Machine learning downscaling of GRACE/GRACE-FO data to capture spatial-temporal drought effects on groundwater storage at a local scale under data-scarcity

Abstract

Abstract The continued threat from climate change and human impacts on water resources demands high-resolution and continuous hydrological data accessibility for predicting trends and availability. This study proposes a novel threefold downscaling method based on machine learning (ML) which integrates: data normalization; interaction of hydrometeorological variables; and the application of a time series split for cross-validation that produces a high spatial resolution groundwater storage anomaly (GWSA) dataset from the Gravity Recovery and Climate Experiment (GRACE) and its successor mission, GRACE Follow-On (GRACE-FO). In the study, the relationship between the terrestrial water storage anomaly (TWSA) from GRACE and other land surface and hydrometeorological variables (e.g., vegetation coverage, land surface temperature, precipitation, and in situ groundwater level data) is leveraged to downscale the GWSA. The predicted downscaled GWSA datasets were tested using monthly in situ groundwater level observations, and the results showed that the model satisfactorily reproduced the spatial and temporal variations in the GWSA in the study area, with Nash-Sutcliffe efficiency (NSE) correlation coefficient values of 0.8674 (random forest) and 0.7909 (XGBoost), respectively. Evapotranspiration was the most influential predictor variable in the random forest model, whereas it was rainfall in the XGBoost model. In particular, the random forest model excelled in aligning closely with the observed groundwater storage patterns, as evidenced by its high positive correlations and lower error metrics (Mean Absolute Error (MAE) of 54.78 mm; R-squared (R²) of 0.8674). The downscaled 5 km GWSA data (based on random forest) showed a decreasing trend in storage associated with variability in the rainfall pattern. An increase in drought severity during El Niño lengthened the full recovery time of groundwater based on historical storage trends. Furthermore, the time lag between the occurrence of precipitation and recharge was likely controlled by the drought intensity and the spatial recharge characteristics of the aquifer. Projected increases in drought severity could further increase groundwater recovery times in response to droughts in a changing climate, resetting storage to a new tipping condition. Therefore, climate change adaptation strategies must recognise that less groundwater will be available to supplement the surface water supply during droughts.

Keywords

• Barotse catchment
• Drought
• Downscaling
• GRACE TWSA
• Groundwater storage anomaly
• Machine learning