Abstract
Dense Global Position System (GPS) arrays can be used to invert the terrestrial water-storage anomaly (TWSA) with higher accuracy. However, the uneven distribution of GPS stations greatly limits the application of GPS to derive the TWSA. Aiming to solve this problem, we grid the GPS array using regression to raise the reliability of TWSA inversion. First, the study uses the random forest (RF) model to simulate crustal deformation in unobserved grids. Meanwhile, the new Machine-Learning Loading-Inverted Method (MLLIM) is constructed based on the traditional GPS derived method to raise the truthfulness of TWSA inversion. Second, this research selects southwest China as the study region, the MLLIM and traditional GPS inversion methods are used to derive the TWSA, and the inverted results are contrasted with datasets of the Gravity Recovery and Climate Experiment (GRACE) Mascon and the Global Land Data Assimilation System (GLDAS) model. The comparison shows that values of Pearson Correlation Coefficient (PCC) between the MLLIM and GRACE and GRACE Follow-On (GRACE-FO) are equal to 0.91 and 0.88, respectively; and the values of R-squared (R2) are equal to 0.76 and 0.65, respectively; the values of PCC and R2 between MLLIM and GLDAS solutions are equal to 0.79 and 0.65. Compared with the traditional GPS inversion, the MLLIM improves PCC and R2 by 8.85% and 7.99% on average, which indicates that the MLLIM can improve the accuracy of TWSA inversion more than the traditional GPS method. Third, this study applies the MLLIM to invert the TWSA in each province of southwest China and combines the precipitation to analyze the change of TWSA in each province. The results are as follows: (1) The spatial distribution of TWSA and precipitation is coincident, which is highlighted in southwest Yunnan and southeast Guangxi; (2) this study compares TWSA of MLLIM with GRACE and GLDAS solutions in each province, which indicates that the maximum value of PCC is as high as 0.86 and 0.94, respectively, which indicates the MLLIM can be used to invert the TWSA in the regions with sparse GPS stations. The TWSA based on the MLLIM can be used to fill the vacancy between GRACE and GRACE-FO.
Highlights
Terrestrial water is crucial for supporting industrial, agricultural, and human life in arid and semiarid areas, while accounting for only 3.5% of global water resources
This study demonstrated that the Global Position System (GPS) array plays a complementary part in Gravity Recovery and Climate Experiment (GRACE) for studying annual changes at a regional scale [38]
The follow-up studies will process the phase difference to achieve better fitting with the. It is essential for the inversion of terrestrial water-storage anomalies (TWSA) to obtain the crustal deformation effectively, which determines the reliability of the inversion
Summary
Terrestrial water is crucial for supporting industrial, agricultural, and human life in arid and semiarid areas, while accounting for only 3.5% of global water resources. In China, the uneven spatiotemporal distribution of terrestrial water has always been a serious problem for the survival and development of human society [1,2]. It is crucial to observe terrestrial water-storage anomalies (TWSA). The GRACE mission is organized by National Aeronautics and Space Administration (NASA) in 2002, which provides a useful and effective method for the large-scale spatiotemporal detection of TWSA [10]. Previous research has demonstrated that GRACE satellites can detect the trend and seasonal features of the water storage with an unprecedented accuracy [11,12]. Due to the rough observation of GRACE satellites, it is negative to derive TWSA for the small-scale regions [13,14]. It is of great urgency to develop an alternative method to fill the gap between GRACE and GRACE-FO [17]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
