Filling Temporal Gaps within and between GRACE and GRACE-FO Terrestrial Water Storage Changes over Indian Sub-Continent using Deep Learning.

Abstract

Terrestrial water storage (TWS) anomalies from Gravity Recovery and Climate Experiment (GRACE) and its follow on GRACE-FO satellite missions provide a unique opportunity to measure the impact of different climate extremes and human intervention on water use at regional and continental scales. However, temporal gaps within GRACE and GRACE-FO mission (GRACE: 20 months, between GRACE and GRACE-FO: 11 months and GRACE-FO: 2 months) pose difficulties in analyzing spatiotemporal variations in TWS. In this study, Convolutional Long Short-Term Memory Neural Networks (CNN-LSTM) model was developed to fill these gaps and reconstruct the TWS for the Indian subcontinent (April 2002-July 2022). Various meteorological and climatic variables, such as precipitation, temperature, run-off, evapotranspiration, and vegetation, have been integrated to predict GRACE TWS. The performance of the models was evaluated with the help of Pearson’s correlation coefficient (PR), Nash-Sutcliffe efficiency (NSE), and Normalised Root Mean Square Error (NRMSE). Results indicate that the CNN-LSTM model yielded a mean PR of 0.94 and 0.89, NSE of 0.87 and 0.8, and NRMSE of 0.075 and 0.101 on training and testing, respectively. Overall, the CNN-LSTM achieved good performance except in the northwestern region of India, which showed a relatively poor performance might be due to high anthropogenic activity and arid climatic conditions. Further reconstructed time series were used to study the Spatiotemporal variations of TWS over the Indian Subcontinent.Keywords: GRACE; Deep Learning; TWSA; Indian subcontinent