4
Climate Change and Water Resource Assessment
in South Asia: Addressing Uncertainties
4.1 INTRODUCTION
Any human or natural system’s environment varies from day to day, month to month, year
to year, decade to decade, and so on. It follows that systematic changes in the mean
conditions that define those environments can actually be experienced most noticeably
through changes in the nature and/or frequency of variable conditions that materialize
across short time scales and that adaptation necessarily involves reaction to this sort of
variability. This is the fundamental point in Hewitt and Burton (1971), Kane et al. (1992),
Yohe et al. (1999), Downing (1996) and Yohe and Schlesinger (1998). Some researchers,
like Smithers and Smit (1997), Smit et al. (2000), and Downing et al. (1997), use the
concept of “hazard” to capture these sorts of stimuli, and claim that adaptation is
warranted whenever either changes in mean conditions or changes in variability have
significant consequences. For most systems, though, changes in mean conditions over
short periods of time fall within a “coping range” - a range of circumstances within which,
by virtue of the underlying resilience of the system, significant consequences are not
observed for short-term variability (see Downing et al. (1997) or Pittock and Jones (2000)).
There are limits to resilience for even the most robust of systems, of course. It is therefore
as important to characterize the boundaries of a system’s coping range as it is to
characterize how the short-term variability that it confronts might change over the longer
term.
This chapter is designed to reflect the sensitivity to short-term climate variability
(expressed in terms of the changes in frequency of flooding events in Bangladesh along the
Ganges, Brahmaputra and Meghna Rivers) to long-term secular change (expressed in terms
of long-term trends in maximum monthly flows) along a wide range of not-implausible
climate futures. It therefore explores a case for which the boundaries of a coping range are
easily defined by flooding thresholds. When we ultimately turn a discussion of how to
evaluate adaptation options that might expand the coping range (exposure to flooding) or
reduce the cost of flooding (sensitivity to flooding in terms of multiple metrics), we will do
so in a way that can accommodate enormous uncertainty.

Figure 4.1 offers a schematic portrait of how the drivers of climate change might
influence the likelihood of flooding events in Bangladesh. Various emissions trajectories
of greenhouse gases and sulfate aerosols are shown there to produce a range of climate
futures, determined in large measure by uncertainty about climate sensitivity and the
radiative forcing of the sulfates. These climate futures produce ranges of change in monthly
precipitation and temperature which, in turn, produce a set of futures expressed in terms of
maximum monthly flows in any given year. Since the severity of possible flooding events in
any year can be related statistically to these maximum flows, trajectories of the likelihood
of small, modest, and extreme flooding are ultimately produced. The expanding size of the
loci in Figure 4.1 illustrates pictorially how the uncertainty that clouds our understanding
of each step in the causal chain cascades down the causal flow. If, for example, we knew
the path of future emissions exactly, we could not precisely define associated climate change.
If we knew how climate change would evolve over the next decades, we still could not
accurately describe how associated patterns of precipitation and temperature would be
altered and how those changes might be translated into river flows. And even if we knew
exactly how flows might change, we could not accurately predict how the likelihood of
flooding events might change.
A second cascade of uncertainty, derived from the methods with which researchers try
to describe each of the links depicted in Figure 4.1, must also be recognized. First of all,
there may not be one accepted model of any given link in the causal structure. Instead,
multiple modeling structures - abstractions of the real world - may exist, and they
sometimes produce wildly different answers to the very same questions. This simple
phenomenon is valuable in examining the relative value of one particular model or another,
78 WATER RESOURCE ASSESSMENT: ADDRESSING UNCERTAINTIES
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but it introduces model uncertainty for analysts who are looking across model results for
a coherent view of the future. In addition, the ability of any particular model to offer
credible scenarios is limited by the statistical boundaries that surround estimates of the
critical parameters (call this calibration uncertainty). These limitations are well
understood, of course, but they can be exacerbated when any one parameterization (with

G. YOHE AND K. STRZEPEK 79
Copyright © 2005 Taylor & Francis Group plc, London, UK
portraits of future climate change in terms of flow, but calibration and prediction
uncertainties will also have an effect behind the scenes. Finally, the evaluation approach
described in Section 4.6 must accommodate contextual uncertainty.
4.3 HYDRO-CLIMATIC ANALSIS OF FLOODING IN BANGLADESH
Bangladesh is very vulnerable to flooding, principally due to intense monsoon
precipitation that falls on the watershed of the Ganges, Brahmaputra and Meghna (GBM)
Rivers. Figure 4.2 shows how these rivers converge into a single delta within Bangladesh.
Mirza (2003) reports that the GBM watershed covers 1.75 million square kilometers of
Bangladesh, China, Nepal, India and Bhutan. According to Ahmed and Mirza (2000),
20.5% of the area of Bangladesh is flooded each year, on average; and in extreme cases,
floods about 70% of Bangladesh can be under water.
Fig. 4.2 The Ganges, Brahmaputra and Meghna Rivers.
The goal of this paper is to analyze the impact of not-implausible climate change
scenarios on the flood frequency in Bangladesh. Mirza (2003) took a statistical approach
to relate monsoon precipitation to peak flood flows. This paper will use a conceptual
hydrologic rainfall-runoff model that incorporates evapo-transpiration, snowmelt, soil
moisture and surface and sub-surface flows. Separate models of the Ganges and Brahmaputra
Rivers are developed and described in the next section. The hydrologic model needs to be
driven by a climate data, of course, but COSMIC reports only spatially averaged climate
change variables at a nation scale. To cope with this problem, Nepal was selected as the
representative country for three reasons. First of all, Nepal is located almost directly in
the geographic center of the GBM watershed. Secondly, its monsoon precipitation
characteristics, in quantity and timing, are representative of the average characteristics
over much of the GBM basins. Finally, using the COSMIC data from China or India,
two very large countries over which COSMIC averages climate variables are not
representative of the conditions in the GBM watershed.
80 WATER RESOURCE ASSESSMENT: ADDRESSING UNCERTAINTIES
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Precipitation
(mm)
Maximum
Monthly
Precipitation
(mm)
Mean
Annual
Temperature
ºC Mean
2,097.1 556.1 8.17
Mode
2,600.2 489.1 8.22
Median
2,084.8 533.9 8.20
Standard Deviation
264.9 98.2 0.37
Skewness
0.102 0.433 0.07
Lag-One Auto Correlation
0.096 -0.100 0.47
Coefficient of Variation
0.13 0.18 0.04
Maximum
2670.4 813.4 9.29
Minimum
1396.0 360.6 7.20






−
−=
u = X - 0.5772a
where S is the standard deviation and 7 is the mean. The mean and standard deviation of
the flood peak as well as the parameters of the EV1 distribution were determined using
100-year time series of climate data with the rainfall-runoff model. Using these statistics
and the EV1 distribution, flood flows for the 2-year, 10-year, 50-year and 100-year return
periods were calculated. They are presented in Table 4.2.
Fig. 4.3 Bangladesh Flood Area from 1954 through 1999.
4.3.2 FLOODED AREA AND SEVERITY
High river flows themselves are not a problem unless they overtop their banks and
flood area in the adjoining floodplain. The determination of flood flows used the science of
hydrology, while determining the extent of and depth of flooding was based on the science
of hydraulics. Mirza et al. (2003) reported on the application of the MIKE 11-GIS
hydrodynamic model for Bangladesh to determine flooded area as a function of peak flood
flows in the Brahmaputra-Ganges-Meghna Rivers system. Figure 4.4 shows the data from
their work and the non-linear relationship that was developed between peak flow and
flooded area with results in an R
2
of 0.59.
Flooded Area (million of hectares) = 4.3095* ln[Flow (cms)] – 45.906
82 WATER RESOURCE ASSESSMENT: ADDRESSING UNCERTAINTIES
0
20
40

0
1
2
3
4
5
6
100000 110000 120000 130000 140000 150000
Peak Flood (CMS)
Flooded Area Millon hectare
Fig. 4.4 The relationship between flood flows and flooded areas in Bangladesh.
Table 4.3 Flood flow frequency statistics 1901-2000
P - Annual Probability
of Flood Exceeding Q
0.5 0.1 0.02 0.01
T - Return Period for Q (years) 2 10 50 100
Q - Peak Flood Flow (cms) 115,000 140,000 162,500 172,000
A- Flood Area (ha 10^6) 4.311256 5.158979 5.801248 6.046099
Level of Flooding Low Modest Moderate Severe P - Annual Probability
of Flood Exceeding Q
0.5 0.1 0.02 0.01
T - Return Period for Q (years) 2 10 50 100
Q - Peak Flood Flow (cms) 115,000 140,000 162,500 172,000
4.4 A HYDROLOGIC MODEL FOR THE RIVERS
Mirza et al. (2003) examined the potential climate change impacts for river discharges
in Bangladesh using an empirical model to analyze changes in the magnitude of floods of
the Ganges, Brahmaputra and Meghna Rivers. The present analysis uses a conceptual

= sub-surface runoff (length/time),
E
v
= evaporation (length/time),
S
max
= maximum storage capacity (length), and
z = relative storage (1
≥ z ≥ 0).
84 WATER RESOURCE ASSESSMENT: ADDRESSING UNCERTAINTIES
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A non-linear relationship describes evapo-transpiration based on Kaczmarek (1990):
Following Yates (1996), surface runoff is described in terms of the storage state and
the effective precipitation according to:
where
ε is a calibration parameter that allows for surface runoff to vary both linearly and
non-linearly with storage. Finally, sub-surface runoff is a quadratic function of the relative
storage state:
where
a is the coefficient for sub-surface discharge.
In certain regions, snowmelt represents a major portion of freshwater runoff and
greatly influences the regional water availability. Ozga-Zielinska et al. (1994) provide a two
parameter, temperature based snowmelt model which was used to compute effective
precipitation and to keep track of snow cover extent. Two temperature thresholds define
accumulation onset through the melt rate (denoted mf
i
). If the average monthly
temperature is below some threshold T
s
, then the all the precipitation in that month

s
= lower temperature threshold at which precipitation is all solid (°C),
i = month
The model was calibrated from the TYN CY 1.1 data for the Ganges and Brahmaputra
separately over using data from monthly flow from the 1970 and 1980 and produced R
2
statistics of 0.89 and 0.87 for the Brahmaputra and Ganges, respectively. Since the climate
change scenarios in COSMIC begin with a base year of 1990, the COSMIC base had to be
correlated with the TYN CY 1.1 average data. Panels A and B of Figure 4.6 show the
relationship between historical average and COSMIC base year data for temperature and
precipitation, respectively.
Fig. 4.6 Panel A - Correlation of COSMIC 1990 to historical monthly temperature.
4.5 FUTURE CLIMATE SCENARIOS
Schlesinger and Williams (1998 and 1999) designed the COSMIC program so that
researchers could produce literally thousands of “not-implausible” climate scenarios that
are internally consistent. Each scenario is defined by a specific global circulation model
(of the 14 included in COSMIC) driven by one of seven emissions scenarios for
greenhouse gases that span virtually the entire range of published scenarios. Each scenario
is also defined by one of three associated sulfate emission trajectories and by choosing a
sulfate forcing parameter between 0 watts per meter and -1.2 watts per meter squared and
a climate sensitivities between 1
o
and 4.5
o
(for a doubling of effective carbon-dioxide
concentration from pre-industrial levels). It would be imprudent if not impossible to
conduct integrated analyses along each one, so there is a fundamental need to limit the
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Fig. 4.6 Panel B - Correlation of COSMIC 1990 to historical monthly precipitation.

G. YOHE AND K. STRZEPEK 87
Copyright © 2005 Taylor & Francis Group plc, London, UK
Panel B of Figure 4.7 reflects the same range of “not-implausible” futures with
Fig. 4.7 Panel A - The distribution of flow pathways from COSMIC displayed in terms of
maximum monthly flows anticipated in 2050 and 2100.
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
100000 120000 140000 160000 180000 200000 220000
FLow in 2050
Flow in 2 1 0 0
Fig. 4.7 Panel B - Representative scenarios for the distribution portrayed in Panel A displayed in
terms of maximum monthly flows anticipated in 2050 and 2100.
during moderate and severe floods. While none of these likelihoods reflected any
additional adaptation to the threat of flooding, it is now certainly appropriate to begin
thinking about interventions over the medium- or long-term (like building dikes or
instituting programs of systematic and repeated dredging) that would be designed to
reduce one or more of these likelihoods. Contemplating precisely how and when
88 WATER RESOURCE ASSESSMENT: ADDRESSING UNCERTAINTIES
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alternative adaptations might be implemented and evaluating their relative efficacy are the
topics of the next section.
4.6 ASSESSING ADAPTATION UNDER CONDITIONS OF PROFOUND

likelihood of modest events. Finally, the observation that the likelihood of modest or
moderate flooding might actually begin to decline at some point in the future adds a time
dimension to the problem. Investments in flood protection for these risks might therefore
have to be maintained over decades rather than centuries. In addition, the value of
protecting against only modest events (in terms of reducing their likelihood) would climb
(as the likelihood of moderate inundation fell). In any case, the message is that the
inter-temporal character and expense of the investments required to achieve any specific
protection goal could be quite different depending on how the future unfolds.
In other adaptations that target exposure (like building dams or periodically dredging
the rivers), the hydrologic model presented in Section 4.4 would have to be adjusted.
Throughout any analysis, though, the proposed changes in variability or coping capacity
would have to be run through each of the climate scenarios of Section 4.5 to produce new
flooding frequency trajectories for specific representative climate scenarios. Differences
between these trajectories and the corresponding baselines could then be used to
G. YOHE AND K. STRZEPEK 89
Copyright © 2005 Taylor & Francis Group plc, London, UK
Table 4.4 Characterization of the representative scenariosGlobal Carbon

Circulation Emission Sulfate Sulfate Climate
Scenario Model Scenario Emissions Forcing Sensitivity

(1) UKMO Low Low -1.0W/m
2
2.5
o
2
2.5
o (8) CCC Medium High -1.0W/m
2
4.5
o Notes: GCM’s are identified by their acronyms; details can be found in Schlesinger and
Williams (1999). Emissions scenarios are qualitatively identified
relative to the distribution described in Yohe et al. (1999).
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0
50000
100000
150000
200000
250000
300000
350000
400000
2000 2020 2040 2060 2080 2100
Year
Maximum Monthly Flow
Scenario 1
Scenario 2

P roba bility of a S e v e r e F lo o d
Scenario 1
Scenario 2
Scenario 3
Scenario 4
Scenario 5
Scenario 6
Scenario 7
Scenario 8
Fig. 4.9 Panel C - The likelihood of a severe flooding event in any year.
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ATER RESOURCE ASSESSMENT: ADDRESSING UNCERTAINTIES
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Table 4.5 The Adaptation Policy Framework of the United Nations Development Programme
Step I: Scope the Project
Define key systems
Review and evaluate existing assessments
Step II: Assess Current Vulnerability
Assess climate risks, impacts and damages (note climate variability and change)
Identify socio-economic and natural resource drivers
Assess experience with adaptation
Assess adaptive capacity in the context of policy and development needs
Step III: Characterize Future Conditions
Characterize future climate trends, risks and opportunities
Characterize future socio-economic trends
Characterize future environmental trends
Characterize a range of development options
Step IV: Prioritize Policies and Measures
Characterize a broad adaptation approach
Evaluate the feasibility and efficacy of alternative adaptations

To apply these notions to a specific adaptation context, Yohe and Tol construct an
index of the potential contribution of any adaptation option (to be denoted by j) to an
indicator of overall coping capacity (denoted by PCC
j
) from a step-by-step evaluation of
feasibility factors - index numbers that are judged to reflect its strength or weakness vis à
vis

the last seven determinants of adaptive capacity. These factors were subjective values
assigned from a range bounded on the low side by 0 and on the high side by 5 according to
systematic consideration of the degree to which each determinant would help or impede its
adoption. Let these factors be denoted by ff
j
(k) for determinants k = 2,… ,8. An overall
feasibility factor for adaptation (j) could then be reflected by the minimum feasibility factor
assigned to any of these determinants; i.e.,
Each factor inserted in equation (4.8) indicates whether the local manifestations of
each determinant of adaptive capacity would work to make it more or less likely that
adaptation (j) might be adopted. A low feasibility factor near 0 for determinant #k would,
for example, indicate a shortcoming in the necessary preconditions for implementing
adaptation (j), and this shortcoming would serve to reduce its feasibility. A high feasibility
factor near 5 would indicate the opposite situation; assessors would, in this case, be
reasonably secure in their judgement that the preconditions included in determinant #k
could and would be satisfied. Notice that the structure of equation (4.1) makes it clear that
high feasibility factors for a limited number of determinants would not be sufficient to
conclude that adaptation (j) could actually contribute to sustaining or improving an overall
coping capacity. The overall feasibility of adaption (j) could still be limited by deficiencies
in meeting the requirements of other determinants - the weakest link.
The ability of adaptation (j) to influence a system’s exposure or sensitivity to an
external stress will meanwhile be reflected in an efficacy factor EF

Indeed, public works are increasingly decided through direct participation of the population;
long postponements result, and radical solutions are disadvantaged. The stock of human
capital, including education and personal security, is very high in the Netherlands, though;
and Dutch water engineers are among the best in the world. The stock of social capital is
also high. The Netherlands is a consensus-oriented society in which the collective need is
an effective counterweight to individual interests. Property rights are clearly defined, and
the judiciary is independent. The system’s access to formal risk spreading processes is
limited because flood insurance cannot be purchased. Decision-makers are quite capable
of managing information and determining which is credible; as a result, their decisions are
generally taken to be credible. Dutch bureaucrats are typically well educated and supported
by able consultancies; but an “old-boy” network of professors, civil servants and consultants
controls water management practices. The public, as well as the water managers, are well
aware of climate change and its implications for flood risk.
Table 4.6 offers expert judgment into how these macro-scale observations might be
translated into the micro-scale determinants of each of the options listed above. The strength
of each determinant was scored on a subjective scale from 0 on the low side to 5 on the
high side. The low score for storing water is a reflection of the international cooperation
that would be required to implement and to manage such a scheme. Accepting floods,
creating a fourth mouth for the river, and constructing a bypass also scored low marks, but
their deficiencies were far less ubiquitous; instead, specific determinants like distributional
ramifications and/or risk spreading were sources of weakness. Higher dikes and
manipulating the riverbed were awarded higher scores, but neither is perfect. Indeed,
manipulating the riverbed would appear to be most feasible, but it is hampered by a
relatively low efficacy factor; i.e., such a plan could not eliminate the risk of flooding. On
the other hand, higher dikes face participation difficulties on the feasibility side, but could
offer extremely effective flood protection. The results of organizing an examination of
adaptive capacity around its underlying determinants are thus surprisingly pessimistic. Each
alternative, for one reason or another, has a weakness that can be discovered by a process
that looks at each determinant in turn.
G. YOHE AND K. STRZEPEK 95
Determinant Store
Water
Accept
Floods
Higher
Dikes
Riverbed 4
th
Mouth Bypass 1. Resources
Total costs
Distribution
a 3
1

5
3

4
4

4
5

4

2
1
3

3
2
2
3. Human Capital

1 2 5 4 4 3
4. Social Capital

1 3 4 5 2 2
5. Risk Spreading

2 1 5 4 4 3
6. Information
Management
Credibility 1
1

3
2

5

moderate flooding could, for example, be much larger than the boundary of the area
vulnerable to moderate flooding. Comparisons of these two trajectories for each scenario
can, however, be used to assess the net value of incurring this greater expense. Panel C of
the degree to which such an expanded investment project would reduce the likelihood of
modest flooding. This is, of course, the first step in computing the expected benefit of
moving protection against modest flooding closer to the riverbank. Notice that the pattern
displayed in Panel C clearly shows the importance of time profiles. In particular, the value
of moving protection against moderate flooding close to the river erodes significantly
overtime after peaking sometime around the middle of the century for most scenarios.
Fig. 4.10 Panel A - Efficacy factor of protecting against modest flooding with or without protection
against moderate or severe flooding inland from the river.
4.6.4 TRUTH IN ADVERTISING - THE UNDERLYING ASSUMPTIONS OF THE
INDICATOR APPROACH
The construction of this indicators of the sort just described clearly depends on
subjective judgments of the relative strengths of underlying determinants. This can be a
virtue, though, for applications in which quality data are scarce. The method also depends
critically on the notion that adaptive capacity is ultimately determined by the “weakest
link” - a hypothesis that requires some justification. Yohe and Tol reported some
suggestive empirical results from international comparisons. They found, for example, that
poorer people are more likely to fall victim to natural violence than are richer people. They
also found that more densely populated areas are more vulnerable. Moreover, they found
a positive relationship between income inequality and vulnerability; i.e., people in more
G. YOHE AND K. STRZEPEK 97
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Figure 4.10 shows the difference between the efficacies of the two strategies and indicates
Fig. 4.10 Panel B - Efficacy factor of protecting against modest and moderate flooding along the
riverbed with or without protection against severe flooding inland from the river.
Fig. 4.10 Panel C - Reduction in the likelihood of modest flooding achieved by moving protection
against moderate flooding to the riverbed with or without protection against severe flooding inland
from the river.

information and to separate signal from noise is equally important; theory tells us that
inefficiencies and market failures can result from the application of asymmetric
information; these are the realms of moral hazard and principal-agent problems. Finally,
the inability to spread risk (the result of market distortions or the vagaries of adverse
selection) can also bring a market to a halt.
4.7 CONCLUDING REMARKS
We have not, in this paper, analyzed the potential efficacy of any specific adaptation with
which decision-makers might be able to reduce the likelihood of flooding in Bangladesh.
We have, though, described one method by which analyses of possible adaptations could
be conducted to accommodate the cascade of uncertainty that explodes from a variety of
sources to cloud our vision of how the future will unfold. Model, calibration and
projection uncertainty can be captured in the range of “not-implausible” climate futures
generated by COSMIC. Calibration and prediction uncertainties can be reflected in
translating the hydrologic model to the likelihood of flooding and in driving it through time
by COSMIC outputs; and contextual uncertainty can certainly be recognized by careful
application of the Adaptation Policy Framework. Moreover, focusing attention on
representative transient scenarios explicitly brings a critical time dimension to bear on the
analyses. “Who know what and when?” are some of the critical questions, but their
answers will not provide any insight into relative vulnerability until they are coupled with
some idea of what decision-makers might do with that information and how effective those
G. YOHE AND K. STRZEPEK 99
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actions might be overtime in reducing climate-driven risks. Bringing some consistent
methodology to the subjective consideration of these final questions, informed by the range
of futures drawn from the COSMIC transients, is the point of constructing time series of
coping capacity indices.
ACKNOWLEDGMENTS
The National Science Foundation of the United States supported both Yohe and Strzepek
in this work under contract SBR 95-21914 with the Center for Integrated Study of the
Human Dimensions of Global Change at Carnegie Mellon University.

Mirza, M. M. Q., Warrick, R.A. and Ericksen, N.J.: “The Implications of Climate Change on Floods
of the Ganges, Brahmaputra and Meghna Rivers in Bangladesh”. Climatic Change 57 (2003),
pp.287-318.
Mitchell, T. D., Carter, T. R., Hones, P. D., Hulme, M. and New, M.: “A Comprehensive Set of
High-Resolution Grids of Monthly Climate for Europe and the Globe: The Observed Record
(1901-2000) and 16 Scenarios (2001-2100). Journal of Climate, Forthcoming, 2004.
100 W
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Copyright © 2005 Taylor & Francis Group plc, London, UK
Ozga-Zielinska, M., Brzezinski, J. and Feluch, W.: “Meso-Scale Hydrologic Modeling for Climate
Impact Assessments: A Conceptual and a Regression Approach”, IIASA CP 94-10, Laxenburg
Austria, 1994.
Pittock, B. and Jones, R. N.: “Adaptation to What and Why?” Environmental Monitoring and
Assessment 61 (2000), pp.9-35.
Ross, N. A., Wolfson, M. C., Dunn, J. R., Berthelot, J. M., Kaplan, G. A. and Lynch, J.A.: “Relation
Between Income Inequality and Mortality in Canada and in the United States: Cross Sectional
Assessment Using Census Data and Vital Statistics”. British Medical Journal 320 (2000),
pp.898-902.
Schlesinger, M. and Williams, L.: “COSMIC - Country Specific Model for Intertemporal Climate”,
Computer Software, Electric Power Research Institute, Palo Alto, CA, USA, 1998.
Schlesinger, M. and Williams, L.: “Country Specific Model for Inter-Temporal Climate”. Climatic
Change 41 (1999), pp.55-67.
Smit, B., Burton, I., Klein, R. J. T. and Wandel, J.: “An Anatomy of Adaptation to Climate Change
and Variability”. Climatic Change 45 (2000), pp.223-251.
Smithers, J. and Smit, B.: “Human Adaptation to Climatic Variability and Change”. Global
Environmental Change 7 (1997), pp.129-146.
Tol, R. S. J., van der Grijp, N. M., Olsthoorn, A. A., and van der Werff, P. E.: “Adapting to Climate
Change: A Case Study on Riverine Flood Risks in the Netherlands”. In R. S. J. Tol and
A. A. Olsthoorn (eds.), Floods, Flood Management and Climate Change in the Netherlands,
Institute for Environmental Studies, Vrije Universiteit, Amsterdam, Netherlands, 2001.