Unsupervised Representation Learning of GRACE Improves Groundwater Predictions

Abstract

Groundwater is a crucial source of the world’s drinking and irrigation water. Nonetheless, it is being rapidly depleted in many parts of the world. To enact policy decisions to preserve this precious resource, policymakers need real-time data on the groundwater levels in their local area. However, groundwater monitoring wells are costly and scarce in supply. The use of satellite imagery is a promising alternative with its ability to provide continuous information over a large area. Machine learning has also emerged as an alternative to computationally intensive physics-based models. However, advancements in machine learning such as unsupervised learning methods have never been translated to groundwater modeling. Thus, in this paper, learned representations were generated for the GRACE satellite for the first time. When used as an input to groundwater prediction models, the learned representations reduce the root mean square error (RMSE) by up to 19% and improve the Nash–Sutcliffe efficiency (NSE) by up to 8x compared to traditional satellite data inputs at three different spatial scales: national, state, and county. The learned representations are able to discern fine-grained patterns from the coarse satellite data, globally downscaling the GRACE satellite. Crucially, the globally trained representations have the potential to improve the performance of virtually every machine learning-based groundwater prediction model. With accurate measurements, local officials are empowered to make proactive decisions to ensure the stability of their region’s water.