Abstract
Densely distributed Global Navigation Satellite System (GNSS) stations can invert the terrestrial water storage anomaly (TWSA) with high precision. However, the uneven distribution of GNSS stations greatly limits the application of TWSA inversion. The purpose of this study was to compensate for the spatial coverage of GNSS stations by simulating the vertical deformation in unobserved grids. First, a new deep learning weight loading inversion model (DWLIM) was constructed by combining the long short-term memory (LSTM) algorithm, inverse distance weight, and the crustal load model. DWLIM is beneficial for improving the inversion accuracy of TWSA based on the GNSS vertical displacement. Second, the DWLIM-based and traditional GNSS-derived TWSA methods were utilized to derive TWSA over mainland China. Furthermore, the TWSA results were compared with the TWSA solutions of the Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) model. The results indicate that the maximum Pearson’s correlation coefficient (PCC), Nash–Sutcliffe efficiency (NSE) coefficient, and root mean square error (RMSE) equal 0.81, 0.61, and 2.18 cm, respectively. The accuracy of DWLIM was higher than that of the traditional GNSS inversion method according to PCC, NSE, and RMSE, which were increased by 67.11, 128.15, and 22.75%. The inversion strategy of DWLIM can effectively improve the accuracy of TWSA inversion in regions with unevenly distributed GNSS stations. Third, this study investigated the variation characteristics of TWSA based on DWLIM in 10 river basins over mainland China. The analysis shows that the TWSA amplitudes of Songhua and Liaohe River basins are significantly higher than those of the other basins. Moreover, TWSA sequences in each river basin contain annual seasonal signals, and the wave peaks of TWSA estimates emerge between June and July. Overall, DWLIM provides a useful measure to derive TWSA in regions where GNSS stations are uneven or sparse.
Highlights
This article is an open access articleTerrestrial water storage (TWS) comprises all of the water stored on the crustal surface and underground, including snow, glaciers, soil water, groundwater, runoff, and biological water components, which is an essential part of the water cycle system [1,2]
The Global Navigation Satellite System (GNSS) vertical sequences were utilized as the true data to verify the accuracy of regression. 9The simulated of 20 results were contrasted with the GNSS vertical sequence according to the root mean square error (RMSE) and Pearson’s correlation coefficient (PCC)
The results show that the simulated according to this strategy,of which provides a reasonable data basis forwell the inverperiod term and annual amplitude the vertical crustal deformation can be simusion of lated according to this strategy, which provides a reasonable data basis for the inversion of terrestrial water storage anomaly (TWSA)
Summary
This article is an open access articleTerrestrial water storage (TWS) comprises all of the water stored on the crustal surface and underground, including snow, glaciers, soil water, groundwater, runoff, and biological water components, which is an essential part of the water cycle system [1,2]. The TWS is extraordinarily limited, only accounting for 3.47% of the total global water resources [3]. The freshwater resources of China account for only 6% of the total global water resources [4]. A series of natural disasters have occurred frequently, for example, droughts, floods, and soil erosion [8,9]. This phenomenon seriously affects human life and the economic development of society. It has become an urgent issue to scientifically and effectively manage regional water resources in China [10]
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