptation
and Impacts Research Group and Sustainable Development
Research Institute/UBC, Canada, and ESSI, Nanjing University, Nanjing, China
2 International Institute for Earth Systems Science (ESSI), Nanjing University
3 Faculty of Engineering, University of Regina, Canada
Outline
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Concept: the resource system vulnerability
Need for new methods in vulnerability assessment
Major questions addressed
Determinants of resource vulnerabilities
IA research methodology
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Resource vulnerability indicators
Identifying Critical Thresholds for Indicators
Measure Vulnerability
Vulnerability Classification by the Fuzzy Set Model
Mapping Vulnerability
• Some examples for illustration
• Prioritizing adaptation options or policies
• Acknowledgement
Study Site – Heihe River Basin
•128,000 Square kilometers (inland watershed)
•Diversity of land cover types
•Diversity of population densities
•Geographically variable climate
Concept of Resource System Vulnerability
A system’s vulnerability is related to a
system’s resilience defined as the capability of
the system to maintaining its functionality in
the face of a particular environmental change.
In this connection, the vulnerability of a
system is defined as its propensity to undergo
impacts or lead to disruptions in the nominal
functionality of the system as a result of
climate variation or change.
Need for new methods in vulnerability assessment
The current status of climate vulnerability
research and vulnerability assessment show a
lack of designing new methods to meet the
increasing demand of policy makers. The main
goal of vulnerability assessment is to develop
effective methods to measure vulnerability and
to assess the environmental risks in dealing with
climate stresses.
Major Questions Addressed
• What factors are driving the changes of system vulnerability?
How might these factors change in the future and what
implications would that have for future vulnerability?
• What are the important climatic and non-climatic exposures
operating on, or expected to operate on, the land and water
system of study region?
• How can vulnerability indicators be used to assess resource
system vulnerabilities to present climate variations and future
climate change?
• Can thresholds in climatic variables be identified which, if
surpassed, would pose substantially greater risk of harm to
the land and water system or sub-systems than would be
expected if the thresholds are not surpassed?
• Does vulnerability (or adaptive capacity) to climatic
exposures vary in character or degree for different sub-units
of the resource system?
Determinants of Resource Vulnerability
Table 1 Potential determinants (climate and other variables with the forcing) and resource
vulnerability indicators in Heihe River region
Climate and other related
determinants (forcing)
Related system attributes and
options
Resource vulnerability indicators
Rainfall - variability
Drought
Temperature - max
Soil moisture
Temperature - min
Wind
Cold snap
Heat stress days
Accumulated degree days
Cropping area
Population growth
Economic growth
Technology
Consumption
Urban expansion
Land resource management
Government policies
Water flows, storage volumes,
and quality
Palmer drought severity index
Evaporation
Soil moisture
Irrigation
Land conversion
Land use plan
Adaptive capacity
Adaptation options and policies
Food sufficiency ratio
Farm income
Water scarcity (withdraw ratio)
Drought hazards
Groundwater stress
Hydro power
Arable land loss
Salinity
Soil erosion
Grassland deterioration
Water quality
Wetland area
Water use conflicts
CO2 and CH4 emission
IA Research Methodology
1. Climate scenarios and extremes

Dr. Xu Ying’s presentation
2. Socio-economic scenarios

CAREERI
3. Data collection: RS, GIS, field work,
literature review, and survey
4. Vulnerability and adaptation
assessment
Vulnerability andMethods
Adaptive Capacity Assessment
• Selecting vulnerability and adaptive capacity
indicators
• Identifying critical thresholds for indicators
• Setting priorities to vulnerability indicators
• Vulnerability classification by the fuzzy set
model
• Adaptive capacity classification by the fuzzy
set model
Some Examples of Calculating Vulnerabilities
• Average annual water withdrawal ratio can be used to identify sub-units
under water stress. WMO suggested that the ratio exceeds 20% and 40% of
annual water availability be considered as medium and high water stress
respectively.
• Reservoir system vulnerability defined as the magnitude of a water supply
failure as a fraction of annual yield can be computed by: Vrf = 0.452 *
(S/Y)1.27 (Where: Vrf is reservoir vulnerability, S is the reservoir storage
capacity, and Y is annual reservoir yield)
• Vulnerability can also be measured as the maximum duration of failure,
should a failure (i.e., an unsatisfactory value falling outside of the coping
range) occur. The Maximum Duration-Vulnerability can be calculated by:
Maximum Duration-Vulnerability (p) of DVF = Maximum duration (number
of time periods) of a continuous series of failure events for indicator F,
occurring with probability p or that may be exceeded with probability 1-p
• With respect to land system vulnerability, the soil erosion rate can be used as
another indicator. Soil loss rate can be calculated by USLE or wind erosion
model. The soil loss tolerance value can be used as the threshold level. Soil
loss tolerance is a concept in the relationship between erosion and
productivity.
Some Examples for Illustration
Input Data: Land cover, population density, rainfall, temperature, soils, DEM,
AVHRR
• Sources: heihe.westgis.ac.cn, edcdaac.usgs.gov,
www.orbit.nesdis.noaa.gov, www.ornl.gov
Computation of Indicators: Part I
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The “Rational Runoff Equation,” Q=cia, to partition rainfall
c = f(curve number, slope, imperviousness, intensity)
i = measured rainfall (interpolated to a grid)
a = 1 square kilometer (one pixel)
X
=
Runoff
Computation of Indicators: Part II
• Evapo-transpiration under irrigated conditions
• Implementation of FAO methodology in GIS
• Inputs: monthly temperature, average range as measured at the
Jiuquan station, crop location data, crop growth parameters
Crop location
Monthly
Temperature
Avenue
Monthly
Evapotranspiration
Avenue
Computation of Indicators: Part IIIa
• In this example, rainfall infiltration is subtracted from crop water
requirements to derive moisture deficit (irrigation requirement)
Computation of Indicators: Part IIIb
• In this example, runoff for year 2000 is divided by population to create
an indicator for water resource availability.
Vulnerability Classification by the Fuzzy Set Model
The sets, U, of classification criteria and V of vulnerability levels
can be specified as follows:
U = {(temperature), (rainfall), (low flow event frequency), (low
flow event duration), (causality and/or injury), (damage to
ecosystem), (water use conflicts), …}
V = {(extremely vulnerable), (high risk), (moderate risk), (low
risk), (acceptable)}
The problem under consideration is how to assign different land
units into proper categories of overall vulnerability level on the
basis of the given data and criteria, and thus partition the whole
region into several sub-regions with unique vulnerability patterns.
Adaptive Capacity Classification by the Fuzzy Set Model
The sets, U, of classification criteria and V of adaptive capacity
levels can be specified as follows:
U = {(economic return), (industry productivity), (technology
advancement), (regulated annual supply), (institutional
frameworks), (water storage capacity), …}
V = {(extremely adaptive), (high adaptive), (moderate adaptive),
(low adaptive), (acceptable)}
Since factors influencing adaptive capacity may be different from
vulnerability indicators, criteria selected in the U set equation are
thus different from the vulnerability criteria set. The factors
affecting a system’s adaptive capacity are usually those economic,
technological, and social in nature.
Prioritizing Adaptation Options or Policies
Adopt a multi-criteria decision making
technique, Analytic Hierarchy Process
(AHP), to identify desirable adaptation
options to reduce climate vulnerabilities
and to improve adaptive capacity.
Identify desirable adaptation options which can be used to
reduce climate change vulnerability
Inventory of climate
change impacts and
adaptation options
RANKING:
1. Government Relief
2. Farm-level
Adjustment
3. Research &
Education
Relative Importance Scale
Adaptation Option
5 4 3 2 1 2 3 4 5
Farm-level Adjustment
X
Farm-level Adjustment
X
Farm-level Adjustment
X
Adaptation Option
Water Management
Government Relief
Research & Education
AHP multi-criteria
evaluation of options
by stakeholders
using online survey
4. Water Management
Prioritized ranking of
options, indicating
overall preference
Acknowledgements
The research project and participation of this
workshop have been made possible through
the financial support of the AIACC,
Adaptation and Impacts Research
Group/Environment Canada, and Sustainable
Development Research Institute/University
of British Columbia.