Improving the Spatial Resolution of GRACE-Derived Terrestrial Water Storage Changes in Small Areas Using the Machine Learning Spatial Downscaling Method

Abstract

Gravity Recovery and Climate Experiment (GRACE) satellites can effectively monitor terrestrial water storage (TWS) changes in large-scale areas. However, due to the coarse resolution of GRACE products, there is still a large number of deficiencies that need to be considered when investigating TWS changes in small-scale areas. Hence, it is necessary to downscale the GRACE products with a coarse resolution. First, in order to solve this problem, the present study employs modeling windows of different sizes (Window Size, WS) combined with multiple machine learning algorithms to develop a new machine learning spatial downscaling method (MLSDM) in the spatial dimension. Second, The MLSDM is used to improve the spatial resolution of GRACE observations from 0.5° to 0.25°, which is applied to Guantao County. The present study has verified the downscaling accuracy of the model developed through the combination of WS3, WS5, WS7, and WS9 and jointed with Random Forest (RF), Extra Tree Regressor (ETR), Adaptive Boosting Regressor (ABR), and Gradient Boosting Regressor (GBR) algorithms. The analysis shows that the accuracy of each combined model is improved after adding the residuals to the high-resolution downscaled results. In each modeling window, the accuracy of RF is better than that of ETR, ABR, and GBR. Additionally, compared to the changes in the TWS time series that are derived by the model before and after downscaling, the results indicate that the downscaling accuracy of WS5 is slightly more superior compared to that of WS3, WS7, and WS9. Third, the spatial resolution of the GRACE data was increased from 0.5° to 0.05° by integrating the WS5 and RF algorithm. The results are as follows: (1) The TWS (GWS) changes before and after downscaling are consistent, decreasing at −20.86 mm/yr and −21.79 mm/yr (−14.53 mm/yr and −15.46 mm/yr), respectively, and the Nash–Sutcliffe efficiency coefficient (NSE) and correlation coefficient (CC) values of both are above 0.99 (0.98). (2) The CC between the 80% deep groundwater well data and the downscaled GWS changes are above 0.70. Overall, the MLSDM can not only effectively improve the spatial resolution of GRACE products but also can preserve the spatial distribution of the original signal, which can provide a reference scheme for research focusing on the downscaling of GRACE products.

Advancing SDGs: Predicting Future Shifts in Saudi Arabia’s Terrestrial Water Storage Using Multi-Step-Ahead Machine Learning Based on GRACE Data

“…The growth and sustainability of human development are reliant on the availability of water resources [1,2] and social development [3]. Water can be considered an essential resource to supply industrial, domestic, and agricultural demands.…”

“…Traditional approaches, such as large-scale monitoring networks, need a significant amount of human and material resources to monitor water resources developments [8,9]. As a result, various initiatives have been undertaken in recent years to better the spatiotemporal aspects of water resources changes, such as hydrological modeling and satellite remote sensing [3].…”

“…To address this issue, various techniques have been applied, including simple interpolation and data assimilation methods. In recent times, the use of artificial intelligence (AI) and machine learning (ML) has shown potential in enhancing the precision and efficiency of modeling changes in water supplies via the analysis of GRACE satellite data [3,[26][27][28][29]. Different models have been employed, including ANN, fuzzy logic, and hybrid learning [30].…”

Improved Drought Characteristics in the Pearl River Basin Based on Reconstructed GRACE Solution with Enhanced Temporal Resolution

“…The purpose of the GRACE satellite is to monitor changes in the time variable gravity field with a time interval of about 30 days [23,38]. Therefore, many scholars reconstructed it to improve its spatial resolution [39,40] or to fill in the gap time [41].…”

Multi-objective optimization of particle gluing operating parameters in particleboard production based on improved machine learning algorithms

“…Yang et al [8] used Random Forest algorithm to develop a prediction model for 23 operating parameters such as particle gluing to predict the mechanical properties of PB. Yet too many dimensions would reduce the prediction accuracy of the Random Forest model [9].…”