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

This chapter is designed to reflect the sensitivity to short-term climate variabilityexpressed in terms of the changes in frequency of flooding events in Bangladesh along theGanges, Brah

Climate Change and Water Resource Assessment

in South Asia: Addressing Uncertainties

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 meanconditions that define those environments can actually be experienced most noticeablythrough changes in the nature and/or frequency of variable conditions that materializeacross 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 iswarranted whenever either changes in mean conditions or changes in variability havesignificant consequences For most systems, though, changes in mean conditions overshort 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 notobserved 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 tocharacterize how the short-term variability that it confronts might change over the longerterm

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 theGanges, 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-implausibleclimate futures It therefore explores a case for which the boundaries of a coping range areeasily defined by flooding thresholds When we ultimately turn a discussion of how toevaluate adaptation options that might expand the coping range (exposure to flooding) orreduce 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 thefuture climate might evolve and our associated expectations about the frequency offlooding Section 4.3 reviews historical records of annual mean flows, annual peak monthly

GARY YOHE

KENNETH STRZEPEK

flows and flooding events A statistically calibrated reduced-form relationship betweenmonthly peak flow and the likelihood of flooding in any one year will summarize thesedata Section 4.4 follows with a description of a simple hydrologic model that relatesprecipitation and temperature to river flow on a monthly basis; calibration and scalingissues are also reviewed Major sources of uncertainty in generating scenarios of futureclimate change are described in Section 4.5 Following a methodology developed in

Yohe et al (1999), a systematic sampling across 14 general circulation models across

three alternative carbon-emissions scenarios associated with two alternative sulfatescenarios, three alternative climate sensitivities, and two alternative sulfate forcing factorswill produce a wide range of future flow scenarios (684 in number) Subsequent analysiswill work with 8 representative scenarios for peak monthly flows selected from the fullsample The representatives will not be chosen to reflect a probabilistic portrait of what thefuture 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 asimpossible The scenarios will, in particular, reflect the possibility that maximum flowsmay or may not climb continuously over time; indeed, they reflect the distinct possibilitythat the monthly maxima may actually begin to fall after 2050 Further adaptation can beexpected to guard against any increase in the frequency of flooding, so Section 4.6describes how these representative trajectories might be employed to characterize therelative efficacy of various adaptation options overtime before a concluding section offerssome thoughts about context

of small, modest, and extreme flooding are ultimately produced The expanding size of theloci 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 knewthe 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 notaccurately describe how associated patterns of precipitation and temperature would bealtered and how those changes might be translated into river flows And even if we knewexactly how flows might change, we could not accurately predict how the likelihood offlooding 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 theysometimes produce wildly different answers to the very same questions This simplephenomenon is valuable in examining the relative value of one particular model or another,

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 offercredible 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 (withassociated 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 andtherefore out of the sample range upon which the model was calibrated Finally, underlyingsocial and economic structures might change overtime; and if they do, this evolutionundermines 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 aframework within which to evaluate adaptation options Calibration, prediction andprojection uncertainties will, for example, cloud our understanding of the link betweenflow in the rivers and the likelihood of flooding events Model and projection uncertaintieswill cascade through the scenarios with which we create representative “not-implausible”

portraits of future climate change in terms of flow, but calibration and predictionuncertainties will also have an effect behind the scenes Finally, the evaluation approachdescribed in Section 4.6 must accommodate contextual uncertainty.

Bangladesh is very vulnerable to flooding, principally due to intense monsoonprecipitation 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 ofBangladesh, 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 changescenarios 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 conceptualhydrologic rainfall-runoff model that incorporates evapo-transpiration, snowmelt, soilmoisture and surface and sub-surface flows Separate models of the Ganges and BrahmaputraRivers are developed and described in the next section The hydrologic model needs to bedriven by a climate data, of course, but COSMIC reports only spatially averaged climatechange variables at a nation scale To cope with this problem, Nepal was selected as therepresentative country for three reasons First of all, Nepal is located almost directly inthe geographic center of the GBM watershed Secondly, its monsoon precipitationcharacteristics, in quantity and timing, are representative of the average characteristicsover 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 notrepresentative of the conditions in the GBM watershed

4.3.1 UNCERTAINTIES IN THE HISTORICAL CLIMATE RECORD

The COSMIC scenario generator provides a base year of 1990, but does not provideany information on the statistics of climate record for the country It is nonethelessnecessary to have data on the moments and probability distributions of the hydro-climaticvariables to perform a flood frequency analysis To supplement the COSMIC scenario datafor 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)

for 289 countries and territories including monthly time series data for seven climatevariables for the 20th 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

Table 4.1 presents the statistics for the annual precipitation and mean annual

(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 totalannual 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

year The risk factor is generally expressed as a return period of T = 1/(probability ofoccurrence) The return period is determined from the cumulative density function of floodfrequency For flood frequency analyses, FAP (1992) recommends using the GumbelType I Distribution (EV1) for the major rivers in Bangladesh; it is defined by:

S

x u

x x

F

π α

α 6

exp exp ) (

Fig 4.3 Bangladesh Flood Area from 1954 through 1999.

High river flows themselves are not a problem unless they overtop their banks andflood area in the adjoining floodplain The determination of flood flows used the science ofhydrology, 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 floodflows in the Brahmaputra-Ganges-Meghna Rivers system Figure 4.4 shows the data fromtheir work and the non-linear relationship that was developed between peak flow andflooded area with results in an R2 of 0.59

Flooded Area (million of hectares) = 4.3095* ln[Flow (cms)] – 45.906

0 20 40 60 80 100

With a relationship between peak flow and flooded area, we have created a linkbetween climate variables and the extent of flooding Subsequent analysis of climate changewill examine the impact of potential climate change on flooding in Bangladesh with fullrecognition of the possibility that this impact may not be symmetric with respect to alllevels of flood risk Table 4.3 shows four levels of flooding (low, modest, moderate andsevere) that were mapped to correspond to the 2-year, 10-year, 50-year and 100-yearreturn periods, respectively.

Table 4.2 Flood flow frequency statistics 1901-2000

y = 4.3095Ln(x) - 45.906

R2 = 0.5912

0 1 2 3 4 5 6

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

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 ofthe Ganges, Brahmaputra and Meghna Rivers The present analysis uses a conceptualrainfall-runoff model, WATBAL, to analyze changes in the magnitude of floods for thesame watershed Yates (1997) describes the model It has been applied in over forty

country studies of climate change impact on runoff including the Nile River basin, a riverbasin 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 depictedschematically in Figure 4.5 Yates and Strzepek (1994) compared this relatively simpleformulation to more detailed distributed hydrologic models and found them in closeagreement with absolute and relative runoff The advantage of this lumped water-balancemodel lies in its use of continuous functions of relative storage to represent surfaceoutflow, sub-surface outflow, and evapo-transpiration in the form of a differential equation

parameters related to surface runoff and sub-surface runoff A third model parameter,

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 soilmoisture balance is written as:

A non-linear relationship describes evapo-transpiration based on Kaczmarek (1990):

Following Yates (1996), surface runoff is described in terms of the storage state andthe effective precipitation according to:

where ε is a calibration parameter that allows for surface runoff to vary both linearly andnon-linearly with storage Finally, sub-surface runoff is a quadratic function of the relativestorage 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 effectiveprecipitation 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 monthaccumulates If the temperature is between the two thresholds, then a fraction of theprecipitation 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 precipitationenters the soil moisture zone If there is any previous monthly accumulation, then this isalso added to the effective precipitation

where,

and snow accumulation is written as,

In writing equations (4.5) through (4.7),

Peffi = effective precipitation,

T

The model was calibrated from the TYN CY 1.1 data for the Ganges and Brahmaputra

statistics of 0.89 and 0.87 for the Brahmaputra and Ganges, respectively Since the climatechange scenarios in COSMIC begin with a base year of 1990, the COSMIC base had to becorrelated with the TYN CY 1.1 average data Panels A and B of Figure 4.6 show therelationship between historical average and COSMIC base year data for temperature andprecipitation, respectively

Fig 4.6 Panel A - Correlation of COSMIC 1990 to historical monthly temperature.

Schlesinger and Williams (1998 and 1999) designed the COSMIC program so thatresearchers could produce literally thousands of “not-implausible” climate scenarios thatare 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 forgreenhouse 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 asulfate forcing parameter between 0 watts per meter and -1.2 watts per meter squared and

a climate sensitivities between 1o and 4.5o (for a doubling of effective carbon-dioxideconcentration from pre-industrial levels) It would be imprudent if not impossible toconduct integrated analyses along each one, so there is a fundamental need to limit the

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 monthlyflows in 2050 and 2100 - monthly flows that were computed by inserting COSMIC monthlyprecipitation and temperature pathways into the hydrologic model described in

identical Notice that many, but by no means all, of the ordered pairs lie below thisdemarcation These pathways indicate the possibility that monthly flows might actuallydecline with secular climate change in the later half of the century even if they began thecentury with an increasing trend It seems that reduced precipitation in the lowlands morethan accommodate increased runoff of melting snowfall in the spring in the later decades

8 representative scenarios whose underlying parameterizations which are displayed inTable 4.4 They clearly do not reflect the relative frequency of model run output across thefull sample; instead, they reasonably span the range of possible outcomes Figure 4.8provides an alternative depiction of the diversity that these representative scenarioscapture in terms of transient trajectories of maximum monthly flows in 10-year incrementsfrom 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 portrayedthere were derived for each year along each flow pathway from the statistical correlationdescribed in Section 4.3 Notice that they fall, for every year along each pathway, as youmove 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; quitesimply, 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” Inthis application, 8 scenarios were therefore chosen and dubbed “representative” of anunderlying 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 internallyconsistent “not-implausible” climate futures that published climate models can produce

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.