Predicting groundwater level changes using GRACE data

The purpose of this work is to investigate the feasibility of downscaling Gravity Recovery and Climate Experiment (GRACE) satellite data for predicting groundwater level changes and, thus, enhancing current capability for sustainable water resources management. In many parts of the world, water management decisions are traditionally informed by in situ observation networks which, unfortunately, have seen a decline in coverage in recent years. Since its launch, GRACE has provided terrestrial water storage change (TWS) data at global and regional scales. The application of GRACE data for local-scale groundwater resources management has been limited because of uncertainties inherent in GRACE data and difficulties in disaggregating various TWS components. In this work, artificial neural network (ANN) models are developed to predict groundwater level changes directly by using a gridded GRACE product and other publicly available hydrometeorological data sets. As a feasibility study, ensemble ANN models are used to predict monthly and seasonal water level changes for several wells located in different regions across the US. Results indicate that GRACE data play a modest but significantly role in the performance of ANN ensembles, especially when the cyclic pattern of groundwater hydrograph is disrupted by extreme climate events, such as the recent Midwest droughts. The statistical downscaling approach taken here may be readily integrated into local water resources planning activities.