The intensity, duration, and geographic extent of floods in Bangladesh mostly depend on the combined influences of three river systems, the Ganges, Brahmaputra and Meghna (GBM). In addition, climate change is likely to have significant effects on the hydrology and water resources of the GBM basin and may ultimately lead to more serious floods in Bangladesh. However, the assessment of climate change impacts on the basin-scale hydrology by using well-calibrated hydrologic modeling has seldom been conducted in the GBM basin due to the lack of observed data for calibration and validation. In this study, a macroscale hydrologic model H08 has been applied over the basin at a relatively fine grid resolution (10 km) by integrating the fine-resolution DEM (digital elevation model) data for accurate river networks delineation. The model has been calibrated via the analysis of model parameter sensitivity and validated based on long-term observed daily streamflow data. The impacts of climate change (considering a high-emissions path) on runoff, evapotranspiration, and soil moisture are assessed by using five CMIP5 (Coupled Model Intercomparison Project Phase 5) GCMs (global circulation models) through three time-slice experiments; the present-day (1979–2003), the near-future (2015–2039), and the far-future (2075–2099) periods. Results show that, by the end of 21st century, (a) the entire GBM basin is projected to be warmed by ~4.3 °C; (b) the changes of mean precipitation (runoff) are projected to be +16.3% (+16.2%), +19.8% (+33.1%), and +29.6% (+39.7%) in the Brahmaputra, Ganges, and Meghna, respectively; and (c) evapotranspiration is projected to increase for the entire GBM (Brahmaputra: +16.4%, Ganges: +13.6%, Meghna: +12.9%) due to increased net radiation as well as warmer temperature. Future changes of hydrologic variables are larger in the dry season (November–April) than in the wet season (May–October). Amongst the three basins, the Meghna shows the highest increase in runoff, indicating higher possibility of flood occurrence. The uncertainty due to the specification of key model parameters in model predictions is found to be low for estimated runoff, evapotranspiration and net radiation. However, the uncertainty in estimated soil moisture is rather large with the coefficient of variation ranging from 14.4 to 31% among the three basins.

The densely populated delta of the three river systems of the Ganges, Brahmaputra and Meghna is highly prone to floods. Potential climate change-related increases in flood intensity are therefore of major societal concern as more than 40 million people live in flood-prone areas in downstream Bangladesh. Here we report on new flood projections using a hydrological model forced by bias-adjusted ensembles of the latest-generation global climate models of CMIP6 (SSP5-8.5/SSP1-2.6) in comparison to CMIP5 (RCP8.5/RCP2.6). Results suggest increases in peak flow magnitude of 36% (16%) on average under SSP5-8.5 (SSP1-2.6), compared to 60% (17%) under RCP8.5 (RCP2.6) by 2070–2099 relative to 1971–2000. Under RCP8.5/SSP5-8.5 (2070–2099), the largest increase in flood risk is projected for the Ganges watershed, where higher flood peaks become the ‘new norm’ as early as mid-2030 implying a relatively short time window for adaptation. In the Brahmaputra and Meghna rivers, the climate impact signal on peak flow emerges after 2070 (CMIP5 and CMIP6 projections). Flood peak synchronization, when annual peak flow occurs simultaneously at (at least) two rivers leading to large flooding events within Bangladesh, show a consistent increase under both projections. While the variability across the ensemble remains high, the increases in flood magnitude are robust in the study basins. Our findings emphasize the need of stringent climate mitigation policies to reduce the climate change impact on peak flows (as presented using SSP1-2.6/RCP2.6) and to subsequently minimize adverse socioeconomic impacts and adaptation costs. Considering Bangladesh’s high overall vulnerability to climate change and its downstream location, synergies between climate change adaptation and mitigation and transboundary cooperation will need to be strengthened to improve overall climate resilience and achieve sustainable development.

The intensity, duration, and geographic extent of floods in Bangladesh mostly depend on the combined influences of three river systems, Ganges, Brahmaputra and Meghna (GBM). In addition, climate change is likely to have significant effects on the hydrology and water resources of the GBM basins and might ultimately lead to more serious floods in Bangladesh. However, the assessment of climate change impacts on basin-scale hydrology by using well-constrained hydrologic modelling has rarely been conducted for GBM basins due to the lack of data for model calibration and validation. In this study, a macro-scale hydrologic model H08 has been applied regionally over the basin at a relatively fine grid resolution (10 km) by integrating the fine-resolution (~0.5 km) DEM data for accurate river networks delineation. The model has been calibrated via analyzing model parameter sensitivity and validated based on a long-term observed daily streamflow data. The impact of climate change on not only the runoff, but also the basin-scale hydrology including evapotranspiration, soil moisture and net radiation have been assessed in this study through three time-slice experiments; present-day (1979–2003), near-future (2015–2039) and far-future (2075–2099) periods. Results shows that, by the end of 21st century (a) the entire GBM basin is projected to be warmed by ~3°C (b) the changes of mean precipitation are projected to be +14.0, +10.4, and +15.2%, and the changes of mean runoff to be +14, +15, and +18% in the Brahmaputra, Ganges and Meghna basin respectively (c) evapotranspiration is predicted to increase significantly for the entire GBM basins (Brahmaputra: +14.4%, Ganges: +9.4%, Meghna: +8.8%) due to increased net radiation (Brahmaputra: +6%, Ganges: +5.9%, Meghna: +3.3%) as well as warmer air temperature. Changes of hydrologic variables will be larger in dry season (November–April) than that in wet season (May–October). Amongst three basins, Meghna shows the largest hydrological response which indicates higher possibility of flood occurrence in this basin. The uncertainty due to the specification of key model parameters in predicting hydrologic quantities, has also been analysed explicitly in this study and found that the uncertainty in estimation of runoff, evapotranspiration and net radiation is relatively less. However, the uncertainty in estimation of soil moisture is quite large (coefficient of variation ranges from 11 to 33% for three basins). It is significant in land use management, agriculture in particular and highlights the necessity of physical observation of soil moisture.

Storage is the only means to smooth out the discharge variation necessary to control hazards and utilize water resources. Necessary storage depends on the levels of discharge variation, required flood control and water use. The main methodologies to calculate necessary storage include mass curve method and simulation. The use of flood duration curve (FDC) and drought duration curve (DDC) that this paper presents is an alternative way that has considerable advantages over the others. The diagram of FDC-DDC serves as the classification indicator of basin hydrology and the FDC-DDC based estimates of necessary storages can be used for reservoir operation. The FDC-DDC based necessary storages pay an attention to the finite term hydrological variation rather than the asymptotic infinite memory of variation that Range analysis focuses. On the other hand the FDC-DDC focus on spatial distribution of necessary storages and try to find spatial rules of hydrological heterogeneity in necessary storages. For a case study, the Ganges-Brahmaputra-Meghna basin was selected and its spatial distribution of the FDC–DDC based necessary storages was calculated based on the discharge dataset obtained by hydrological simulation of the MRI-AGCM3.2S projections using the hydrological model BTOPMC developed at University of Yamanashi and ICHARM. The spatial distribution of necessary storages indicate the relative difficulty of managing the temporal variation of river discharges for human use at respective locality. The climate change impacts on the necessary storages were analysed and found the increase in difficulty of managing high flows and the general ease of managing low flows. But local differences were rather large that indicates the need of careful study to respond unique spatial structure of local discharge variation. The relation to catchment area was also analysed and found such regional heterogeneity diminishes into a basin average slowly in several 10,000 km². A representative elementary area of necessary storages for discharge smoothing is a new concept that deserves for further study. A creation of global maps would be a useful challenge for assessment of current state of water resources, climate change impact on water resources and for study of hydrological heterogeneity and its scale effect.