Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia

• Main Authors: Md. Safat Sikder, Cédric H. David, George H. Allen, Xiaohui Qiao, E. James Nelson, Mir A. Matin
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2019-10-01
• Series: Frontiers in Environmental Science
• Subjects: water balance; discharge; global LSM; GLDAS; ERA-interim; GBM
• Online Access: https://www.frontiersin.org/article/10.3389/fenvs.2019.00171/full

Numerical models have become essential tools for simulating and forecasting hydro-meteorological variability, and to help better understand the Earth's water cycle across temporal and spatial scales. Hydrologic outputs from these numerical models are widely available and represent valuable alternatives for supporting water management in regions where observations are scarce, including in transboundary river basins where data sharing is limited. Yet, the wide range of existing Land Surface Model (LSM) outputs makes the choice of datasets challenging in the absence of detailed analysis of the hydrological variability and quantification of associated physical processes. Here we focus on two of the world's most populated transboundary river basins—the combined Ganges-Brahmaputra-Meghna (GBM) in South Asia and the Mekong in Southeast Asia—where downstream countries are particularly vulnerable to water related disasters in the absence of upstream hydro-meteorological information. In this study, several freely-available global LSM outputs are obtained from NASA's Global Land Data Assimilation System (GLDAS) and from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-interim/Land (ERA-interim/Land) and used to compute river discharge across these transboundary basins using a river network routing model. Simulations are then compared to historical discharge to assess runoff data quality and identify best-performing models with implications for the terrestrial water balance. This analysis examines the effects of meteorological inputs, land surface models, and their spatio-temporal resolution, as well as river network fineness and routing model parameters on hydrologic modeling performance. Our results indicate that the most recent runoff datasets yield the most accurate simulations in most cases, and suggest that meteorological inputs and the selection of the LSM may together be the most influential factors affecting discharge simulations. Conversely, the spatial and temporal resolution of the LSM and river model might have the least impact on the quality of simulated discharge, although the routing model parameters affect the timing of hydrographs.