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We propose a deep learning model: long short-term memory (LSTM) networks to spatially downscale Global Recovery and Climate Experiment (GRACE)-derived terrestrial water storage anomalies (TWSA) with an objective to map groundwater level anomalies (GWLA) at 0.25° resolution for basin-scale applications. Monthly TWSA from global spherical harmonic (GSH) and global mascons (GM) during 2002 to 2017 were obtained at 1° scales for the Krishna River. Eleven hydro-climatic variables were considered to observe their dependence on TWSA and further reduced to three principal components. The LSTM’s recurrent neural networks, with a 12-month lag to control flow of information in the memory units, were applied to downscale TWSA. At basin scale, downscaled GWLA from the two GRACE solutions have reasonably captured the observed trends (r &gt; 0.6); however, GSH has underestimated the peaks (BIAS = 7.83 cm). The strong signal amplitude resulting from reduced leakage made GM a better choice over GSH in downscaling TWSA, particularly for the land–ocean mixed pixels (r GM  = 0.74, r GSH  = 0.62).

Sequential downscaling of GRACE products to map groundwater level changes in Krishna River basin