Development of a Remote-Sensing-Based Method to Monitor Changes in Groundwater Storage

Abstract:

Near-real-time estimates of changes in groundwater storage are critical to the effective monitoring and management of major aquifer systems worldwide. However, few methods exist that can assess both local and regional aquifer dynamics, especially in regions where groundwater monitoring wells are sparse, or data are not publicly available. We present a remote sensing approach to water mass balance estimation - the Hybrid Data Remote Sensing Assimilation (HyDRA) system, which integrates remotely sensed and in situ hydrologic data and models to estimate changes in groundwater storage at multiple spatial scales. To demonstrate this method, we quantified monthly groundwater storage changes in California’s Central Valley Watershed (CVWS; 160,000 km²) and Central Valley Alluvial Aquifer (CV; 55,000 km²). Groundwater storage changes are calculated as the residual of inflows (precipitation, streamflow, runoff) minus outflows (evapotranspiration, streamflow), and corrected for fluctuations in reservoir levels, snowpack, and soil moisture. Uncertainty is estimated using model ensembles and Triple Collocation. At the scales of the CV and CVWS, groundwater storage change estimates agree with trends, timing, and magnitude of independent estimates calculated from (a) water levels measured in groundwater wells, (b) the Gravity Recovery and Climate Experiment (GRACE) and (c) regional groundwater flow models. The assimilation of remotely sensed and in situ data to provide high spatiotemporal frequency measurements of key local to sub-regional components of the water cycle can improve water budgets, thereby contributing significantly to the sustainable management of aquifers, especially in areas with limited access to monitoring well data and hydrogeologic information.