Transcript Yin Yongyuan - global change SysTem for Analysis, Research

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 University
2. State key laboratory of frozen soil engineering (CAREERI), Lanzhou
Outline
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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
Socio-Economic Scenarios:
Current climate variability
and extreme events, and
climate change scenarios
Population increase, economic growth
National West Development Strategy
Urbanisation
1
Identifying present-day climate impacts and stresses, and vulnerabilities of
ecosystems and sensitive sectors to climate changes scenarios in the Western
Region of China (including integrated impact assessment)
3
Identification and inventory of existing and
Sustainable development indicators or
potential adaptation measures or options
multiple evaluation criteria
Desirable adaptation options
Multiple stakeholders, planners, analysts, and public
Domain of the multi-criteria adaptation options evaluation system
4
2
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
Results: land degradation change
Fractional vegetation cover changed as well
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)
High
Climate and related
variables (forcing)
Activities (sensitivity)
Rainfall - variability
Drought
Evaporation
Soil moisture
Stream flow
Water supply, cropping, Grazing
Water management, cropping, Grazing
Water supply, cropping, Grazing
cropping, irrigation salinity
water supply
Moderate Temperature - min
Wind
Irrigation
Cropping
Soil erosion, sand storm
cropping, irrigation salinity, soil erosion
Low
Cropping, properties
cropping yield, carbon sequestration
Hail
CO2
Vulnerability and Adaptive
Capacity Assessment
Methods
Environmental Risk = exposure frequency (probability)  consequence
Consequence = 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
Methods
Both quantitative and qualitative methods will be employed.
• Numerical numbers can be derived for those climate and
physical variables: drought index, soil loss tolerance, and
EVf = 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.
Summary on ecological footprint in China
State or
province
EF
hm2/cap
Biocapacity
Hm2//cap
Ecological
deficit/surplus
hm2/cap
GDP’s EF
hm2/ten
thousand
RMB
State or
province
EF
hm2/cap
Biocapacity
hm2//cap
Ecologic
al
deficit/su
rplus
Hm2/cap
GDP’s EF
hm2/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
Guangdon
g
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
Ecological footprint’s diversity, capacity and intensity
in China and provinces
State or province
Ecological
footprint’s diversity
Development
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).
Distribution of survey willingness to pay responses
Response
Percent of respondents(%)
Main valley
Surrounding
district
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)
100.00(485)
100.00(215)
6.60
6.98
Willing to pay some amount
Total**
Deleted as protest
*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
Regions
Household
annual
Median
WTP
Number Number
of house- of households
holds
which
have
WTP
Annual
aggregate
WTP
(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
*calculated by compound interest.
8.84
55.33
(RMB)
Vulnerability and adaptive capacity indicators
Sectors Indicators
Water resources
VI 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 frameworks
Agriculture
VI population growth, water resource consumption, arable land loss,
food consumption
EI cold snap, heat stress days, monsoon pattern, accumulated degree days,
water supply, Palmer drought severity index
ACI
farm income, agricultural product price, agricultural production,
Ecosystems
VI soil erosion, desertification, sand storm, population growth rate, population density
EI 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 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
AHP (developed by Saaty), can be used:
• 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.
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.