Estimating High-Resolution Groundwater Storage from GRACE: A Random Forest Approach

Abstract

Gravity Recovery and Climate Experiment (GRACE) data have become a widely used global dataset for evaluating the variability in groundwater storage for the different major aquifers. Moreover, the application of GRACE has been constrained to the local scale due to lower spatial resolution. The current study proposes Random Forest (RF), a recently developed unsupervised machine learning method, to downscale a GRACE-derived groundwater storage anomaly (GWSA) from 1° × 1° to 0.25° × 0.25° in the Northern High Plains aquifer. The RF algorithm integrated GRACE to other satellite-based geospatial and hydro-climatological variables, obtained from the Noah land surface model, to generate a high-resolution GWSA map for the period 2009 to 2016. This RF approach replicates local groundwater variability (the combined effect of climatic and human impacts) with acceptable Pearson correlation (0.58 ~ 0.84), percentage bias (−14.67 ~ 2.85), root mean square error (15.53 ~ 46.69 mm), and Nash-Sutcliffe efficiency (0.58 ~ 0.84). This developed RF model has significant potential to generate finer scale GWSA maps for managing groundwater at both local and regional scales, especially for areas with sparse groundwater monitoring wells.

Future Changes in Water Supply and Demand for Las Vegas Valley: A System Dynamic Approach based on CMIP3 and CMIP5 Climate Projections

“…This study assumes a constant groundwater contribution and evaporation loss in the future due to the difficulty in assessing the future groundwater condition in LVV under the changing climate. However, this assumption cannot be entirely justified, especially for long-term decision making, as it has been shown by previous studies that the change in precipitation trends, changes in subsurface conditions, and many other factors severely affect the status of groundwater [71,72]. Rise in temperature due to climate change in the future will tend to increase evaporation loss, resulting in reduced storage.…”

Machine Learning application for modeling high-resolution groundwater storage variations in North China Plain

“…Random Forest (RF) model has been used in various geoscience applications such as global soil property mapping (Hengl et al, 2017), downscaling GRACE data (Milewski et al, 2019, Rahaman et al, 2019, or assessing the groundwater overextraction related contamination (Smith et al, 2018). The algorithm is well-described in Breiman, (2001) and we discuss it concisely here.…”

Groundwater Prediction Using Machine-Learning Tools

“…In addition to these techniques, a range of machine-learning techniques have been applied to the problem, including MLP in [12,[19][20][21][22][23], SVR in [19,24] and recently RFs in [25,26]. The use of XGB is rare in the scheme of groundwater prediction, and is found in only a few studies such as [27,28].…”