Analysis and Prediction of Temporal and Spatial Evolution of Groundwater Storage by Combined SAR-GRACE Satellite Data

被引:1
|
作者
An, Yan [1 ]
Yang, Fan [1 ,2 ]
Xu, Jia [1 ]
Ren, Chuang [1 ]
Hu, Jin [3 ]
Luo, Guona [4 ]
机构
[1] Liaoning Tech Univ, Sch Geomat, Fuxin 123000, Peoples R China
[2] Liaoning Tech Univ, Acad Sci & Technol, Fuxin 123000, Peoples R China
[3] POWERCHINA Beijing Engn Corp Ltd, Beijing 100000, Peoples R China
[4] Tarim Univ, Coll Water Hydraul & Architectural Engn, Alar 843300, Peoples R China
关键词
Climate change; Water resources; Water storage; Surface morphology; Long short term memory; Predictive models; Deformation; Synthetic aperture radar; Satellites; Gravity measurement; Water cycle; Urban areas; Interferometry; Information retrieval; Time series analysis; GRACE; InSAR; groundwater storage; surface deformation; VMD; LSTM; LUIS VALLEY; VARIABILITY; ALGORITHM; FIELD; AREA;
D O I
10.1109/ACCESS.2024.3368423
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Regional surface deformation resulting from the development and utilization of underground space resources poses a significant threat to the safety of urban areas, and the combination of Synthetic Aperture Radar and Gravity Recovery and Climate Experiment (GRACE) satellite data has become a new means to study the impact of underground space evolution on surface deformation. We combine the Interferometric Synthetic Aperture Radar (InSAR) technology and gravity satellite data to extract information on regional surface deformation and groundwater storage changes in Shanxi Province, to explore the patterns of their temporal and spatial variations, to discover their links with seasonal climate change, to re-conceptualize the laws of the regional water cycle, and to quantify the contribution of multiple fields to the evolution of the surface. Furthermore, we propose a novel multi-source neural network prediction model (LSTM/BP) based on signal decomposition (VMD) and algorithm optimization to handle the complex time series characteristics of groundwater storage. Our findings reveal that groundwater storage in Shanxi Province has been consistently declining, with a monthly deficit rate of approximately 1.05 mm. Additionally, there is a notable spatial variation in the annual rate of change, ranging from -21 to 4 mm/year from north to south. Furthermore, we observe a close relationship between inter-annual and seasonal groundwater storage changes and local rainfall patterns, and we find that regional surface deformation is influenced by these groundwater storage changes. The new prediction model outperforms other models, with a root mean square error of 1.56 mm and a correlation coefficient of more than 0.98 on the test set. The model improves the prediction accuracy of the groundwater reserves in the basin, and it can be used to provide a reference for the comprehensive management of the groundwater in Shanxi Province, the rational development of mineral resources, and other major national needs.
引用
收藏
页码:33671 / 33686
页数:16
相关论文
共 50 条
  • [31] Influence of Land Use and Land Cover Changes and Precipitation Patterns on Groundwater Storage in the Mississippi River Watershed: Insights from GRACE Satellite Data
    Dash, Padmanava
    Shekhar, Sushant
    Paul, Varun
    Feng, Gary
    REMOTE SENSING, 2024, 16 (22)
  • [32] Groundwater storage loss in the central valley analysis using a novel method based on in situ data compared to GRACE-derived data
    Stevens, Michael D.
    Ramirez, Saul G.
    Martin, Eva-Marie H.
    Jones, Norman L.
    Williams, Gustavious P.
    Adams, Kyra H.
    Ames, Daniel P.
    Pulla, Sarva T.
    ENVIRONMENTAL MODELLING & SOFTWARE, 2025, 186
  • [33] Analysis of Groundwater Storage Fluctuations Using GRACE and Remote Sensing Data in Wadi As-Sirhan, Northern Saudi Arabia
    Alshehri, Fahad
    Mohamed, Ahmed
    WATER, 2023, 15 (02)
  • [34] Spatio-temporal evolution and teleconnection factor analysis of groundwater drought based on the GRACE mascon model in the Yellow River Basin
    Wang, Fei
    Lai, Hexin
    Li, Yanbin
    Feng, Kai
    Tian, Qingqing
    Guo, Wenxian
    Qu, Yanping
    Yang, Haibo
    JOURNAL OF HYDROLOGY, 2023, 626
  • [35] Upwelling dynamics in the Baltic Sea studied by a combined SAR/infrared satellite data and circulation model analysis
    Gurova, Evgenia
    Lehmann, Andreas
    Ivanov, Andrei
    OCEANOLOGIA, 2013, 55 (03) : 687 - 707
  • [36] Analysis of terrestrial water storage variations in South Korea using GRACE satellite and GLDAS data in Google Earth Engine
    Cho, Younghyun
    HYDROLOGICAL SCIENCES JOURNAL, 2024, 69 (08) : 1032 - 1045
  • [37] Quantification of Groundwater Storage Variations and Stressed Areas Using Multi-temporal GRACE Data: A Case Study of Upper Indus Plains, Pakistan
    Amin, M.
    Khan, M. R.
    Jamil, Ahsan
    ADVANCES IN REMOTE SENSING AND GEO INFORMATICS APPLICATIONS, 2019, : 299 - 304
  • [38] The spatial and temporal variation of the terrestrial water storage anomaly (TWSA) of Iraq for the period 2002-2019 based on GRACE gravity data
    Al-Abadi, Alaa M.
    Al-Mohammdawi, Jawad A.
    Abass, Ali K.
    Jabbar, Fadhil K.
    Mohamod, Majid S.
    Alzahrani, Hassan
    KUWAIT JOURNAL OF SCIENCE, 2024, 51 (04)
  • [39] Multi-Temporal Analysis of Cropping Patterns and Intensity Using Optical and SAR Satellite Data for Sustaining Agricultural Production in Tamil Nadu, India
    Pazhanivelan, Sellaperumal
    Kumaraperumal, Ramalingam
    Priya, Manchuri Vishnu
    Rengabashyam, Kalpana
    Shankar, Kanaka
    Raj, Moorthi Nivas
    Yadav, Manoj Kumar
    SUSTAINABILITY, 2025, 17 (04)
  • [40] Spatial-temporal analysis of groundwater well features from neural network prediction of hexavalent chromium concentration
    de la Noval, Alejandro J.
    Upadhyay, Himanshu
    Lagos, Leonel
    Soni, Jayesh
    Prabakar, Nagarajan
    SCIENTIFIC REPORTS, 2024, 14 (01):