Estimation of daily hydrological mass changes using continuous GNSS measurements in mainland China

Jiang, Zhongshan; Hsu, Ya‐Ju; Yuan, Linguo; Cheng, Shuai; Li, Qinzheng; Li, Meng

doi:10.1016/j.jhydrol.2021.126349

Cited by 24 publications

(13 citation statements)

References 52 publications

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“…Moreover, TWS suffers from uneven interannual distribution, apparent conflicts between water supply and demand, and low utilization of water resources [7]. In recent years, 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.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, there are many advantages with regard to GNSS observations, such as high accuracy and all-weather and real-time measurements [31]. Currently, the GNSS is constantly utilized to derive TWSA in distinct regions around the world, such as California [32,33], the western United States [34,35], southwest China [3,12], and mainland China [8]. In regions with dense GNSS stations, TWSA can be effectively derived using GNSS vertical arrays.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China

et al. 2022

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“…However, the uneven spatial distribution of GPS greatly limits its application to analyze geophysical phenomena [32][33][34]. The GPS stations are concentrated in Sichuan and Yunnan provinces [35]. It has become a hotpot to simulate the crustal deformation on the unobserved regions.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the success of these efforts, few studies concentrate on the application of GPS on the regions where GPS spatial distribution is coarse. Jiang et al (2021) represented a new derived algorithm of TWSA, which combined the traditional loading model and spherical Slepian basis functions in China. This study emphasized that the sparse GPS array also can be used as a method to detect the spatiotemporal variations of TWSA [35].…”

Section: Introductionmentioning

confidence: 99%

“…Jiang et al (2021) represented a new derived algorithm of TWSA, which combined the traditional loading model and spherical Slepian basis functions in China. This study emphasized that the sparse GPS array also can be used as a method to detect the spatiotemporal variations of TWSA [35]. However, these above studies focused on the density of GPS spatial distribution and did not consider how to encrypt the GPS array.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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GNSS2TWS: an open-source MATLAB-based tool for inferring daily terrestrial water storage changes using GNSS vertical data

et al. 2022

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Estimation of daily hydrological mass changes using continuous GNSS measurements in mainland China

Cited by 24 publications

References 52 publications

Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China

Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China

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

GNSS2TWS: an open-source MATLAB-based tool for inferring daily terrestrial water storage changes using GNSS vertical data

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