Skip to main content

Advertisement

Log in

Mapping method of rainfall-induced landslide hazards by infiltration and slope stability analysis

A case study in Marumori, Miyagi, Japan, during the October 2019 Typhoon Hagibis

  • Original Paper
  • Published:
Landslides Aims and scope Submit manuscript

A Correction to this article was published on 23 February 2021

This article has been updated

Abstract

By utilizing the Green-Ampt infiltration equation and the infinite slope stability model, a method for analyzing shallow slope failures caused by rainfall is developed. With rainfall intensity, soil characteristics, and topography, the modified Green-Ampt infiltration equation is used to estimate the rainfall infiltration capacity and depth of infiltration in a given slope. Assigning the calculated depth of infiltration as the depth of slip surface, the factor of safety of the slope is obtained through the infinite slope stability model. A time-series visualization map of the space-time varying factor of safety is generated when the method is implemented with the aid of Geographic Information System (GIS) software. The model is applied and validated with the landslides that occurred during the October 2019 Typhoon Hagibis in Marumori, Japan. The model results show good agreement with the reported time and depths of failure, and the analysis of the spatial distribution of predicted failures yielded receiver operating characteristic-area under the curve (ROC-AUC) value of 0.90. The applicability of the model can be extended for post-analysis, real-time, or projected assessment of slope stability, depending on the nature of input rainfall data (e.g., historical, real-time, forecast, hypothetical).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Change history

References

  • Abella EAC, Van Westen CJ (2008) Qualitative landslide suscepti- bility assessment by multicriteria analysis: a case study from san antonio del sur, guanta´namo, cuba. Geomorphol 94(3-4):453–466

    Article  Google Scholar 

  • Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58(1):21–44

    Article  Google Scholar 

  • Al-Hashemi HMB, Al-Amoudi OSB (2018) A review on the angle of repose of granular materials. Powder Technol 330:397–417

    Article  Google Scholar 

  • Andriola P, Chirico GB, De Falco M, Di Crescenzo G, Santo A (2009) A comparison between physically-based models and a semi- quantitative methodology for assessing susceptibility to flowslides triggering in pyroclastic deposits of southern italy. Geogr Fis Din Quat 32(2):213–226

    Google Scholar 

  • Baum RL, Savage WZ, Godt JW et al (2002) Trigrs—a fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis. US Geol Surv Open-File Rep 424:38

    Google Scholar 

  • Baum, R.L., Savage, W.Z., Godt, J.W.: TRIGRS: a Fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis, version 2.0. US Geological Survey Re- ston, VA (2008)

  • Beguería S (2006) Validation and evaluation of predictive models in hazard assessment and risk management. Nat Hazards 37(3):315–329

    Article  Google Scholar 

  • Cabinet Office, Government of Japan: White paper on disaster management in japan 2019. http://www.bousai.go.jp/ kaigirep/hakusho/pdf/R1_hakusho_english.pdf (2019). Accessed August 3, 2020

  • Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) Gis techniques and statistical models in evaluating landslide hazard. Earth surface processes and landforms 16(5):427–445

    Article  Google Scholar 

  • Cervi F, Berti M, Borgatti L, Ronchetti F, Manenti F, Corsini A (2010) Comparing predictive capability of statistical and de- terministic methods for landslide susceptibility mapping: a case study in the northern apennines (reggio emilia province, italy). Landslides 7(4):433–444

    Article  Google Scholar 

  • Cevasco A, Pepe G, Brandolini P (2014) The influences of geological and land use settings on shallow landslides triggered by an intense rainfall event in a coastal terraced environment. Bulletin of Engi- neering Geology and the Environment 73(3):859–875

    Article  Google Scholar 

  • Chacón J, Irigaray C, Fernández T, El Hamdouni R (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ 65(4):341–411

    Article  Google Scholar 

  • Chaithong T (2017) Analysis of extreme rainfall-induced slope failure using a rainfall infiltration-infinite slope analysis model. Int J Geomate 13(35):156–165

    Article  Google Scholar 

  • Chawla, N.V.: Data mining for imbalanced datasets: An overview. In: Data mining and knowledge discovery handbook, pp. 875–886. Springer (2009)

  • Chen L, Young MH (2006) Green-ampt infiltration model for sloping surfaces. Water Resour Res 42(7)

  • Cho SE (2017) Prediction of shallow landslide by surficial stability analysis considering rainfall infiltration. Eng Geol 231:126–138

    Article  Google Scholar 

  • Cho SE, Lee SR (2002) Evaluation of surficial stability for homogeneous slopes considering rainfall characteristics. J Geotech Geoenviron Eng 128(9):756–763

    Article  Google Scholar 

  • Chu ST (1978) Infiltration during an unsteady rain. Water Resour Res 14(3):461–466

    Article  Google Scholar 

  • Cui P, Guo Cx, Zhou Jw, Hao Mh, Xu Fg (2014) The mechanisms behind shallow failures in slopes comprised of landslide deposits. Eng Geo 180:34–44

    Article  Google Scholar 

  • Cui Y, Jiang Y, Guo C (2019) Investigation of the initiation of shallow failure in widely graded loose soil slopes considering interstitial flow and surface runoff. Landslides 16(4):815–828

    Article  Google Scholar 

  • Dai F, Lee C, Ngai YY (2002) Landslide risk assessment and management: an overview. Engineering geology 64(1):65–87

    Article  Google Scholar 

  • Di Napoli M, Carotenuto F, Cevasco A, Confuorto P, Di Martire D, Firpo M, Pepe G, Raso E, Calcaterra D (2020) Machine learning ensemble modelling as a tool to improve landslide sus- ceptibility mapping reliability. Landslides 17(8):1897–1914

    Article  Google Scholar 

  • Dietrich, W.E., Montgomery, D.R.: SHALSTAB: a digital terrain model for mapping shallow landslide potential. University of California (1998)

  • Fredlund D, Morgenstern NR, Widger R (1978) The shear strength of unsaturated soils. Canadian geotechnical journal 15(3):313–321

    Article  Google Scholar 

  • Froude MJ, Petley DN (2018) Global fatal landslide occurrence from 2004 to 2016. Nat Hazards Earth Syst Sci 18(8):2161–2181

    Article  Google Scholar 

  • Fujita, Y.: Geology of the kakuda district. Geol Sheet Map at 1: 50, 000 99 (1988)

  • Garcia Gaines RA, Frankenstein S (2015) Uscs and the usda soil classification system: development of a mapping scheme. Tech. rep. US Army Eng Res Dev Center

  • Geospatial Information Authority of Japan: Basic map infor- mation digital elevation model. https://fgd.gsi.go.jp/ download/mapGis.php?tab=dem. Accessed December 19, 2020a

  • Geospatial Information Authority of Japan: Information about the east japan typhoon 2019. https://www.gsi.go.jp/BOUSAI/ R1.taihuu19gou.html (2019). Accessed December 19, 2020b

  • Goetz J, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for land- slide susceptibility modeling. Comput Geosci 81:1–11

    Article  Google Scholar 

  • Green WH, Ampt G (1911) Studies on soil physics. J Agric Sci 4(1):1–24

    Article  Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, central italy. Geomorphology 31(1-4):181–216

    Article  Google Scholar 

  • He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263–1284

    Article  Google Scholar 

  • International Soil Reference and Information Centre: Isric world soil information data hub. https://data.isric.org/ geonetwork/srv/eng/catalog.search#/home. Accessed December 19, 2020

  • Irasawa M, Koi T, Tsou CY, Kato N, Matsuo S, Arai M, Kaibori M, Yamada T, Kasai M, Wakahara T et al (2020) October 2019 sediment disaster in the tohoku region owing to typhoon no. 19 (tyhpoon hagibis). Int J Erosion Control Eng 13(2):48–55

    Article  Google Scholar 

  • Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36(7):1897–1910

    Article  Google Scholar 

  • Japan Meteorological Agency: Amedas past weather data search. http://www.data.jma.go.jp/obd/stats/etrn/index.php (2019). Accessed December 19, 2020

  • Jiang SH, Li DQ, Zhang LM, Zhou CB (2014) Slope reliability analysis considering spatially variable shear strength parameters using a non-intrusive stochastic finite element method. Engineer- ing Geology 168:120–128

    Article  Google Scholar 

  • Kale RV, Sahoo B (2011) Green-ampt infiltration models for varied field conditions: A revisit. Water Resour Manag 25(14):3505

    Article  Google Scholar 

  • Kawagoe S, Kazama S, Sarukkalige PR (2010) Probabilistic modelling of rainfall induced landslide hazard assessment. Hydrology and Earth System Sciences 14(6):1047–1061

    Article  Google Scholar 

  • Komori D, Rangsiwanichpong P, Inoue N, Ono K, Watanabe S, Kazama S (2018) Distributed probability of slope failure in thailand under climate change. Clim Risk Manag 20:126–137

    Article  Google Scholar 

  • Kubo K, Yamamoto T (1990) Cretaceous intrusive rocks of the haramachi district, eastern margin of the abukuma mountains- petrography and k-ar age. J. Geol Soc Japan 96:731–743

    Article  Google Scholar 

  • Kubo K, Yanagisawa Y, Yamamoto T, Komazawa M, Hiroshima T, Sudo S (2003) Geological map of japan 1: 200,000, fukushima. Geol Surv Jpn

  • Liu G, Craig JR, Soulis ED (2011) Applicability of the green-ampt infiltration model with shallow boundary conditions. J Hydrol Eng 16(3):266–273

    Article  Google Scholar 

  • Lombardo, L., Opitz, T., Ardizzone, F., Guzzetti, F., Huser, R.: Space-time landslide predictive modelling. Earth-Science Reviews p. 103318 (2020)

  • Maidment, D.R., et al.: Handbook of hydrology, vol. 9780070. McGraw-Hill New York (1993)

  • Mein RG, Larson CL (1973) Modeling infiltration during a steady rain. Water Resour Res 9(2):384–394

    Article  Google Scholar 

  • Meisina C, Scarabelli S (2007) A comparative analysis of terrain stability models for predicting shallow landslides in colluvial soils. Geomorphology 87(3):207–223

    Article  Google Scholar 

  • Michel GP, Kobiyama M, Goerl RF (2014) Comparative analysis of shalstab and sinmap for landslide susceptibility mapping in the cunha river basin, southern brazil. J Soils Sediments 14(7):1266–1277

    Article  Google Scholar 

  • Ministry of Land, Infrastructure, Transport and Tourism: Press release: Number of sediment disasters for 2019. https:// www.mlit.go.jp/report/press/content/001334184.pdf (2020). Accessed August 3, 2020

  • Montgomery DR, Dietrich WE (1994) A physically based model for the topographic control on shallow landsliding. Water Resour Res 30(4):1153–1171

    Article  Google Scholar 

  • Montrasio L, Valentino R (2008) A model for triggering mechanisms of shallow landslides. Nat Hazards Earth Syst Sci 8(5):1149–1159

    Article  Google Scholar 

  • Montrasio L, Valentino R, Losi G (2011) Towards a real-time sus- ceptibility assessment of rainfall-induced shallow landslides on a regional scale. Nat Hazards Earth Syst Sci 11(7):1927–1947

    Article  Google Scholar 

  • Muntohar AS, Liao HJ (2010) Rainfall infiltration: infinite slope model for landslides triggering by rainstorm. Nat hazards 54(3):967–984

    Article  Google Scholar 

  • Ochiai, T., Ueno, S., Inagaki, H., Suzuki, M., Yoshikawa, S., Matugi, H.: Findings (2) of the slope disaster caused by ty- phoon no.19 of october, 2019. disaster caused by river erosion of marumorimachi,igu-gun, miyagi. The 55th Geotechnical Re- search Conference, Japanese Geotechnical Society (2020)

  • Ogden FL, Saghafian B (1997) Green and ampt infiltration with redis- tribution. J Irrig Drain Eng 123(5):386–393

    Article  Google Scholar 

  • Pack RT, Tarboton DG (1998) Goodwin. C.N, The sinmap approach to terrain stability mapping

    Google Scholar 

  • Rawls WJ, Brakensiek DL, Saxtonn K (1982) Estimation of soil wa- ter properties. Trans ASAE 25(5):1316–1320

    Article  Google Scholar 

  • Rawls WJ, Brakensiek DL, Miller N (1983) Green-ampt infiltra- tion parameters from soils data. J Hydraul Eng 109(1):62–70

    Article  Google Scholar 

  • Richards LA (1931) Capillary conduction of liquids through porous mediums. Phys 1(5):318–333

    Article  Google Scholar 

  • Rossi G, Catani F, Leoni L, Segoni S, Tofani V (2013) Hiresss: a physically based slope stability simulator for hpc applications. Nat Hazards Earth Syst Sci 13(1):151–166

    Article  Google Scholar 

  • Segoni S, Leoni L, Benedetti A, Catani F, Righini G, Falorni G, Gabellani S, Rudari R, Silvestro F, Rebora N (2009) Towards a definition of a real-time forecasting network for rain- fall induced shallow landslides. Nat Hazards Earth Syst Sci 9(6):2119–2133

    Article  Google Scholar 

  • Simoni S, Zanotti F, Bertoldi G, Rigon R (2008) Modelling the probability of occurrence of shallow landslides and channelized debris flows using geotop-fs. Hydrol Process: An Int J 22(4):532–545

    Article  Google Scholar 

  • Soeters R, Van Westen C (1996) Slope instability recognition, analysis and zonation. Landslides: Investig Mitig 247:129–177

    Google Scholar 

  • Sorbino G, Sica C, Cascini L (2010) Susceptibility analysis of shal- low landslides source areas using physically based models. Nat Hazards 53(2):313–332

    Article  Google Scholar 

  • Takara K, Yamashiki Y, Sassa K, Ibrahim AB, Fukuoka H et al (2010) A distributed hydrological–geotechnical model using satellite-derived rainfall estimates for shallow landslide prediction system at a catchment scale. Landslides 7(3):237–258

    Article  Google Scholar 

  • Te Chow V (2010) Applied hydrology. Tata McGraw-Hill Education

  • Tofani V, Bicocchi G, Rossi G, Segoni S, D’Ambrosio M, Casagli N, Catani F (2017) Soil characterization for shallow landslides modeling: a case study in the northern apennines (central italy). Landslides 14(2):755–770

    Article  Google Scholar 

  • Tsuchiya N, Kimura JI, Kagami H (2007) Petrogenesis of early cretaceous adakitic granites from the kitakami mountains, japan. J Volcanol Geotherm Res 167(1-4):134–159

    Article  Google Scholar 

  • Tsuchiya N, Takeda T, Tani K, Adachi T, Nakano N, Osanai Y, Kimura JI (2014) Zircon u–pb age and its geological significance of late carboniferous and early cretaceous adakitic granites from eastern margin of the abukuma mountains, japan. J Geol Soc Jpn 120(2):37–51

    Article  Google Scholar 

  • Tsutsumi, Y., Ohtomo, Y., Horie, K., Nakamura, K.i., Yokoyama, K.: Granitoids with 300 ma in the joban coastal region, east of abukuma plateau, northeast japan. J Mineral Petrol Sci pp. 1005260146–1005260146 (2010)

  • Van Westen C, Seijmonsbergen A, Mantovani F (1999) Comparing landslide hazard maps. Nat Hazards 20(2-3):137–158

    Article  Google Scholar 

  • Van Westen C, Rengers N, Soeters R (2003) Use of geomorpholog- ical information in indirect landslide susceptibility assessment. Natural hazards 30(3):399–419

    Article  Google Scholar 

  • Whisler FD, Bouwer H (1970) Comparison of methods for calculating vertical drainage and infiltration for soils. J Hydrol 10(1):1–19

    Article  Google Scholar 

  • Xie M, Esaki T, Cai M (2004) A time-space based approach for map- ping rainfall-induced shallow landslide hazard. Environ Geol 46(6-7):840–850

    Article  Google Scholar 

  • Yoshikawa, S., Suzuki, M., , Ueno, S., Inagaki, H., Ochiai, T., Matugi, H.: Findings (1) of the slope disaster caused by typhoon no.19 of october, 2019. the general gamage gondition of maru- morimachi, igu-gun, miyagi. The 55th Geotechnical Research Conference, Japanese Geotechnical Society (2020)

  • Young, R., Farrar, J., Howard, A.: Earth Manual Part 1, 3 edn. US Government Printing Office (1998). U.S. Department of the Interior Bureau of Reclamation

  • Zizioli D, Meisina C, Valentino R, Montrasio L (2013) Comparison between different approaches to modeling shallow landslide sus- ceptibility: a case history in oltrepo pavese, northern italy. Nat Hazards Earth Syst Sci 13(3):559–573

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nilo Lemuel J. Dolojan.

Additional information

The original online version of this article was revised due to a retrospective Open Access cancellation.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dolojan, N.L.J., Moriguchi, S., Hashimoto, M. et al. Mapping method of rainfall-induced landslide hazards by infiltration and slope stability analysis. Landslides 18, 2039–2057 (2021). https://doi.org/10.1007/s10346-020-01617-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10346-020-01617-x

Keywords

Navigation