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

A 9-km daily gridded streamflow dataset is generated using the Variable Infiltration Capacity-River Routing Model (VIC-RRM) across the Ganges-Brahmaputra-Meghna River basins over 1951-2023, forced by the ERA5-Land reanalysis data for naturalized streamflow. Physically consistent streamflow forecast data is also generated forced by the ECMWF S2S forecasts. The performance of the dataset is evaluated using observed streamflow data from three gauge stations in Bangladesh along the streams of Ganges, Brahmaputra, and Meghna Rivers, calculating the modified Kling-Gupta Efficiency (mKGE) metric for the 365-day climatology. For Ganges, Brahmaputra, and Meghna Rivers, the mKGE values of reconstructed streamflow data are 0.50, 0.75, and 0.25, respectively. Comparing with the reconstructed streamflow data, the streamflow forecasts show a good agreement with mKGE values of 1.00, 0.97, and 0.91 at three gauge stations, respectively. This dataset provides physically consistent reconstructed and forecasted streamflow data at high resolution, offering a valuable resource for the assessment of climate variability and change and the development of river basin-specific water management strategies in the Ganges-Brahmaputra-Meghna Rivers in Bangladesh.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-04-22
DOI
https://doi.org/10.1038/s41597-025-05014-8
Citations
4
Primary Topic
Hydrology and Watershed Management Studies
Type
article
Field-Weighted Citation Impact
3.27

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Byeong-Hee Kim, Jonghun Kam, Sazzad Hossain
4 citations
Scientific Data
Hydrology and Watershed Management Studies
3.27
article

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

Byeong-Hee Kim, Jonghun Kam, Sazzad Hossain
article en
4 citations

Abstract

A 9-km daily gridded streamflow dataset is generated using the Variable Infiltration Capacity-River Routing Model (VIC-RRM) across the Ganges-Brahmaputra-Meghna River basins over 1951-2023, forced by the ERA5-Land reanalysis data for naturalized streamflow. Physically consistent streamflow forecast data is also generated forced by the ECMWF S2S forecasts. The performance of the dataset is evaluated using observed streamflow data from three gauge stations in Bangladesh along the streams of Ganges, Brahmaputra, and Meghna Rivers, calculating the modified Kling-Gupta Efficiency (mKGE) metric for the 365-day climatology. For Ganges, Brahmaputra, and Meghna Rivers, the mKGE values of reconstructed streamflow data are 0.50, 0.75, and 0.25, respectively. Comparing with the reconstructed streamflow data, the streamflow forecasts show a good agreement with mKGE values of 1.00, 0.97, and 0.91 at three gauge stations, respectively. This dataset provides physically consistent reconstructed and forecasted streamflow data at high resolution, offering a valuable resource for the assessment of climate variability and change and the development of river basin-specific water management strategies in the Ganges-Brahmaputra-Meghna Rivers in Bangladesh.

Scientific DataVol. 12(1)
Pohang University of Science and Technology (KR), Institute for Forecasting of the Slovak Academy of Sciences (SK), University of Reading (GB)
National Research Foundation of Korea
Climate action
Openalex Percentile: Top 9%
Hydrology and Watershed Management Studies
3.27
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.