Abstract
Groundwater has a significant contribution to water storage and is considered to be one of the sources for agricultural irrigation; industrial; and domestic water use. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a unique opportunity to evaluate terrestrial water storage (TWS) and groundwater storage (GWS) at a large spatial scale. However; the coarse resolution of GRACE limits its ability to investigate the water storage change at a small scale. It is; therefore; needed to improve the resolution of GRACE data at a spatial scale applicable for regional-level studies. In this study; a machine-learning-based downscaling random forest model (RFM) and artificial neural network (ANN) model were developed to downscale GRACE data (TWS and GWS) from 1° to a higher resolution (0.25°). The spatial maps of downscaled TWS and GWS were generated over the Indus basin irrigation system (IBIS). Variations in TWS of GRACE in combination with geospatial variables; including digital elevation model (DEM), slope; aspect; and hydrological variables; including soil moisture; evapotranspiration; rainfall; surface runoff; canopy water; and temperature; were used. The geospatial and hydrological variables could potentially contribute to; or correlate with; GRACE TWS. The RFM outperformed the ANN model and results show Pearson correlation coefficient (R) (0.97), root mean square error (RMSE) (11.83 mm), mean absolute error (MAE) (7.71 mm), and Nash–Sutcliffe efficiency (NSE) (0.94) while comparing with the training dataset from 2003 to 2016. These results indicate the suitability of RFM to downscale GRACE data at a regional scale. The downscaled GWS data were analyzed; and we observed that the region has lost GWS of about −9.54 ± 1.27 km3 at the rate of −0.68 ± 0.09 km3/year from 2003 to 2016. The validation results showed that R between downscaled GWS and observational wells GWS are 0.67 and 0.77 at seasonal and annual scales with a confidence level of 95%, respectively. It can; therefore; be concluded that the RFM has the potential to downscale GRACE data at a spatial scale suitable to predict GWS at regional scales.
Highlights
Groundwater has become the main source of irrigation and domestic water supplies.It is being abstracted at an alarming rate due to its easy access and wider distribution.Recently, groundwater abstraction and consumption have been increased due to an increase in population and, stress on agriculture
The results show that canopy water, soil moisture, and snow water equivalent are closely related to terrestrial water storage (TWS)
The results showed that random forest model (RFM) has higher accuracy than the artificial neural network (ANN) model
Summary
Groundwater has become the main source of irrigation and domestic water supplies.It is being abstracted at an alarming rate due to its easy access and wider distribution.Recently, groundwater abstraction and consumption have been increased due to an increase in population and, stress on agriculture. Groundwater has become the main source of irrigation and domestic water supplies. It is being abstracted at an alarming rate due to its easy access and wider distribution. The situation demands continuous groundwater monitoring and management at a regional scale [1,2,3,4]. A network of observational wells is used to monitor groundwater behavior at a regional scale. Establishing such networks is costly and tiresome to collect data. The study of changes in groundwater storage at a large scale is normally constrained because observational wells data have less density and limitation of the spatial extent [6]
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