THE AS25 PROJECT: THE IA METHODOLOGY

(Presentation at the AIACC Asia Regional Workshop)March 22-27, 2003, Bangkok, Thailand

By Yongyuan Yin1, Zhongmin Xu2 and Jiaguo Qi1

1. International Institute for Earth System Science, Nanjing University2. State key laboratory of frozen soil engineering (CAREERI), Lanzhou

Outline

• Study Objective

• IA Research Framework

• Data Collection

• Sensitivity Identification

• Vulnerability Assessment

• Adaptive Capacity Assessment

• Vulnerability under Climate Change

• Adaptation Policy Evaluation

Figure 1. Flow-chart showing the research structure of the proposal

Current climate variabilityand extreme events, andclimate change scenarios

1

Socio-Economic Scenarios:

Population increase, economic growth 2National West Development StrategyUrbanisation

Identifying present-day climate impacts and stresses, and vulnerabilities ofecosystems and sensitive sectors to climate changes scenarios in the WesternRegion of China (including integrated impact assessment)

3

Identification and inventory of existing and

potential adaptation measures or options

Sustainable development indicators or

multiple evaluation criteria

Desirable adaptation options

Multiple stakeholders, planners, analysts, and public

Domain of the multi-criteria adaptation options evaluation system 4

Research Methodology

1. Data collection with RS and GIS 2. Climate scenarios and extremes

Prof. Ding Yihui: RCM of China CIDA C5 project CC Scenario Workshop

3. Socio-economic scenarios Dr. Shuming Bao: Database of China National West China Development Strategy

4. Field work, literature review, and survey Dr. Zhongmin Xu: EF and CVM Methods

Remote Sensing Land Use and Land Cover Dynamics of Zhangye Region in Western China

(Source: Qi et al., 2002)

Image Processing Methods:

• Unsupervised classification

• Supervised classification

• Continuous field: fractional vegetation

• Change detection of urban expansion

Study Area

Zhangye Region is a typical representation of climate, social, geology, ecology and

hydrology of western China

Image Sources

Three Landsat images over a span of 25 years have been used

Results: land cover change

Agricultural land expansion is obvious

Results_land degradation

Fractional vegetation cover changed as well

Results: land degradation change

Potential sensitivity matrix showing the climate variables with the greatest forcing and activities with the broadest sensitivity in Western China (Modified from: Hennessy and Jones, 1999)

Climate and related variables (forcing)

Activities (sensitivity)

High Rainfall - variabilityDrought EvaporationSoil moisture Stream flow

Water supply, cropping, GrazingWater management, cropping, GrazingWater supply, cropping, Grazingcropping, irrigation salinitywater supply

Moderate Temperature - minWindIrrigation

CroppingSoil erosion, sand stormcropping, irrigation salinity, soil erosion

Low HailCO2

Cropping, propertiescropping yield, carbon sequestration

MethodsEnvironmental Risk = exposure frequency (probability) consequenceConsequence = F{intensity, sensitivity, adaptive capacity}

• 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

Vulnerability and Adaptive Capacity Assessment

MethodsBoth quantitative and qualitative methods will be

employed. • Numerical numbers can be derived for those climate and

physical variables: drought index, soil loss tolerance, andEVf = Max [0, LFt-Ft, Ft-UFt]

Where: EVf is water system’s maximum-extent vulnerability based on river flow indicator; LFt and UFt are the lower and upper critical thresholds of the coping range respectively; and Ft is the observed river flow data.

• Yohe and Tol (2001) suggest that the relationships between adaptive capacity and its determinants are difficult to quantify.

Vulnerability and Adaptive Capacity Assessment

Summary on ecological footprint in China

State or province

EFhm2/cap

Bio-capacityHm2//cap

Ecological deficit/surplushm2/cap

GDP’s EFhm2/tenthousand RMB

State or province

EFhm2/cap

Bio-capacityhm2//cap

Ecological deficit/surplusHm2/cap

GDP’s EFhm2/ten thousand RMB

China 1.325 0.681 -0.645 2.037 Henan 1.478 0.481 -0.997 3.032

Beijing 2.682 0.934 -1.748 1.550 Hubei 1.595 0.395 -1.200 2.455

Tianjin 0.895 0.385 -0.510 0.592 Hunan 1.006 0.432 -0.575 1.975

Hebei 0.947 0.626 -0.321 1.371 Guangdong

1.232 0.462 -0.770 1.058

Shanxi 2.555 0.741 -1.741 5.433 Guangxi 1.022 0.425 -0.597 2.466

Monoglia 2.371 2.353 -0.018 4.415 Hainan 0.891 0.336 -0.555 1.441

Liaoling 2.571 0.700 -1.871 2.571 Sichuan 0.951 0.385 -0.566 2.141

Jilin 1.789 1.054 -0.734 2.848 Zhongqin 1.042 0.303 -0.738 2.163

Heilongjiang 2.387 1.625 -0.761 3.124 Guizhou 1.228 0.352 -0.876 4.998

Shanghai 2.242 0.256 -1.987 0.819 Yunnan 0.477 0.755 0.277 1.078

Jiangsu 1.568 0.459 -1.109 1.469 Shaanxi 1.085 0.742 -0.344 2.641

Zhejiang 0.529* 0.4205 -0.108 0.441 Gansu 1.337 0.806 -0.531 3.596

Anhui 1.382 0.502 -0.880 2.963 Qinghai 1.573 1.173 -0.401 3.365

Fujian 1.447 0.482 -0.760 2.094 Ningxia 1.278 1.100 -0.178 2.875

Jiangxi 1.058 1.288 0.229 2.280 Xinjiang 2.413 1.152 -1.261 3.665

Shandong 1.447 0.497 -0.951 1.667 Tibet 2.153 7.584 5.431 5.208

State or provinceEcological

footprint’s diversityDevelopment

capacity

GDP/cap(ten thousand

RMB）

State or province

Ecological footprint’s diversity

Development capacity

GDP/cap(ten thousand

RMB)

China 1.29 1.71 0.65 Hubei 1.14 1.82 0.65

Beijing 1.05 2.82 1.73 Hunan 1.09 1.1 0.51

Tianjin 1.25 1.12 1.51 Guangdong 1.34 1.65 1.16

Shanxi 0.68 1.74 0.47 Guangxi 0.94 0.96 0.41

Monoglia 0.82 1.94 0.54 Hainan 1.19 1.06 0.62

Liaoling 0.89 2.29 1 Sichuan 1.08 1.03 0.43

Jilin 1.08 1.93 0.63 Zhongqin 1.17 1.22 0.48

Heilongjiang 0.89 2.12 0.76 Guizhou 1.09 1.34 0.25

Shanghai 1.22 2.74 2.74 Yunnan 0.96 0.46 0.44

Jiangsu 1.28 2.01 1.07 Shaanxi 1.23 1.33 0.41

Anhui 1.09 1.51 0.47 Gansu 0.98 1.31 0.37

Fujian 1.16 1.68 1.07 Qinghai 0.86 1.35 0.47

Jiangxi 1.19 1.26 0.46 Ningxia 0.85 1.09 0.44

Shandong 1.26 1.82 0.86 Xinjiang 0.93 2.24 0.66

Henna 1.11 1.64 0.49 Tibet 0.73 1.57 0.41

Notes: In the analysis of diversity, because of some flaws in the data, we deleted two provinces (Hebei and Zhejiang).

Ecological footprint’s diversity, capacity and intensity in China and provinces

Distribution of survey willingness to pay responses

Response Percent of respondents(%)

Main valley Surrounding district

Willing to pay some amount 92.37(448) 92.09(198)

“restoring ecosystem service is not worth this money to me”

0.00(0) 0.00(0)

“I can’t afford to pay this amount” 1.03(5) 0.93(2)

“It is unfair to expect me to pay for increasing ecosystem services”*

2.06(10) 3.26(7)

“Restoring Ejina ecosystem services cannot get expected effect”*

1.65(8) 0.00(0)

“I am opposed to paying for this government program”*

2.27(11) 2.79(6)

Other reasons* 0.62(3) 0.93(2)

Total** 100.00(485) 100.00(215)

Deleted as protest 6.60 6.98

*Classified as a protest response.** Due to numeric rounding, the totals do not equal to one hundred percent.

Total benefits of households in Hei valley

RegionsHouse-hold annual Median WTP

Number of house-holds

Number of house-holds which have WTP

Annual aggregateWTP (millions)

Discount rate (%)

Time scale (year)

Present value Aggregate benefits(millions)*

Main valley 20.78 223895 222187 4.62 15 20 28.90 (RMB)

Surrounding district

16.41 259328 257277 4.22 15 20 26.43 (RMB)

Total 8.84 55.33 (RMB)

*calculated by compound interest.

Sectors IndicatorsWater resourcesVI water demand, water storage stress, water stress, hydropower, EI water supply climate variables, Palmer drought severity index,

low flow event frequency and duration, ACI economic return, industry productivity, regulated annual supply,

institutional frameworksAgricultureVI population growth, water resource consumption, arable land loss,

food consumptionEI cold snap, heat stress days, monsoon pattern, accumulated degree days,

water supply, Palmer drought severity indexACI farm income, agricultural product price, agricultural production, EcosystemsVI soil erosion, desertification, sand storm, population growth rate, population densityEI water supply, high winds Number of days, sand storms, Palmer drought severity index,

heat stress days, cold snap days, ACI forest area protection, emission reduction of CO2, ecological protection--------------------------Note: VI=vulnerability indicator; EI= Exposure indicators; ACI=adaptive capacity

indicator

Vulnerability and adaptive capacity indicators

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.

Measure Vulnerabilities to Future Climate Change

Various methods can be applied to estimate indicator values in the future. This will produce future data for each indicator. Since water system vulnerability is critical in Western China, we use it as an example to illustrate the research steps.

• Hydrologic simulation models will be employed to project the levels of vulnerabilities indicators of the hydrologic system (e.g. stream-flow, velocities and qualities) under climate change.

• Water Resources System (Integrated Assessment) model can provide a means for integrating climate change vulnerabilities and regional adaptive capacity in the structure of the model by a clear articulation and reconciliation of objective functions and decision variables.

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.

Applying AHP (Analytical Hierarchy Process) to identify desirable adaptation options

• to provide a means by which alternative options can be compared and evaluated in an orderly and systematic manner;

• to evaluate alternative policies, allocate resources, and select desirable project locations.

AHP (developed by Saaty), can be used:

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.