Assessment of Continental Scale Water Balance for Indian Land Data Assimilation System (ILDAS)

Abstract

A land data assimilation system plays a pivotal role in effective water resources management by providing reliable estimates of various land surface states and fluxes including water balance components. An Indian Land Data Assimilation System (ILDAS) has been setup that aims to provide spatially consistent, high resolution and reliable estimates of land surface states, water balance and energy fluxes over the Indian mainland. The ILDAS is built on NASA’s Land Information System Framework (LISF), which is an open-source framework that enables a multi-model, multi-data approach to terrestrial modeling. In this study, we evaluated the water balance components simulated using ILDAS while driven by three reanalysis meteorological forcing datasets: Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and ECMWF's ERA5. The multi-site autocalibration is performed on multiple catchments to further improve the performance of ILDAS. We have used Kling-Gupta Efficiency (KGE) and its components to evaluate the major components of the terrestrial water balance: surface runoff, soil moisture, terrestrial water storage anomalies, evapotranspiration, and monthly mean streamflow, against a combination of satellite and in-situ observation datasets. We also assessed the uncertainty and bias due to spatio-temporal heterogeneity in the forcing precipitation by validating against gauge-based gridded precipitation provided by Indian Meteorological Department (IMD). Overall, considering nationwide median KGE scores, CHIRPS showed the highest performance for surface runoff and streamflow. However, MERRA-2 performed the highest in soil moisture simulation whereas ERA5 showed best results in total evapotranspiration.