Published: April 9, 2021 By

The Ganges-Brahmaputra-Meghna (GBM) rivers carry a massive sediment load that feeds the world鈥檚 largest depositional system: the GBM megadelta. The mass of sediment transported annually by the GBM rivers has not been well constrained; previous estimates range between 0.5 and 2.4 BT/year. The present study attempts to resolve the sediment load of the GBM rivers to estimate their future sediment discharge regimes, a task of utmost importance for informing coastal management and predicting the sustainability of the GBM delta into the 21st century. A comprehensive modeling effort was conducted using HydroTrend, a well-established, climate-driven, water balance and sediment transport model. HydroTrend simulated daily water and sediment discharge from 1975 to 2100 at the Ganges and Brahmaputra basin outlets, driven by precipitation and temperature data spanning five global climate models (GFDL-ESM2M, HadGEM2, IPSL_CM5A, MIROC-ESM-CHEM, and NORESM-M) and three emission scenarios (reference, RCP4.5, and RCP8.5). The model was calibrated and validated using biweekly water discharge and suspended sediment concentration (SSC) data newly collected in 2019-2020, established sediment rating curves, and historic water discharge data over the reference scenario: 1975-2000. Results suggest HydroTrend simulates mean water discharge well, with percent errors of 2% and -7% for the Ganges and Brahmaputra rivers, respectively, over the reference scenario. The annual sediment load for the combined rivers was found to be on the low end of the previously estimated range (<1 BT/yr), determined from recently collected water discharge and SSC measurements. Simulations show that mean water and sediment discharge may increase by ~30% and ~50%, respectively, by end of century for the combined Ganges-Brahmaputra rivers. Refined sediment flux results will fuel macro-scale modeling efforts of GBM delta morphology, which will enhance our understanding of this complex deltaic system and can support the design of climate resilient adaptation measures.