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Landslides

pp 1–17 | Cite as

Evaluating the performances of satellite-based rainfall data for global rainfall-induced landslide warnings

  • Guoqiang Jia
  • Qiuhong TangEmail author
  • Ximeng Xu
Original Paper
  • 174 Downloads

Abstract

Satellite-based precipitation estimates (SPEs) show great promise for promoting landslide warning and mitigating landslide disaster risk with quasi-global coverage, near real-time monitoring, increasing spatial-temporal resolution, and accuracy. In this study, we evaluated the performances of four SPE products in detecting the initiation of rainfall-induced landslides globally using Hanssen-Kuiper (HK) skill score based on rainfall frequentist thresholds. The results show that SPEs can distinguish rainfall events responsible for landslides from those not related to landslides, suggesting that SPEs can capture rainfall conditions corresponding to landslide occurrence well and are of great use for landslide detecting. Further investigation indicates that performances at the global scale vary with products. CMORPH-3h V1 (HK = 0.43) and TMPA-3B42RT V7 (HK = 0.42) are superior to two other rainfall products with high HK values. Rainfall threshold establishment and evaluation for specific landslide types can improve SPEs’ performances in landslide modeling with higher HK values compared to results based on all landslide records. Performances also vary spatially with HK values ranging from 0.1 to 0.9 at a spatial grid of 5° × 5°. Linear relationship analysis reveals the variation in mean annual precipitation can partially explain the heterogeneous spatial distribution of rainfall threshold parameters. These findings serve to promote the application of satellite-based rainfall data in landslide warnings.

Keywords

Rainfall-induced landslides Satellite-based precipitation estimates Landslide warning Empirical rainfall thresholds Skill scores 

Notes

Funding information

This research is supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20060402), the National Natural Science Foundation of China (41425002, 41730645, and 41790424), the International Partnership Program of Chinese Academy of Sciences (131A11KYSB20170113), and Newton Advanced Fellowship.

References

  1. Aleotti P (2004) A warning system for rainfall-induced shallow failures. Eng Geol 73:247–265.  https://doi.org/10.1016/j.enggeo.2004.01.007 CrossRefGoogle Scholar
  2. Apip TK, Yamashiki Y et al (2010) A distributed hydrological-geotechnical model using satellite-derived rainfall estimates for shallow landslide prediction system at a catchment scale. Landslides 7:237–258.  https://doi.org/10.1007/s10346-010-0214-z CrossRefGoogle Scholar
  3. Arnone E, Noto LV, Lepore C, Bras RL (2011) Physically-based and distributed approach to analyze rainfall-triggered landslides at watershed scale. Geomorphology 133:121–131.  https://doi.org/10.1016/j.geomorph.2011.03.019 CrossRefGoogle Scholar
  4. Beck HE, van Dijk A, Levizzani V et al (2017) MSWEP: 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data. Hydrol Earth Syst Sci 21:589–615.  https://doi.org/10.5194/hess-21-589-2017 CrossRefGoogle Scholar
  5. Beck HE, Wood EF, Pan M, Fisher CK (2019) MSWEP V2 global 3-hourly 0.1° precipitation: methodology and quantitative assessment. Bull Am Meteorol Soc.  https://doi.org/10.1175/BAMS-D-17-0138.1 CrossRefGoogle Scholar
  6. Broeckx J, Vanmaercke M, Duchateau R, Poesen J (2018) A data-based landslide susceptibility map of Africa. Earth-Science Rev 185:102–121.  https://doi.org/10.1016/j.earscirev.2018.05.002 CrossRefGoogle Scholar
  7. Brunetti MT, Melillo M, Peruccacci S, Ciabatta L, Brocca L (2018) How far are we from the use of satellite rainfall products in landslide forecasting? Remote Sens Environ 210:65–75.  https://doi.org/10.1016/j.rse.2018.03.016 CrossRefGoogle Scholar
  8. Brunetti MT, Peruccacci S, Rossi M, Luciani S, Valigi D, Guzzetti F (2010) Rainfall thresholds for the possible occurrence of landslides in Italy. Nat Hazards Earth Syst Sci 10:447–458.  https://doi.org/10.5194/nhess-10-447-2010 CrossRefGoogle Scholar
  9. Caine N (1980) The rainfall intensity-duration control of shallow landslides and debris flows. Geogr Ann Ser A-Physic Geogr 62:23–27.  https://doi.org/10.2307/520449 CrossRefGoogle Scholar
  10. Camici S, Ciabatta L, Massari C, Brocca L (2018) How reliable are satellite precipitation estimates for driving hydrological models: a verification study over the Mediterranean area. J Hydrol 563:950–961.  https://doi.org/10.1016/j.jhydrol.2018.06.067 CrossRefGoogle Scholar
  11. Chleborad AF, Baum RL, Godt JW (2006) Rainfall Thresholds for Forecasting Landslides in the Seattle, Washington , Area —— Exceedance and Probability US Geol Surv Open file Rep 2006–1064.  https://doi.org/10.3133/ofr20061064
  12. Corominas J, van Westen C, Frattini P, Cascini L, Malet JP, Fotopoulou S, Catani F, van den Eeckhaut M, Mavrouli O, Agliardi F, Pitilakis K, Winter MG, Pastor M, Ferlisi S, Tofani V, Hervás J, Smith JT (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263.  https://doi.org/10.1007/s10064-013-0538-8 CrossRefGoogle Scholar
  13. Cruden DM, Varnes DJ (1996) Landslide types and processes. In: turner AK, Shuster RL (eds) landslides investigation and mitigation. Transportation Research Board, US National Research Council. Special report no. 247, Washington DC, Chapter 3, pp 36–75Google Scholar
  14. Dahal RK, Hasegawa S (2008) Representative rainfall thresholds for landslides in the Nepal Himalaya. Geomorphology 100:429–443.  https://doi.org/10.1016/j.geomorph.2008.01.014 CrossRefGoogle Scholar
  15. Dai FC, Lee CF, Ngai YY (2002) Landslide risk assessment and management: An overview. Eng Geol 64:65–87.  https://doi.org/10.1016/S0013-7952(01)00093-X CrossRefGoogle Scholar
  16. Devoli G, Ingeborg K, Monica S et al (2015) Landslide early warning system and web tools for real-time scenarios and for distribution of warning messages in Norway. In: Lollino G, Giordan D, Crosta GB et al (eds) Engineering geology for society and territory volume 2: landslide processes. Springer, Cham, pp 625–629.  https://doi.org/10.1007/978-3-319-09057-3_104 CrossRefGoogle Scholar
  17. Farahmand A, Aghakouchak A (2013) A satellite-based global landslide model. Nat Hazards Earth Syst Sci 13:1259–1267.  https://doi.org/10.5194/nhess-13-1259-2013 CrossRefGoogle Scholar
  18. Froude MJ, Petley DN (2018) Global fatal landslide occurrence from 2004 to 2016. Nat Hazards Earth Syst Sci 18:2161–2181.  https://doi.org/10.5194/nhess-18-2161-2018 CrossRefGoogle Scholar
  19. Gariano SL, Brunetti MT, Iovine G, Melillo M, Peruccacci S, Terranova O, Vennari C, Guzzetti F (2015) Calibration and validation of rainfall thresholds for shallow landslide forecasting in Sicily, southern Italy. Geomorphology 228:653–665.  https://doi.org/10.1016/j.geomorph.2014.10.019 CrossRefGoogle Scholar
  20. Gariano SL, Guzzetti F (2016) Landslides in a changing climate. Earth-Science Rev 162:227–252.  https://doi.org/10.1016/j.earscirev.2016.08.011 CrossRefGoogle Scholar
  21. Gebremichael M, Hossain F (2010) Satellite rainfall applications for surface hydrology. Springer, DordrechtCrossRefGoogle Scholar
  22. Guha-Sapir D, Hoyois P, Wallemacq P, Below R (2016) Annual disaster statistical review 2016: the numbers and trends. CRED https://www.emdat.be/sites/default/files/adsr_2016.pdf. Accessed on 31 October 2017
  23. Guzzetti F, Peruccacci S, Rossi M, Stark CP (2007) Rainfall thresholds for the initiation of landslides in central and southern Europe. Meteorog Atmos Phys 98:239–267.  https://doi.org/10.1007/s00703-007-0262-7 CrossRefGoogle Scholar
  24. Guzzetti F, Peruccacci S, Rossi M, Stark CP (2008) The rainfall intensity-duration control of shallow landslides and debris flows: an update. Landslides 5:3–17.  https://doi.org/10.1007/s10346-007-0112-1 CrossRefGoogle Scholar
  25. Hanssen AW, Kuipers WJ (1965) On the relationship between the frequency of rain and various meteorological parameters. Koninklijk Nederlands Meteorologisch Instituut, Meded Verhand (81):86Google Scholar
  26. Harp EL, Reid ME, McKenna JP, Michael JA (2009) Mapping of hazard from rainfall-triggered landslides in developing countries: examples from Honduras and Micronesia. Eng Geol 104:295–311.  https://doi.org/10.1016/j.enggeo.2008.11.010 CrossRefGoogle Scholar
  27. Hong Y, Alder RF, Huffman GJ (2006) Evaluation of the potential of NASA multi-satellite precipitation analysis in global landslide hazard assessment. Geophys Res Lett 33:1–5.  https://doi.org/10.1029/2006GL028010 CrossRefGoogle Scholar
  28. Hong Y, Adler RF, Huffman GJ (2007) An experimental global monitoring system for rainfall-triggered landslides using satellite remote sensing information. IEEE Trans Geosci Remote Sens 45:1671–1680.  https://doi.org/10.1109/TGRS.2006.888436 CrossRefGoogle Scholar
  29. Hossain F, Siddique-E-Akbor AHM, Yigzaw W, Shah-Newaz S, Hossain M, Mazumder LC, Ahmed T, Shum CK, Lee H, Biancamaria S, Turk FJ, Limaye A (2014) Crossing the “valley of death”: lessons learned from implementing an operational satellite-based flood forecasting system. Bull Am Meteorol Soc 95:1201–1207.  https://doi.org/10.1175/BAMS-D-13-00176.1 CrossRefGoogle Scholar
  30. Hsu K, Gao X, Sorooshian S, Gupta HV (1997) Precipitation estimation from remotely sensed information using artificial neural networks. J Appl Meteorol 36:1176–1190.  https://doi.org/10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2 CrossRefGoogle Scholar
  31. Huffman GJ, Adler RF, Bolvin DT et al (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55.  https://doi.org/10.1175/JHM560.1 CrossRefGoogle Scholar
  32. Huffman GJ, Adler RF, Bolvin DT, Nelkin EJ (2010) The TRMM multi-satellite precipitation analysis (TMPA). In: Gebremichael M, Hossain F (eds) Satellite rainfall applications for surface hydrology. Springer, Dordrecht, pp 3–22.  https://doi.org/10.1007/978-90-481-2915-7_1 CrossRefGoogle Scholar
  33. Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11:167–194.  https://doi.org/10.1007/s10346-013-0436-y CrossRefGoogle Scholar
  34. Innes JL (1983) Debris flows. Prog Phys Geogr 7:469–501.  https://doi.org/10.1177/030913338300700401 CrossRefGoogle Scholar
  35. Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36:1897–1910.  https://doi.org/10.1029/2000WR900090 CrossRefGoogle Scholar
  36. Joyce RJ, Janowiak JE, Arkin PA, Xie P (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5:487–503.  https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2 CrossRefGoogle Scholar
  37. Kirschbaum DB, Adler RF, Hong Y, Hill S, Lerner-Lam A (2010) A global landslide catalog for hazard applications: method, results, and limitations. Nat Hazards 52:561–575.  https://doi.org/10.1007/s11069-009-9401-4 CrossRefGoogle Scholar
  38. Kirschbaum DB, Adler RF, Hong Y, Kumar S, Peters-Lidard C, Lerner-Lam A (2012) Advances in landslide nowcasting: evaluation of a global and regional modeling approach. Environ Earth Sci 66:1683–1696.  https://doi.org/10.1007/s12665-011-0990-3 CrossRefGoogle Scholar
  39. Kirschbaum DB, Adler RF, Hong Y, Lerner-Lam A (2009) Evaluation of a preliminary satellite-based landslide hazard algorithm using global landslide inventories. Nat Hazards Earth Syst Sci 9:673–686.  https://doi.org/10.5194/nhess-9-673-2009 CrossRefGoogle Scholar
  40. Kirschbaum DB, Stanley T (2018) Satellite-based assessment of rainfall-triggered landslide hazard for situational awareness. Earth’s Futur 6:505–523.  https://doi.org/10.1002/2017EF000715 CrossRefGoogle Scholar
  41. Kirschbaum DB, Stanley T, Zhou Y (2015) Spatial and temporal analysis of a global landslide catalog. Geomorphology 249:4–15.  https://doi.org/10.1016/j.geomorph.2015.03.016 CrossRefGoogle Scholar
  42. Lewkowicz AG, Way RG (2019) Extremes of summer climate trigger thousands of thermokarst landslides in a high Arctic environment. Nat Commun 10:1–11.  https://doi.org/10.1038/s41467-019-09314-7 CrossRefGoogle Scholar
  43. Martelloni G, Segoni S, Fanti R, Catani F (2012) Rainfall thresholds for the forecasting of landslide occurrence at regional scale. Landslides 9:485–495.  https://doi.org/10.1007/s10346-011-0308-2 CrossRefGoogle Scholar
  44. Melillo M, Brunetti MT, Peruccacci S, Gariano SL, Guzzetti F (2015) An algorithm for the objective reconstruction of rainfall events responsible for landslides. Landslides 12:311–320.  https://doi.org/10.1007/s10346-014-0471-3 CrossRefGoogle Scholar
  45. Melillo M, Brunetti MT, Peruccacci S, Gariano SL, Guzzetti F (2016) Rainfall thresholds for the possible landslide occurrence in Sicily (southern Italy) based on the automatic reconstruction of rainfall events. Landslides 13:165–172.  https://doi.org/10.1007/s10346-015-0630-1 CrossRefGoogle Scholar
  46. Melillo M, Brunetti MT, Peruccacci S, Gariano SL, Roccati A, Guzzetti F (2018) A tool for the automatic calculation of rainfall thresholds for landslide occurrence. Environ Model Softw 105:230–243.  https://doi.org/10.1016/j.envsoft.2018.03.024 CrossRefGoogle Scholar
  47. Nadim F, Kjekstad O, Peduzzi P, Herold C, Jaedicke C (2006) Global landslide and avalanche hotspots. Landslides 3:159–173.  https://doi.org/10.1007/s10346-006-0036-1 CrossRefGoogle Scholar
  48. Peres DJ, Cancelliere A (2014) Derivation and evaluation of landslide-triggering thresholds by a Monte Carlo approach. Hydrol Earth Syst Sci 18:4913–4931.  https://doi.org/10.5194/hess-18-4913-2014 CrossRefGoogle Scholar
  49. Peruccacci S, Brunetti MT, Gariano SL, Melillo M, Rossi M, Guzzetti F (2017) Rainfall thresholds for possible landslide occurrence in Italy. Geomorphology 290:39–57.  https://doi.org/10.1016/j.geomorph.2017.03.031 CrossRefGoogle Scholar
  50. Peruccacci S, Brunetti MT, Luciani S, Vennari C, Guzzetti F (2012) Lithological and seasonal control on rainfall thresholds for the possible initiation of landslides in Central Italy. Geomorphology 139–140:79–90.  https://doi.org/10.1016/j.geomorph.2011.10.005 CrossRefGoogle Scholar
  51. Piciullo L, Calvello M, Cepeda JM (2018) Territorial early warning systems for rainfall-induced landslides. Earth-Science Rev 179:228–247.  https://doi.org/10.1016/j.earscirev.2018.02.013 CrossRefGoogle Scholar
  52. Piciullo L, Gariano SL, Melillo M, Brunetti MT, Peruccacci S, Guzzetti F, Calvello M (2017) Definition and performance of a threshold-based regional early warning model for rainfall-induced landslides. Landslides 14:995–1008.  https://doi.org/10.1007/s10346-016-0750-2 CrossRefGoogle Scholar
  53. Pisano L, Zumpano V, Parisea M et al (2017) Variations in the susceptibility to landslides, as a consequence of land cover changes: a look to the past, and another towards the future. Sci Total Environ 601–602:1147–1159.  https://doi.org/10.1016/j.scitotenv.2017.05.231 CrossRefGoogle Scholar
  54. Premchitt J, Brand EW, Chen PYM (1994) Rain-induced landslides in Hong Kong, 1972-1992. Asia Eng 43–51Google Scholar
  55. Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth-Science Rev 180:60–91.  https://doi.org/10.1016/j.earscirev.2018.03.001 CrossRefGoogle Scholar
  56. Rossi M, Luciani S, Valigi D, Kirschbaum D, Brunetti MT, Peruccacci S, Guzzetti F (2017) Statistical approaches for the definition of landslide rainfall thresholds and their uncertainty using rain gauge and satellite data. Geomorphology 285:16–27.  https://doi.org/10.1016/j.geomorph.2017.02.001 CrossRefGoogle Scholar
  57. Segoni S, Piciullo L, Gariano SL (2018) A review of the recent literature on rainfall thresholds for landslide occurrence. Landslides 15:1483–1501.  https://doi.org/10.1007/s10346-018-0966-4 CrossRefGoogle Scholar
  58. Sorooshian S, Nguyen P, Sellars S, Braithwaite D, AghaKouchak A, Hsu K (2014) Satellite-based remote sensing estimation of precipitation for early warning systems. In: Ismail-Zadeh A, Fucugauchi JU, Kijko A, Zaliapin I et al (eds) Extreme Natural Hazards. Disaster Risks and Societal Implications. Cambridge University Press, Cambridge, pp 99–112.  https://doi.org/10.1017/CBO9781139523905.011 CrossRefGoogle Scholar
  59. Sun Q, Miao C, Duan Q, Ashouri H, Sorooshian S, Hsu KL (2018) A review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev Geophys 56:79–107.  https://doi.org/10.1002/2017RG000574 CrossRefGoogle Scholar
  60. United Nations Office for Disaster Risk Reduction (UNISDR) (2017) UNISDR annual report 2017. UNISDR https://www.unisdr.org/files/58158_unisdr2017annualreport.pdf. Accessed on 15 December 2018
  61. United States Geological Survey (USGS) (2004) Landslide types and processes. Facts Sheet 2004-3072:1–4 https://pubs.usgs.gov/fs/2004/3072/. Accessed on 29November 2016Google Scholar
  62. Xie P, Joyce RJ, Wu S, Yoo SH, Yarosh Y, Sun F, Lin R (2017) Reprocessed, bias-corrected CMORPH global high-resolution precipitation estimates from 1998. J Hydrometeorol 18:1617–1641.  https://doi.org/10.1175/JHM-D-16-0168.1 CrossRefGoogle Scholar
  63. Zhang G, Chen L, Dong Z (2011) Real-time warning system of regional landslides supported by WEBGIS and its application in Zhejiang Province, China. Procedia Earth Planet Sci 2:247–254.  https://doi.org/10.1016/j.proeps.2011.09.040 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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