Precipitation data and their uncertainty as input for rainfall-induced shallow landslide models

Abstract

Physical models used to forecast the temporal occurrence of rainfall-induced shallow landslides are based on deterministic laws. Owing to the existing measuring technology and our knowledge of the physical laws controlling landslide initiation, model uncertainties are due to an inability to accurately quantify the model input parameters and rainfall forcing data. An uncertainty analysis of slope instability prediction provides a rationale for refining the geotechnical models. The Transient Rainfall Infiltration and Grid-based Regional Slope Stability-Probabilistic (TRIGRS-P) model adopts a probabilistic approach to compute the changes in the Factor of Safety (FS) due to rainfall infiltration. Slope Infiltration Distributed Equilibrium (SLIDE) is a simplified physical model for landslide prediction. The new code (SLIDE-P) is also modified by adopting the same probabilistic approach to allow values of the SLIDE model input parameters to be sampled randomly. This study examines the relative importance of rainfall variability and the uncertainty in the other variables that determine slope stability. The precipitation data from weather stations, China Meteorological Administration Land Assimilation System 2.0 (CLDAS2.0), China Meteorological Forcing Data set precipitation (CMFD), and China geological hazard bulletin are used to drive TRIGRS, SLIDE, TRIGRS-P and SLIDE-P models. The TRIGRS-P and SLIDE-P models are used to generate the input samples and to calculate the values of FS. The outputs of several model runs with varied input parameters and rainfall forcings are analyzed statistically. A comparison suggests that there are significant differences in the simulations of the TRIGRS-P and SLIDE-P models. Although different precipitation data sets are used, the simulation results of TRIGRS-P are more concentrated. This study can inform the potential use of numerical models to forecast the spatial and temporal occurrence of regional rainfall-induced shallow landslides.

Keywords

rainfall-induced landslide / SLIDE / TRIGRS / FS

References

[1] Abbaszadeh M, Shahriar K, Sharifzadeh M, Heydari M (2011). Uncertainty and reliability analysis applied to slope stability: a case study from Sungun copper mine. Geotech Geol Eng, 29(4): 581–596

[2] Arnone E, Noto L V, Lepore C, Bras R L (2011). Physically-based and distributed approach to analyze rainfall-triggered landslides at watershed scale. Geomorphology, 133(3–4): 121–131

[3] Arnone E, Dialynas Y G, Noto L V, Bras R L (2014). Parameter uncertainty in shallow rainfall-triggered landslide modeling at basin scale: a probabilisticapproach. Proced Earth Plan Sc, 9: 101–111

[4] Baum R L, Savage W Z, Godt J W (2008). TRIGRS-A FORTRAN program for transient rainfall infiltration and grid-based regional slope-stability analysis, Version 2.0. USGS Open File Report, 2008–1159

[5] Baum R L, Godt J W, Savage W Z (2010). Estimating the timing and location of shallow rainfall-induced landslides using a model for transient, unsaturated infiltration. J Geophys Res Earth Surf, 115: F03013

[6] Bogaard T, Greco R (2014). Preface “Hillslope hydrological modelling for landslides prediction”. Hydrol Earth Syst Sci, 18(10): 4185–4188

[7] Chen T, Feng Z, Chuang Y (2011). An application of TRIGRS on slope failture probability analyses—a case study of Aowanda. J Chin Soil Water Con, 42(3): 228–239

[8] Fenton G A, Griffiths D V, Service W I O (2008) Risk Assessment in Geotechnical Engineering. New Jersey: John Wiley & Sons

[9] Fredlund D G, Xing A, Fredlund M D, Barbour S L (1996). The relationship of the unsaturated soil shear strength to the soil-water characteristic curve. Can Geotech J, 33(3): 440–448

[10] Godt J W, Baum R L, Savage W Z, Salciarini D, Schulz W H, Harp E L (2008). Transient deterministic shallow landslide modeling: requirements for susceptibility and hazard assessments in a GIS framework. Eng Geol, 102(3–4): 214–226

[11] Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005). Probabilistic landslide hazard assessment at the basin scale. Geomorphology, 72(1–4): 272–299

[12] Guzzetti F, Galli M, Reichenbach P, Ardizzone F, Cardinali M (2006). Landslide hazard assessment in the Collazzone area, Umbria, Central Italy. Nat Hazards Earth Syst Sci, 6(1): 115–131

[13] Guzzetti F, Peruccacci S, Rossi M, Stark C P (2008). The rainfall intensity-duration control of shallow landslides and debris flows: an update. Landslides, 5(1): 3–17

[14] Haneberg W (2004). A rational probabilistic method for spatially distributed landslide hazard assessment. Environ Eng Geosci, 10(1): 27–43

[15] Hong Y, Adler R, Huffman G (2006). Evaluation of the potential of NASA multi-satellite precipitation analysis in global landslide hazard assessment. Geophys Res Lett, 33(22): L22402

[16] Iverson R M (2000). Landslide triggering by rain infiltration. Water Resour Res, 36(7): 1897–1910

[17] Li W, Liu C, Hong Y, Saharia M, Sun W, Yao D, Chen W (2016). Rainstorm-induced shallow landslides process and evaluation—a case study from three hot spots, China. Geomat Nat Haz Risk, 7(6): 1908–1918

[18] Liao Z, Hong Y, Wang J, Fukuoka H, Sassa K, Karnawati D, Fathani F (2010). Prototyping an experimental early warning system for rainfall-induced landslides in Indonesia using satellite remote sensing and geospatial datasets. Landslides, 7(3): 317–324

[19] Liao Z, Hong Y, Kirschbaum D F, Adler R, Gourley J, Wooten R (2011). Evaluation of TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis)’s predictive skill for hurricane-triggered landslides: a case study in Macon County, North Carolina. Nat Hazards, 58(1): 325–339

[20] Liao Z, Hong Y, Kirschbaum D, Liu C (2012). Assessment of shallow landslides from Hurricane Mitch in Central America using a physically based model. Environ Earth Sci, 66(6): 1697–1705

[21] Montgomery D R, Dietrich W E (1994). A physically based model for the topographic control on shallow landsliding. Water Resour Res, 30(4): 1153–1171

[22] Montgomery D R, Sullivan K, Greenberg H M (1998). Regional test of a model for shallow landsliding. Hydrol Processes, 12(6): 943–955

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

[24] Pack R, Tarboton D, Goodwin C N (1998). Terrain Stability Mapping with SINMAP, Technical Description and Users Guide for Version 1.00

[25] Raia S, Alvioli M, Rossi M, Baum R L, Godt J W, Guzzetti F (2014). Improving predictive power of physically based rainfall-induced shallow landslide models: a probabilistic approach. Geosci Model Dev, 7(2): 495–514

[26] Shi C, Jiang L, Zhang T, Xu B, Han S (2014). Status and Plans of CMA Land Data Assimilation System (CLDAS) Project. Paper presented at EGU General Assembly Conference Abstracts

[27] 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 Processes, 22(4): 532–545

[28] The Ministry of Land and Resources (2010). China Geological Hazard Bulletin. Beijing: The Ministry Of Land and Resources

[29] Vieira B C, Fernandes N F, Filho O A (2010). Shallow landslide prediction in the Serra do Mar, São Paulo, Brazil. Nat Hazards Earth Syst Sci, 10(9): 1829–1837

[30] Wang Y, Cao Z, Au S (2010). Efficient Monte Carlo Simulation of parameter sensitivity in probabilistic slope stability analysis. Comput Geotech, 37(7–8): 1015–1022

[31] Wu W, Sidle R C (1995). A distributed slope stability model for steep forested basins. Water Resour Res, 31(8): 2097–2110

[32] Yang K, He J, Tang W, Qin J, Cheng C C K (2010). On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau. Agric Meteorol, 150(1): 38–46

Acknowledgments

This study was funded by the National Key R&D Program of China (Grant No. 2018YFC1506600), the Chinese Ministry of Science and Technology Project (No. 2015CB452806), the National Natural Science Foundation of China (No. 41475044), and the Basic Research Special Project of the Chinese Academy of Meteorological Sciences (No. 2019Z008). There are no conflicts of interest to report. We gratefully acknowledge the anonymous reviewers for reviewing the manuscript and providing constructive comments and suggestions.

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