5 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 of Bangladesh 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 is generated outside of Bangladesh. However, there is a high seasonal difference in the availability of water. For example, for the Ganges River, the ratio of dry and monsoon runoff is 1:6 (Fig. 5.2). This illustrates that Bangladesh has an abundance of water in the monsoon while the country still faces surface water scarcity in the dry season. Irrigated agriculture 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 occurred during the floods of 1988 and 1998 (Ahmed and Mirza, 2000). Hydrological droughts are very common in the rivers of Bangladesh. The magnitude of precipitation over the GBM basins is very high and more than three-quarters occurs during the summer monsoon (June-September) (Table 5.1). The resulting huge volume of cross-border monsoon runoff, together with the locally generated runoff and some physical factors, either singly or in combination, causes floods in Bangladesh. The physical factors, either singly or in combination, include snow and glacier melt, El Niño Southern Oscillation (ENSO) induced conditions, loss of drainage capacity due to the siltation of principal distributaries, backwater effect, unplanned infrastructure development, deforestation and the synchronization of flood peaks of the major rivers. Recently Mirza (2003), compared three recent extreme floods (1987, 1988 and 1998) in Bangladesh and found that the intense monsoon precipitation was the principal cause of flooding. However, there are differences in opinions concerning the role of deforestation in upstream areas in the flooding process in Bangladesh. Deforestation of steep 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. Copyright © 2005 Taylor & Francis Group plc, London, UK during monsoon precipitation. This in turn is believed to contribute to devastating floods in Bangladesh (Khalequzzaman, 1994; Hamilton, 1987). Hofer (1998) concluded that land-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 recent increase 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). 0 10000 20000 30000 40000 50000 60000 70000 80000 4/1/67 5/1/67 6/1/67 7/1/67 8/1/67 9/1/67 10/1/67 11/1/67 12/1/67 1/1/68 2/1/68 3/1/68 104 IMPLICATIONS ON RIVER DISCHARGE IN BANGLADESH Copyright © 2005 Taylor & Francis Group plc, London, UK Table 5.1 Mean annual precipitation in the Ganges, Brahmaputra and Meghna basins Basin Country Mean Annual Precipitation (mm) Ganges Nepal India Bangladesh 1,860 450-2,000 1,570 Brahmaputra Tibet (China) Bhutan India Bangladesh 400-500 500-5,000 2,500 2,400 Meghna/Barak India Bangladesh 2,640 3,575 Source: Mirza, 1997. Bangladesh generally experiences four main types of floods: flash, riverine, rain and storm-surge (Fig. 5.3). Eastern and Northern areas of Bangladesh adjacent to its border with India are vulnerable to flash floods. Rivers in these regions are characterized by sharp rises and high flow velocities resulting from exceptionally heavy rainfall occurring over the hilly and mountainous regions in the neighboring India. Riverine floods occur when flood water of the major rivers and their tributaries and distributaries spill. With the onset of the monsoon in June, all of the major rivers start swelling to the brim and bring flood water from the upstream basin areas. Rain floods are caused by intense local rainfall of long duration in the monsoon months. Heavy pre-monsoon rainfall (April-May) causes local runoff to accumulate in depressions. Later (June-September), local rainwater is increasingly ponded on the land by the rising water levels in the adjoining rivers. Coastal areas of Bangladesh, which consist of large estuaries, extensive tidal flats, and low-lying offshore 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 floods help the growth of rice crops because of the fertilization produced by nitrogen supplying blue-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 key economic sectors: agriculture, infrastructure and housing. Based on the reported crop damage due to floods, average annual loss is estimated to be 0.47 million tons (Paul and Rasid, 1993). However, in a year of an extreme flood such as 1998, food grain loss may exceed 3.5 million tons (Ahmed, 2001). The total monetary loss caused by the extreme floods of 1998 and 1988 was US$ 3.4 billion and US$ 2.0 billion, respectively or 10% of the 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 hardest hit. M. M. Q. MIRZA 105 Copyright © 2005 Taylor & Francis Group plc, London, UK 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 hydrological events 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 climate models simulate an enhanced hydrological cycle and increases in annual mean rainfall over South Asia (under non-aerosol forcing). In all periods of simulation (GHG and GHG + aerosol forcing), summer precipitation shows an increase. The magnitude of increase in summer precipitation with GHG + aerosol forcing is smaller than that seen in the GHG forcing. The difference in change with aerosol forcing is due to its dampening 106 IMPLICATIONS ON RIVER DISCHARGE IN BANGLADESH Copyright © 2005 Taylor & Francis Group plc, London, UK 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 of factors. 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 that peak wind intensities will increase by 5% to 10% and the mean and peak precipitation intensities 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 on Bangladesh have been undertaken (Ahmad and Warrick, 1996; ADB, 1994; and Resource Analysis, 1993). The consensus was that over the past 100 years, the broad region encompassing Bangladesh had warmed by 0.5 o C (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 to climate change, flooding in Bangladesh might worsen. However, no specific research has assessed changes in flooding in terms of magnitude, depth and spatial extent in Bangladesh taking into account possible changes in precipitation in the cross-border basin areas of the GBM Rivers. 5.2 OBJECTIVES As indicated above, the annual runoff in the GBM basins may be changed due to possible changes in future climate and it may also exacerbate the flood problem in Bangladesh. Most experiments using GCMs show increases in monsoon precipitation as a consequence of enhanced greenhouse effect. However, it is not known exactly what the magnitude of climate change 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 to examine possible changes in flooding in Bangladesh under climate change. The BDCLIM is a large 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 peak discharge at the boundary of Bangladesh to future climate change and 2) estimating the consequent 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 M. M. Q. MIRZA 107 Copyright © 2005 Taylor & Francis Group plc, London, UK Manabe, 1986), and LLNL (Whener and Convey, 1995) GCMs in the SCENGEN software 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, water quality and sediment transport in river basins, estuaries, irrigation systems, channels and other 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 Bangladesh context by the Surface Water Modeling Center (SWMC), Dhaka and is currently being used 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 modeling approach was compared to the water-balance, lumped-parameter and physically-based distributed models and found to be preferable on the basis of the constraints imposed by the 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 Meghna River basins was carried out using the model R = P - E. Here R = runoff, P = precipitation and 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 runoff was 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 empirical model building but it may be considered as an explanatory variable as part of a future research undertaking. The results of the sensitivity analysis also shows that, in percentage terms, runoff is more sensitive to precipitation and temperature changes in relatively dry stations than wet stations. As an example, in the case of the New Delhi station (a drier station in the Ganges basin) 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 Meghna basins, respectively), the changes in runoff are +6% and +8%, respectively. In the extreme case, a 5 o C increase in temperature and a 20% increase in precipitation could increase runoff by 29% at the New Delhi station, whereas for Gauhati and Syhet stations the expected changes are 22% and 21%, respectively. Accordingly, time-series data for precipitation were collected from various primary and recognized secondary sources for the three river basins. Sources of precipitation data were: 1) Carbon Dioxide Information Analysis Center (CDIAC)/Oak Ridge National Laboratory (ORNL), Tennessee, USA; 2) Climatic Research Unit (CRU), University of East Anglia, U.K.; 3) Nepal Water Conservation Foundation (NWCF), Kathmandu; 4) The Bangladesh Water Development Board (BWDB), Dhaka; 5) United Nations; and 108 IMPLICATIONS ON RIVER DISCHARGE IN BANGLADESH Copyright © 2005 Taylor & Francis Group plc, London, UK (c) Fig. 5.4 Sensitivity of runoff to temperature and precipitation changes in the: (a) Ganges basin (New Delhi), (b) Brahmaputra basin (Gauhati) and (c) Meghna basin (Syhet). (a) (b) M. M. Q. M IRZA 109 Copyright © 2005 Taylor & Francis Group plc, London, UK 6) Center for Ocean-Land-Atmosphere Studies (COLA), Maryland, USA. Discharge data was received from the Bangladesh Water Development Board. Details of these datasets are given in Mirza, 1997. Selection of the dataset for the development of empirical models was made with regard to length of record, spatial coverage and missing observations. The selected datasets were the COLA dataset and the NWCF dataset for the Ganges basin; the COLA dataset and selected four stations from the UN dataset within Bangladesh for the Brahmaputra basin; and the COLA dataset and the UN and BWDB datasets for the Meghna basin. 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. After filling in the missing observations, the means and standard deviations were computed for the complete time series and compared with those of the incomplete time series. The difference 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 of empirical modeling. Statistical tests show that the precipitation observations in all meteorological sub-divisions are normally distributed. Over the periods of record, one meteorological sub-division (The East Madhaya Pradesh) (V10 in Fig. 5.5a) in the Ganges basin shows a statistically significant decreasing trend. In the Brahmaputra basin, a decreasing 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 any discernible trend. On the other hand, each of these two sub-divisions covers a small area over the respective river basin. Therefore, they would not have a major effect on the predictive capability of the empirical models. Precipitation observations of all meteorological sub-divisions are found to be random, with a few exceptions. Analysis shows the presence of Markov linear type “persistence” only in the observations of the North 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 for the 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 not statistically significant. Therefore, on an annual basis, the regulation effect of the Farakka Barrage (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 divert 1,134 m 3 /sec water to make the Hooghly-Bhagirathi River channel (on which the port of Kolkata is situated) navigable (Mirza, 2002). A sequence of empirical models that describe the relationship between precipitation and annual mean and peak discharge was developed. One of the advantages of such a relationship is, for example, that in absence of precipitation data, peak discharge can be estimated from known values of annual discharge. Initially, in order to examine the independence of the explanatory variables, annual mean discharges of the Ganges River at Hardinge Bridge and Brahmaputra River at Bahadurabad in Bangladesh (Fig. 5.1) were regressed on the meteorological sub-division wide annual precipitation data. Initial examination indicated the presence of multi-collinearity in the precipitation data. This is the condition where at least one explanatory variable is highly correlated with another explanatory variable or with some combination of other explanatory variables (Maidment, 1993). Multi-collinearity may cause a number of consequences. (1) In extreme cases, the least 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 of regression coefficient estimators occur as the correlations among the independent 110 IMPLICATIONS ON RIVER DISCHARGE IN BANGLADESH Copyright © 2005 Taylor & Francis Group plc, London, UK 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 point estimates are produced; and (4) significance tests and confidence intervals for regression coefficients, due to increases in the standard errors of coefficient estimates, are affected. The principal components analyses (Dunteman, 1989; Manly, 1986) of the precipitation data were carried out to minimize the problems of collinearity and to generate relatively independent, contiguous precipitation regions (Table 5.2 and Fig. 5.5c). Selection of components 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 discharge for the Ganges and Brahmaputra Rivers. For the Meghna River, a multiple regression model 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 peak discharge in relation to mean annual discharge, regression models between annual mean and peak discharges were developed for the Ganges and Brahmaputra Rivers. Standard procedures (Berry and Feldman, 1985; Bowerman and O’Connell, 1990; Cook and Wesberg, M. M. Q. MIRZA 111 Copyright © 2005 Taylor & Francis Group plc, London, UK Fig. 5.5b Independent and contiguous precipitation regions of the Brahmaputra basin. 1982) were followed to examine the model parameters. The precipitation annual mean discharge 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 Ganges V1-V11* V3, V5-V11 V1 and V4 V2 Brahmaputra V1-V4** V1, V2 and V4 V3 - Meghna V1-V3*** V2 and V3 V1 - * V1 - Sub-Himalayan West Bengal; V2 - Gangetic West Bengal; V3 - Bihar Plateau; V4 - Bihar Plain; V5 - East Up; V6 West Up; V7 - Haryana; V8 - East Rajasthan; V9 - West Madhaya Pradesh; V10 - East Madhaya Pradesh; and V11- Nepal ** 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 IMPLICATIONS ON RIVER DISCHARGE IN BANGLADESH Copyright © 2005 Taylor & Francis Group plc, London, UK [...]... may change by -1 7% and -1 8%, respectively, for the CSIRO9 and LLNL models This is perhaps due to increased peak discharges and local rainfall being unable to inundate new areas, due to land elevation For F1, F2 and F3, the changes are -2 % and -2 %, +31% and +34%, and +34% and +36%, respectively Similarly, for a 6oC temperature rise, changes in the F0 inundation category may increase to -1 8% and -1 9%,... Hazards 28(1) (2003), pp.3 5- 6 4 Mirza, M M Q and Dixit, A.: Climate Change and Water Resources in the GBM Basins Water Nepal 5( 1) (1997), pp.7 1-1 00 Mirza, M M Q, Warrick, R A., Ericksen, N J and Kenny, G J.: Trends and Persistence in Precipitation in the Ganges, Brahmaputra and Meghna Basins in South Asia Hydrological Sciences Journal 43(6) (1998), pp.84 5- 8 58 Copyright © 20 05 Taylor & Francis Group plc,... months (F3) Current 25. 0 33.0 42.0 CSIRO9 20.0 35. 0 45. 0 UKTR 14.0 31.0 55 .0 GFDL 15. 0 32.0 53 .0 LLNL 19 .5 35. 0 45. 5 Changes in land categories may affect cropping intensity in Bangladesh Farmers do not plant when the risk of flooding is too high A flood can damage the aus (a type of rice crop which is grown in the kharif-I season during April-July) crop at the end of the growing period and the aman (a... expected to be in the range of -2 0% to -2 % The F2 and the F3 categories increase by +31% to +36% and +34% to +66%, respectively Changes in the F1 category are expected to be in the range of -2 0% to -2 % At a 6oC temperature rise, changes in the F0 category may be in the range of -2 7% to -1 8%, which is very negligible The F1 category changes to a -2 5% to -4 % range from a -2 0% to -2 % The F2 category, in a broad... indicates drastic changes in the flooded areas under the F3 inundation category (Figs 5. 15, 5. 16 and 5. 17) At 2oC, 4oC and 6oC temperature changes, for the F3 inundation category, the changes may be as great as 47%, 66% and 85% , respectively The sixth point is that under the climate change scenarios, deeply flooded areas will be the largest of all flooded areas (Figs 5. 15, 5. 16, 5. 17 and Table 5. 10) Of the... Applications in New Zealand and Bangladesh In L Erda, W C Bolhofer, S Huq, S Lenhart, S K Mukerjee, J B Smith and J Wisniewski (eds.): Climate Change Vulnerability and Adaptation in Asia and the Pacific, Kluwer Academic Publishers, Dordrecht, the Netherlands, 1996, pp.21 5- 2 27 Wetherald, R T and Manabe, S.: An Investigation of Cloud Cover Change in Response to Thermal Forcing Climate Change 8 (1986), pp. 5- 2 3... pattern of change at 2oC, 4oC and 6oC increases in temperature (Figs 5. 14, 5. 15, 5. 16, 5. 17 and Table 5. 10) At a 2oC temperature change, the CSIRO9 and LLNL models imply equal changes (-1 7%) for the Fo and no change for the F1 inundation category, respectively The F2 category may change by 30% and 32%, respectively For both models, the F3 category may change by 32% For a 4oC temperature rise, the F0 inundation... planted in January-February and harvested in April-May) crop Changes in the F0 and F1 land categories may affect the HYVs of rice cultivation Under the climate change scenarios, reduction in the F0 and F1 categories is expected to be within the range of -2 1% to -1 7% and -2 5% to -3 %, respectively for the four GCMs (Table 5. 10) This may have a significant effect on the HYV aman rice production in Bangladesh... Himalayan Deforestation on the Ganges-Brahmaputra Lowlands and Delta? Mountain Research and Development 7(3) (1987), pp. 25 6-2 63 Hofer, T.: Do Land-Use Changes in the Himalayas Affect Downstream Flooding: Traditional Understanding and New Evidences In V S Kale (ed.): Flood Studies in India, Geological Society of India, Bangalore, 1998, pp.11 9-1 41 Hulme, M.: Regional Climate Change Scenarios Based on IPCC... ( 0-3 0 cm) F1 (3 1-9 0 cm) F2 (9 1-1 80 cm) F3>180 cm CSIRO9 UKTR GFDL LLNL 4.42 (-1 7%) 4.03 (-2 5% ) 4.09 (-2 3%) 4.37 (-1 7%) 0. 95 (-2 %) 0.81 (-2 0%) 0. 85 (-1 2%) 0.94 (-2 %) 1.63 (+31%) 1.67 (+ 35% ) 1.68 (+36%) 1.66 (+34%) 2.11 (+34%) 2.61 (+66%) 2.48 (+43%) 2.13 (+36%) 6oC Temperature rise GCM Fo ( 0-3 0 cm) F1 (3 1-9 0 cm) F2 (9 1-1 80 cm) F3>180 cm CSIRO9 UKTR GFDL LLNL 4.40 (-1 8%) 3.86 (-2 7%) 3.94 (-2 6%) 4.32 (-1 9%) . V1-V11* V3, V5-V11 V1 and V4 V2 Brahmaputra V1-V4** V1, V2 and V4 V3 - Meghna V1-V3*** V2 and V3 V1 - * V1 - Sub-Himalayan West Bengal; V2 - Gangetic West Bengal; V3 - Bihar Plateau; V4 -. has assessed changes in flooding in terms of magnitude, depth and spatial extent in Bangladesh taking into account possible changes in precipitation in the cross-border basin areas of the GBM Rivers. 5. 2. Bihar Plain; V5 - East Up; V6 West Up; V7 - Haryana; V8 - East Rajasthan; V9 - West Madhaya Pradesh; V10 - East Madhaya Pradesh; and V1 1- Nepal ** V1 - North Assam; V2 - South Assam; V3 - Sub-Himalayan