Climate Change and Water Resources in South Asia - Chapter 5 pdf

Taking into account the range of uncertainty in the climate scenarios, the overall goals of this research include: 1 determining the sensitivity of mean annual and mean peakdischarge at

The Implications of Climate Change on River Discharge in Bangladesh

5.1 INTRODUCTION

5.1.1 WATER RESOURCES PROBLEM OF BANGLADESH

Bangladesh lies in the delta of three large rivers - the Ganges, Brahmaputra and Meghna(GBM), which is often termed as a “land of rivers and water.” With a complex network of

230 rivers, including 57 cross boundary rivers, about 92.5% of the 175 million hectares(mha) of combined basin area of the GBM Rivers (Fig 5.1) is beyond the boundary ofBangladesh and is located in China, Nepal, India and Bhutan Therefore, Bangladesh acts

as a drainage outlet for the cross-border runoff More than 90% of the annual runoff isgenerated outside of Bangladesh However, there is a high seasonal difference in theavailability of water For example, for the Ganges River, the ratio of dry and monsoonrunoff is 1:6 (Fig 5.2) This illustrates that Bangladesh has an abundance of water in themonsoon while the country still faces surface water scarcity in the dry season Irrigatedagriculture is highly dependent on dry season surface water availability On average,annually floods engulf roughly 20.5% of the area of the country, or about 3.03 mha (Mirza,2003) In extreme cases, floods may inundate about 70% of Bangladesh, as it occurredduring the floods of 1988 and 1998 (Ahmed and Mirza, 2000) Hydrological droughts arevery common in the rivers of Bangladesh

The magnitude of precipitation over the GBM basins is very high and more thanthree-quarters occurs during the summer monsoon (June-September) (Table 5.1) Theresulting huge volume of cross-border monsoon runoff, together with the locallygenerated runoff and some physical factors, either singly or in combination, causes floods

in Bangladesh The physical factors, either singly or in combination, include snow andglacier melt, El Niño Southern Oscillation (ENSO) induced conditions, loss of drainagecapacity due to the siltation of principal distributaries, backwater effect, unplannedinfrastructure development, deforestation and the synchronization of flood peaks of themajor rivers Recently Mirza (2003), compared three recent extreme floods (1987, 1988and 1998) in Bangladesh and found that the intense monsoon precipitation was theprincipal cause of flooding However, there are differences in opinions concerning the role

of deforestation in upstream areas in the flooding process in Bangladesh Deforestation ofsteep slopes in the Himalayas is assumed to lead to accelerated soil erosion and landslides

M MONIRUL QADER MIRZA

A part of this chapter was published in the Climatic Change 57 (2003), pp.287-318 and reprinted

with permission.

during monsoon precipitation This in turn is believed to contribute to devastating floods

in Bangladesh (Khalequzzaman, 1994; Hamilton, 1987) Hofer (1998) concluded thatland-use changes in the Himalayas were not responsible for floods in India and Bangladesh.With regard to sedimentation, the existing publications do not report any significant recentincrease in the sediment load of the major rivers and their tributaries (Ives and Messerli,1989)

Fig 5.1 The Ganges, Brahmaputra and Meghna basins.

Fig 5.2 Hydrograph of the Ganges (lighter solid line) and Brahmaputra (thicker solid line) Rivers for the typical water year 1967-1968 The values are in m 3 /sec Data source: Bangladesh Water Development Board (BWDB, 1995).

Table 5.1 Mean annual precipitation in the Ganges, Brahmaputra and Meghna basins

Precipitation (mm)

India Bangladesh

1,860 450-2,000 1,570

Bhutan India Bangladesh

400-500 500-5,000 2,500 2,400

Bangladesh

2,640 3,575

Source: Mirza, 1997.

Bangladesh generally experiences four main types of floods: flash, riverine, rain andstorm-surge (Fig 5.3) Eastern and Northern areas of Bangladesh adjacent to its borderwith India are vulnerable to flash floods Rivers in these regions are characterized by sharprises and high flow velocities resulting from exceptionally heavy rainfall occurring over thehilly and mountainous regions in the neighboring India Riverine floods occur when floodwater of the major rivers and their tributaries and distributaries spill With the onset of themonsoon in June, all of the major rivers start swelling to the brim and bring flood waterfrom the upstream basin areas Rain floods are caused by intense local rainfall of longduration in the monsoon months Heavy pre-monsoon rainfall (April-May) causes localrunoff to accumulate in depressions Later (June-September), local rainwater isincreasingly ponded on the land by the rising water levels in the adjoining rivers Coastalareas of Bangladesh, which consist of large estuaries, extensive tidal flats, and low-lyingoffshore islands, are vulnerable to storm-surge floods, which occur during cyclonic storms.Cyclonic storms usually occur during April-May and October-November

Flood is a necessity as well as a danger in Bangladesh For example, normal floodshelp the growth of rice crops because of the fertilization produced by nitrogen supplyingblue-green algae, which grow in the ponded clear flood water (World Bank, 1989) The

extra moisture provided by large floods to higher lands also benefits rabi crops such as vegetables, lintels, onion, mustard, etc (Brammer, 1990) Rabi refers to a cropping season

from November-May But, high flood levels can cause substantial damage to keyeconomic sectors: agriculture, infrastructure and housing Based on the reported cropdamage due to floods, average annual loss is estimated to be 0.47 million tons (Paul andRasid, 1993) However, in a year of an extreme flood such as 1998, food grain loss mayexceed 3.5 million tons (Ahmed, 2001) The total monetary loss caused by the extremefloods of 1998 and 1988 was US$ 3.4 billion and US$ 2.0 billion, respectively or 10% ofthe GDP of Bangladesh in the respective years (Bhattacharya, 1998; World Bank, 1989)

For a country like Bangladesh with a transitional economy and a low per capita income ($360 in 2001) (World Bank, 2003), this amount of loss is very high Although flood

affects people of all socio-economic status, the rural and urban poor have been the hardesthit

Fig 5.3 Bangladesh and various flood types.

5.1.2 RATIONALE OF THE RESEARCH

Future climate change may affect water resources availability and extreme hydrologicalevents such as floods in Bangladesh in many ways The IPCC (2001) indicated a likelihood

of increased intensity of extreme precipitation over the South Asian region All climatemodels simulate an enhanced hydrological cycle and increases in annual mean rainfallover South Asia (under non-aerosol forcing) In all periods of simulation (GHG andGHG + aerosol forcing), summer precipitation shows an increase The magnitude ofincrease in summer precipitation with GHG + aerosol forcing is smaller than that seen inthe GHG forcing The difference in change with aerosol forcing is due to its dampening

106 I MPLICATIONS O N R IVER D ISCHARGE I N B ANGLADESH

effect on Indian summer monsoon precipitation (Lal et al., 2001; Cubasch et al., 1996; Roeckner et al., 1999).

Annual runoff may increase as a result of increased precipitation However,uncertainty remains in dry season availability of river flow as it is related to a number offactors They include: amount of monsoon precipitation and ground water recharge, amount

of snowfall, temperature gradient, snowmelt, evaporation, upstream water demand, etc

More frequent extreme precipitation could increase the possibility of flash floods.

Increased precipitation in the GBM basins may increase the magnitude, depth and spatial

extent of riverine and rain floods Based on a series of theoretical and model-based

studies, including the use of a high resolution hurricane prediction model, it is likely thatpeak wind intensities will increase by 5% to 10% and the mean and peak precipitationintensities by 20% to 30%, in some regions (IPCC, 2001) Therefore, stronger

storm-surges can aggravate coastal flooding Of all of these flood types, the riverine floods

are the most pervasive and have long-term impacts on land-use, the economy and most

development strategies for Bangladesh Thus, it is with the changes in riverine flooding

that the effects of climate change may be most strongly felt

In the past, a number of studies on climate change and its possible implications onBangladesh have been undertaken (Ahmad and Warrick, 1996; ADB, 1994; and ResourceAnalysis, 1993) The consensus was that over the past 100 years, the broad regionencompassing Bangladesh had warmed by 0.5oC (Ahmad and Warrick, 1996) However,

overall increases in precipitation were not found (Mirza et al., 1998) These studies also

indicated that with increases in precipitation in Bangladesh and surrounding areas due toclimate change, flooding in Bangladesh might worsen However, no specific research hasassessed changes in flooding in terms of magnitude, depth and spatial extent in Bangladeshtaking into account possible changes in precipitation in the cross-border basin areas of theGBM Rivers

5.2 OBJECTIVES

As indicated above, the annual runoff in the GBM basins may be changed due to possiblechanges in future climate and it may also exacerbate the flood problem in Bangladesh Mostexperiments using GCMs show increases in monsoon precipitation as a consequence ofenhanced greenhouse effect However, it is not known exactly what the magnitude of climatechange will be in the future or how it will affect precipitation, and thereby flooding in Bangladesh.Therefore, a study was carried out under the BDCLIM (Bangladesh Climate) project toexamine possible changes in flooding in Bangladesh under climate change The BDCLIM is alarge integrated model system developed for assessing the effects of future climate change

scenarios on Bangladesh (Warrick et al., 1996).

Taking into account the range of uncertainty in the climate scenarios, the overall goals

of this research include: 1) determining the sensitivity of mean annual and mean peakdischarge at the boundary of Bangladesh to future climate change and 2) estimating theconsequent changes in depth and spatial extent of flooding in Bangladesh

5.3 METHODOLOGY

In order to meet the first objective, four major steps were followed First, an empirical relationship between precipitation and discharge was determined Second, climate

change scenarios were constructed for the three river basins using the results of CSIRO9

(McGregor et al., 1993), UKTR (Murphy and Mitchell, 1995), GFDL (Whetherland and

Manabe, 1986), and LLNL (Whener and Convey, 1995) GCMs in the SCENGENsoftware of the Climatic Research Unit (CRU), University of East Anglia, U.K (CRU,

1995) Third, the climate change scenarios were applied to empirical models in order to determine the magnitude of changes in discharge at the boundaries of Bangladesh Fourth,

the MIKE 11-GIS hydrodynamic model was forced with current and future peak discharges

to simulate river flood stages and depth and spatial extent of flooding within Bangladesh.The MIKE 11 is a professional engineering software tool that simulates flows, waterquality and sediment transport in river basins, estuaries, irrigation systems, channels andother water bodies The Danish Hydraulic Institute (DHI) developed the software.The GIS interface was developed and applied during the Flood Action Plan (FAP) Study(1990-1995) in Bangladesh The model has been calibrated and validated in a Bangladeshcontext by the Surface Water Modeling Center (SWMC), Dhaka and is currently beingused for water resource development, planning and management

5.3.1 DEVELOPMENT OF EMPIRICAL DISCHARGE MODELS

As a first step for determining the sensitivity of mean peak discharge at the boundary of

Bangladesh, different approaches of modeling were envisaged The empirical modelingapproach was compared to the water-balance, lumped-parameter and physically-baseddistributed models and found to be preferable on the basis of the constraints imposed bythe large areal extent of the river basins and the lack of available data and resources.Sensitivity analyses for three selected stations in the Ganges, Brahmaputra and MeghnaRiver basins was carried out using the model R = P - E Here R = runoff, P = precipitationand E = actual evapo-transpiration, which was calculated using the relationship

E = ( 1 ( )2)

PE P

P

+

(Pike, 1964), where PE = potential evapo-transpiration The analysis showed that runoffwas far more sensitive to precipitation than to temperature (Mirza, 1997; Mirza and Dixit,1997) (Fig 5.4) Therefore, temperature was excluded as an explanatory variable for empiricalmodel building but it may be considered as an explanatory variable as part of a futureresearch undertaking

The results of the sensitivity analysis also shows that, in percentage terms, runoff ismore sensitive to precipitation and temperature changes in relatively dry stations than wetstations As an example, in the case of the New Delhi station (a drier station in the Gangesbasin) no change in temperature and a 4% increase in precipitation changes runoff by+11%, while for the Gauhati and Syhet (the wetter stations in the Brahmaputra and Meghnabasins, respectively), the changes in runoff are +6% and +8%, respectively In the extremecase, a 5oC increase in temperature and a 20% increase in precipitation could increaserunoff by 29% at the New Delhi station, whereas for Gauhati and Syhet stations theexpected changes are 22% and 21%, respectively

Accordingly, time-series data for precipitation were collected from various primaryand recognized secondary sources for the three river basins Sources of precipitation datawere: 1) Carbon Dioxide Information Analysis Center (CDIAC)/Oak Ridge NationalLaboratory (ORNL), Tennessee, USA; 2) Climatic Research Unit (CRU), University ofEast Anglia, U.K.; 3) Nepal Water Conservation Foundation (NWCF), Kathmandu;4) The Bangladesh Water Development Board (BWDB), Dhaka; 5) United Nations; and

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6) Center for Ocean-Land-Atmosphere Studies (COLA), Maryland, USA Discharge datawas received from the Bangladesh Water Development Board Details of these datasetsare given in Mirza, 1997 Selection of the dataset for the development of empirical modelswas made with regard to length of record, spatial coverage and missing observations Theselected datasets were the COLA dataset and the NWCF dataset for the Ganges basin; theCOLA dataset and selected four stations from the UN dataset within Bangladesh for theBrahmaputra basin; and the COLA dataset and the UN and BWDB datasets for the Meghnabasin Missing observations were between 1%-12% for the NWCF, UN and BWDB datasets.These observations were filled in by applying the method stated by Salinger, 1980 Afterfilling in the missing observations, the means and standard deviations were computed forthe complete time series and compared with those of the incomplete time series Thedifference in the means and standard deviations were found to be statistically insignificant

at a 5% level of significance

The precipitation and discharge data were examined with respect to their adequacy ofempirical modeling Statistical tests show that the precipitation observations in allmeteorological sub-divisions are normally distributed Over the periods of record, onemeteorological sub-division (The East Madhaya Pradesh) (V10 in Fig 5.5a) in the Gangesbasin shows a statistically significant decreasing trend In the Brahmaputra basin, adecreasing trend is found only in the precipitation time series of South Assam (V2 in

Fig 5.5b) However, the basin-wide average precipitation series does not show anydiscernible trend On the other hand, each of these two sub-divisions covers a small areaover the respective river basin Therefore, they would not have a major effect on thepredictive capability of the empirical models Precipitation observations of allmeteorological sub-divisions are found to be random, with a few exceptions Analysisshows the presence of Markov linear type “persistence” only in the observations of theNorth Assam and South Assam meteorological sub-divisions in the Brahmaputra basin(Mirza, 1997)

Annual mean and peak discharge series have been found to be normally distributed forthe GBM Rivers Statistical tests indicate that the difference in mean annual discharge

of the Ganges River at Hardinge Bridge for the pre- and post-Farakka period is notstatistically significant Therefore, on an annual basis, the regulation effect of the FarakkaBarrage (Fig 5.1) can be overlooked (Mirza, 1997) The barrage was constructed at Farakka(18 km from the border of Bangladesh) and commissioned in April of 1975 to divert1,134 m3/sec water to make the Hooghly-Bhagirathi River channel (on which the port ofKolkata is situated) navigable (Mirza, 2002)

A sequence of empirical models that describe the relationship between precipitationand annual mean and peak discharge was developed One of the advantages of such arelationship is, for example, that in absence of precipitation data, peak discharge can beestimated from known values of annual discharge Initially, in order to examine theindependence of the explanatory variables, annual mean discharges of the Ganges River atHardinge Bridge and Brahmaputra River at Bahadurabad in Bangladesh (Fig 5.1) wereregressed on the meteorological sub-division wide annual precipitation data Initialexamination indicated the presence of multi-collinearity in the precipitation data This isthe condition where at least one explanatory variable is highly correlated with anotherexplanatory variable or with some combination of other explanatory variables (Maidment,1993) Multi-collinearity may cause a number of consequences (1) In extreme cases, theleast square point estimates can be far from the true values of the regression parameters,and some estimates may even have the incorrect sign; (2) increases in standard error ofregression coefficient estimators occur as the correlations among the independent

110 I MPLICATIONS O N R IVER D ISCHARGE I N B ANGLADESH

Fig 5.5a Independent and contiguous precipitation regions of the Ganges basin.

variables increase; (3) serious rounding errors in the calculation of the least square pointestimates are produced; and (4) significance tests and confidence intervals for regressioncoefficients, due to increases in the standard errors of coefficient estimates, are affected.The principal components analyses (Dunteman, 1989; Manly, 1986) of the precipitationdata were carried out to minimize the problems of collinearity and to generate relativelyindependent, contiguous precipitation regions (Table 5.2 and Fig 5.5c) Selection ofcomponents and a procedure for regionalization are discussed in Cattel, 1966; Kaiser,1960; Morgan, 1971; Ogallo, 1989 and Regemortel, 1995

Multiple regression models were then developed for estimating mean annual dischargefor the Ganges and Brahmaputra Rivers For the Meghna River, a multiple regressionmodel was developed between annual precipitation and the peak discharge This was due

to the absence of adequate annual discharge data In order to determine mean annual peakdischarge in relation to mean annual discharge, regression models between annual meanand peak discharges were developed for the Ganges and Brahmaputra Rivers Standardprocedures (Berry and Feldman, 1985; Bowerman and O’Connell, 1990; Cook and Wesberg,

Fig 5.5b Independent and contiguous precipitation regions of the Brahmaputra basin.

1982) were followed to examine the model parameters The precipitation annual meandischarge regression models for the Ganges, Brahmaputra and Meghna basins are given in

Table 5.3

Table 5.2 New variables (regions) derived by the principal components analysis

River Basin Variables

(Sub-Divisions)

New Variables Region 1 Region 2 Region 3

** V1 - North Assam; V2 - South Assam; V3 - Sub-Himalayan West Bengal; and V4 - Teesta Basin in Bangladesh

*** V1 - North Assam; V2 - South Assam; and V3 - Meghna basin (Bangladesh part)

112 I MPLICATIONS O N R IVER D ISCHARGE I N B ANGLADESH

5.3.2 CONSTRUCTION OF CLIMATE CHANGE SCENARIOS

For the first objective outlined above, the second step was to construct climate change

scenarios for the three river basins Seven alternatives for scenario generation suggested

by Carter et al., 1994 and WMO, 1987 were reviewed These alternatives include direct

use of GCM runoff changes, GCM-generated regional temperature and precipitationchanges, addition of GCM-predicted changes to baseline conditions, scaling of thestandardized patterns of change from GCMs, temporal analogues, spatial analogues andhypothetical scenarios For this research, the empirical models were developed based onthe spatial distribution of precipitation in the three river basins Therefore, preference wasgiven to the method of scenario construction, which predicts spatial changes inprecipitation For this purpose, the results of the GCMs are useful in that they indicatepossible spatial changes in climate

For the scenario construction, a method of scaling “standardized” patterns ofprecipitation derived from GCMs was adopted Hulme (1994) recommendedstandardizing GCM results for climate change scenario construction in order to overcomethe problem of variation of equilibrium global mean temperature and overcome theproblem of variation of equilibrium global mean temperature and precipitation changesfrom GCM to GCM This arises mainly because of the way the GCMs treat clouds andoceans Moreover, some of the atmospheric GCMs (For example, LLNL and MPILSG -

Fig 5.5c Independent and contiguous precipitation regions of the Meghna basin.

Table 5.3 Precipitation-annual mean discharge and annual mean-peak discharge regression models

The model excludes the part of the basin in

China

Qa = 7201 + 5.23 * P (R2 = 62.33%) (2) where Qa is the estimated annual mean discharge at Bahadurabad and P is the area weighted annual precipitation in the basin

The model excludes the part of the basin

in Bhutan and China

Multiple regression of Region 1 and Region 2 produced negative parameter (not significant) for the latter, which was unrealistic from physical point of view

This might have caused by error inherent

in the data Therefore two regions were treated as one homogeneous region

Qp = -10531 + 3.43 *P1 + 5.69 * P2 (R2=87.1%) (3)

where Qp is annual peak discharge and P1 is the area weighted annual precipitation in Region 1 (North Assam) and P2

is the average of the precipitation in Region 2 (South Assam and the Bangladesh part

of the basin upstream of Bhairab Bazar)

Annual

Mean-Peak Discharge

Qp = 14,844 + 3.26 Qa

(R2 =49.3%) (4) where Qp is annual peak discharge

MPI large scale geotropic ocean) were coupled with Ocean General Circulation Models(OGCMs), while others include prescribed ocean heat This produces substantialinter-model differences in their simulation of current climate and response to a doubling of

CO2

The results of 11 GCMs were compared, and four were selected - CSIRO9(3.2 x 5.6o L9), UKTR (2.5 x 3.75o L19), GFDL (2.25 x 3.75o L14) and LLNL(4 x 5o L15) This maximizes the range of predicted changes in precipitation amounts andspatial variability within the GBM basins window The other selection criterion was

goodness-of-fit of a GCM with respect to regional bias (control-observed) The CSIRO9,

UKTR and GFDL models showed a slight negative bias for summer precipitation butshowed a close fit, compared to the other GCMs Note that the selected four GCMexperiments were based on only GHG forcing These spatial patterns of precipitation changewere then “standardized” to account for the different climate sensitivity values of the GCMs.This gave a pattern of change per degree of global warming The standardized patternswere then scaled for global mean temperature changes of 2oC, 4oC and 6oC giving a total of

12 scenarios (4 GCMs x 3 DTs) for each river basin and the Bangladesh window

5.3.3 APPLICATION OF THE CLIMATE CHA NGE SCENA RIOS TO THE

EMPIRICAL MODELS

The third step was to apply the constructed climate change scenarios to the empirical

models to determine the magnitude of changes in discharges at the boundary of Bangladesh.This was carried out in two stages: (1) changes in the mean annual discharge wereestimated by applying the scenarios of changes in the mean precipitation generated fromthe results of four GCMs These GCMs represent the range of uncertainty in climate modelprojections of future climate change; and (2) the calculated mean annual discharge wasused to estimate changes in the mean annual peak discharge

5.3.4 ESTIMATION OF CHA NGES IN DEPTH A ND EXTENT OF FLOODING IN

BANGLADESH

The fourth step was to force the MIKE 11-GIS model with current and future peak

discharges to simulate depth and spatial extent of flooding in Bangladesh This was carriedout in three stages: (1) current and future mean precipitation was scaled for the Bangladeshwindow as the MIKE 11-GIS model needed input of local precipitation for the simulationpurpose (Table 5.4); (2) current and future mean peak discharges were scaled to 1991discharges for the Ganges, Brahmaputra and Meghna Rivers (Table 5.5) For scalingpurposes, the year ‘1991’ was selected because the monsoon of that year represented atemporal distribution which was considered fairly ‘typical’ with regard to the usualpeaking time of the three rivers; and (3) the MIKE 11-GIS model was forced with currentand future peak discharges to simulate present and future depth and extent of flooding

5.4 ESTIMATION OF CHANGES IN ANNUAL DISCHARGE

The precipitation change scenarios for the Ganges and Brahmaputra basins are applied tothe empirical models in order to assess the possible changes in the mean annual dischargefor 2oC, 4oC and 6oC increases in global temperature For the Ganges basin, the empiricalmodel (Table 5.6) was developed between annual precipitation in the basin area in Indiaand Nepal and annual mean discharge at Hardinge Bridge in Bangladesh This station is

located very close to the border of Bangladesh with India Therefore, the measureddischarge takes account of the total cross-border inflow Annual mean discharge ispredicted from the area weighted annual precipitation from three regions comprising thetotal basin area (excluding parts of the basin area in China and Bangladesh).

116 I MPLICATIONS O N R IVER D ISCHARGE I N B ANGLADESH

An empirical model was developed between annual precipitation in the Brahmaputrabasin area (in India and Bangladesh) and the annual mean discharge of the Brahmaputra atBahadurabad, Bangladesh The discharge measurement station (Bahadurabad) takes intoaccount the discharge generated in the Bangladesh part of the basin The discharge ispredicted from the area weighted annual precipitation in the four meteorologicalsub-divisions of the Brahmaputra basin.

The overall results with regard to changes in the mean annual discharge for the Gangesand Brahmaputra Rivers are presented in Table 5.6 It is evident from Table 5.6 that themean discharge of the Brahmaputra River is less sensitive than the Ganges River to thechanges in precipitation The results of the empirical model support the contention thatrunoff or discharge of a wetter basin will be less sensitive to climate changes than arelatively drier basin Details regarding the changes in mean annual discharges for theGanges and Brahmaputra basins for the four selected GCMs are discussed below

The Ganges Basin

Three precipitation change scenarios for global temperature increases of 2oC, 4oC and 6oCwere considered for determining changes in the mean annual discharge Changes in themagnitude of mean discharges from present to the future conditions for the Ganges basinfor the four GCMs (CSIRO9, UKTR, GFDL and LLNL) are shown graphically in

Figure 5.6 For all four models, the figure shows an increase in discharge as globaltemperature and thereby basin-wide precipitation, increases The figure also shows a marked

increase in discharge (for each of the scenarios) across the four models, with LLNLshowing least change and UKTR showing most change Three of the models showsignificant increases in mean discharge The application of scenarios from the UKTR modelshows that a 39% change in precipitation (at a 6oC rise in global mean temperature)produces a 63.4% change in the mean discharge In absolute terms, this implies an increase

in mean discharge from the current 11,606 m3/sec to 18,970 m3/sec

In contrast, the LLNL model shows the least changes for all temperature scenarios.The LLNL model shows that for a 6oC rise in global mean temperature, there is a 1.5%increase in precipitation, which gives an increase in mean discharge of 2.4% (from11,606 m3/sec to 11,888 m3/sec) As seen from Figure 5.6, the other models (CSIRO9 andGFDL) fall almost linearly between these two extremes.1

The Brahmaputra Basin

The changes in mean annual discharge in the Brahmaputra River due to changes in themean annual precipitation predicted by the four GCMs are displayed graphically in

Figure 5.7 Two general patterns are evident from the figure First, a very marked difference

in the mean discharge predicted by the UKTR and GFDL models as compared with theCSIRO9 and LLNL models The UKTR and GFDL models show large, step-like increases indischarge as temperature increases (2oC, 4oC and 6oC) Second, the LLNL model shows avery small increase in discharge across the temperature changes, while the CSIRO9 modelshows a decrease The largest increase in the basin-wide precipitation are predicted by theUKTR model With a 30.6% precipitation change for a 6oC global warming, the meandischarge may increase from 19,350 m3/sec, to 23,069 m3/sec The smallest increase in the

118 I MPLICATIONS O N R IVER D ISCHARGE I N B ANGLADESH