Skip to main content Accessibility help
Hostname: page-component-59b7f5684b-s82fj Total loading time: 0.928 Render date: 2022-09-26T19:35:36.486Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "displayNetworkTab": true, "displayNetworkMapGraph": false, "useSa": true } hasContentIssue true

13 - Broadening and Deepening the Rainfall-Induced Landslide Detection

Practices and Perspectives at a Global Scale

from Part II - Climate Risk to Human and Natural Systems

Published online by Cambridge University Press:  17 March 2022

Qiuhong Tang
Chinese Academy of Sciences, Beijing
Guoyong Leng
Oxford University Centre for the Environment
Get access


A better detection of landslide occurrence is critical for disaster prevention and mitigation. Over the past four decades, great achievements have been made, ranging from inventories to mapping, susceptibility analysis to triggering threshold identification. Here, we proposed a model to establish global distributed rainfall thresholds, by linking triggering rainfall with geo-environmental causes related to landslide events. The model was based on multiple linear regression method, to define rainfall thresholds as a function of diverse geo-environmental variables, fitted and validated by a combined and relatively accurate landslide dataset. Results show primarily feasible performances for training and testing datasets, with low mean absolute error (0.22 log(mm)) and a high coefficient of determination (0.67) totally. We further prepared global distributed threshold maps for sub- and multi-daily rainfall durations. They share similar spatial distributions in line with previous research. The normalized rainfall index, defined as the ratio of precipitation amount over distributed rainfall thresholds, can be an index of possible landslide occurrence, that is, regions with a normalized index over 1.0 correspond to high probability. We argue that distributed rainfall threshold models are an improvement of empirical threshold models and susceptibility assessments by considering the interaction between triggering rainfall and geo-environmental causes, and promising for better hazard assessment.

Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)


Aleotti, P. (2004). A warning system for rainfall-induced shallow failures. Engineering Geology 73(3–4): 247265.CrossRefGoogle Scholar
Alvioli, M., Melillo, M., Guzzetti, F., et al. (2018). Implications of climate change on landslide hazard in Central Italy. Science of the Total Environment 630: 15281543.CrossRefGoogle ScholarPubMed
Benz, S. A., & Blum, P. (2019). Global detection of rainfall-triggered landslide clusters. Natural Hazards and Earth System Sciences 19(7): 14331444.CrossRefGoogle Scholar
Bogaard, T., & Greco, R. (2018). Invited perspectives: Hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: Proposing hydro-meteorological thresholds. Natural Hazards and Earth System Sciences 18(1): 3139.CrossRefGoogle Scholar
Broeckx, J., Vanmaercke, M., Duchateau, R., & Poesen, J. (2018). A data-based landslide susceptibility map of Africa. Earth-Science Reviews 185: 102121.CrossRefGoogle Scholar
Brunetti, M., Peruccacci, S., Rossi, M., Luciani, S., Valigi, D., & Guzzetti, F. (2010). Rainfall thresholds for the possible occurrence of landslides in Italy. Natural Hazards and Earth System Sciences 10(3): 447458.CrossRefGoogle Scholar
Caine, N. (1980). The rainfall intensity-duration control of shallow landslides and debris flows. Geografiska Annaler Series A 62(1–2): 2327.Google Scholar
Chen, W., Pourghasemi, H. R., Kornejady, A., & Zhang, N. (2017). Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma 305(3): 314327.CrossRefGoogle Scholar
Clarizia, M., Gullà, G., & Sorbino, G. (1996). Sui meccanismi di innesco dei soil slip. In International Conference on Prevention of Hydrogeological Hazards: The Role of Scientific Research, Alba, Italy, pp. 585597. (in Italian)Google Scholar
Crosta, G. B., & Frattini, P. (2000). Rainfall thresholds for triggering soil slips and debris flows. In Mediterranean Storms 2000 – Proceedings of the Second EGS Plinius Conference, Siena, Italy, pp. 463487.Google Scholar
Dai, F. C., Lee, C. F., & Ngai, Y. Y. (2002). Landslide risk assessment and management: An overview. Engineering Geology 64(1): 6587.CrossRefGoogle Scholar
Danielson, J. J., & Gesch, D. (2011). Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010). US Geological Survey. Available from (Last accessed 29 March 2020).Google Scholar
Ermini, L., Catani, F., & Casagli, N. (2005). Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66(1–4): 327343.CrossRefGoogle Scholar
Evans, S. G., & Delaney, K. B. (2019). Taking the pulse of global landslide occurrence 2010–2018. Geophysical Research Abstracts 21: EGU2019–11815.Google Scholar
FAO, IIASA, ISRIC, ISS-CAS, & JRC (2012). Harmonized World Soil Database V 1.2. Rome: FAO.Google Scholar
Farahmand, A., & Aghakouchak, A. (2013). A satellite-based global landslide model. Natural Hazards and Earth System Science 13(5): 12591267.CrossRefGoogle Scholar
Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37(12): 43024315.CrossRefGoogle Scholar
Froude, M. J., & Petley, D. N. (2018). Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences 18(8): 21612181.CrossRefGoogle Scholar
Gariano, S. L., & Guzzetti, F. (2016). Landslides in a changing climate. Earth Science Reviews 162: 227252.CrossRefGoogle Scholar
Giardini, D., Gruenthal, G., Shedlock, K., & Zhang, P. (2003). The GSHAP global seismic hazard map. In Lee, W. H. K., Kanamori, H., Jennings, P. C., & Kisslinger, C. (eds.), International Handbook of Earthquake and Engineering Seismology (Vol. 81, pp. 12331239). Amsterdam: Academic Press.CrossRefGoogle Scholar
Giuliani, G., & Peduzzi, P. (2011). The PREVIEW global risk data platform: A geoportal to serve and share global data on risk to natural hazards. Natural Hazards and Earth System Science 11(1): 5366.CrossRefGoogle 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): 181216.CrossRefGoogle Scholar
Guzzetti, F., Peruccacci, S., Rossi, M., & Stark, C. P. (2007). Rainfall thresholds for the initiation of landslides in central and southern Europe. Meteorology and Atmospheric Physics 98(3–4): 239267.CrossRefGoogle Scholar
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): 317.CrossRefGoogle Scholar
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., & Ardizzone, F. (2005). Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72(1–4): 272299.CrossRefGoogle Scholar
Haque, U., Blum, P., da Silva, P. F., et al. (2016). Fatal landslides in Europe. Landslides 13(5): 15451554.CrossRefGoogle Scholar
Haque, U., da Silva, P. F., Devoli, G., et al. (2019). The human cost of global warming: Deadly landslides and their triggers (1995–2014). Science of the Total Environment 682: 673684.CrossRefGoogle Scholar
Harp, E. L., Reid, M. E., McKenna, J. P., & Michael, J. A. (2009). Mapping of hazard from rainfall-triggered landslides in developing countries: Examples from Honduras and Micronesia. Engineering Geology 104(3–4): 295311.CrossRefGoogle Scholar
Hong, Y., Adler, R. F., & Huffman, G. (2007a). An experimental global prediction system for rainfall-triggered landslides using satellite remote sensing and geospatial datasets. IEEE Transactions on Geoscience and Remote Sensing 45(6): 16711680.CrossRefGoogle Scholar
Hong, Y., Adler, R., & Huffman, G. (2007b). Use of satellite remote sensing data in the mapping of global landslide susceptibility. Natural Hazards 43(2): 245256.CrossRefGoogle Scholar
Hong, Y., Alder, R., & Huffman, G. (2006). Evaluation of the potential of NASA multi-satellite precipitation analysis in global landslide hazard assessment. Geophysical Research Letters 33(22): 15.CrossRefGoogle Scholar
Huang, Y., & Zhao, L. (2018). Review on landslide susceptibility mapping using support vector machines. Catena 165(3): 520529.CrossRefGoogle Scholar
Huffman, G. J., Bolvin, D. T., Braithwaite, D., et al. (2019). Algorithm Theoretical Basis Document (ATBD) Version 06: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG). Greenbelt, MD: National Aeronautics and Space Administration (NASA). Available from (Last accessed 29 March 2020).Google Scholar
Innes, J. L. (1983). Debris flows. Progress in Physical Geography 7(4): 469501.CrossRefGoogle Scholar
Intrieri, E., Gigli, G., Casagli, N., & Nadim, F. (2013). Early warning system: Toolbox and general concepts. Natural Hazards and Earth System Science 13(1): 8590.CrossRefGoogle Scholar
Jia, G., Tang, Q., & Xu, X. (2020). Evaluating the performances of satellite-based rainfall data for global rainfall-induced landslide warnings. Landslides 17(2): 283299.CrossRefGoogle Scholar
Juang, C. S., Stanley, T. A., & Kirschbaum, D. B. (2019). Using citizen science to expand the global map of landslides: Introducing the Cooperative Open Online Landslide Repository (COOLR). PLoS One 14(7): 128.CrossRefGoogle Scholar
Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: Method, results, and limitations. Natural Hazards 52(3): 561575.CrossRefGoogle Scholar
Kirschbaum, D. B., Adler, R., Hong, Y., & Lerner-Lam, A. (2009). Evaluation of a preliminary satellite-based landslide hazard algorithm using global landslide inventories. Natural Hazards and Earth System Science 9(3): 673686.CrossRefGoogle Scholar
Kirschbaum, D. B., & Stanley, T. (2018). Satellite-based assessment of rainfall-triggered landslide hazard for situational awareness. Earth’s Future 6(3): 505523.CrossRefGoogle ScholarPubMed
Kirschbaum, D. B., Stanley, T., & Zhou, Y. (2015). Spatial and temporal analysis of a global landslide catalog. Geomorphology 249: 415.CrossRefGoogle Scholar
Latham, J., Cumani, R., Rosati, I., & Bloise, M. (2014). Global Land Cover SHARE (GLC-SHARE). Rome: Food and Agriculture Organization of the United Nations.Google Scholar
Lin, L., Lin, Q., & Wang, Y. (2017). Landslide susceptibility mapping on a global scale using the method of logistic regression. Natural Hazards and Earth System Sciences 17(8): 14111424.CrossRefGoogle Scholar
Ma, T., Li, C., Lu, Z., & Bao, Q. (2015). Rainfall intensity-duration thresholds for the initiation of landslides in Zhejiang Province, China. Geomorphology 245: 193206.CrossRefGoogle Scholar
Melillo, M., Brunetti, M. T., Peruccacci, S., Gariano, S. L., & Guzzetti, F. (2015). An algorithm for the objective reconstruction of rainfall events responsible for landslides. Landslides 12(2): 311320.CrossRefGoogle Scholar
Melillo, M., Brunetti, M. T., Peruccacci, S., Gariano, S. L., & Guzzetti, F. (2016). Rainfall thresholds for the possible landslide occurrence in Sicily (Southern Italy) based on the automatic reconstruction of rainfall events. Landslides 13(1): 165172.CrossRefGoogle Scholar
Melillo, M., Brunetti, M. T., Peruccacci, S., Gariano, S. L., Roccati, A., & Guzzetti, F. (2018). A tool for the automatic calculation of rainfall thresholds for landslide occurrence. Environmental Modelling and Software 105: 230243.CrossRefGoogle Scholar
Monsieurs, E., Dewitte, O., & Demoulin, A. (2018). A susceptibility-based rainfall threshold approach for landslide occurrence. Natural Hazards and Earth System Sciences 19(4): 775789.CrossRefGoogle Scholar
Nadim, F., Kjekstad, O., Peduzzi, P., Herold, C., & Jaedicke, C. (2006). Global landslide and avalanche hotspots. Landslides 3(2): 159173.CrossRefGoogle Scholar
Palladino, M. R., Viero, A., Turconi, L., et al. (2018). Rainfall thresholds for the activation of shallow landslides in the Italian Alps: The role of environmental conditioning factors. Geomorphology 303: 5367.CrossRefGoogle Scholar
Peruccacci, S., Brunetti, M. T., Gariano, S. L., Melillo, M., Rossi, M., & Guzzetti, F. (2017). Rainfall thresholds for possible landslide occurrence in Italy. Geomorphology 290(4): 3957.CrossRefGoogle Scholar
Peruccacci, S., Brunetti, M. T., Luciani, S., Vennari, C., & Guzzetti, F. (2012). Lithological and seasonal control on rainfall thresholds for the possible initiation of landslides in central Italy. Geomorphology 139–140: 7990.CrossRefGoogle Scholar
Petley, D. (2012). Global patterns of loss of life from landslides. Geology 40(10): 927930.CrossRefGoogle Scholar
Petley, D. (2019). Global Fatal Landslide Database (GFLD) Version 2, pp. 1–24. University of Sheffield. Online publication.Google Scholar
Petley, D. N., Dunning, S. A., & Rosser, N. J. (2005). The analysis of global landslide risk through the creation of a database of worldwide landslide fatalities. In Hungr, O., Fell, R., Couture, R., & Eberhardt, E. (eds.), Landslide Risk Management (pp. 367374). Amsterdam: Academic Press.Google Scholar
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-Science Reviews 180(3): 6091.CrossRefGoogle Scholar
Sassa, K. (2004). The International Consortium on Landslides. Landslides 1(1): 9194.CrossRefGoogle Scholar
Schneider, A., Jost, A., Coulon, C., Silvestre, M., Théry, S., & Ducharne, A. (2017). Global-scale river network extraction based on high-resolution topography and constrained by lithology, climate, slope, and observed drainage density. Geophysical Research Letters 44(6): 27732781.CrossRefGoogle Scholar
Segoni, S., Piciullo, L., & Gariano, S. L. (2018). A review of the recent literature on rainfall thresholds for landslide occurrence. Landslides 15(8): 14831501.CrossRefGoogle Scholar
Stanley, T., & Kirschbaum, D. B. (2017). A heuristic approach to global landslide susceptibility mapping. Natural Hazards 87(1): 145164.CrossRefGoogle ScholarPubMed
UNDRR. (2019). Global Assessment Report on Disaster Risk Reduction, Geneva: UNDRR.Google Scholar
UNESCO. (1973). Annual Summary of Information on Natural Disasters No.6:1971. Paris: UNESCO.Google Scholar
University of East Anglia Climatic Research Unit, Harris, I. C., & Jones, P. D. (2020). CRU TS4.03: Climatic Research Unit (CRU) Time-Series (TS) version 4.03 of High-Resolution Gridded Data of Month-by-Month Variation in Climate (Jan. 1901–Dec. 2018). Centre for Environmental Data Analysis, 22 January. Online publication.Google Scholar
Van Den Eeckhaut, M., Hervás, J., Jaedicke, C., Malet, J. P., Montanarella, L., & Nadim, F. (2012). Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data. Landslides 9(3): 357369.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the or variations. ‘’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats