A study on time lags between groundwater changes and land subsidence based on GRACE and InSAR data

doi:10.6046/zrzyyg.2023208

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(5341 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

The increasing dependence on groundwater in the Hexi region has led to a significant drop in the groundwater table, which has induced land subsidence in some areas. Studying the relationship between groundwater changes and land subsidence hysteresis in the Hexi region holds great significance for local water resource management, land use planning, and agricultural development. This study determined the changing rate of groundwater in the study area from 2010 to 2017 using the GRACE and GLDAS data and verified the reliability of the inverted groundwater changes by combining measured data from monitoring wells. Then, this study derived the surface deformation rate of the local subsidence areas from October 2014 to June 2017 using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique, as well as comparing and validating the results using the persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technique. Finally, this study analyzed the relationship between groundwater changes and surface subsidence data using fast Fourier transform and time-delay correlation analysis. The results indicate that the time lags between land subsidence and groundwater changes were 74~86 d, 61~80 d, 80~99 d, and 74~99 d, respectively in the Linze, Ganzhou, Liangzhou, and Jinchuan subsidence areas, with respective correlation coefficients ranging from 0.541 to 0.593, from 0.589 to 0.689, from 0.600 to 0.750, and 0.543 to 0.630, respectively. The results of this study will provide a scientific basis for water resource management, land use planning, and agricultural development in the Hexi region.

Keywords GRACE SBAS-InSAR PS-InSAR groundwater change land subsidence hysteresis

ZTFLH:

TP79

Issue Date: 17 February 2025

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Xiaoqiang WEI
	Guolin YANG
	Tao LIU
	Ming SHAO
	Zhigang MA

Cite this article:

Xiaoqiang WEI,Guolin YANG,Tao LIU, et al. A study on time lags between groundwater changes and land subsidence based on GRACE and InSAR data[J]. Remote Sensing for Natural Resources, 2025, 37(1): 122-130.

URL:

https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023208 OR https://www.gtzyyg.com/EN/Y2025/V37/I1/122

Fig.1 Flowchart of PS-InSAR data processing

Fig.2 Flowchart of SBAS-InSAR data processing

Fig.3 Annual average subsidence rate of the subsidence area

Fig.4-1 Distribution of annual average subsidence rate

Fig.4-2 Distribution of annual average subsidence rate

Fig.5 Correlation between PS and SBAS annual average subsidence rate

Fig.6 Annual rate of change in water storage

Fig.7 Changes in inverted groundwater and measured groundwater changes

Tab.1 Lag time of land subsidence compared with groundwater change

Tab.2 Correlation coefficient between land subsidence and groundwater change

Fig.8 Relationship between land subsidence and groundwater change

Fig.9 Spatial distribution of lag time and spatial variation of correlation coefficient

[1]	于海若, 宫辉力, 陈蓓蓓, 等. 新水情下利用InSAR-GRACE卫星的新兴风险预警与城市地下空间安全展望[J]. 国土资源遥感, 2020, 32(4):16-22.doi:10.6046/gtzyyg.2020.04.03.
[1]	Yu H R, Gong H L, Chen B B, et al. Emerging risks and the prospect of urban underground space security based on InSAR-GRACE satellite under the new hydrological background[J]. Remote Sen-sing for Land and Resources, 2020, 32(4):16-22.doi:10.6046/gtzyyg.2020.04.03.
[2]	Castellazzi P, Martel R, Galloway D L, et al. Assessing groundwater depletion and dynamics using GRACE and InSAR: Potential and limitations[J]. Ground Water, 2016, 54(6): 768-780. doi: 10.1111/gwat.12453 pmid: 27576068
[3]	Castellazzi P, Longuevergne L, Martel R, et al. Quantitative mapping of groundwater depletion at the water management scale using a combined GRACE/InSAR approach[J]. Remote Sensing of Environment, 2018,205: 408-418.
[4]	Rodell M, Velicogna I, Famiglietti J S. Satellite-based estimates of groundwater depletion in India[J]. Nature, 2009, 460(7258): 999-1002.
[5]	虎小强, 杨树文, 闫恒, 等. 基于时序InSAR的新疆阿希矿区地表形变监测与分析[J]. 自然资源遥感, 2023, 35(1):171-179.doi:10.6046/zrzyyg.2021415.
[5]	Hu X Q, Yang S W, Yan H, et al. Time-series InSAR-based monitoring and analysis of surface deformation in the Axi mining area,Xinjiang[J]. Remote Sensing for Natural Resources, 2023, 35(1):171-179.doi:10.6046/zrzyyg.2021415.
[6]	张庆洁, 赵争, 贾李博, 等. 黄河三角洲地面沉降现状及影响因素分析[J]. 测绘科学, 2022, 47(12):165-173.
[6]	Zhang Q J, Zhao Z, Jia L B, et al. Analysis of land subsidence status and influencing factors in Yellow River Delta[J]. Science of Surveying and Mapping, 2022, 47(12):165-173.
[7]	杨旺, 何毅, 张立峰, 等. 甘肃金川矿区地表三维形变InSAR监测[J]. 自然资源遥感, 2022, 34(1):177-188.doi:10.6046/zrzyyg.2021107
[7]	Yang W, He Y, Zhang L F, et al. InSAR monitoring of 3D surface deformation in Jinchuan mining area,Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1):177-188.doi:10.6046/zrzyyg.2021107.
[8]	Guo J, Zhou L, Yao C, et al. Surface subsidence analysis by multi-temporal InSAR and GRACE: A case study in Beijing[J]. Sensors, 2016, 16(9): 1495.
[9]	Vasco D W, Kim K H, Farr T G, et al. Using Sentinel-1 and GRACE satellite data to monitor the hydrological variations within the Tulare Basin,California[J]. Scientific Reports, 2022, 12(1): 3867.
[10]	Massoud E C, Liu Z, Shaban A, et al. Groundwater depletion signals in the Beqaa Plain,Lebanon:Evidence from GRACE and Sentinel-1 data[J]. Remote Sensing, 2021, 13(5): 915.
[11]	费太政, 常晓涛, 朱广彬, 等. 利用GRACE与Sentinel-1反演地下水变化与地表沉降研究[J]. 测绘科学, 2023, 48(1):140-147.
[11]	Fei T Z, Chang X T, Zhu G B, et al. Study on inversion of ground-water change and surface subsidence using GRACE and Sentinel-1[J]. Science of Surveying and Mapping, 2023, 48(1):140-147.
[12]	李红英, 李岩瑛, 王云鹏, 等. 河西走廊西部沙尘暴时空差异及其动力分析[J]. 干旱区资源与环境, 2022, 36(10):104-112.
[12]	Li H Y, Li Y Y, Wang Y P, et al. Temporal and spatial differences and dynamic analysis of sandstorms in the west of Hexi Corridor[J]. Journal of Arid Land Resources and Environment, 2022, 36(10):104-112.
[13]	李平平. 甘肃省地下水超采区地面沉降控制区判定方法和结果探讨[J]. 中国农村水利水电, 2019(6):74-77.
[13]	Li P P. Determination method and discussion of ground subsidence control area of groundwater overmining area in Gansu Province[J]. China Rural Water and Hydropower, 2019(6):74-77.
[14]	Zheng M, Deng K, Fan H, et al. Monitoring and analysis of surface deformation in mining area based on InSAR and GRACE[J]. Remote Sensing, 2018, 10(9):1392.
[15]	Wang Q, Zheng W, Yin W, et al. Improving the resolution of GRACE/InSAR groundwater storage estimations using a new subsidence feature weighted combination scheme[J]. Water, 2023, 15(6):1017.
[16]	Agarwal V, Kumar A, Gomes R L, et al. Monitoring of ground movement and groundwater changes in London using InSAR and GRACE[J]. Applied Sciences, 2020, 10(23):8599.
[17]	Khorrami M, Shirzaei M, Ghobadi-Far K, et al. Groundwater vo-lume loss in Mexico City constrained by InSAR and GRACE observations and mechanical models[J]. Geophysical Research Letters, 2023, 50(5): e2022GL101962.
[18]	Liu Z, Liu P W, Massoud E, et al. Monitoring groundwater change in California’s central valley using Sentinel-1 and GRACE observations[J]. Geosciences, 2019, 9(10):436.
[19]	Ferretti A, Prati C, Rocca F. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5): 2202-2212.
[20]	Berardino P, Fornaro G, Lanari R, et al. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2375-2383.
[21]	杨国林, 孙学先, 胡栋, 等. 利用GRACE数据研究柴达木盆地区域水储量时空变化及干旱特征[J]. 导航定位学报, 2023, 11(1):107-112.
[21]	Yang G L, Sun X X, Hu D, et al. Application of GRACE data in analysis on temporal and spatial changes of water reserves and drought characteristics of Qaidam Basin[J]. Journal of Navigation and Positioning, 2023, 11(1):107-112.
[22]	韦振锋, 任志远, 张翀. 气候因子与植被的时滞相关分析——以广西为例[J]. 生态环境学报, 2013, 22(11):1757-1762.
[22]	Wei Z F, Ren Z Y, Zhang C. Analysis on the time-lag correlation between vegetation and climatic factors:Take Guangxi as an example[J]. Ecology and Environmental Sciences, 2013, 22(11):1757-1762.
[23]	Hussain M A, Chen Z, Zheng Y, et al. PS-InSAR based monitoring of land subsidence by groundwater extraction for Lahore Metropolitan City,Pakistan[J]. Remote Sensing, 2022, 14(16):3950.
[24]	Béjar-Pizarro M, Ezquerro P, Herrera G, et al. Mapping groundwater level and aquifer storage variations from InSAR measurements in the Madrid aquifer,Central Spain[J]. Journal of Hydrology, 2017,547:678-689.
[25]	范军, 左小清, 李涛, 等. PS-InSAR和SBAS-InSAR技术对昆明主城区地面沉降监测的对比分析[J]. 测绘工程, 2018, 27(6):50-58.
[25]	Fan J, Zuo X Q, Li T, et al. Analysis and comparison of PS-InSAR and SBAS-InSAR for ground subsidence monitoring in the main city of Kunming[J]. Engineering of Surveying and Mapping, 2018, 27(6):50-58.

[1]	WU Dehong, HAO Lina, YAN Lihua, TANG Fengshun, ZHENG Guang. InSAR-based detection and deformation factor analysis of landslide clusters in the Jinsha River[J]. Remote Sensing for Natural Resources, 2024, 36(3): 259-266.
[2]	ZHANG Lijun, HE Sirui, ZHANG Jiandong, PENG Guangxiong, XU Zhibin, XIE Jiancheng, TANG Kai, BU Jiancai. Identification of landslide hazards based on multi-source remote sensing technology:A case study of the Changli area in Hunan Province[J]. Remote Sensing for Natural Resources, 2024, 36(2): 173-187.
[3]	CAI Jian’ao, MING Dongping, ZHAO Wenyi, LING Xiao, ZHANG Yu, ZHANG Xingxing. Integrated remote sensing-based hazard identification and disaster-causing mechanisms of landslides in Zayu County[J]. Remote Sensing for Natural Resources, 2024, 36(1): 128-136.
[4]	MING Xiaoyong, TIAN Yichao, ZHANG Qiang, TAO Jin, ZHANG Yali, LIN Junliang. Monitoring and analyzing land subsidence in Qinfang, Guangxi based on Sentinel-1A data[J]. Remote Sensing for Natural Resources, 2024, 36(1): 35-48.
[5]	ZHAO Huawei, ZHOU Lin, TAN Minglun, TANG Minggao, TONG Qinggang, QIN Jiajun, PENG Yuhui. Early identification of potential landslides for the Sichuan-Chongqing power grid based on optical remote sensing and SBAS-InSAR[J]. Remote Sensing for Natural Resources, 2023, 35(4): 264-272.
[6]	GAO Chen, MA Dong, QU Man, QIAN Jianguo, YIN Haiquan, HOU Xiaozhen. Exploring the anomaly mechanism of borehole strain at the Huailai seismic station based on PS-InSAR[J]. Remote Sensing for Natural Resources, 2023, 35(3): 153-159.
[7]	YU Hairuo, GONG Huili, CHEN Beibei, ZHOU Chaofan. Emerging risk assessment of areas subject to land subsidence in the southern plain of Tianjin, China[J]. Remote Sensing for Natural Resources, 2023, 35(2): 182-192.
[8]	PAN Jianping, FU Zhanbao, DENG Fujiang, CAI Zhuoyan, ZHAO Ruiqi, CUI Wei. A time-series InSAR-based analysis of surface deformation of hydro-fluctuation belts and the effects of hydrological elements[J]. Remote Sensing for Natural Resources, 2023, 35(2): 212-219.
[9]	HU Xiaoqiang, YANG Shuwen, YAN Heng, XUE Qing, ZHANG Naixin. Time-series InSAR-based monitoring and analysis of surface deformation in the Axi mining area, Xinjiang[J]. Remote Sensing for Natural Resources, 2023, 35(1): 171-179.
[10]	YU Wen, GONG Huili, CHEN Beibei, ZHOU Chaofan. Spatial-temporal evolution characteristics and prediction of land subsidence in the eastern plain of Beijing[J]. Remote Sensing for Natural Resources, 2022, 34(4): 183-193.
[11]	LIN Xuemin, LI Weifeng, WANG Hong, MING Dongping, HAN Lijian. Analysis of the groundwater storage variations and their driving factors in the three eastern coastal urban agglomerations of China[J]. Remote Sensing for Natural Resources, 2022, 34(4): 262-271.
[12]	LUO Xuewei, XIANG Xiqiong, LYU Yadong. PS correction of InSAR time series deformation monitoring for a certain collapse in Longli County[J]. Remote Sensing for Natural Resources, 2022, 34(3): 82-87.
[13]	ZHANG Zhihua, HU Changtao, ZHANG Zhen, YANG Shuwen. PS-InSAR-based monitoring and analysis of surface subsidence in Shanghai[J]. Remote Sensing for Natural Resources, 2022, 34(3): 106-111.
[14]	XU Zixing, JI Min, ZHANG Guo, CHEN Zhenwei. Method for dynamic prediction of mining subsidence based on the SBAS-InSAR technology and the logistic model[J]. Remote Sensing for Natural Resources, 2022, 34(2): 20-29.
[15]	YANG Wang, HE Yi, ZHANG Lifeng, WANG Wenhui, CHEN Youdong, CHEN Yi. InSAR monitoring of 3D surface deformation in Jinchuan mining area, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 177-188.

Viewed

Full text

Abstract

Cited

Shared

Discussed