A non-stationary downscaling and gap-filling approach for GRACE/GRACE-FO data under climatic and anthropogenic influences

Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) are being increasingly used as valuable data sources for hydrological monitoring. However, their coarse spatial resolution is considered as a limitation for regional studies, especially in areas with remarkable hydroclimate variability. In this study, a novel approach is presented for downscaling, and gap filling of terrestrial water storage (TWS) in Tehran province, Iran. Non-stationarity in the GRACE/GRACE-FO derived TWS is a significant challenge for predictive models. In this regard, the Hodrick–Prescott filter was adopted to detrend the TWS data. Afterward, several machine learning and deep learning techniques are employed for TWS prediction using Global Land Data Assimilation System and the fifth-generation ECMWF reanalysis (ERA5) datasets. The methodology is employed for bridging the gap between GRACE and GRACE-FO as well. Subsequently, the models are trained with different combinations of input variables and their performance is evaluated against the actual values. In parallel, a separate regression model based on the temporal index of the sample is developed for trend estimation and highlighting the role of anthropogenic activities. The proposed methodology is employed for bridging the gap between GRACE and GRACE-FO as well. The models with the highest accuracy are fed by input data with a spatial resolution of 0.25° × 0.25° to obtain fine-resolution TWS. Finally, the downscaled TWS derived from the predictive model is applied to calculate groundwater storage (GWS). The monthly TWS prediction results exhibit a strong correlation (CC = 0.93) and a low error (RMSE = 4.75 cm), underscoring the effectiveness of the proposed approach. TWS and GWS computations reveal rapid declines in groundwater-level prevailing by anthropogenic factors which exacerbate water crisis issues and environmental problems in the study area.