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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES Volume 1, No 5, 2011

© Copyright 2010 All rights reserved Integrated Publishing Association

Research article ISSN 0976 – 4402

Received on January, 2011 Published on January 2011 884

Application of Regional Climate Model Simulation and Flow Data for Assessing Future Water Availability in the River Jamuna Mohammad Adnan Rajib 1 , Md. Mujibur Rahman 2 , Edward A. McBean 3

1 Lecturer, Department of Civil Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh

2 Professor, Department of Civil Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh

3 Professor of Engineering & Canada Research Chair in Water Supply Security, University of Guelph, Ontario, Canada

[email protected]

ABSTRACT

Among the multifarious dimensions of climate change, alteration of precipitation pattern with associated changes in runoff and subsurface flow, rise in surface temperature and consequent increase in evaporation rate are sure to have direct impact on the future water availability of major river systems in Bangladesh like the Jamuna. The characteristic trends of historical riverdischarge data, as developed in this paper, indicate that the river Jamuna has undergone a generous amount of amplification of flow through the years. Subsequently, this increase can be attributed to the effects of climate change mainly in terms of extensive precipitation on the rivercatchments and rise of water level by inflating snowmelt at its source as a result of the global warming. However, the main objective of this paper is to develop and analyze the characteristic trends of average monthly flow of the river Jamuna in Bangladesh using historical records, and to correlate the hydrometeorological waterbalance as calculated from the climate model projections of precipitation and evaporation, which would therefore aid the assessment of climate change impact on the future possible flow condition and water availability in the Jamuna River system.

Keywords: Climate, Evaporation, Flow, Precipitation, River

1. Introduction

Being a riverine country, it is momentous to observe and predict the flow patterns of the major river systems of Bangladesh, since an enormous part of the whole population is affianced in river transportation, fishing, agriculture etc. Enormous challenges associated with the river systems in Bangladesh are already present. Bangladesh possesses high population density and corresponding huge withdrawal of river water for irrigation needs, massive erosion and siltation in river bed, loss of flow due to transboundary extractions and so on. The combined effects of ongoing climate change and above mentioned challenges will have vast implications on the future ability of such river systems both in terms of water quality and quantity.

In this paper, average discharges at a particular gauge station of the river Jamuna in Bangladesh has been analyzed for the duration of 1954 to 2006, both in individual months as

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Application of Regional Climate Model Simulation and Flow Data for Assessing Future Water Availability in the River Jamuna

Mohammad Adnan Rajib,Md. Mujibur Rahman, Edward A. McBean International Journal of Environmental Sciences Volume 1 No.5, 2011

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well as in different seasons, to get the observed flow trend characteristics through time. Also, projections of the changes in precipitation and evaporation quantities for individual months in Bangladesh, in each of the future year from 2011 to 2100, are developed from a Regional Climate Model called PRECIS. The model projections illustrate that the hydro meteorological water balance, that is, the net amount of precipitation minus evaporation is virtually increasing in future years, compared to a climatological baseperiod of 19712000. This paper analytically corelates the characteristic trends of riverdischarge with the calculated future possible hydrometeorological water balance and thereby, qualitatively assesses the future state of water availability in the river Jamuna in Bangladesh.

2. Hydrological Background of River Jamuna

The river Jamuna, Bangladesh, originating in the Himalayas, entering Bangladesh through the northern boundary and covering a basinarea of approximately 13330 sq km within the country premises, is one of the world's great rivers, ranking in the top three in terms of both sediment and water discharge (Gupta, 2008). The high water and sediment discharges are generated by the monsoondominated floods and the tectonic setting. The discharge of the river during the rainy season is enormous, averaging 40,000 cumec. The maximum velocity ranges from 34 m/sec with a depth of 2122m. The average discharge of the river is about 20,000 cumec with average annual silt load of 1,370 tons/sq km. The average slope of the Jamuna is about 1:11,400; however, the local gradient differs quite considerably from the average picture (Bristow, 2009; Schumm and Winkley, 2004).

Thus, by any definition, the BrahmaputraJamuna is one of the world's truly great rivers, and has a direct and significant influence on the overall water availability of the country.

3. Data and Methodology

3.1. Developing Characteristic Flow Curves

To analyze the historical flow patterns as well as relating them to future water balance parameters, characteristic curves of monthly river discharge are developed herein. The methodology of developing these curves is instigated by collecting data from Bangladesh Water Development Board (BWDB). BWDB has historical records of Jamuna river discharge from a particular gauging station named Bahadurabad (see Figure 1).

Gauged discharge data at the Bahadurabad station are available at daily interval. The monthly average flow is obtained by averaging daily data of a month. The average flows of individual months for the period of 19562006 are then plotted separately in MS Excel to analyze the variation of flow in that particular month over the years. However, to overcome the effect of the missing data of flow, curves of “2 year running average” and “5 year running average” are drawn respectively for every significant plot depicting the monthly as well as seasonal flow characteristics.

3.2. Application of a Regional Climate Model for Future Projections

Climate models are the main tools available for developing projections of climate change in the future. There are a number of mathematical models of global circulation that indicate expectations of future climate scenarios. A Regional Climate Model (RCM) generally covers a limited area of the globe at a higher resolution (typically around 50 km), for which conditions at its boundary are specified from a Global Climate Model (GCM) (Jones et al.,



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2004). The Hadley Centre of United Kingdom has developed PRECIS, a regional climate model (RCM) system which has been adopted in this research for generating projections of some specific climatic parameters for Bangladesh, such as total monthly precipitation and evaporation, giving way to future state of hydrometeorological water balance. In this regard, the PRECIS projections of total monthly precipitation and evaporation in individual months for each of the future year from 2011 to 2100 have been developed with respect to a climatological baseperiod of 19712000 in terms of ‘Applied Change Field’ (e.g., IPCC TGICA, 2007; Booty et al., 2005; DiazNieto and Wilby, 2005; Zhang, 2005). However, all the climatologic projections from PRECIS are made considering A1B scenario of the Special Report on Emissions Scenarios (SRES) by the Intergovernmental Panel on Climate Change (IPCCTGICA, 2007). “Scenario” is a plausible description of how the future may develop based on a coherent and internally consistent set of assumptions about key driving forces (e.g., rate of technological change and gas emissions). These are neither predictions nor forecasts, but are useful to provide a view of the implications of future developments and actions.

Figure 1: Image showing the three major river systems in Bangladesh: Padma, Meghna and Jamuna, along with BWDB gauge stations for stream flow recording (e.g. Bahadurabad)

4. PRECIS: Model Domain for Bangladesh and Development of ‘Change Field’

The PRECIS (Providing Regional Climates for Impacts Studies) is a hydrostatic, primitive equation grid point model containing 19 levels as described by a hybrid vertical coordinate Jones et al., 2004). The present version of PRECIS (PRECIS 1.7.1) has a horizontal resolution of 50 km horizontal grid with the option of downscaling to 25 km horizontal grid having 0.44 X 0.44 degree resolution (latitude X longitude) and it can generate outputs for more than 150 parameters (Islam et al., 2008). A regional model like PRECIS generally covers a limited area of the globe for which conditions at its boundary are specified from a GCM (CCSP, 2008). In this regard, the simulations of PRECIS being applied in this paper are based on the atmospheric component of the GCM called HadCM3. Figure 2 shows the



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Figure 2: Schematization of PRECIS domain for Bangladesh

schematization of PRECIS domain over Bangladesh that has been adopted for developing future climate change projections. The selected PRECIS domain consists of 50 km 88 X 88 number of gridpoints, which essentially beholds not only the geographic area of the country but also the parts of significant landmass and sea area that might have potential impacts on the country’s climatologic circulation. As such, the whole reach of the river Jamuna has been included within this domain of PRECISsimulation. A rim of 8 pixels along the domain boundary is basically the GCMintegration ‘Buffer Area’ which is ultimately excluded from the analysis.

Once climate model outputs have been developed for use in an impact study like this one, there are numerous procedures available for processing and applying the data (i.e. constructing the scenarios). GCM or RCM outputs are not generally of a sufficient resolution or reliability to be applied directly as actual prediction of future climatic conditions. There are often significant biases in the model control simulations (Islam et al., 2008; IPCCTGICA, 2007; Jones et al., 2004). In this regard, it is found usual for baseline observational data, which are commonly in the form of time series of annual or monthly data for several variables over a period such as 19712000, to be used as a reference for correcting or adjusting future timeseries of model outputs. Here, a scenario of future climate is obtained



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by adjusting the baseline observations by the difference or ratio (where necessary) between results of the model experiment on past baseline years and the corresponding results for the model control simulations for future. Differences are usually applied for temperature changes (e.g. 20412070 minus 19712000) while ratios are commonly used for precipitation and evaporation changes (e.g. 20412070 divided by 19712000).

Therefore, this approach of modelbias correction features the use of climate model data for both periods (recent and future) and then the difference or ratio between the two responses will represent the change with respect to the baseline data of particular climatic parameter (Jones et al., 2004). A pattern of such differences or ratios of particular climate output variables are known as a “Change Field" or “Change Factor" (CCSP, 2008; IPCCTGICA, 2007). With improvements in control simulations, which are now being realized with improved models and the use of higher resolution, this approach of modelbias correction and construction of ‘change field’ is becoming increasingly attractive. It is conceptually simpler and allows direct application of the changes in all climate change projections.

5. Characteristic Flow Curves of the River Jamuna

5.1. Seasonal Variability of River Discharge: Dry and Wet Period

The historical flow pattern in individual years has been shown in Figure 3 below for a range of period starting from 1956 to 2006, which aids to explore the dry and wet period for this river in terms of high and low discharge within a year at the particular station. Wet period in terms of high flow for the river Jamuna appears to be between June and October, as there is a visible peak in this period. The dry period, in contrast, appears between January to April in terms of very low flow.

Figure 3:Monthly distribution of flow from 19562006, showing wet and dry period

The pie chart illustrates the average portion of annual discharge in various months. This chart is important, from the point of view that, they can be used to identify the ‘driest’ and ‘wettest’ month in the river as obtained from the historical record of 50 years. For the river Jamuna, the driest month in terms of the lowest flow is found to be any of the months from January, February and March, while the wettest month in terms of highest flow is July. Figure 4 exemplifies the change pattern of seasonal flows both in the designated dry and wet period, as identified above. Typically the wet period (r = 0.266) show large fluctuations of flow in a

Months

Discharge, x 10 3

m 3 /s



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y = 58.11x + 4600. r = 0.617

y = 53.65x + 35706 r = 0.266

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et Season Discharge, m

3 /s

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Average Dry Season Discharge, m

3 /s

Wet Period Flow

Dry Period Flow

range of 30000 to 50000 m 3 /s over past 50 years, with a general increasing pattern. Again, the wet period flow of river Jamuna shows relatively lower discharge almost in every alternative year (shown in Figure 4 by the alternate high and low peaks). This erratic change in flow might be caused due to the variability in precipitation pattern as a result of climate change. Also, the wet period curve shows a visible peak in the year 1998, indicating the occurrence of high discharge due to a catastrophic flood. On the other hand, the dry period (r = 0.617) have less fluctuations (3000 to 12000 m 3 /s) with a general increasing pattern as well.

Figure 4: Trends of dry and wet period flows in the river Jamuna (19562006)

5.2. Trend Analysis of Historical Flow in Individual Months

The average flows of individual months for the period of 19562006 are then plotted separately to analyze the variation of flow in particular months over the years. “2 year moving average curve” is adopted, overcoming the effect of missing gaugedischarge data. The trend of average flow condition of individual months has also been analyzed assuming a linear variation and it is observed that in all the months, discharge through the river Jamuna, as seen in Figure 5, has been significantly amplified over the years.

However, a couple of months like June and August show more or less an unchanged trend through time. Noteworthy that, alike seasonal variation as shown in Figure 4, in the individual monthly trend analysis, the river discharge in monsoon months like July appear to be more dispersive compared to the dry month January. Thus, frequent fluctuations of flow are prominent specifically in monsoon and postmonsoon months for which magnitudes of the Coefficient of Dependency, R 2 of the linear DischargeTime regression equations are in the range of only 0.065 (see Figure 5), which can turn as low as 0.0009 in the month of June. Above all, the general trend of flow in river Jamuna, as obtained from the particular gauge station data, is increasing.

Table 1 shows the changes in monthly flow of the river Jamuna in individual months, being averaged and then compared for two 25year periods, such as 19812005 and 19561980. Such periodaveraged comparison of average monthly flow or discharge is graphically represented in Figure 8(a) also.



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Figure 5: Trend analysis of historical flow in the river Jamuna for January and July

y = 142.73x 235787 R 2 = 0.0656

0

10000

20000

30000

40000

50000

60000

70000

1950 1960 1970 1980 1990 2000 2010

July Linear (July) 2 per. Mov. Avg. (July)

y = 35.608x 65275 R 2 = 0.1905

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1950 1960 1970 1980 1990 2000 2010

January Linear (January)

2 per. Mov. Avg. (January)

Year (19562006)

Average M

onthly Discharge, m

3 /s JANUARY JULY

Table 1: Change in monthly flow for river Jamuna (cumec), averaged over 25year periods

6. Calculated Future Water Balance from Climate Model Simulations

The Regional Climate Model PRECIS has been applied to make future climate projections in Bangladesh regarding the hydrometeorological parameters like precipitation and evaporation. Future monthly projections of precipitation and evaporation in Bangladesh, being averaged over the three 30 year future periods, have been shown in Figure 6 below.

The change in precipitation pattern for Bangladesh, as projected by the Regional Climate Model PRECIS, indicates that the precipitation will continue to increase in all the months in future years. Even though the percentage of precipitation increase is higher in a dry month like December or January, the monsoon season month like June or July is still going to have extensive precipitation events in future times. With higher surface temperatures, substantial increase in the amount of free water surface evaporation has also been projected by PRECIS.

Month 25 year average (1956 1980)

25 year average (19812005) Change Ratio

January 4846.87 5495.68 1.13 February 4104.20 4587.12 1.12 March 4577.10 5298.85 1.16 April 7412.17 9178.59 1.24 May 15545.60 16177.50 1.04 June 31532.08 31440.85 1.00 July 44375.50 49725.81 1.12 August 43765.32 43382.95 0.99 September 35963.75 40224.34 1.12 October 22662.64 26303.46 1.16 November 10510.71 13384.92 1.27 December 6710.25 7832.05 1.17



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19712000 20112040 20412070 20712100

Month; 1: January, 12: December

Average Precipitatio

n (P), mm

Average Evaporatio

n (E), mm

The premonsoon months (March to May) are showing significant increase in the amount of possible monthly future evaporation than the dry and monsoon months (Rajib et al., 2010). However, the PRECISprojections that have been induced herein are being averaged for the 55 gridpoints of the total 88 X 88 number of points of the model domain (Figure 2), which fall within country’s geometry. This averaging approach provides a generalized overall future climatologic scenario for Bangladesh in terms of precipitation and evaporation, although localized projections could have been made from individual gridpoint outputs of the model. But such localized future climate change projections are supposed to have slight variation from the countryaverage considering the small geographical area of Bangladesh. Needless to say that as the model domain contains the entire stretch of the river Jamuna from its source in the Himalayas, which basically lies way aside from Bangladesh territory, developing future projections of precipitation and evaporation by extracting model outputs covering all the model gridpoints along the riverpathway and adjacent designated watershed area can provide more realistic assessment of future hydrometeorological water availability to the riversystem.

Figure 6: Periodaveraged monthly precipitation and evaporation as projected by PRECIS

Such projections for monthly precipitation and evaporation pattern in Bangladesh gives the scope for approximation of future hydrometeorological water balance, that is, the net monthly total precipitation (P) minus the total evaporation (E). In Table 2 and Table 3, modelprojected precipitation (P), evaporation (E) and the calculated net hydro meteorological water balance (PE) are shown respectively, being averaged over a 30year period. Each of the future 30year segments is then compared with the climatological base period of 19712000.

Figure 7 shows the future water balance (PE) for Bangladesh in January and July, being calculated from the PRECIS projections of Precipitation (P) and Evaporation (E) in those particular months. The diverse nature of the seasonal variability in monthly precipitation and evaporation projections produce significant inconsistency in the net water balance of individual months. For example,

(i) The dry months (November to February) show significant deficit in terms of calculated net water balance (PE) in almost all of the future years (see Figure 7 (a) for the month of January). The possible reason might be the higher amount of free water surface evaporation compared to very little or literally ‘no’ incident rainfall as projected by the climate model in these months.



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Total M

onthly Precipitatio

n (P), Evaporation (E)

and Water Balance (P

E) , mm

Year (20112100)

Total M

onthly Precipitation (P), Evaporatio

n (E)

and Water Balance (P

E) , mm

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January E

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2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

July (PE) July P July E

JANUARY

(a)

JULY

(b)

Year (20112100)

(ii) Again, extensive amount of precipitation is expected in monsoon months and also, evaporation rates in these months are not projected to be the maximum, resulting into more water availability compared to dry season.

(iii) But large fluctuations both in modelprojected precipitation quantity as well as in the calculated water balance are prominent in monsoon season months like July (as shown in Figure 7 (b)), which reflect the expectation of either severe water shortage or excessive flowcondition in many of the future years as a result of climate change. In this way, climate variability can be of utmost importance.

Figure 7: Calculated Future hydrometeorological water balance (PE) for Bangladesh (a) January and (b) July



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Table 2: PRECIS projections of precipitation (P) and evaporation (E) for Bangladesh

P (mm) E (mm) Observed Climate Model Observed Climate Model

19712000 7.53 73.08 January 20112040 23.98 79.10

20412070 41.84 93.39 20712100 38.64 100.99 19712000 22.80 92.80

February 20112040 43.57 98.14 20412070 64.54 109.31 20712100 51.06 137.36 19712000 50.54 146.48

March 20112040 58.11 168.59 20412070 82.01 182.02 20712100 120.88 206.65 19712000 121.56 157.02

April 20112040 188.15 196.61 20412070 207.33 209.52 20712100 215.50 222.30 19712000 277.32 144.65

May 20112040 374.80 175.86 20412070 410.26 172.95 20712100 392.39 229.61 19712000 457.68 118.77

June 20112040 603.91 134.00 20412070 546.18 172.33 20712100 525.84 214.03 19712000 530.33 111.49

July 20112040 650.60 116.28 20412070 605.32 140.49 20712100 754.80 139.50 19712000 430.99 113.63

August 20112040 462.94 128.71 20412070 495.81 149.09 20712100 454.11 175.14 19712000 320.94 102.66

September 20112040 354.13 117.01 20412070 391.98 130.03 20712100 416.40 145.22 19712000 169.04 107.10

October 20112040 241.71 127.92 20412070 209.36 154.17 20712100 294.25 160.38 19712000 47.51 89.56

November 20112040 127.13 99.99 20412070 97.12 141.81 20712100 140.33 159.88 19712000 10.72 75.36

December 20112040 13.28 80.21 20412070 34.59 100.48 20712100 25.82 114.34



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Table 3: Calculated future water balance (PE) for Bangladesh from PRECIS projections

Calculated PE (mm) Observed Future Change (percentage)

19712000 65.55 January 20112040 55.12 15.91

20412070 51.55 21.35 20712100 62.35 4.88 19712000 70.00

February 20112040 54.56 22.05 20412070 44.77 36.04 20712100 86.31 23.30 19712000 95.94

March 20112040 110.48 15.16 20412070 100.01 4.24 20712100 85.77 10.60 19712000 35.46

April 20112040 8.46 76.14 20412070 2.19 93.83 20712100 6.80 80.83 19712000 132.67

May 20112040 198.94 49.95 20412070 237.31 78.87 20712100 162.78 22.70 19712000 338.91

June 20112040 469.91 38.65 20412070 373.85 10.31 20712100 311.82 7.99 19712000 418.84

July 20112040 534.32 27.57 20412070 464.84 10.98 20712100 615.30 46.91 19712000 317.36

August 20112040 334.23 5.32 20412070 346.72 9.25 20712100 278.97 12.10 19712000 218.28

September 20112040 237.12 8.63 20412070 261.96 20.01 20712100 271.18 24.23 19712000 61.94

October 20112040 113.79 83.71 20412070 55.20 10.89 20712100 133.87 116.13 19712000 42.05

November 20112040 27.14 164.55 20412070 44.69 6.29 20712100 19.55 53.50 19712000 64.64

December 20112040 66.93 3.54 20412070 65.89 1.93 20712100 88.52 36.95



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Month; 1: January, 12: December

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E), mm

Average Discharge, m 3 /s

(b)

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25 year average (1956 1980) 25 year average (19812005)

(a)

7. Future State of Water Availability in River Jamuna

Figure 8 shows historical change in monthly flow in river Jamuna, being averaged over two 25year periods and also the change in periodaveraged future hydrometeorological water balance (PE) in Bangladesh. General trend of flow through the river Jamuna is observed to have been increasing with time. The particular monthly variationpattern of riverdischarge in Jamuna, with relatively higher rate of increase in dry months as analyzed in this paper, can be attributed to the process of climate change. Bangladesh as well as other adjacent region is expected to have more warming in dry months than in monsoon (Rajib et al., 2010; Islam et al., 2008). Obviously thus, climate change and the consequential higher rate of temperature increment in future dry months, is surely going to accelerate the glacier melt even in winters, and thereby the riverdischarge. Future hydrometeorological water balance (PE) will also shift to a higher side as observed in Figure 8(b), giving way for more water availability compared to current conditions. Therefore, the conjugal effect of the climate change interference into the riversource in terms of temperature increase, associated glacier melt etc., along with the change in future precipitation and evaporation will aid into possible future flow intensification of Jamuna. However, large fluctuations in riverdischarge and trans boundary exploitation might very often result into severe waterstress in future years.

Figure 8: (a) Change in average monthly flow in River Jamuna from gaugestation data, (b) Future change in calculated hydrometeorological water balance for Bangladesh

8. Conclusions

Evidence of climate change in Bangladesh is apparent, and the implications on a lifeline river system like the Jamuna are multidimensional. From the detail analysis of historical flow data and the climate model projections on future precipitation and evaporation, it can be expected that that rate of change in average monthly discharges in Jamuna will continue to increase in future years as a result of ongoing climate change, with a higher rate particularly in dry months, except for possible transboundary exploitations over which Bangladesh has minimal control.

9. References

1. Bristow, C.S. (2009). Gradual Avulsion, River Metamorphosis and Reworking by Underfit Streams: a Modern Example from the Brahmaputra River in Bangladesh and a



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Possible Ancient Example in the Spanish Pyrenees. In Fluvial Sedimentology VI, Eds: Smith, N. D. and Rogers, J., Blackwell Publishing Ltd., Oxford, UK, doi: 10.1002/9781444304213.ch17.

2. Booty, W. G., Lam, D.C.L., Bowen, G.S., Resler, O. and Leon, L. (2005). “Modeling Changes in Stream Water Quality Due to Climate Change in a Southern Ontario Watershed,” Canadian Water Resources Journal, 30(3),pp 211226.

3. CCSP (2008). Climate Models: An Assessment of Strengths and Limitations. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research; Authors: Bader D.C., Covey, C., Gutowski Jr., W.J., Held, I.M., Kunkel, K.E. Miller, R.L., Tokmakian, R.T. and Zhang, M.H.; Department of Energy, Office of Biological and Environmental Research, Washington, D.C., USA,pp 124.

4. DiazNieto, J. and Wilby, R.L. (2005). “A Comparison of Statistical Downscaling and Climate Change Factor Methods: Impacts on Low Flows in the River Thames, United Kingdom,” Climatic Change, 69(23), pp 245268.

5. Gupta, A. (2008). Large Rivers: Geomorphology and management, Chapter 20, John Wiley & Sons, ISBN: 9780470849873.

6. Islam, M.N., Rafiuddin, M., Ahmed, A.U. and Kolli, R.K. (2008). “Calibration of PRECIS in Employing Future Scenarios in Bangladesh,” International Journal of Climatology, Wiley InterScience, 28: 617628, DOI: 10.1002/joc.1559.

7. IPCCTGICA (2007). General Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment, Version 2. Prepared by T. R. Carter on behalf of the Intergovernmental Panel and Climate Change. Task Group on Data and Scenario Support for Impact and Climate Analysis.

8. Jones, R.G., Noguer, M., Hassell, D.C., Hudson, D., Wilson, S.S., Jenkins, G.J. and Mitchell, J.F.B. (2004). Generating High Resolution Climate Change Scenarios Using PRECIS, Met Office Hadley Centre, Exeter, UK,pp 40.

9. Rajib, M.A., Rahman, M.M. and McBean, E.A. (2010). “Validation of Observed EvaporationTemperature Trend Models with GCM Projections for Bangladesh,” proceedings of the 5th International Water Association Young Water Professionals Conference, 57 July, The University of New South Wales, Sydney, Australia.

10. Schumm, S.A. and Winkley, B.R. (2004). The Variability of Large Alluvial Rivers, New York: ASCE Press,pp 467.

11. Zhang, X.C. (2005). “Spatial Downscaling of Global Climate Model Output for Site Specific Assessment of Crop Production and Soil Erosion,” Agricultural and Forest Meteorology, 135(14), pp 215229.

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