Inverted Algorithm of Terrestrial Water-Storage Anomalies Based on Machine Learning Combined with Load Model and Its Application in Southwest China

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.

Spatial-Temporal Dynamic Evolution of Land Deformation Driven by Hydrological Signals around Chaohu Lake

“…Coefficient of determination takes the value of 0 to 1, it close to 1 indicates that the model is of good quality and the model is credible, and the simulation results are generally consistent with the observation results. The calculation equation is as follows [ 40 ]: …”

Inversion of GNSS Vertical Displacements for Terrestrial Water Storage Changes Using Slepian Basis Functions

“…2 of 18 such as the United States (Argus et al, 2017;Borsa et al, 2014;Enzminger et al, 2018;Fu et al, 2015;Jin & Zhang, 2016;Shen et al, 2020), China (Fok & Liu, 2019;Hsu et al, 2020;Jiang, Hsu, Yuan, & Huang, 2021;Lai et al, 2020;Li et al, 2022;Shen et al, 2021;Zhang et al, 2016;Zhong et al, 2020) and South America (Ferreira et al, 2019). For example, Borsa et al (2014) used the GNSS-derived TWS changes to reveal the ongoing drought in the western United States, and the results showed that GNSS-derived TWS changes presented good consistency with precipitation and streamflow.…”

“…(2014) first inverted the GNSS vertical displacements for regional TWS changes using the Green's function method in California, and the spatial resolution of GNSS‐derived TWS changes could reach ∼50 km. Subsequently, the Green's function method has been widely used to invert GNSS observations for regional TWS changes in different regions, such as the United States (Argus et al., 2017; Borsa et al., 2014; Enzminger et al., 2018; Fu et al., 2015; Jin & Zhang, 2016; Shen et al., 2020), China (Fok & Liu, 2019; Hsu et al., 2020; Jiang, Hsu, Yuan, & Huang, 2021; Lai et al., 2020; Li et al., 2022; B. Liu et al., 2022; Shen et al., 2021; Zhang et al., 2016; Zhong et al., 2020) and South America (Ferreira et al., 2019). For example, Borsa et al.…”

Evaluating the Hydrological Components Contributions to Terrestrial Water Storage Changes in Inner Mongolia with Multiple Datasets

“…As GRACE/GFO is not able to measure vertical profile of TWS changes, auxiliary data are used to isolate TWS changes into individual hydrological component [ 46 ], such as evapotranspiration [ 47 , 48 ], basin discharge [ 49 , 50 ], soil moisture [ 51 , 52 ] and groundwater storage (GWS) [ 53 , 54 , 55 ]. Machine learning techniques are employed to extract valuable insights from the large volumes of remote sensing multiply data that are generated for mapping terrestrial water changes [ 56 ] and monitoring areas with high hydrological changes [ 57 ] and might be used for evaluate component contributions as well. Revealing the contributions of different storage components to total TWS changes is crucial for understanding the water cycle processes and water resources management.…”