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Lu S, Li W, Yao G, Zhong Y, Bao L, Wang Z, Bi J, Zhu C, Guo Q. The changes prediction on terrestrial water storage in typical regions of China based on neural networks and satellite gravity data. Sci Rep 2024; 14:16855. [PMID: 39039111 PMCID: PMC11263337 DOI: 10.1038/s41598-024-67611-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 07/12/2024] [Indexed: 07/24/2024] Open
Abstract
Accurate prediction of regional terrestrial water storage change (TWSA) is of great significance for water resources planning and management, and early warning of extreme climate disasters. Aiming at the problem that the conventional methods on prediction of TWSA time series are difficult to be accurate, the six typical regions are selected in China as examples, including the upper reaches of the Yangtze River (UYR), the southwest region (SWR), the Liaohe River Basin (LRB), the North China Plain (NCP), the Qinghai-Tibet Plateau (QTP), and the Pearl River Basin (PRB). The mascon product from GRACE/GRACE-FO provided by CSR is used to extract TWSA time series in six typical areas. The improved Back Propagation (BP) neural network, Long Short-Term Memory (LSTM) neural network and the latest Bidirectional LSTM (BiLSTM-attention) neural network model based on attention mechanism are proposed to predict and analyze the regional TWSA. In the experiment, the selection of the optimal model parameters such as the number of hidden layer nodes and the number of hidden units of the neural network model is tested and analyzed in detail. Meanwhile, the model prediction results are compared with the traditional least squares method and random forest (RF) prediction method. The root mean square error (RMSE), determination coefficient (R2), Nash-Sutcliffe efficiency coefficient (NSE) and mean absolute percentage error (MAPE) were used to evaluate the accuracy of the predicted results. The results show that the improved BP, LSTM and Bi-LSTM-attention neural network models all achieve higher prediction accuracy in UYR and SWR areas. RMSE is less than 2.641 cm, R2 is as high as 0.8 or more, NSE is above 0.6, and MAPE is within 0.1. Compared with the least square method, the RMSE of the predicted results from three neural network decreased by 0.998 cm, 0.700 cm and 0.7563 on average, and the R2 increased by 81.75%, 69.89% and 72% on average. Compared with RFML method, the RMSE from three neural network is reduced by 0.601 cm, 0.316 cm and 0.360, and R2 is increased by 38.20%, 24.60% and 27.06% on average. NSE and RMSE are improved to varying degrees in the above regions. It shows that the improved BP, LSTM and BiLSTM-attention model used can effectively predict TWSA. The research methods and results in this paper can provide important reference for the rational utilization of regional water resources and disaster risk assessment.
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Affiliation(s)
- Shanbo Lu
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Shandong, Jinan, 250101, China
| | - Wanqiu Li
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Shandong, Jinan, 250101, China.
- State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, CAS, Wuhan, 430077, China.
| | - Guobiao Yao
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Shandong, Jinan, 250101, China
| | - Yulong Zhong
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, 430074, China
| | - Lifeng Bao
- State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, CAS, Wuhan, 430077, China
| | - Zhiwei Wang
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Shandong, Jinan, 250101, China
| | - Jingxue Bi
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Shandong, Jinan, 250101, China
| | - Chengcheng Zhu
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Shandong, Jinan, 250101, China
| | - Qiuying Guo
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Shandong, Jinan, 250101, China
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Li W, Bao L, Yao G, Wang F, Guo Q, Zhu J, Zhu J, Wang Z, Bi J, Zhu C, Zhong Y, Lu S. The analysis on groundwater storage variations from GRACE/GRACE-FO in recent 20 years driven by influencing factors and prediction in Shandong Province, China. Sci Rep 2024; 14:5819. [PMID: 38461310 PMCID: PMC10925065 DOI: 10.1038/s41598-024-55588-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/26/2024] [Indexed: 03/11/2024] Open
Abstract
Monitoring and predicting the regional groundwater storage (GWS) fluctuation is an essential support for effectively managing water resources. Therefore, taking Shandong Province as an example, the data from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) is used to invert GWS fluctuation from January 2003 to December 2022 together with Watergap Global Hydrological Model (WGHM), in-situ groundwater volume and level data. The spatio-temporal characteristics are decomposed using Independent Components Analysis (ICA), and the impact factors, such as precipitation and human activities, which are also analyzed. To predict the short-time changes of GWS, the Support Vector Machines (SVM) is adopted together with three commonly used methods Long Short-Term Memory (LSTM), Singular Spectrum Analysis (SSA), Auto-Regressive Moving Average Model (ARMA), as the comparison. The results show that: (1) The loss intensity of western GWS is significantly greater than those in coastal areas. From 2003 to 2006, GWS increased sharply; during 2007 to 2014, there exists a loss rate - 5.80 ± 2.28 mm/a of GWS; the linear trend of GWS change is - 5.39 ± 3.65 mm/a from 2015 to 2022, may be mainly due to the effect of South-to-North Water Diversion Project. The correlation coefficient between GRACE and WGHM is 0.67, which is consistent with in-situ groundwater volume and level. (2) The GWS has higher positive correlation with monthly Global Precipitation Climatology Project (GPCP) considering time delay after moving average, which has the similar energy spectrum depending on Continuous Wavelet Transform (CWT) method. In addition, the influencing facotrs on annual GWS fluctuation are analyzed, the correlation coefficient between GWS and in-situ data including the consumption of groundwater mining, farmland irrigation is 0.80, 0.71, respectively. (3) For the GWS prediction, SVM method is adopted to analyze, three training samples with 180, 204 and 228 months are established with the goodness-of-fit all higher than 0.97. The correlation coefficients are 0.56, 0.75, 0.68; RMSE is 5.26, 4.42, 5.65 mm; NSE is 0.28, 0.43, 0.36, respectively. The performance of SVM model is better than the other methods for the short-term prediction.
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Affiliation(s)
- Wanqiu Li
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China.
| | - Lifeng Bao
- State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, CAS, Wuhan, 430077, China
| | - Guobiao Yao
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China
| | - Fengwei Wang
- College of Surveying and Geo-Informatics, Tongji University, Shanghai, 200092, China
| | - Qiuying Guo
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China
| | - Jie Zhu
- China Earthquake Networks Center, Beijing, 100045, China
| | - Jinjie Zhu
- 801 Institute of Hydrogeology and Engineering Geology, Shandong Provincial Bureau of Geology & Mineral Resources, Jinan, 250014, China
| | - Zhiwei Wang
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China.
| | - Jingxue Bi
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China
| | - Chengcheng Zhu
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China
| | - Yulong Zhong
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, 430074, China
| | - Shanbo Lu
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China
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Zhong Y, Hu E, Wu Y, An Q, Wang C, Bai H, Gao W. Reconstructing a long-term water storage-based drought index in the Yangtze River Basin. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 883:163403. [PMID: 37059147 DOI: 10.1016/j.scitotenv.2023.163403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 03/30/2023] [Accepted: 04/05/2023] [Indexed: 06/03/2023]
Abstract
Drought is a prolonged dry period in the natural climate cycle, and is one of the most costly weather events. The Gravity Recovery and Climate Experiment (GRACE) derived terrestrial water storage anomalies (TWSA) have been widely used to assess drought severity. However, the relatively short cover period of GRACE and GRACE Follow-On limit our knowledge about the characterization and evolution of drought over decades time scale. This study proposes a standardized GRACE reconstructed TWSA index (SGRTI) to assess the drought severity based on a statistical reconstruction method calibrated by GRACE observations. Results show that the SGRTI correlates well with 6-month scale SPI and SPEI, with correlation coefficients reaching 0.79 and 0.81 in the YRB from 1981 to 2019. Soil moisture can capture drought condition like the SGRTI, while cannot further reflect deeper water storage depletion. The SGRTI is also comparable to the SRI and in-situ water level. As a case study for the Yangtze River Basin, its three sub-basins experience more frequent droughts, shorter drought duration, and lower severity drought, as identified by SGRTI during 1992-2019 relative to 1963-1991. The presented SGRTI in this study can provide a valuable supplement to the drought index before the GRACE era.
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Affiliation(s)
- Yulong Zhong
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China; Artificial Intelligence School, Wuchang University of Technology, Wuhan 430223, China
| | - E Hu
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China
| | - Yunlong Wu
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China
| | - Qing An
- Artificial Intelligence School, Wuchang University of Technology, Wuhan 430223, China
| | - Changqing Wang
- State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China.
| | - Hongbing Bai
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China
| | - Wei Gao
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China
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Cui L, He M, Zou Z, Yao C, Wang S, An J, Wang X. The Influence of Climate Change on Droughts and Floods in the Yangtze River Basin from 2003 to 2020. SENSORS (BASEL, SWITZERLAND) 2022; 22:8178. [PMID: 36365876 PMCID: PMC9658109 DOI: 10.3390/s22218178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/19/2022] [Accepted: 10/22/2022] [Indexed: 06/16/2023]
Abstract
In recent decades, extreme floods and droughts have occurred frequently around the world, which seriously threatens the social and economic development and the safety of people's lives and properties. Therefore, it is of great scientific significance to discuss the causes and characteristic quantization of extreme floods and droughts. Here, the terrestrial water storage change (TWSC) derived from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) data was used to characterize the floods and droughts in the Yangtze River basin (YRB) during 2003 and 2020. To reduce the uncertainty of TWSC results, the generalized three-cornered hat and least square methods were used to fuse TWSC results from six GRACE solutions. Then combining precipitation (PPT), evapotranspiration, soil moisture (SM), runoff, and extreme climate index data, the influence of climate change on floods and droughts in the YRB was discussed and analyzed. The results show that the fused method can effectively improve the uncertainty of TWSC results. And seven droughts and seven floods occurred in the upper of YRB (UY) and nine droughts and six floods appeared in the middle and lower of YRB (MLY) during the study period. The correlation between TWSC and PPT (0.33) is the strongest in the UY, and the response time between the two is 1 month, while TWSC and SM (0.67) are strongly correlated with no delay in the MLY. The reason for this difference is mainly due to the large-scale hydropower development in the UY. Floods and droughts in the UY and MLY are more influenced by the El Niño-Southern Oscillation (ENSO) (correlation coefficients are 0.39 and 0.50, respectively) than the Indian Ocean Dipole (IOD) (correlation coefficients are 0.19 and 0.09, respectively). The IOD event is usually accompanied by the ENSO event (the probability is 80%), and the hydrological hazards caused by independent ENSO events are less severe than those caused by these two extreme climate events in the YRB. Our results provide a reference for the study on the formation, development, and recovery mechanism of regional floods and droughts on a global scale.
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Affiliation(s)
- Lilu Cui
- School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China
| | - Mingrui He
- School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China
| | - Zhengbo Zou
- Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan 430071, China
- Gavitation and Earth Tide, National Observation and Research Station, Wuhan 430071, China
- Institute of Disaster Prevention, Sanhe 065201, China
| | - Chaolong Yao
- College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
| | - Shengping Wang
- College of Geomatics, East China University of Technology, Nanchang 330013, China
- Key Laboratory of Marine Environment Exploration Technology and Application, Ministry of Natural Resources, Guangzhou 510030, China
| | - Jiachun An
- Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China
| | - Xiaolong Wang
- Nanning Survey and Design Institute Group Co., Ltd., Nanning 530022, China
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Natural- and Human-Induced Influences on Terrestrial Water Storage Change in Sichuan, Southwest China from 2003 to 2020. REMOTE SENSING 2022. [DOI: 10.3390/rs14061369] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A quantitative understanding of changes in water resources is crucial for local governments to enable timely decision-making to maintain water security. Here, we quantified natural-and human-induced influences on the terrestrial water storage change (TWSC) in Sichuan, Southwest China, with intensive water consumption and climate variability, based on the data from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-on (GRACE-FO) during 2003–2020. We combined the TWSC estimates derived from six GRACE/GRACE-FO solutions based on the uncertainties of each solution estimated from the generalized three-cornered hat method. Metrics of correlation coefficient and contribution rate (CR) were used to evaluate the influence of precipitation, evapotranspiration, runoff, reservoir storage, and total water consumption on TWSC in the entire region and its five economic regions. The results showed that a significant improvement in the fused TWSC was found compared to those derived from a single model. The increase in regional water storage with a rate of 3.83 ± 0.54 mm/a was more influenced by natural factors (CR was 53.17%) compared to human influence (CR was 46.83%). Among the factors, the contribution of reservoir storage was the largest (CR was 42.32%) due to the rapid increase in hydropower stations, followed by precipitation (CR was 35.16%), evapotranspiration (CR was 15.86%), total water consumption (CR was 4.51%), and runoff (CR was 2.15%). Among the five economic regions, natural influence on Chengdu Plain was the highest (CR was 48.21%), while human influence in Northwest Sichuan was the largest (CR was 61.37%). The highest CR of reservoir storage to TWSC was in Northwest Sichuan (61.11%), while the highest CRs of precipitation (35.16%) and evapotranspiration (15.86%) were both in PanXi region. The results suggest that TWSC in Sichuan is affected by natural factors and intense human activities, in particular, the effect of reservoir storage on TWSC is very significant. Our study results can provide beneficial help for the management and assessment of regional water resources.
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Using the Local Drought Data and GRACE/GRACE-FO Data to Characterize the Drought Events in Mainland China from 2002 to 2020. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11209594] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Accurate quantification of drought characteristics helps to achieve an objective and comprehensive analysis of drought events and to achieve early warning of drought and disaster loss assessment. In our study, a drought characterization approach based on drought severity index derived from Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) data was used to quantify drought characteristics. In order to improve drought detection capability, we used the local drought data as calibration criteria to improve the accuracy of the drought characterization approach to determine the onset of drought. Additionally, the local precipitation data was used to test drought severity determined by the calibrated drought characterization approach. Results show that the drought event probability of detection (POD) of this approach in the four study regions increased by 61.29%, 25%, 94.29%, and 66.86%, respectively, after calibration. We used the calibrated approach to detect the drought events in Mainland China (MC) during 2016 and 2019. The results show that CAR of the four study regions is 100.00%, 92.31%, 100.00%, and 100.00%. Additionally, the precipitation anomaly index (PAI) data was used to evaluate the severity of drought from 2002 to 2020 determined by the calibrated approach. The results indicate that both have a strong similar spatial distribution. Our analysis demonstrates that the proposed approach can serve a useful tool for drought monitoring and characterization.
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