Climate change (CC) is impacting the hydrology in the basins of the Himalayan region. Thus, this could have significant implications for people who rely on basin water for their lives and livelihoods. However, there are very few studies on the Himalayan river basins. This study aims to fill this gap by presenting a water balance for the Brahmaputra River Basin using the Soil and Water Assessment Tool (SWAT). Results show that snowmelt contributed about 6% of the total annual flow of the whole Brahmaputra, 21% of the upper Brahmaputra, and 5% of the middle Brahmaputra. The basin-wide average annual water yield (AWY) is projected to increase by 8%, with the maximum percentage increase in the pre-monsoon season. The annual snowmelt is projected to decrease by 17%, with a marked decrease during the monsoon but an increase in other seasons and the greatest percentage reduction in the upper Brahmaputra (22%). The contribution of snowmelt to AWY is projected to decrease while rain runoff will increase across the entire Brahmaputra and also in the upper and middle Brahmaputra. The impact assessment suggests that the upper Brahmaputra will be most affected by CC, followed by the middle Brahmaputra. The results can be used to support future water management planning in the basin taking into account the potential impact of CC.

Estimation of the flow generated in the Brahmaputra river basin is important for establishing an effective flood prediction and warning services as well as for water resources assessment and management. But this is a data scarce region with few and unevenly distributed hydrometeorological stations. Five high-resolution satellite rainfall products (CPC RFE2.0, RFE2.0-Modified, CMORPH, GSMaP, and TRMM 3B42) were evaluated at different spatial and temporal resolutions (daily, dekadal, monthly, and seasonal) with observed rain gauge data from 2004 to 2006 to determine their ability to fill the data gap and suitability for use in hydrological and water resources management applications. Grid-to-grid (G-G) and catchment-to-catchment (C-C) comparisons were performed using the verification methods developed by the International Precipitation Working Group (IPWG). Comparing different products, RFE2.0-Modified, TRMM 3B42, and CMORPH performed best; they all detected heavy, moderate, and low rainfall but still significantly underestimated magnitude of rainfall, particularly in orographically influenced areas. Overall, RFE2.0-Modified performed best showing a high correlation coefficient with observed data and low mean absolute error, root mean square error, and multiple bias and is reasonably good at detecting the occurrence of rainfall. TRMM 3B42 showed the second best performance. The study demonstrates that there is a potential use of satellite rainfall in a data scarce region.

A forecasting lead time of 5–10 days is desired to increase the flood response and preparedness for large river basins. Large uncertainty in observed and forecasted rainfall appears to be a key bottleneck in providing reliable flood forecasting. Significant efforts continue to be devoted to developing mechanistic hydrological models and statistical and satellite-driven methods to increase the forecasting lead time without exploring the functional utility of these complicated methods. This paper examines the utility of a data-based modeling framework with requisite simplicity that identifies key variables and processes and develops ways to track their evolution and performance. Findings suggest that models with requisite simplicity—relying on flow persistence, aggregated upstream rainfall, and travel time—can provide reliable flood forecasts comparable to relatively more complicated methods for up to 10 days lead time for the Ganges, Brahmaputra, and upper Meghna (GBM) gauging locations inside Bangladesh. Forecasting accuracy improves further by including weather-model-generated forecasted rainfall into the forecasting scheme. The use of water level in the model provides equally good forecasting accuracy for these rivers. The findings of the study also suggest that large-scale rainfall patterns captured by the satellites or weather models and their “predictive ability” of future rainfall are useful in a data-driven model to obtain skillful flood forecasts up to 10 days for the GBM basins. Ease of operationalization and reliable forecasting accuracy of the proposed framework is of particular importance for large rivers, where access to upstream gauge-measured rainfall and flow data are limited, and detailed modeling approaches are operationally prohibitive and functionally ineffective.