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.
Highlights
Groundwater availability assessment highly depends on groundwater monitoring [1]
The performance of the Random Forest (RF) model mostly depends on selecting appropriate biophysical variables that are highly correlated with Gravity Recovery and Climate Experiment (GRACE)
Different predictors, i.e., aspect, base flow groundwater runoff (BFGR), digital elevation model model (DEM), plant canopy surface waterwater (PCSW), ET, HF, precipitation, RZSM, slope, soilstorm moisture (SM), SSR, snow water equivalent (SWE), snow precipitation (SP), temperature, and wind speed (WS), were used to develop RF, as those are strongly co-related with groundwater
Summary
Groundwater availability assessment highly depends on groundwater monitoring [1]. Local wells are a dependable source of groundwater measurement around the world [2]. Inadequate monitoring well networks make groundwater observation less reliable [3]. Comprehending the magnitude of groundwater pumping and seasonal variability is very complex due to limited monitoring well networks at regional or local scales. Many advanced statistical solutions have been developed to model groundwater variability, such as geo-statistical analysis of groundwater data [4,5]. Groundwater modeling [6,7], and groundwater monitoring network design [8]. Imbalanced spatial distribution and irregular measurement of groundwater wells are among the main challenges for modeling groundwater
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