HYDRO-METEOROLOGICAL VARIABILITY IN THE GREATER GANGES BRAHMAPUTRA MEGHNA BASINS

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 24: (2004) Published online in Wiley InterScience ( DOI: /joc.1076 HYDRO-METEOROLOGICAL VARIABILITY IN THE GREATER GANGES BRAHMAPUTRA MEGHNA BASINS MD. RASHED CHOWDHURY* and NEIL WARD International Research Institute for Climate Prediction, Columbia University, 61 Route 9W, Palisades NY 10964, USA Received 18 February 2003 Revised 26 May 2004 Accepted 26 May 2004 ABSTRACT The flows of the Ganges, Brahmaputra and Meghna (GBM) are highly seasonal, and heavily influenced by monsoon rainfall. As a result, these rivers swell to their banks and often overflow during the monsoon months. This is most pronounced in the downstream regions, particularly in Bangladesh, which is the lowest riparian country. The objective of this paper is to study this hydro-meteorological variability in the greater GBM regions, including the headwater regions in India and their role in streamflows in Bangladesh, and explore the large-scale oceanic factors affecting this hydrometeorological variability. Global precipitation data, Bangladesh rainfall and streamflow records have been analysed and related to large-scale climate patterns, including upstream rainfall, regional atmospheric circulation and patterns of sea-surface temperature. The findings have quantified how the streamflows of these rivers in Bangladesh are highly correlated with the rainfall in the upper catchments with typically a lag of about 1 month. Therefore, streamflows in Bangladesh could be reasonably estimated for 1 to 3 months in advance (especially for the Ganges and Brahmaputra rivers) by employing simple correlation, if rainfall data from countries further up are available on a real-time and continuous basis. In the absence of rainfall data, streamflow forecasts are still possible from unusually warm or cold sea-surface temperatures in the tropics. The study concludes that hydro-meteorological information flow between Bangladesh and other neighbouring countries is essential for developing a knowledge base for evaluating the potential implications of seasonal streamflow forecast in the GBM basins in Bangladesh. Copyright 2004 Royal Meteorological Society. KEY WORDS: Ganges Brahmaputra Meghna (GBM); rainfall; streamflow; sea-surface temperature (SST); India; Bangladesh 1. INTRODUCTION The Ganges Brahmaputra Meghna (GBM) river system is the third largest freshwater outlet to the world s oceans; it is exceeded only by the Amazon and the Congo rivers. The Brahmaputra and Ganges encompass a number of countries in the South Asian region, including China, India, Nepal, and Bangladesh (Figure 1). Of these, China contributes solely to the flow of the Brahmaputra, and Nepal to the flow of the Ganges (Nishat and Faisal, 2000). The Brahmaputra, after travelling about 1800 km through Tibet and India, enters northern Bangladesh through the northern border; the Ganges flows for about 2000 km through India, and enters through the western side of Bangladesh. Inside Bangladesh, the run off of all these main rivers is primarily generated by the accumulation of basin-wide rainfall of the Ganges, Brahmaputra, and Meghna, the areas of which are km 2, km 2 and km 2 respectively. When intensive rainfall occurs simultaneously over several tributary basins for a long duration, the combined runoff from these tributaries is a critical factor in causing high floods in the main channels downstream (Bangladesh). The contribution of local rainfall in generating these flows is less dominant, especially in the Ganges and Brahmaputra basins; * Correspondence to: MD. Rashed Chowdhury, Pacific ENSO Applications Center, University of Hawaii, 2525 Correa Road, HIG 350, Honolulu, HI 96822, USA; rashed@hawaii.edu Copyright 2004 Royal Meteorological Society

2 1496 MD. R. CHOWDHURY AND N. WARD Area: 583,000 sq-km Area: 907,000 sq-km Ganges R Brahmaputra R Bahadurabad Area: 65,000 sq-km Hardinge Bridge Bhairab Bazar Figure 1. Bangladesh, India, China, and Nepal along the principal channels of the Ganges, Brahmaputra, and Meghna however, on some occasions prolonged localized rainfall within Bangladesh aggravates the local flooding situation. The effect of climate on hydrology in tropical Asia has many facets. In the Himalayas, the storage of precipitation in the form of snow and ice (in glaciers) over a long period provides a large water reservoir that regulates annual water distribution. The majority of rivers originating in the Himalayas have their upper catchments in snow-covered areas and flow through steep mountains. If there is any climatic variability in the Himalayas the impacts could be felt in countries downstream, i.e. India and Bangladesh. In particular, the streamflow within the GBM rivers in Bangladesh displays significant variations, both in terms of seasonal flow and in timing of onset, peak, and recession of flooding. Essentially, climatic factors (sea-surface temperature (SST), atmospheric circulation system) are hypothesized to be responsible for these changes; it is also expected that climate-change-induced alterations in temperature have been affecting the timing and rate of snow melt in the upper Himalayas. As a result, the hydrological aspects of the eastern Himalayan rivers and the GBM rivers are likely to undergo significant changes in future. Usually, the seasonal rainfall upstream in India is a major factor that contributes significantly to the streamflow generation in the major rivers of Bangladesh. Therefore, in Bangladesh, the streamflow in the major rivers is the result of monsoon rainfall upstream in India. Although the Himalayan snowpack may have some local influences on the regime of tributaries, this could not be independently verified due to lack of snow data. Therefore, the influence of the Himalayan snowpack on the streamflows in Bangladesh remained beyond the scope of this study. However, various unofficial sources estimated that the volume due to snowmelt is much smaller than due to the volume of rainfall. Therefore, the flood peaks are essentially determined by basin-wide rainstorms (Chowdhury and Sato, 1996). Although there are many new initiatives in conducting research on Bangladesh climate and El Niño southern oscillation (ENSO; e.g. see Hossain et al., 2001; Douglas et al., 2001; Chowdhury and Ward, 2003; also see Chowdhury (2003)), there are insufficient studies quantifying the current basin-wide rainfall and runoff relationship. This is mainly because rainfall data from the upstream countries is either not available or, if available, is not accessible to scientists in other countries (Nishat and Faisal, 2000). Here, an attempt is made to explore the globally available precipitation data record for (popularly known as Hulme data) to gather rainfall information from the upstream GBM basins (see Hulme (1994) for details). Therefore,

3 SOUTH ASIAN HYDRO-METEOROLOGICAL VARIABILITY 1497 the primary objective of this study is to identify relationships of a predictive nature from the rainfall and runoff data in the greater GBM basins on a seasonal-to-monthly scale, and then, in addition to scientific interest, explore the physical mechanism responsible for the links between SST and monsoon climate that influence these hydro-meteorological changes. The motivation for this research comes from the fact that the hydrologic forecasts available now in Bangladesh provide a short forecasting lead time of 3 days or so. There is a demand for a forecasting lead time of a month to a season. Such a forecasting lead time is expected to have far-reaching economic ramifications Rainfall data 2. DATA, BASIC INDICES, AND METHOD Bangladesh rainfall data from 1962 to 2000 (basin-wide rainfall data before 1962 are not available) were collected from various locally available sources (i.e. published and unpublished documents, and personal contact). Rainfall data were compiled from 35 monitoring points (Ganges: 12; Brahmaputra: 12; Meghna: 11) in Bangladesh. The daily flood bulletin of the Flood Forecasting and Warning Center (FFWC) of the Bangladesh Water Development Board (BWDB) was an important source of information in this study ( A year-to-year standardized anomaly of basin-wide average annual rainfall index is presented in Figure 2. The other rainfall data (outside Bangladesh: ) in the GBM basins were collected from the available Hulme global precipitation data. Hulme (1994) adopted a Thiessen polygon approach to calculate grid-box monthly average rainfall from monthly station reports. To qualify for inclusion in the dataset, a station must have 83% of data present in the analysis period. Then all missing data are interpolated using the mean percentage anomaly in surrounding stations using an inverse distanceweighting scheme (Ward, 1998). Seasonal datasets were formed by summing the monthly grid-box values from the Ganges (Lat N, Lon E (total of eight, along the border of India and Nepal), the Brahmaputra (Lat N, Lon E, total of six, along the borders of India, Bhutan, and China), and the Meghna (Lat N, Lon E, total of two, along the border of India and Myanmar); (see also the following Website for more on the rainfall data: NW_N(B) NE_N(M) NW_S(G) Standardized annual rainfall Years Figure 2. Year-to-year standardized deviation of seasonal rainfall in the downstream Bangladesh Basin-wide: NW N (B): northwestern (north) region in the Brahmaputra basin; NE N (M): northeastern (north) region in the Meghna basin; NW S (G): northwestern (south) region in the Ganges basin

4 1498 MD. R. CHOWDHURY AND N. WARD 4 Brahmaputra Ganges Meghna JJAS standardized rainfall Year Figure 3. Year-to-year standardized deviation of seasonal (JJAS) rainfall in upstream India, basin-wide: Source: UEA Climatic Research Unit (CRU), Hulme (1994), global precipitation data; grid: (1 = 111 km) A standardized anomaly of this area-average rainfall index is presented in Figure Streamflow data Streamflow data were collected directly from three major river points i.e. Ganges at Hardinge Bridge (Hbr), Brahmaputra at Bahadurabad (Bbd), and Meghna at Bhairab Bazar (Bbz) (Figure 1). Normally, discharges are measured weekly at all these stations by the velocity-area method. Since the River Brahmaputra is highly braided, the discharge measurement at Bahadurabad is carried out on multiple channels. On the other hand, the River Meghna at Bhairab Bazar is seasonally tidal after withdrawal of the monsoon the river at this station becomes tidal and from December to about May the river shows both a horizontal and a vertical tide. Under this condition, during the dry season, tidal discharge measurements are made at this station once per month (Matin, 2001, personal communication). Standardized anomalies of maximum annual flow at the three monitoring points on the Ganges (at Hardinge Bridge), Brahmaputra (at Bahadurabad), and Meghna (at Bhairab Bazar) are also computed (Figure 4) Linear Correlations in Atmospheric Seasonal/Monthly Averages Using the Climate Diagnostics Center (CDC) Website ( correlations of seasonally averaged variables (SST and atmospheric circulation at 850 hpa) from the National Centers for Environmental Protection (NCEP) reanalysis with specified teleconnection and ocean index time-series (rainfall and streamflow) were plotted (see Figure 6 8) Data limitations As data accumulation is a major task in this study, and assuming that the globally available data are accurate, concerted efforts were made to collect data from reliable sources in Bangladesh. The accuracy of precipitation and river flow data depends primarily on the number of data-collection stations, and the number of rainfall stations is quite high in Bangladesh. However, rainfall is usually more accurate than discharge assessments. Although it is certainly possible to get accurate discharge information for a small test area, it is extremely difficult to measure the depth and velocities of mighty rivers like the Ganges, Brahmaputra, and

5 SOUTH ASIAN HYDRO-METEOROLOGICAL VARIABILITY 1499 Bbd_max Hbr_max Bbz_max 2 Standardized discharge cu-meter/sec Year Figure 4. Year-to-year standardized deviation of annual maximum flows in downstream Bangladesh. Bbd max: maximum flows at Bahadurabad in River Brahmaputra; Hbr max: maximum flows at Hardinge Bridge in River Ganges; Bbz max: maximum flows at Bhairab Bazar in River Meghna Meghna. We have no other model-based methods to verify this discharge data independently. Therefore, it is possible that some of estimates may be rather uncertain. 3. FINDINGS Basin-wide monsoon rainfall patterns control the flood peaks of the GBM rivers. The progression of the monsoon air mass is from the southeast part of the GBM basin to the northwest (Mirza, 2003). The Ganges begins to rise in May and the period of maximum flow is centred on July and August, with a clear influence of the Himalayas; also, September can occasionally be a month of severe floods. In general, the Brahmaputra and Meghna are characterized by a first flood occurring in July and August and a second one of lesser volume occurring in September (Chowdhury and Sato, 1996). Therefore, the flows of the Brahmaputra and Meghna are highly correlated to each other (r 2 = 0.631, p<0.01), whereas the correlations of flow between the Ganges and the other two rivers are virtually nonexistent. However, some interesting features came out from the correlation of basin-wide rainfall (in India) and downstream flows (in Bangladesh), which are summarized as follows. In the Ganges basin the correlation between the discharge at Hardinge Bridge (on the River Ganges) and the rainfall in the upper Ganges basin (outside the country) has been found to be significant (Table I). Interestingly, the successive flows in the months of July, August, and September were found to be highly correlated to the rainfall of June, July, and August respectively (r 2 = 0.54, 0.48, and 0.53, and all are significant at p<0.01). In addition, some significant correlations between June rainfall and August flow (r 2 = 0.39, p<0.01) and between July rainfall and July flow (r 2 = 0.44, p<0.01) were evident (Table I). Most importantly, the seasonal (June September, JJAS) flow at Hardinge Bridge on the River Ganges is highly correlated to the monsoon (JJAS) rainfall in the upper catchments of India (r 2 = 0.565, p<0.01). On the other hand, the flow at Hardinge Bridge and the seasonal rainfall (JJAS) in this basin (in Bangladesh) were found to be insignificant. The findings, therefore, clearly indicate that the flow at Hardinge Bridge (main boundary point) in the River Ganges is primarily dependent on the rainfall in the same basin outside the country. From a pre-monsoon minimum, the discharge of the River Ganges increases sharply with the increase in upstream

6 1500 MD. R. CHOWDHURY AND N. WARD Table I. Rainfall runoff correlations in the greater GBM basin Basin-wide correlation Bangladesh flow, correlation coefficient Rainfall (India) Ganges, Hardinge Bridge Brahmaputra, Bahadurabad Meghna, Bhairab Bazar Jun b July b Aug b Sep b JJAS c seasonal Jun b July b Aug b Sep b JJAS c seasonal Jun b July b Aug b Sep b JJAS c seasonal May a ns ns ns ns ns ns ns June a ns ns ns ns July a ns ns ns ns August a ns ns ns ns ns ns JJAS d (India) JJAS e (Bangladesh) Significance 0.05; Significance 0.01; ns: not significant. a Monthly average rainfall (mm) in India. b Monthly average discharge (m 3 /s) in Bangladesh. c Total average seasonal (JJAS) discharge (m 3 /s) in Bangladesh. d Total average seasonal (JJAS) rainfall (mm) in India. e Total average seasonal (JJAS) rainfall (mm) in Bangladesh.

7 SOUTH ASIAN HYDRO-METEOROLOGICAL VARIABILITY 1501 monsoon rainfall; a peak is usually attained in late August or early September, with some influences from the mountain snowpacks in the Himalayan tributaries. In the Brahmaputra basin, in terms of 1 month lag correlation, the River Brahmaputra displayed a slightly different, but somewhat expected, relation: the monthly flow in June and September at Bahadurabad is significantly correlated to the rainfall of May and August in the upper basin areas (r 2 = 0.36 and 0.36; both significant at p<0.01; Table I). In addition, the monthly rainfall of July and August displayed significant associations with the monthly flows at Bahadurabad (r 2 = 0.453, p<0.01; r 2 = 0.627, p<0.01), and the July rainfall shows statistically significant correlation (r 2 = 0.575, p<0.01) with the September flow at Bahadurabad. Also, the basin-wide seasonal (JJAS) flow and rainfall remained strongly associated (r 2 = 0.657, p<0.01; Table I). The rise of water in the Brahmaputra, with the increase in upstream rainfall, starts as early as April. The high stages last from June to August, and in some cases even until September. Several peaks occur during the monsoon with the first flooding event occurring in July and August and a second one of lesser volume occurring in September. Unlike the Ganges, the flow at Bahadurabad and the seasonal rainfall (JJAS) in the Brahmaputra basin (in Bangladesh) displayed some moderate correlation in July (r 2 = 0.32, p<0.05). However, the relation was found to be very weak for other months. So, the findings here further emphasize the influences that rainfall outside Bangladesh has on shaping the major flows in the Rivers Ganges and Brahmaputra. In the Meghna basin, like the River Brahmaputra, the Meghna displayed a somewhat expected correlation: the monthly flows in July and September at Bhairab Bazar are related to the rainfall of June and August in the upper basin areas (r 2 = 0.513, p<0.01; r 2 = 0.585, p<0.05; Table I). Also, the seasonal (JJAS) flow at Bhairab Bazar in Meghna is highly correlated to the monsoon rainfall in the upper catchments of India (r 2 = 0.464, p<0.01; Table I). However, the most important finding clearly appeared to be that, unlike the Ganges, the seasonal flow at Bhairab Bazar and seasonal rainfall (JJAS) in the Meghna basin (in Bangladesh) are significantly correlated (r 2 = 0.410, p<0.05). This indicates the influence that rainfall both internal and external to Bangladesh has on affecting the major flow in the River Meghna. Based on the correlation results, the following statistical models are proposed: 1. y = x 1 α 1 + c for the Ganges and Brahmaputra basins 2. y = x 1 α 1 + x 2 α 2 + c for the Meghna basin where y is streamflow (m 3 /s) in Bangladesh, x 1 is rainfall (mm) in upstream India, x 2 is rainfall (mm) in downstream Bangladesh, α 1 and α 2 are regression coefficients, and c is the intercept. The scatterplot diagram for the Ganges basin provided a clear picture of the rainfall runoff relation (Figure 5). However, similar relations from the monthly to seasonal scale in the Brahmaputra and Meghna basins were not distinct and, therefore, are not reported here. Further, the stepwise regression results identified that streamflows in the Ganges are highly dependent on rainfall in the upper catchments, with typically a lag of 1 month. The results of the regression analyses are summarized in Table II. It is evident that some of the variables have significant to moderate correlation (at the 1, 5, and 10% levels) with the dependent variables, but the rest are statistically insignificant (Table II). The monthly flow for July September is found to be highly dependent on the upstream rainfall of the previous month in the Ganges, and predictive equations are clearly evident (Table II). The seasonal flow of the Ganges (in Bangladesh) was found to be highly dependent on the rainfall (in India), which has indicated the possibilities of a predictive equation: the variance explained was 33% and the t-statistic suggests rainfall to be strongly associated with flow (Table II). Bangladesh rainfall remained statistically insignificant throughout the series. In the case of the Brahmaputra, the stepwise regression identified that the September flow of the Brahmaputra is highly dominated by the consecutive 2 months rainfall (July and August) in the upper basin areas. Despite any significant association with Bangladesh rainfall, the seasonal flow displayed a close association with the upstream rainfall and provided a predictive equation: the variance explained is 36% and the t-statistic displays a moderately strong association with flow (Table II).

8 1502 MD. R. CHOWDHURY AND N. WARD Ganges - HBR: flow (jul) vs. rainfall (jun) Ganges - HBR: flow (aug) vs. rainfall (jul) Flow (July), Bangladesh y = x R 2 = Flow (Aug), Bangladesh y = x R 2 = Rainfall (June), India Rainfall (Jul), India Ganges - HBR: flow (sep) vs. rainfall (aug) Ganges - HBR: flow vs. rainfall Flow (Sep), Bangladesh y = 75.95x R 2 = Seasonal flow (cumec), Bangladesh y = x R 2 = Rainfall (Aug), India Seasonal rainfall (mm), India Figure 5. Scatterplot and regression line, Ganges at Hardinge Bridge The Meghna basin displayed similar trends: September flow is strongly related to the August rainfall in the upper basin area. With the exception of a moderately strong association with seasonal flow, no other significant associations between the flow of June August versus monthly rainfall in Bangladesh, plus 1 month lag rainfall in India, could be estimated from the stepwise analyses. Instead, the seasonal rainfall in Bangladesh tended to provide a moderate association with flow in the Meghna (rainfall in India remained statistically insignificant): the variance explained was 10% and the t-statistic displays a moderately strong association with flow (Table II). 4. PHYSICAL MECHANISM FOR SST MONSOON LINKS With the intention of drawing a general pattern of the climatic factors affecting rainfall and streamflow variability in the greater GBM basins, the linear correlations in seasonal/monthly averages are plotted for all three rivers (Figures 6 8). The left panels describe the correlation between the SST and streamflow in Bangladesh, and the right panels indicate the correlation between SST and rainfall in India. The analysis provides an improved description of how (in the absence of real-time rainfall data) streamflow forecasts can be made from SSTs in the tropics and Indian Ocean. Discussion has been limited to those statistically significant relations that were deemed to be interest or importance, and also subject to logical interpretation (see Table II for significant relations). The seasonal correlation between the SST (and circulation pattern) versus rainfall (India) and streamflow (Bangladesh) identifies that both the rainfall and flows in the greater Ganges basin are highly correlated to:

9 SOUTH ASIAN HYDRO-METEOROLOGICAL VARIABILITY 1503 Table II. Stepwise regression results Rainfall average (India) Independent a Correlation coefficient, dependent variable Flow at Ganges Flow at Brahmaputra Flow at Meghna q Jul b q Aug b q Sep b JJAS c q Sep b JJAS c q Sep b JJAS c June a ns ns ns ns (3.63) July a ns ns ns (4.43) (2.92) August a ns ns (3.39) (2.98) (3.07) JJAS d Ns (4.9) (1.98) Rainfall d (Bangladesh) (2.1) Intercept (6.15) (3.09) (3.16) (2.14) (1.54) (2.25) (5.74) (0.25) R F Stepwise regression results (stepwise criteria: probability-of-f -to-enter 0.050), probability-of-f -to-remove Numbers in parentheses are t-values. Significant at 1%, 5%,and 10 % level; ns: not significant. a Monthly average rainfall (mm). b Monthly-average discharge (m 3 /s). c Total-average seasonal (JJAS) discharge (m 3 /s). d Total-average seasonal (JJAS) rainfall (mm). (i) the negative SST anomaly in the domain of the Niño 3.4 region (160 E 120 W, 20 N 20 S) and the Indian Ocean ( E, 20 N 10 S), and (ii) the positive SST anomaly in the western Pacific ( E, N; and 120 E 180, 0 30 E) (Figure 6). The zonal wind flow in both cases is strong and easterly (Figure 6: bottom panel). The monthly correlation plot displays a similar pattern, with slight exceptions in August September (left panel) and July August (right panel), and closely resembles the seasonal plot. The correlation plots suggest that the natural variability of the downstream Ganges flow and the upstream rainfall are significantly associated with the El Niño southern oscillation (ENSO) signal, indicating that an anomaly of the SST (cooling from the long-term average) produces increased streamflow and heavy rainfall in the greater Ganges basin (Figure 6). This is due to a positive ENSO index or La Niña effect the underlying mechanism of which is when the ENSO index is positive and high, the Walker circulation is strong, upper tropospheric wind in the Australasian region is easterly. Consequently, the tropical disturbances are transported westwards, which move into the region of the Ganges and cause heavy rainfall in the upstream regions and produce high streamflow in downstream Bangladesh (Figure 6). When the southern oscillation index is negative, however, the Walker circulation is weakened and then the easterly wind is also weakened or completely reversed (Glantz et al., 1991). As a result, the Hadley circulation gets stronger, along with the upper tropospheric wind in the Pacific, which is westerly, and the tropical disturbances formed are transported northwards or northeastwards, depriving the Ganges region of rain (Chowdhury, 1994; also see Chowdhury (2003)). Douglas et al. (2001) also identified a significant relationship between the natural variability of the Ganges annual flow and the ENSO index (also see Ropelewski and Halpert (1996)). Therefore, scope for rainfall (in India) and streamflow (in Bangladesh) forecasts has been identified in the Ganges basin from the unusually warm or cold SST, and (in the absence of rainfall data) a forecast for Ganges flow is possible from unusually warm/cool SSTs. Despite some significant association between upstream rainfall variability and ENSO, the influence of ENSO appears to be very weak and nonexistent in the case of streamflow variability in the River Brahmaputra

10 1504 MD. R. CHOWDHURY AND N. WARD SST vs. Ganges-flow (B): Correlation i) June-July SST vs. Ganges-rainfall (I): Correlation vi) June-July ( - ) ( - ) ( - ) ( - ) ii) July-August vii) July-August ( - ) (- ) iii) August-September viii) August-September ( - ) (- ) iv) Seasonal (JJAS) - JJAS ix) Seasonal (JJAS) - JJAS ( - ) ( - ) ( - ) ( - ) Zonal-wind vs. Ganges-flow (B): Correlation v) Seasonal (JJAS) - JJAS Zonal-wind vs. Ganges-rainfall (I): Correlation x) Seasonal (JJAS) - JJAS Figure 6. SST and monsoon links in the Ganges basin ( for positive and ( ) for negative correlations; bold for strong association; left arrow for easterly). Left: SST versus streamflow in Bangladesh; Right: SST versus rainfall in India

11 SOUTH ASIAN HYDRO-METEOROLOGICAL VARIABILITY 1505 SST vs. Brahmaputra-flow (B): Correlation i) June-July SST vs. Brahmaputra-rainfall (I): Correlation vi) June-July ( + ) ii) July-August vii) July-August ( + ) iii) August-September viii) August-September iv) Seasonal (JJAS) - JJAS ix) Seasonal (JJAS) - JJAS Zonal-wind vs. BPutra-flow (B): Correlation v) Seasonal (JJAS) - JJAS Zonal-wind vs. BPutra-rainfall (I): Correlation x) Seasonal (JJAS) - JJAS Figure 7. SST and monsoon links in the Brahmaputra basin ( for positive and ( ) for negative correlations; bold for strong association; left arrow for easterly). Left: SST versus streamflow in Bangladesh; Right: SST versus rainfall in India

12 1506 MD. R. CHOWDHURY AND N. WARD SST vs. Meghna-flow (B): Correlation i) June-July SST vs. Meghna-rainfall (I): Correlation vi) June-July ii) July-August vii) July-August iii) August-September viii) August-September iv) Seasonal (JJAS) - JJAS ix) Seasonal (JJAS) - JJAS Zonal-wind vs. Meghna-flow (B): Correlation v) Seasonal (JJAS) - JJAS Zonal-wind vs. Meghna-rainfall (I): Correlation x) Seasonal (JJAS) - JJAS Figure 8. SST and monsoon links in the Meghna basin ( for positive and ( ) for negative correlations; bold for strong association; left arrow for easterly). Left: SST versus streamflow in Bangladesh; Right: SST versus rainfall in India

13 SOUTH ASIAN HYDRO-METEOROLOGICAL VARIABILITY 1507 in Bangladesh (Figure 7). This indicates that the flows in the River Brahmaputra are influenced by the combinations of local causes and SST anomalies that are positive in the domain of the western Pacific ( E, 0 30 N) and Indian Ocean (50 90 E, 0 20 N) (Figure 7). This is relatively more distinct in the months of August September and for seasonal (JJAS) correlation. This finding is consistent with the natural variability of the River Brahmaputra, which reaches its peak during the months of August September. For other months, no significant correlations between SST and flow or rainfall were observed. Also, while relating flows of these months to rainfall, all these relations were found to be statistically insignificant (Table II). The Meghna displays similar trends to that of the Brahmaputra: upstream rainfall shows no significant association with the ENSO signal or with unusually warm/cool SSTs. However, in addition to some very weak association with ENSO, the streamflow in this basin appeared to maintain a stronger relation with the positive SST anomalies in the domain of the western Pacific ( E, 0 30 N), and partly in the Indian Ocean (60 90 E, 0 30 N) (Figure 8). Like the River Brahmaputra, this is relatively more distinct in the months of August September and for the season (JJAS); its peak in August September influences the natural variability of the River Meghna. The findings have identified that the prediction potential for both the rainfall (in India) and streamflows (in Bangladesh) in the Ganges basin from unusually warm or cold SSTs is encouraging. However, this is not the case for other basins, where the scope for rainfall and streamflow prediction from the unusually warm or cold SSTs is relatively limited. Therefore, a more detailed study to explore the synergetic effects of these factors on climate variability is essential (research related to this issue is in progress and will be reported elsewhere). 5. CONCLUSIONS Climate- and ocean-driven factors like the SST and circulation process significantly affect climate variability in the greater GBM basin. Also, along with local rainfall, rainfall in the basin areas adjacent to Bangladesh generally control the flood peaks and the flooding process. The findings have quantified the rainfall runoff relation in the greater GBM basin and identified the possibilities of month-to-seasonal (1 3 months in advance) streamflow forecast (especially in the Ganges basin) by employing a simple correlation if rainfall data from countries further upstream are available on a real-time and continuous basis. Unfortunately, with reference to the sharing of information among these riparian countries, the water experts in one country had surprisingly little access to information from the other co-basin countries. Thus, some operational mechanism is needed for wider sharing of meteorological and hydrological information between the countries concerned. A proactive role from these countries to foster a closer regional cooperation is essential to mitigate the sufferings of humanity in the three river basin areas. Despite the problem of regular exchange of data among the neighbouring countries, the process of seasonal climate forecasts can still be enhanced from the unusually warm or cold SSTs in the tropics. For example, unusually warm or cold SSTs in the tropical Pacific or Indian Ocean can cause major shifts in seasonal climate in nearby continents. Although, knowing the ENSO condition (SST, circulation pattern) ahead of time would provide substantial opportunities to provide useful climate forecasts (rainfall, streamflow) in Bangladesh, the present technology in Bangladesh is not in a position to identify the uncertain impacts of an El Niño episode. The country, therefore, has to rely on the latest ideas and technology from developed countries for this purpose. Regular exchange of data and a close cooperation between the local institutions (e.g. Space Research and Remote Sensing Organization (SPARRSO), Bangladesh Meteorological Department (BMD), and FFWC of the BWDB) and those climate research institutes in the developed countries is essential. ACKNOWLEDGEMENTS We are particularly grateful to Dr Reid Basher for his supervision during the entire period of this research. Thanks are extended to Dr Upmanu Lall and Dr Carolyn Mutter for their comments. We express our gratitude to the Climate Diagnostics Center (CDC; http// for providing easy access, manipulation, and visualization of Earth science data. In this connection, we also express our special thanks

14 1508 MD. R. CHOWDHURY AND N. WARD to Mr Ousmane Ndiaye. Thanks are also due to the Pacific ENSO Center (PEAC) of the University of Hawaii for providing a very congenial atmosphere for research. We also express our thanks to the officials of the Bangladesh Water Development Board (BWDB). Finally we express our thanks to the anonymous referees for thoughtful comments and insights regarding an earlier version of this paper. This research was supported by the Postdoctoral Program at the International Research Institute for Climate Prediction (IRI) (the Earth Institute of Columbia University, USA), administered by the University Corporation for Atmospheric Research (UCAR). REFERENCES Chowdhury AM Bangladesh floods, cyclones and ENSO. In International Conference on Monsoon Variability and Prediction, International Center for Theoretical Physics (ICTP), Italy, 9 13 May. Chowdhury MR The El Niño southern oscillation (ENSO) and seasonal flooding: Bangladesh. Theoretical and Applied Climatology 76: Chowdhury MR, Sato Y Flood monitoring in Bangladesh: experience from normal and catastrophic floods. Hydrology (Journal of the Japanese Association of Hydrological Sciences) 26: Chowdhury MR, Ward N Seasonal rainfall and stream-flow in the Ganges Brahmaputra basins of Bangladesh: variability and predictability, In ASCE, World Water & Environmental Resources Congress, Philadelphia, June. Douglas W, Wasimi SA, Islam S The El Niño southern oscillation and long-range forecasting of flows in the Ganges. International Journal of Climatology 21: Glantz MH, Katz RW, Nicholls N (eds) Teleconnections Linking World wide Climate Anomalies. Cambridge University Press. Hossain E, Alam SS, Imam KH, Hoque MM Bangladesh country case study: impacts and response to the El Nino event. In Once Burned Twice Shy?, Glantz MH (ed.). United Nations University Press; Hulme M Validataion of large-scale precipitation fields in general circulation models. In Global Precipitation and Climate Change, Desbois M, Desalmand F (eds). Springer-Verlag; Mirza MMQ Three recent extreme floods in Bangladesh: a hydro-meteorological analysis. Natural Hazards 28: Nishat A, Faisal IM An assessment of the Institutional Mechanism for Water Negotiations in the Ganges Brahmaputra Meghna system. International Negotiations 5: Ropelewski CF, Halpert MS Quantifying southern oscillation precipitation relationship. Journal of Climate 9(5): Ward MN Diagnosis and short-lead time prediction of summer rainfall in tropical North Africa an interannual and multidecadal timescales. Journal of Climate 11: