Climate Change and Water Resources in South Asia - Chapter 4 docx

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.

We begin by characterizing the sources of uncertainty in our perception of how the
future climate might evolve and our associated expectations about the frequency of
flooding. Section 4.3 reviews historical records of annual mean flows, annual peak monthly
GARY YOHE
KENNETH STRZEPEK
Copyright © 2005 Taylor & Francis Group plc, London, UK
flows and flooding events. A statistically calibrated reduced-form relationship between
monthly peak flow and the likelihood of flooding in any one year will summarize these
data. Section 4.4 follows with a description of a simple hydrologic model that relates
precipitation and temperature to river flow on a monthly basis; calibration and scaling
issues are also reviewed. Major sources of uncertainty in generating scenarios of future
climate change are described in Section 4.5. Following a methodology developed in
Yo h e et al. (1999), a systematic sampling across 14 general circulation models across
three alternative carbon-emissions scenarios associated with two alternative sulfate
scenarios, three alternative climate sensitivities, and two alternative sulfate forcing factors
will produce a wide range of future flow scenarios (684 in number). Subsequent analysis
will work with 8 representative scenarios for peak monthly flows selected from the full
sample. The representatives will not be chosen to reflect a probabilistic portrait of what the
future might hold. They will, rather, be selected to span a full-range of “not-implausibility”
futures so that the associated inter-temporal trajectories of the annual likelihood of
flooding events absent any additional adaptation presented in Section 4.5 offer pictures
of profound uncertainty - possible futures that cannot, at this point, be dismissed as
impossible. The scenarios will, in particular, reflect the possibility that maximum flows
may or may not climb continuously over time; indeed, they reflect the distinct possibility
that the monthly maxima may actually begin to fall after 2050. Further adaptation can be
expected to guard against any increase in the frequency of flooding, so Section 4.6
describes how these representative trajectories might be employed to characterize the
relative efficacy of various adaptation options overtime before a concluding section offers
some thoughts about context.
4.2 DEFINING UNCERTAINTIES

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
Copyright © 2005 Taylor & Francis Group plc, London, UK
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

associated error bounds) is used to produce predictions of critical state variables (call this
prediction uncertainty). Things get even worse when researchers take account of
uncertainty about the track that the critical drivers of the model might take in the future
(call this projection uncertainty). This compounding effect, really the point of Figure 4.1,
can be especially troublesome when these drivers move beyond past experience and
therefore out of the sample range upon which the model was calibrated. Finally, underlying
social and economic structures might change overtime; and if they do, this evolution
undermines the credibility of using historically-founded modeling structures as
representations of future conditions to produce what might be called contextual
uncertainty.
Fig. 4.1 The cascade of uncertainty from emissions to a source of vulnerability.
Our depiction of climate uncertainty in terms of the annual likelihood of flooding will,
at least implicitly, confront each of these sources of uncertainty by the time we describe a
framework within which to evaluate adaptation options. Calibration, prediction and
projection uncertainties will, for example, cloud our understanding of the link between
flow in the rivers and the likelihood of flooding events. Model and projection uncertainties
will cascade through the scenarios with which we create representative “not-implausible”


Emissions

Precipitation

Concentrations

Temperature

Maximum Flow

Flooding Likelihood

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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4.3.1 UNCERTAINTIES IN THE HISTORICAL CLIMATE RECORD
The COSMIC scenario generator provides a base year of 1990, but does not provide
any information on the statistics of climate record for the country. It is nonetheless
necessary to have data on the moments and probability distributions of the hydro-climatic
variables to perform a flood frequency analysis. To supplement the COSMIC scenario data
for Nepal, we employed historical climate data gathered by the Tyndall Center for Climate
Change Research and recorded in their TYN CY 1.1 dataset. Mitchell et al. (2004)
reported that the TYN CY 1.1 data provide a summary of the climate of the 20
th
century
for 289 countries and territories including monthly time series data for seven climate
variables for the 20
th
century (1901-2000). Interestingly, the dataset creators provide the
following warning: “This dataset is intended for use in trans-boundary research, where it
is necessary to average climatic behavior over a wide area into statistics that are
representative of the whole area.” This warming endorses the use of TYN CY 1.1 and
COSMIC data for Nepal as appropriate for this modeling approach.
4.3.1.1 CLIMATE VARIABILITY
Table 4.1 presents the statistics for the annual precipitation and mean annual
temperature for Nepal from the TYN CY 1.1 monthly time series data for the 20
th
century
(1901-2000). The data shows that mean annual temperature varies very little with a COV
of 0.04 and a lag-one correlation of 0.47. Precipitation exhibits variability at the total
annual level. More importantly for predicting the likelihood of flooding events, though,
maximum monthly precipitation per year is even more variable and strongly (positively)
skewed with a high coefficient of variation.
Table 4.1 Climate Statistics 1901-2000
Annual

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

4.3.1.2 FLOOD FREQUENCY
Figure 4.3 shows that the flooded area in Bangladesh varies greatly from year to year.
Flood risk is characterized by the probability that a certain level of flood will occur each
G. YOHE AND K. STRZEPEK 81
Copyright © 2005 Taylor & Francis Group plc, London, UK
year. The risk factor is generally expressed as a return period of T = 1/(probability of
occurrence). The return period is determined from the cumulative density function of flood
frequency. For flood frequency analyses, FAP (1992) recommends using the Gumbel
Type I Distribution (EV1) for the major rivers in Bangladesh; it is defined by:
S
x
ux
xF
π
α
α
6
expexp)(
=
∞<<∞−













−
−=
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

60
80
100
120
1954
1961
1965
1969
1973
1977
1981
1985
1989
1993
1999
Year
Flooded Area (000 sq.km)
Copyright © 2005 Taylor & Francis Group plc, London, UK
With a relationship between peak flow and flooded area, we have created a link
between climate variables and the extent of flooding. Subsequent analysis of climate change
will examine the impact of potential climate change on flooding in Bangladesh with full
recognition of the possibility that this impact may not be symmetric with respect to all
levels of flood risk. Table 4.3 shows four levels of flooding (low, modest, moderate and
severe) that were mapped to correspond to the 2-year, 10-year, 50-year and 100-year
return periods, respectively.
Table 4.2 Flood flow frequency statistics 1901-2000
y = 4.3095Ln(x) - 45.906
R
2
= 0.5912

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

rainfall-runoff model, WATBAL, to analyze changes in the magnitude of floods for the
same watershed. Yates (1997) describes the model. It has been applied in over forty
G. YOHE AND K. STRZEPEK 83
Copyright © 2005 Taylor & Francis Group plc, London, UK
country studies of climate change impact on runoff including the Nile River basin, a river
basin of the same spatial scale as the GBM basin.
More specifically, the WATBAL model predicts changes in soil moisture according to
an accounting scheme based on the one-dimensional bucket conceptualization depicted
schematically in Figure 4.5. Yates and Strzepek (1994) compared this relatively simple
formulation to more detailed distributed hydrologic models and found them in close
agreement with absolute and relative runoff. The advantage of this lumped water-balance
model lies in its use of continuous functions of relative storage to represent surface
outflow, sub-surface outflow, and evapo-transpiration in the form of a differential equation
(see Kaczmarek (1993) or Yates (1996)). The monthly water-balance contains two
parameters related to surface runoff and sub-surface runoff. A third model parameter,
maximum catchment water-holding capacity (S
max
), was obtained from a global dataset
based on the work of Dunne and Willmott (1996).
Fig. 4.5 A schematic conceptualization of the water-balance model.
The precise structure of WATBAL is easily described. To begin with, the monthly soil
moisture balance is written as:
where P
eff
= effective precipitation (length/time),
R
s
= surface runoff (length/time),
R
ss

= 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

accumulates. If the temperature is between the two thresholds, then a fraction of the
precipitation enters the soil moisture budget and the remaining fraction accumulates.
Temperatures above some higher threshold T
l
give a mf
i
value of 0, so all the precipitation
enters the soil moisture zone. If there is any previous monthly accumulation, then this is
also added to the effective precipitation.
where,
and snow accumulation is written as,
G. YOHE AND K. STRZEPEK 85
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In writing equations (4.5) through (4.7),
mf
i
= melt factor,
A
i
= snow accumulation,
Pm
i
= observed precipitation,
Peff
i
= effective precipitation,
T
l
= upper temperature threshold at which precipitation is all liquid (°C),
T

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
86 WATER RESOURCE ASSESSMENT: ADDRESSING UNCERTAINTIES
Copyright © 2005 Taylor & Francis Group plc, London, UK
Fig. 4.6 Panel B - Correlation of COSMIC 1990 to historical monthly precipitation.

Panel A of Figure 4.7 depicts the full set of 684 scenarios in terms of maximum monthly
flows in 2050 and 2100 - monthly flows that were computed by inserting COSMIC monthly
precipitation and temperature pathways into the hydrologic model described in
Section 4.4. Panel A also plots a 45
o
line along which these two annual maxima would be
identical. Notice that many, but by no means all, of the ordered pairs lie below this
demarcation. These pathways indicate the possibility that monthly flows might actually
decline with secular climate change in the later half of the century even if they began the
century with an increasing trend. It seems that reduced precipitation in the lowlands more
than accommodate increased runoff of melting snowfall in the spring in the later decades.
8 representative scenarios whose underlying parameterizations which are displayed in
Table 4.4. They clearly do not reflect the relative frequency of model run output across the
full sample; instead, they reasonably span the range of possible outcomes. Figure 4.8
provides an alternative depiction of the diversity that these representative scenarios
capture in terms of transient trajectories of maximum monthly flows in 10-year increments
from 2000 through 2100.
The three panels of Figure 4.9 offers insight into the likelihood of modest, moderate,
and severe flooding events in any year along each of the 8 scenarios. The values portrayed
there were derived for each year along each flow pathway from the statistical correlation
described in Section 4.3. Notice that they fall, for every year along each pathway, as you
move from modest to severe events. This is because some of the modest events are,
statistically speaking at least, included in episodes of moderate and severe flooding; quite
simply, the area that would be vulnerable to modest flooding would surely be exposed
number of scenarios under study while still spanning the range of “not-implausibility”. In
this application, 8 scenarios were therefore chosen and dubbed “representative” of an
underlying set of 684 possibilities, but care must be taken in interpreting their content.
They were not chosen to be representative in any statistically significant sense. They were,
instead, chosen to represent the diversity displayed by the multitude of internally
consistent “not-implausible” climate futures that published climate models can produce.

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
Copyright © 2005 Taylor & Francis Group plc, London, UK
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

UNCERTAINTY
Several unifying methodologies have been developed recently to aid researchers who are
trying to evaluate adaptation options in the face of extreme uncertainty. The United
Nations Development Programme (2003), for example, just completed several years of work
involving multiple experts from across the globe in its creation Adaptation Policy
Framework (APF). The basic structure of the APF is illustrated schematically in Table 4.5;
it is particularly well suited for the risk-hazard approach characterized in the introduction.
It is therefore particularly well suited for handling the profound uncertainty depicted in
Figure 4.9. The need to reduce vulnerability to flooding in Bangladesh set the stage for this
work, so we have a reasonably focused context within which Step I of the APF might be
accomplished. Descriptions of current and (possible) future climate conditions, as
required in Steps II and III, are similarly provided in Sections 4.3 and 4.5, respectively. We
now turn to assessing adaptation options (Step IV).
The risk-hazard approach to assessing how adaptation might increase a system’s
long-term sustainability in the face of climate change and climate variability builds on the
notion that its exposure to the impacts of climate, its baseline sensitivity to those impacts,
and its adaptive capacity determine its vulnerability. The trajectories displayed in
Section 4.5 offer a wide range of “not-implausible” baselines along which we can judge the
relative efficacy of various adaptations in terms of reducing the annual likelihood of
flooding events. For some adaptations designed to reduce exposure (like building dikes
and/or levies), tracking the sensitivity of the correlation estimated in Section 4.3 to higher
thresholds for flooding events could measure the change in flooding frequency. The
diversity of futures, as well as the reported differentiation in the severity of flooding events,
would add richness to the analysis and depth to the range of alternatives to be considered.
Building dikes along the rivers could, for example, reduce exposure to modest flooding,
but do nothing to diminish the likelihood of moderate or severe events. It follows that the
likelihood of flooding in areas vulnerable during modest episodes would not fall to zero,
but the likelihood of moderate events (that themselves would include some chance of
severe inundation). Building a different set of dikes inland from the rivers could meanwhile
reduce exposure to moderate or extreme flooding, but do nothing to diminish the

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 scenarios


Global Carbon

Circulation Emission Sulfate Sulfate Climate
Scenario Model Scenario Emissions Forcing Sensitivity

(1) UKMO Low Low -1.0W/m
2
2.5
o

(2) POLLS High Low -1.0W/m
2
4.5
o


(3) GISS Low High -1.0 W/m
2
2.5
o


(4) UIUC High High -1.0W/m
2
4.5
o


(5) BMRC Medium High -1.0W/m
2
2.5
o

(6) CCC Medium Low -1.0W/m
2
2.5
o


(7) CCC Low High -1.0W/m

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).
90 WATER RESOURCE ASSESSMENT: ADDRESSING UNCERTAINTIES
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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

Scenario 3
Scenario 4
Scenario 5
Scenario 6
Scenario 7
Scenario 8
Fig. 4.8 The representative scenarios reflected in terms of transient trajectories of maximum monthly
flows.
Fig. 4.9 Panel A - The likelihood of a modest flooding event in any year.
characterize the degree to which any adaptation would reduce flooding frequency
overtime. Moreover, casting the same adaptations across the wide range of possible baselines
can be used to test its robustness against profound uncertainty over the long-run.
Analysis of a different set of adaptations designed to reduce sensitivity of surrounding
systems to flooding events would not have to adjust these portraits of future climate change
G. YOHE AND K. STRZEPEK 91
Copyright © 2005 Taylor & Francis Group plc, London, UK
(unless they were to be combined with exposure-limiting options). In these cases,
however, a final link between flooding and some metrics of social, economic, or ecological
impact would be required to produce adaptation baselines and to reflect the effect of
adaptation. The metrics of impact would, of course, now be the appropriate indicators of
relative efficacy.
Fig. 4.9 Panel B - The likelihood of a moderate flooding event in any year.
0.000
0.200
0.400
0.600
0.800
1.000
2000 2020 2040 2060 2080 2100
Year

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

Prioritize measures and adaptations within and across sectors
Step V: Continue the Adaptation Process
Incorporate adapting to climate risks into development plans
Review, monitor and evaluate policies, measures and adaptations
4.6.1 MOVING TOWARD A MORE COMPLETE ASSESSMENT OF
VULNERABILITY AFTER ADAPTATION
A complete assessment of the ability adaptation to reduce vulnerability cannot stop with
estimates of relative efficacy, because these evaluations must include some
consideration of relative feasibility. Recent work by Yohe and Tol (2002) builds on the
notion the determinants of adaptive capacity that are path dependent and geographically
idiosyncratic to suggest how to accomplish this task. The determinants of adaptive
capacity that they cite include:
1. The range of available technological options for adaptation,
2. The availability of resources and their distribution across the population,
3. The structure of critical institutions, the derivative allocation of decision-making
authority, and the decision criteria that would be employed,
4. The stock of human capital including education and personal security,
5. The stock of social capital including the definition of property rights,
6. The system’s access to risk spreading processes,
G. YOHE AND K. STRZEPEK 93
Copyright © 2005 Taylor & Francis Group plc, London, UK
7. The ability of decision-makers to manage information, the processes by which
these decision-makers determine which information is credible, and the credibility
of the decision-makers, themselves, and
8. The public’s perceived attribution of the source of stress and the significance of
exposure to its local manifestations.
The approach that Yohe and Tol (2002) suggest also relies fundamentally on the
notion that a system’s adaptive capacity is fundamentally determined by the weakest link
- the underlying determinant that provides the least support in its ability to cope with
variability and change in local environmental conditions.

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

j
- a subjective index
number assigned from a range running from 0 to 1. Efficacy factors, like changes in the
expected frequency of flooding events supported by analyses just described and computed
along the exposure pathways reported in Section 4.5, reflect the likelihood that adaptation
(j) will perform as expected to influence exposure and/or sensitivity compounded by
the likelihood that actual experience would exceed critical thresholds if it were adopted.
Yohe and Tol define potential contribution of any adaptation to a system’s social and
economic coping capacity as the simple product of its overall feasibility factor and its
efficacy factor; i.e.,
94 WATER RESOURCE ASSESSMENT: ADDRESSING UNCERTAINTIES
Copyright © 2005 Taylor & Francis Group plc, London, UK
Note that the potential contribution is a unit-less number bounded from below by 0 and
above by 5.
4.6.2 AN EXAMPLE OF THE INDICATOR APPROACH
Tol et al. (2001) reported on an extensive assessment of adaptation against the increased
risk of climate-induced flooding in the Rhine Delta; and their work can support an instructive
application of the vulnerability model to examine this issue. Six feasible options for the
Netherlands were identified by major consultancies: (1) store excess water in Germany;
(2) accept more frequent floods; (3) build higher dikes; (4) deepen and widen the riverbed;
(5) dig a fourth river mouth; and (6) dig a bypass and create a Northerly diversion. The
Netherlands is the 11
th
largest economy in the world (measured in terms of purchasing
power parity), and the distribution of resources across the population is irrelevant because
flood protection is administered by the national government. The structure of critical
institutions, the derivative allocation of decision-making authority, and the decision criteria
could be more problematic. However, water management and land-use planning are
administered by separate agencies; as a result, pressure to expand into the floodplain can
limit the options for water management because of conflicts among many stakeholders.

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

Copyright © 2005 Taylor & Francis Group plc, London, UK
Table 4.6 Quantifying the details of adaptation options for the lower Rhine Delta
Source: Table 4.4 in Yohe and Tol (2002).
Notes:
a
The distribution of the costs and benefits of implementing an option.
b
The degree to which the current mandates of bureaucracies are inadequate for the
problem, essentially, how much integration of land-use and water management is needed
for successful implementation.
c
The degree to which the decision-making process is likely to be hindered by “not in my
backyard” phenomena.
d
The degree to which the option fits in with current decision-making criteria.
e
Ranking (minimum of the weighted scores).
4.6.3 COMPUTING EFFICACY FACTORS FROM ALONG THE REPRESENTATIVE
SCENARIOS
The introduction to this section described generically a few of the adaptation targets that
might be pursued along the Ganges, Brahmaputra and/or Meghna Rivers, but it stopped
short of explaining exactly how to use the likelihood pathways of Figure 4.9 to compute
what have now be termed efficacy factors. Suppose, for the sake of illustration, that
protection against only modest flooding were pursued. If it were designed to be completely
successful, then the efficacy factor would equal to one minus the likelihood of moderate
inland protection against moderate or severe inundation. Panel A of Figure 4.10 shows
these factors - the likelihood of avoiding flooding. By way of contrast, Panels B depicts the
efficacy of protecting against modest and moderate flooding with projects that might be

Options

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

1
1

2
1
2. Institutions

Structure
b
Participation
c
Criteria
d


1
2
2

4
2
1

5
3
5

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

4

4
5

2
3

2
3
7. Awareness

3 3 5 5 3 3
Fe asibility Fac tor (FF)
e

1 1 3 4 1 1
Efficacy Factor (EF)

0.8 1.0 1.0 0.6 0.8 0.6
Coping Index (PCC)

0.8 1.0 3 2.4 0.8 0.6

96 WATER RESOURCE ASSESSMENT: ADDRESSING UNCERTAINTIES
Copyright © 2005 Taylor & Francis Group plc, London, UK
flooding (portrayed in Panel B of Fig. 4.9) even if these investments were coupled with
constructed alongside the river; they are equal to one minus the likelihood of extreme
flooding. They are higher, indicating a more effective program, but this program could be
significantly more expensive to implement. The boundary of the area vulnerable to

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
Copyright © 2005 Taylor & Francis Group plc, London, UK
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.

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ATER RESOURCE ASSESSMENT: ADDRESSING UNCERTAINTIES
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egalitarian societies seem to be less likely to fall victim of natural violence than are people
in a society with a highly skewed income distribution. A growing body of literature has
reached similar conclusions regarding income inequality and mortality (see for example,
Lynch et al., 2000; Kaplan et al., 1996; Ross et al., 2000). Even more recently, McGuire
(2002) looked for statistically significant explains for variability in infant mortality across
developing countries.
First principles of the microeconomics of product markets provide even stronger
evidence in support of the hypothesis. Market responses to price signals of surplus or
scarcity need not be orchestrated from above. They happen automatically as rational
actors individually pursue their own best interest; as a result, markets can be viewed as a
paragon of autonomous adaptation to external stress. To an economic theorist, however,
the list of the determinants of adaptive capacity holds special meaning. It is a list of sources
for market inefficiency and/or (perhaps) failure. Indeed, weakness in any determinant could
doom a market to under-performance or even collapse. To see how, simply move through
the list of determinants provided above. Note, to begin with, that the price elasticities of
demand (and supply, for that matter) for any market good increase with time because the
list of available response options expands. If energy prices rose, for example, short-term
responses might be confined to driving less or lowering the indoor temperature. Over the
long-term, though, individuals might add insulation to their house, replace old windows,
buy a more fuel-efficient car, and so on; the result would be a larger quantity response to
the higher price. We can also rest assured that access to resources with which to
underwrite the implementation of alternative responses can increase these critical
elasticities. Social capital is required to construct and to sustain the definition of property
rights and the institutional foundations upon which market transactions rely; and human
capital is necessary if market participants are to respond “rationally”. Both of these types
of capital require appropriately designed institutions as well as decision-makers whose
primary goal is to safeguard the integrity of the marketplace. Agents’ abilities to process

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
Copyright © 2005 Taylor & Francis Group plc, London, UK
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.

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