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Research paper | Open Access

Exploring the groundwater response to rainfall in a translational landslide using the master recession curve method and cross-correlation function

Cheng-peng Ling1Qiang Zhang1,2( )
Chengdu University of Technology, Chengdu 610059, China
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu 610059, China
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Abstract

Rainfall is a common trigger for landslide reactivation, as it raises groundwater levels and reduces bedrock or soil shear resistance. This study focuses on the Kualiangzi landslide in the southern region of Sichuan Province, China. Real-time monitoring of groundwater levels and rainfall from July 2013 to September 2016 is analyzed. Groundwater table increments, considering groundwater drainage rate, were calculated using the water-table fluctuation and master recession curve method and the response time of the groundwater table to rainfall events was estimated using the cross-correlation function. Results reveal that groundwater level declines from tension troughs to landslide fronts in the rainy season, with a significant positive correlation between the groundwater level in the tension trough and landslide surface displacement. Evaluated spring elevations for groundwater discharge range from 410 m to 440 m, which is in agreement with the actual spring elevations (390–423 m). Lag times of groundwater response to rainfall decreases with cumulative rainfall of the rainy periods. In the middle part of the landslide, two responses between rainfall and groundwater levels indicate two water movement pathways: Vertical cracks or fractures resulting from the slow landslide movement, and matrix pore space in unconsolidated sediment. Variations in peak values of the cross-correlation function suggest early dominance of the uniform matrix flow and later dominance of preferential flow during the rainy period.

References

 

Bernardie S, Desramaut N, Malet JP, et al. 2015. Prediction of changes in landslide rates induced by rainfall. Landslides, 12(3): 481−494. DOI:10.1007/s10346-014-0495-8.

 

Cai ZS, Ofterdinger U. 2016. Analysis of groundwater-level response to rainfall and estimation of annual recharge in fractured hard rock aquifers, NW Ireland. Journal of Hydrology, 535: 71−84. DOI:10.1016/j.jhydrol.2016.01.066.

 

Ciupak M, Ozga-Zielinski B, Adamowski J, et al. 2015. The application of Dynamic Linear Bayesian Models in hydrological forecasting: Varying Coefficient Regression and Discount Weighted Regression. Journal of Hydrology, 530: 762−784. DOI:10.1016/j.jhydrol.2015.10.023.

 

Crosbie RS, Doble RC, Turnadge C, et al. 2019. Constraining the magnitude and uncertainty of specific yield for use in the water table fluctuation method of estimating recharge. Water Resources Research, 55(8): 7343−7361. DOI:10.1029/2019wr025285.

 

Fan XM, Xu Q, Zhang ZY, et al. 2009. The genetic mechanism of a translational landslide. Bulletin of Engineering Geology and the Environment, 68(2): 231−244. DOI:10.1007/ s10064-009-0194-1.

 
Glade T, Crozier MJ. 2005. The nature of landslide hazard impact. In: Glade T, Anderson M, Crozier MJ. (eds) Landslide hazard and risk. Wiley, New York: Academic Press: 43–74.
 

Healy RW, Cook PG. 2002. Using groundwater levels to estimate recharge. Hydrogeology Journal, 10(1): 91−109. DOI:10.1007/s10040-001-0178-0.

 

Hong YM, Wan S. 2011. Forecasting groundwater level fluctuations for rainfall-induced landslide. Natural Hazards, 57(2): 167−184. DOI:10.1007/s11069-010-9603-9.

 

Hou RN, Chen NS, Hu GS, et al. 2022. Characteristics, mechanisms, and post-disaster lessons of the delayed semi-diagenetic landslide in Hanyuan, Sichuan, China. Landslides, 19(2): 437−449. DOI:10.1007/s10346-021-01751-0.

 
Jan CD, Chen TH, Lo WC. 2007. Effect of rainfall intensity and distribution on groundwater level fluctuations. Journal of Hydrology, 332(3−4): 348−360. DOI:10.1016/j.jhydrol.2006.07.010.
 

Labrecque G, Chesnaux R, Boucher MA. 2020. Water-table fluctuation method for assessing aquifer recharge: Application to Canadian aquifers and comparison with other methods. Hydrogeology Journal, 28(2): 521−533. DOI:10.1007/s10040-019-02073-1.

 

Leng YY, Kong XZ, He JY, et al. 2022. The July 10, 2020, red-bed landslide triggered by continuous rainfall in Qianxi, Guizhou, China. Landslides, 19(6): 1421−1433. DOI:10.1007/s10346-022-01851-5.

 

Ling CP, Xu Q, Zhang Q, et al. 2016. Application of electrical resistivity tomography for investigating the internal structure of a translational landslide and characterizing its groundwater circulation (Kualiangzi landslide, Southwest China). Journal of Applied Geophysics, 131: 154−162. DOI:10.1016/j.jappgeo.2016.06.003.

 

Luna LV, Korup O. 2022. Seasonal landslide activity lags annual precipitation pattern in the Pacific northwest. Geophysical Research Letters, 49(18): e2022GL098506. DOI:10.1029/2022gl098506.

 

Lv HB, Ling CP, Hu BX, et al. 2019. Characterizing groundwater flow in a translational rock landslide of southwestern China. Bulletin of Engineering Geology and the Environment, 78(3): 1989−2007. DOI:10.1007/s10064-017-1212-3.

 

Maréchal JC, Perrochet P, Caballero Y. 2023. Computing natural recharge using the water-table fluctuation method: Where to site an observation well. Hydrogeology Journal, 31(7): 1991−1995. DOI:10.1007/s10040-023-02707-5.

 

Nimmo JR, Horowitz C, Mitchell L. 2015. Discrete-storm water-table fluctuation method to estimate episodic recharge. Ground Water, 53(2): 282−292. DOI:10.1111/gwat.12177.

 

Sahoo S, Jha MK. 2013. Groundwater-level prediction using multiple linear regression and artificial neural network techniques: A comparative assessment. Hydrogeology Journal, 21(8): 1865−1887. DOI:10.1007/s10040-013-1029-5.

 

Shah B, Alam A, Bhat MS, et al. 2023. Extreme precipitation events and landslide activity in the Kashmir Himalaya. Bulletin of Engineering Geology and the Environment, 82(8): 328. DOI:10.1007/s10064-023-03350-w.

 

Song LF, Yu X, Xu B, et al. 2021. 3D slope reliability analysis based on the intelligent response surface methodology. Bulletin of Engineering Geology and the Environment, 80(2): 735−749. DOI:10.1007/s10064-020-01940-6.

 

Tesfaldet YT, Puttiwongrak A, Arpornthip T. 2020. Spatial and temporal variation of groundwater recharge in shallow aquifer in the Thepkasattri of Phuket, Thailand. Journal of Groundwater Science and Engineering, 8(1): 10−19. DOI:10.19637/j.cnki.2305-7068.2020.01.002.

 

Vallet A, Charlier JB, Fabbri O, et al. 2016. Functioning and precipitation-displacement modelling of rainfall-induced deep-seated landslides subject to creep deformation. Landslides, 13(4): 653−670. DOI:10.1007/s10346-015-0592-3.

 

van Asch TWJ. 2005. Modelling the hysteresis in the velocity pattern of slow-moving earth flows: The role of excess pore pressure. Earth Surface Processes and Landforms, 30(4): 403−411. DOI:10.1002/esp.1147.

 

Wen BP, Wang SJ, Wang EZ, et al. 2004. Characteristics of rapid giant landslides in China. Landslides, 1(4): 247−261. DOI:10.1007/s10346-004-0022-4.

 

Xu Q, Liu HX, Ran JX, et al. 2016. Field monitoring of groundwater responses to heavy rainfalls and the early warning of the Kualiangzi landslide in Sichuan Basin, southwestern China. Landslides, 13(6): 1555−1570. DOI:10.1007/s10346-016-0717-3.

 

Yan Q, Ma C. 2016. Application of integrated ARIMA and RBF network for groundwater level forecasting. Environmental Earth Sciences, 75(5): 396. DOI:10.1007/s12665-015-5198-5.

 

Yoon H, Hyun Y, Ha K, et al. 2016. A method to improve the stability and accuracy of ANN- and SVM-based time series models for long-term groundwater level predictions. Computers & Geosciences, 90: 144−155. DOI:10.1016/j.cageo.2016.03.002.

 
Zhai GJ. 2011. Analysis of the basic characteristics and deformation mechanism of Kualiangzi landslide in Zhongjiang. M. S. thesis. Chengdu: Chengdu University of Technology: 16–36.
 

Zhang M, Yin YP, Huang BL. 2015. Mechanisms of rainfall-induced landslides in gently inclined red beds in the eastern Sichuan Basin, SW China. Landslides, 12(5): 973−983. DOI:10.1007/s10346-015-0611-4.

Journal of Groundwater Science and Engineering
Pages 237-252
Cite this article:
Ling C-p, Zhang Q. Exploring the groundwater response to rainfall in a translational landslide using the master recession curve method and cross-correlation function. Journal of Groundwater Science and Engineering, 2024, 12(3): 237-252. https://doi.org/10.26599/JGSE.2024.9280018

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Received: 22 November 2023
Accepted: 26 April 2024
Published: 10 August 2024
2305-7068/© 2024 Journal of Groundwater Science and Engineering Editorial Office

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0)

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