High-resolution gridded streamflow data for Ganges-Brahmaputra-Meghna River Basins in Bangladesh (1951-2023)

Area of Science:

• Hydrology
• Climate Science
• Environmental Modeling

Background:

• The Ganges-Brahmaputra-Meghna (GBM) river basins face significant water resource challenges due to climate variability and change.
• Accurate streamflow data is essential for effective water management and disaster preparedness in the region.

Purpose of the Study:

• To generate a high-resolution, daily gridded streamflow dataset for the GBM basins from 1951-2023.
• To produce physically consistent streamflow forecast data using advanced meteorological forecasts.
• To evaluate the dataset's performance against observed streamflow data.

Main Methods:

• Utilized the Variable Infiltration Capacity-River Routing Model (VIC-RRM) for streamflow reconstruction.
• Forced the VIC-RRM model with ERA5-Land reanalysis data for historical streamflow.
• Employed ECMWF S2S forecasts to generate streamflow forecast data.
• Assessed model performance using the modified Kling-Gupta Efficiency (mKGE) metric.

Main Results:

• Reconstructed streamflow data achieved mKGE values of 0.50 (Ganges), 0.75 (Brahmaputra), and 0.25 (Meghna).
• Streamflow forecasts demonstrated high agreement with observed data, with mKGE values of 1.00, 0.97, and 0.91 for the respective rivers.
• The dataset provides a valuable resource for understanding hydrological processes and predicting future streamflow.

Conclusions:

• The generated dataset offers a reliable and high-resolution source of both historical and forecasted streamflow for the GBM basins.
• This resource supports climate variability assessments and the development of targeted water management strategies.
• The study highlights the utility of physically consistent modeling for hydrological applications in data-scarce regions.