Decomposing land surface total water storage in the Indus, Ganges, and Brahmaputra basins

Introduction: The goal of this study is to decompose the influence of specific hydrologic reservoirs in the Earth’s critical zone that interact to create observed total water supply (TWS) anomalies in the highly altered and densely populated Indus, Ganges, and Brahmaputra drainage basins. Understanding the contributions to TWS anomalies can help find potential solutions for the sustainability of human water supply. Methods: We compare changes in the macroscale hydrology of three important High Mountain Asian drainage basins through seasonal and long-term trends in TWS. Statistical time-series analysis of nine individual TWS components modeled by a hydrologic model are used to simulate water storage terms. Results: Long-term TWS trends look similar across the study basins, we find that the drivers and causes of trends and their seasonal variability are fundamentally different in each basin. TWS declines in the Indus and Ganges watersheds are primarily driven by the depletion of aquifers (67% and 76%, respectively) due to irrigated land expansion and water overuse. The Brahmaputra lower aquifer water use stress, and its TWS drop is mostly due to the melting of glaciers, the highest rate over all three basins. The Ganges and Brahmaputra have a quasi-monotonic decline of TWS, and the Indus basin exhibits a non-monotonic trend line of TWS due to different stages of its aquifer depletion relevant to aquifer water accessibility limited by well depth thresholds. Seasonal variability is primarily controlled by soil moisture saturation, shallow groundwater levels, reservoir storage, and snow accumulation for the Ganges and Brahmaputra basins. The Indus is driven by high mountain storage of snow and glaciers. Discussion: The combination of hydrologic modeling and gravity observations show the effectiveness of identifying the critical components that make up TWS. Understanding the spatially heterogeneous drivers of observed TWS decline allows us to translate satellite observations into policy-relevant information. Because this functionality is built within a process-based hydrological model, future projections can illuminate those aspects of the hydrological cycle that require additional attention by decision makers to ensure adequate water resources are available for all.