Estimation of Shallow Landslide Susceptibility Incorporating the Impacts of Vegetation on Slope Stability

Hu Jiang , Qiang Zou , Bin Zhou , Yao Jiang , Junfang Cui , Hongkun Yao , Wentao Zhou

International Journal of Disaster Risk Science ›› 2023, Vol. 14 ›› Issue (4) : 618 -635.

PDF
International Journal of Disaster Risk Science ›› 2023, Vol. 14 ›› Issue (4) : 618 -635. DOI: 10.1007/s13753-023-00507-9
Article

Estimation of Shallow Landslide Susceptibility Incorporating the Impacts of Vegetation on Slope Stability

Author information +
History +
PDF

Abstract

This study aimed to develop a physical-based approach for predicting the spatial likelihood of shallow landslides at the regional scale in a transition zone with extreme topography. Shallow landslide susceptibility study in an area with diverse vegetation types as well as distinctive geographic factors (such as steep terrain, fractured rocks, and joints) that dominate the occurrence of shallow landslides is challenging. This article presents a novel methodology for comprehensively assessing shallow landslide susceptibility, taking into account both the positive and negative impacts of plants. This includes considering the positive effects of vegetation canopy interception and plant root reinforcement, as well as the negative effects of plant gravity loading and preferential flow of root systems. This approach was applied to simulate the regional-scale shallow landslide susceptibility in the Dadu River Basin, a transition zone with rapidly changing terrain, uplifting from the Sichuan Plain to the Qinghai–Tibet Plateau. The research findings suggest that: (1) The proposed methodology is effective and capable of assessing shallow landslide susceptibility in the study area; (2) the proposed model performs better than the traditional pseudo-static analysis method (TPSA) model, with 9.93% higher accuracy and 5.59% higher area under the curve; and (3) when the ratio of vegetation weight loads to unstable soil mass weight is high, an increase in vegetation biomass tends to be advantageous for slope stability. The study also mapped the spatial distribution of shallow landslide susceptibility in the study area, which can be used in disaster prevention, mitigation, and risk management.

Keywords

Physical-based model / Qinghai–Tibet Plateau / Shallow landslide / Susceptibility analysis / Vegetation effect

Cite this article

Download citation ▾
Hu Jiang, Qiang Zou, Bin Zhou, Yao Jiang, Junfang Cui, Hongkun Yao, Wentao Zhou. Estimation of Shallow Landslide Susceptibility Incorporating the Impacts of Vegetation on Slope Stability. International Journal of Disaster Risk Science, 2023, 14(4): 618-635 DOI:10.1007/s13753-023-00507-9

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Aksoy B, Ercanoglu M. Landslide identification and classification by object-based image analysis and fuzzy logic: An example from the Azdavay region (Kastamonu, Turkey). Computers and GeoSciences, 2012, 38: 87-98

[2]

Allen, R.G., L. Pereira, and D. Raes. 1998. Crop evapotranspiration: Guidelines for computing crop water requirements. FAO irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization (FAO).

[3]

Araújo JR, Ramos AM, Soares PMM, Melo R, Oliveira SC, Trigo RM. Impact of extreme rainfall events on landslide activity in Portugal under climate change scenarios. Landslides, 2022, 19: 2279-2293

[4]

Arnone E, Caracciolo D, Noto LV, Preti F, Bras RL. Modeling the hydrological and mechanical effect of roots on shallow landslides. Water Resources Research, 2016, 52: 8590-8612

[5]

Ba RJ, Wang L, Zhen WM, Li ZL, Li MH, Liu YJ, Ni HY, Xu RG. Characteristics and distribution of the geology disasters of the Dadu River in Sichuan, China. Journal of Chengdu University of Technology (Science & Technology Edition), 2011, 38: 529-537.

[6]

Baum, R.L., W.Z. Savage, and J.W. Godt. 2008. TRIGRS—A Fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis, Version 2.0. U.S. Geological Survey open-file report. Reston, VA: U.S. Geological Survey.

[7]

Beven KJ, Kirkby MJ. A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant. Hydrological Sciences Bulletin, 1979, 24: 43-69

[8]

Bischetti GB, Chiaradia EA, Simonato T, Speziali B, Vitali B, Vullo P, Zocco A. Root strength and root area ratio of forest species in Lombardy (northern Italy). Plant and Soil, 2005, 278: 11-22

[9]

Bordoloi, S., and C.W.W. Ng. 2020. The effects of vegetation traits and their stability functions in bio-engineered slopes: A perspective review. Engineering Geology 275: Article 105742.

[10]

Bordoni M, Vivaldi V, Lucchelli L, Ciabatta L, Brocca L, Galve JP, Meisina C. Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale. Landslides, 2021, 18: 1209-1229

[11]

Camera, C.A.S., G. Bajni, I. Corno, M. Raffa, S. Stevenazzi, and T. Apuani. 2021. Introducing intense rainfall and snowmelt variables to implement a process-related non-stationary shallow landslide susceptibility analysis. Science of the Total Environment 786: Article 147360.

[12]

Chirico GB, Dani A, Preti F. Coupling root reinforcement and subsurface flow modeling in shallow landslides triggering assessment. Landslide Science and Practice: Risk Assessment, Management and Mitigation, 2013, 6: 761-766

[13]

Daneshvar MRM. Landslide susceptibility zonation using analytical hierarchy process and GIS for the Bojnurd region, northeast of Iran. Landslides, 2014, 11: 1079-1091

[14]

Didan, K. 2015. MYD13A1 MODIS/Aqua vegetation indices 16-day L3 global 500 m SIN grid V006. Greenbelt, Maryland: Level-1 and Atmosphere Archive & Distribution System, Distributed Active Archive Center.

[15]

Ding J, Yan Y, Yue CT, Wang DW, Mao Y, Wei LW, Chang XJ, Wang J. Analysis of the geological hazards’ distribution and development trend in Dadu River catchments of Sichuan Province. The Chinese Journal of Geological Hazard and Control, 2007, 18(S0): 22-25.

[16]

Dou J, Yamagishi H, Pourghasemi HR, Yunus AP, Song X, Xu YR, Zhu ZF. An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Natural Hazards, 2015, 78: 1749-1776

[17]

Dou J, Yunus AP, Tien Bui D, Merghadi A, Sahana M, Zhu Z, Chen C-W, Khosravi K Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Science of the Total Environment, 2019, 662: 332-346

[18]

Escobar-Wolf R, Sanders JD, Vishnu CL, Oommen T, Sajinkumar KS. A GIS tool for infinite slope stability analysis (GIS-TISSA). Geoscience Frontiers, 2021, 12: 756-768

[19]

Feng, S., H.W. Liu, and C.W.W. Ng. 2020. Analytical analysis of the mechanical and hydrological effects of vegetation on shallow slope stability. Computers and Geotechnics 118: Article 103335.

[20]

Fick SE, Hijmans RJ. WorldClim 2: New 1 km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 2017, 37: 4302-4315

[21]

Garcia-Delgado H, Petley DN, Bermúdez MA, Sepúlveda SA. Fatal landslides in Colombia (from historical times to 2020) and their socio-economic impacts. Landslides, 2022, 19: 1689-1716

[22]

Ghosh S, Carranza EJM, van Westen CJ, Jetten VG, Bhattacharya DN. Selecting and weighting spatial predictors for empirical modeling of landslide susceptibility in the Darjeeling Himalayas (India). Geomorphology, 2011, 131: 35-56

[23]

Grima, N., D. Edwards, F. Edwards, D. Petley, and B. Fisher. 2020. Landslides in the Andes: Forests can provide cost-effective landslide regulation services. Science of the Total Environment 745: Article 141128.

[24]

Ha ND, Sayama T, Sassa K, Takara K, Uzuoka R, Dang K, Van Pham T. A coupled hydrological-geotechnical framework for forecasting shallow landslide hazard—A case study in Halong City, Vietnam. Landslides, 2020, 17: 1619-1634

[25]

Hao L, Rajaneesh A, van Westen C, Sajinkumar KS, Martha TR, Jaiswal P, McAdoo BG. Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis. Earth System Science Data, 2020, 12: 2899-2918

[26]

Hao, L., C. van Westen, A. Rajaneesh, K.S. Sajinkumar, T.R. Martha, and P. Jaiswal. 2022. Evaluating the relation between land use changes and the 2018 landslide disaster in Kerala, India. CATENA 216: Article 106363.

[27]

Hargreaves GH, Samani ZA. Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture, 1985, 1: 96-99

[28]

Harris I, Jones PD, Osborn TJ, Lister DH. Updated high-resolution grids of monthly climatic observations—The CRU TS3.10 dataset. International Journal of Climatology, 2014, 34: 623-642

[29]

Hess DM, Leshchinsky BA, Bunn M, Mason HB, Olsen MJ. A simplified three-dimensional shallow landslide susceptibility framework considering topography and seismicity. Landslides, 2017, 14: 1677-1697

[30]

Hou, X. 2019. 1:1 million vegetation map of China. A Big Earth Data Platform for Three Poles. Lanzhou, China: Northwest Institute of Eco-Environment and Resources, CAS.

[31]

Hungr O, Leroueil S, Picarelli L. The Varnes classification of landslide types, an update. Landslides, 2014, 11: 167-194

[32]

Hwang I-T, Park H-J, Lee J-H. Probabilistic analysis of rainfall-induced shallow landslide susceptibility using a physically based model and the bootstrap method. Landslides, 2023, 20: 829-844

[33]

Jiang, H., Q. Zou, B. Zhou, Z. Hu, C. Li, S. Yao, and H. Yao. 2022. Susceptibility assessment of debris flows coupled with ecohydrological activation in the eastern Qinghai–Tibet Plateau. Remote Sensing 14(6): Article 1444.

[34]

Jotisankasa A, Sirirattanachat T. Effects of grass roots on soil–water retention curve and permeability function. Canadian Geotechnical Journal, 2017, 54: 1612-1622

[35]

Jr-Chuan H, Shuh-Ji K, Mei-Ling H, Jiun-Chuan L. Stochastic procedure to extract and to integrate landslide susceptibility maps: An example of mountainous watershed in Taiwan. Natural Hazards and Earth System Sciences, 2006, 6(5): 803-815

[36]

Keles, F., and H.A. Nefeslioglu. 2021. Infinite slope stability model and steady-state hydrology-based shallow landslide susceptibility evaluations: The Guneysu catchment area (Rize, Turkey). CATENA 200: Article 105161.

[37]

Kim D, Im S, Lee SH, Hong Y, Cha KS. Predicting the rainfall-triggered landslides in a forested mountain region using TRIGRS model. Journal of Mountain Science, 2010, 7: 83-91

[38]

Kim D, Im S, Lee C, Woo C. Modeling the contribution of trees to shallow landslide development in a steep, forested watershed. Ecological Engineering, 2013, 61: 658-668

[39]

Kjekstad O, Highland L. Sassa K, Canuti P. Economic and social impacts of landslides. Landslides-disaster risk reduction, 2009, Berlin: Springer 573-587

[40]

Kozak JA, Ahuja LR, Green TR, Ma L. Modelling crop canopy and residue rainfall interception effects on soil hydrological components for semi-arid agriculture. Hydrological Processes, 2007, 21: 229-241

[41]

Li X, Zhu WZ, Sun SQ, Shu SM, Sheng ZL, Zhang J, Liu T, Zhang ZC. Influence of habitat on the distribution pattern and diversity of plant community in dry and warm valleys of the middle reaches of the Dadu River, China. Biodiversity Science, 2020, 28: 117-127

[42]

Li J, Wang X, Jia H, Liu Y, Zhao Y, Shi C, Zhang F, Wang K. Assessing the soil moisture effects of planted vegetation on slope stability in shallow landslide-prone areas. Journal of Soils and Sediments, 2021, 21: 2551-2565

[43]

Lin, Q., P. Lima, S. Steger, T. Glade, T. Jiang, J. Zhang, T. Liu, and Y. Wang. 2021. National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data. Geoscience Frontiers 12: Article 101248.

[44]

Liu HW, Feng S, Ng CWW. Analytical analysis of hydraulic effect of vegetation on shallow slope stability with different root architectures. Computers and Geotechnics, 2016, 80: 115-120

[45]

Liu, Q., L. Su, C. Zhang, B. Hu, and S. Xiao. 2022a. Dynamic variations of interception loss-infiltration-runoff in three land-use types and their influence on slope stability: An example from the eastern margin of the Tibetan Plateau. Journal of Hydrology 612: Article 128218.

[46]

Liu, X., H. Lan, L. Li, and P. Cui. 2022b. An ecological indicator system for shallow landslide analysis. CATENA 214: Article 106211.

[47]

Mizutori, M. 2018. SRSG statement for the international landslide consortium conference Kyoto, Japan. In Proceedings of the 2018 IPL Symposium on Landslides, 3 December 2018, Kyoto University, Uji Campus, Kyoto, Japan.

[48]

Murgia, I., F. Giadrossich, Z. Mao, D. Cohen, G.F. Capra, and M. Schwarz. 2022. Modeling shallow landslides and root reinforcement: A review. Ecological Engineering 181: Article 106671.

[49]

Ng CWW, Leung AK, Woon KX. Effects of soil density on grass-induced suction distributions in compacted soil subjected to rainfall. Canadian Geotechnical Journal, 2014, 51: 311-321

[50]

Nguyen, B.-Q.-V., S.-R. Lee, and Y.-T. Kim. 2020a. Spatial probability assessment of landslide considering increases in pore-water pressure during rainfall and earthquakes: Case studies at Atsuma and Mt. Umyeon. CATENA 187: Article 104317.

[51]

Nguyen P, Ombadi M, Gorooh VA, Shearer EJ, Sadeghi M, Sorooshian S, Hsu KL, Bolvin D, Ralph MF. PERSIANN Dynamic Infrared-Rain Rate (PDIR-Now): A near-real-time, quasi-global satellite precipitation dataset. Journal of Hydrometeorology, 2020, 21: 2893-2906

[52]

Oommen T, Cobin PF, Gierke JS, Sajinkumar KS. Significance of variable selection and scaling issues for probabilistic modeling of rainfall-induced landslide susceptibility. Spatial Information Research, 2018, 26: 21-31

[53]

Pack, R.T., D.G. Tarbotan, and C.N. Goodwin. 2005. SINMAP 2—A stability index approach to terrain stability hazard mapping. User’s manual. Salmon Arm, Canada: Terratech Consulting Ltd.

[54]

Peng D, Zhang B, Liu L. Comparing spatiotemporal patterns in Eurasian FPAR derived from two NDVI-based methods. International Journal of Digital Earth, 2012, 5: 283-298

[55]

Pradhan A, Kang H-S, Lee JS, Kim Y-T. Shallow landslide hazard modeling by incorporating heavy rainfall statistics and quasi-dynamic wetness index: A case study from Korean mountain. Japanese Geotechnical Society Special Publication, 2016, 2: 1012-1016

[56]

Pradhan AMS, Lee SR, Kim YT. A shallow slide prediction model combining rainfall threshold warnings and shallow slide susceptibility in Busan, Korea. Landslides, 2019, 16: 647-659

[57]

Qin M, Cui P, Jiang Y, Guo J, Zhang G, Ramzan M. Occurrence of shallow landslides triggered by increased hydraulic conductivity due to tree roots. Landslides, 2022, 19: 2593-2604

[58]

Qin M, Guo J, Zou Q. Preliminary study on the distribution characteristics of potentially unstable vegetated-slopes: A case study of Dadu River basin. Journal of Engineering Geology, 2023, 31(2): 628-637.

[59]

Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F. A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 2018, 180: 60-91

[60]

Schwarz M, Preti F, Giadrossich F, Lehmann P, Or D. Quantifying the role of vegetation in slope stability: A case study in Tuscany (Italy). Ecological Engineering, 2010, 36: 285-291

[61]

Sellers P, Los S, Tucker C, Justice C, Dazlich D, Collatz J, Randall D. A revised land surface parameterization (SiB2) for atmospheric GCMS. Part II: The generation of global fields of terrestrial biophysical parameters from satellite data. Journal of Climate, 1996, 9: 706-737

[62]

Shao W, Bogaard TA, Bakker M, Greco R. Quantification of the influence of preferential flow on slope stability using a numerical modelling approach. Hydrology and Earth System Sciences, 2015, 19: 2197-2212

[63]

Shao X, Ma S, Xu C. Distribution and characteristics of shallow landslides triggered by the 2018 Mw 7.5 Palu earthquake, Indonesia. Landslides, 2022, 20: 157-175

[64]

Terink W, Lutz AF, Simons GWH, Immerzeel WW, Droogers P. SPHY v2.0: Spatial processes in hydrology. Geoscientific Model Development Discussions, 2015, 8: 2009-2034

[65]

Tiranti D, Nicolo G, Gaeta AR. Shallow landslides predisposing and triggering factors in developing a regional early warning system. Landslides, 2019, 16: 235-251

[66]

Torizin J, Schüßler N, Fuchs M. Landslide susceptibility assessment tools v1.0.0b—Project manager suite: A new modular toolkit for landslide susceptibility assessment. Geoscientific Model Development, 2022, 15: 2791-2812

[67]

Tron S, Dani A, Laio F, Preti F, Ridolfi L. Mean root depth estimation at landslide slopes. Ecological Engineering, 2014, 69: 118-125

[68]

Vandromme R, Desramaut N, Garnier C, Bernardie S. Lollino G, Giordan D, Crosta GB, Corominas J, Azzam R, Wasowski J, Sciarra N. A novel approach to integrate effects of vegetation changes on slope stability. Engineering geology for society and territory, 2015, Cham: Springer 975-978

[69]

Wang Z, Zhao Q, Han J, Kong W. Physical modeling of the effect of vegetation on slope stability under typhoon. Journal of Natural Disasters, 2013, 22: 145-152.

[70]

Wang GH, Jiang Y, Chang CR, Doi I, Kamai T. Volcaniclastic debris avalanche on Motomachi area of Izu-Oshima, Japan, triggered by severe storm: Phenomenon and mechanisms. Engineering Geology, 2019, 251: 24-36

[71]

Wang X, Ma C, Wang Y, Wang Y, Li T, Dai Z, Li M. Effect of root architecture on rainfall threshold for slope stability: Variabilities in saturated hydraulic conductivity and strength of root–soil composite. Landslides, 2020, 17: 1965-1977

[72]

Wang, F., G. Wang, J. Cui, L. Guo, C.R. Mello, E.W. Boyer, X. Tang, and Y. Yang. 2022. Preferential flow patterns in forested hillslopes of the east Tibetan Plateau revealed by dye tracing and soil moisture network. European Journal of Soil Science 73(4): Article e13294.

[73]

Wu W, Sidle RC. A distributed slope stability model for steep forested basins. Water Resources Research, 1995, 31: 2097-2110

[74]

Wu T, Iii W, Swanston D. Strength of tree roots and landslides on Prince of Wales Island, Alaska. Canadian Geotechnical Journal, 1979, 16(1): 19-33

[75]

Xiong, K., B.R. Adhikari, C.A. Stamatopoulos, Y. Zhan, S.L. Wu, Z.T. Dong, and B.F. Di. 2020. Comparison of different machine learning methods for debris flow susceptibility mapping: A case study in the Sichuan Province, China. Remote Sensing 12(2): Article 295.

[76]

Yan, F.P., S.G. Wei, J. Zhang, and B.F. Hu. 2020. Depth-to-bedrock map of China at a spatial resolution of 100 meters. Scientific Data 7: Article 2.

[77]

Yong C, Jinlong D, Fei G, Bin T, Tao Z, Hao F, Li W, Zhan Q. Review of landslide susceptibility assessment based on knowledge mapping. Stochastic Environmental Research and Risk Assessment, 2022, 36: 2399-2417

[78]

Zêzere JL, Pereira S, Melo R, Oliveira SC, Garcia RAC. Mapping landslide susceptibility using data-driven methods. Science of the Total Environment, 2017, 589: 250-267

[79]

Zhang P-Z. A review on active tectonics and deep crustal processes of the Western Sichuan region, eastern margin of the Tibetan Plateau. Tectonophysics, 2013, 584: 7-22

[80]

Zhang, Y.H., T.T. Ge, W. Tian, and Y.A. Liou. 2019. Debris flow susceptibility mapping using machine-learning techniques in Shigatse area, China. Remote Sensing 11(23): Article 2801.

[81]

Zhu H, Zhang L. Evaluating suction profile in a vegetated slope considering uncertainty in transpiration. Computers and Geotechnics, 2015, 63: 112-120

[82]

Zhu H, Zhang L. Root–soil–water hydrological interaction and its impact on slope stability. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2019, 13(4): 349-359.

[83]

Zhu J, Leung AK, Wang Y. Modelling root–soil mechanical interaction considering root pull-out and breakage failure modes. Plant and Soil, 2022, 480: 675-701

[84]

Zhuang, Y., A.G. Xing, Y.H. Jiang, Q. Sun, J.K. Yan, and Y.B. Zhang. 2022. Typhoon, rainfall and trees jointly cause landslides in coastal regions. Engineering Geology 298(1): Article 106561.

[85]

Zou, Q., H. Jiang, P. Cui, B. Zhou, Y. Jiang, M.Y. Qin, Y.G. Liu, and C. Li. 2021. A new approach to assess landslide susceptibility based on slope failure mechanisms. CATENA 204: Article 105388.

AI Summary AI Mindmap
PDF

273

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/