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Landslides

, Volume 12, Issue 6, pp 1179–1196 | Cite as

Soil moisture and precipitation thresholds for real-time landslide prediction in El Salvador

  • Ari J. PosnerEmail author
  • Konstantine P. Georgakakos
Technical Note

Abstract

Described is the development of a regional forecasting system for landslide hazard threat level, suitable for use operationally by forecasting and disaster management agencies. The system utilizes spatially distributed operational hydrologic models to estimate depth-integrated soil moisture on basin scales of order 160 km2, with forcing of remotely sensed and on-site precipitation data. The depth-integrated soil moisture data and the precipitation forcing are used together with regional databases of landslide occurrence to develop threshold curves in the precipitation/soil moisture space that allow the prediction of landslide hazard threat level on satellite-derived rainfall pixel scales. Predetermined susceptibility maps may then be used together with the real-time prediction of hazard threat level for a particular rainfall pixel to determine the slopes within the pixel that are more likely to fail in real time and to characterize a given pixel as susceptible or non-susceptible to landsliding for real-time prediction. The operational system development requires global satellite precipitation estimates with short latency, real-time precipitation data from sparse rain gauges in the region, and a regional database of historical landslide events with location and timing information. Parametric databases that support the operational hydrologic model consist of soil texture by depth and land-use/land-cover information. The case study presented is for the country of El Salvador. The study shows the feasibility of the regional system development and the validation of the assumed existence of a threshold curve in two-dimensional space consisting of the depth-integrated soil moisture and of the forcing precipitation. The resulting threshold curve, when examined with data from the period 2006–2011 in El Salvador, resulted in warnings of landslide occurrence with frequency that spanned the range between 1 and 5 % of the days for the basins identified to be susceptible to landsliding.

Keywords

Landslide prediction Distributed soil moisture accounting Remote sensing of precipitation 

Notes

Acknowledgments

We wish to thank SNET staff Manuel Diaz and Mario Reyes. We also wish to thank the Editor and two anonymous reviewers for their constructive criticism to earlier versions of this article, which undoubtedly led to a clearer presentation.

References

  1. Acker JG, Leptoukh G (2007) Online analysis enhances use of NASA earth science data. Eos, Trans AGU 88(2):14–17CrossRefGoogle Scholar
  2. Aguilar M, Pacheco T, Tobar J, Quiñónez J (2009) Vulnerability and adaptation to climate change of rural inhabitants in the central coastal plain of El Salvador. Clim Res 40(2–3):187–198CrossRefGoogle Scholar
  3. Baum RL, Godt JW (2009) Early warning of rainfall-induced shallow landslides and debris flows in the USA. Landslides 7(3):259–272CrossRefGoogle Scholar
  4. Baxter S (1984) Lexico estratigrafico de El Salvador. comision ejecutiva hidroelectrica del Rio Lempa. San Salvador, El SalvadorGoogle Scholar
  5. Brocca L, Melone F, Moramarco T (2008) On the estimation of antecedent wetness condition in rainfall–runoff modeling. Hydrol Process 22:629–642CrossRefGoogle Scholar
  6. Burnash RJC, Ferral RL, McGuire RA (1973) A generalized streamflow simulation system: conceptual modeling for digital computers. US National Weather Service and California Department of Water Resources Rep., Joint Federal State River Forecast Center, SacramentoGoogle Scholar
  7. Caine N (1980) Rainfall intensity-duration control of shallow landslides and debris flows. Geograf Ann 62A:23–27CrossRefGoogle Scholar
  8. Capparelli G, Versace P (2010) FLaIR and SUSHI: two mathematical models for early warning of landslides induced by rainfall. Landslides. doi: 10.1007/s10346-010-0228-6 Google Scholar
  9. Carpenter TM, Georgakakos KP (2004) Continuous streamflow simulation with the HRCDHM distributed hydrologic model. J Hydrology 298(1):61–79CrossRefGoogle Scholar
  10. Crozier MJ (1999) Prediction of rainfall-triggered landslides: a test of the antecedent water status model. Earth Surf Process Landforms 24:825–833CrossRefGoogle Scholar
  11. Devoli G, Morales A, Hoeg K (2007) Historical landslides in Nicaragua—collection and analysis of data. Landslides 4(1):5–18CrossRefGoogle Scholar
  12. Dewey JW, White RA, Hernandez DA (2004) Seismicity and tectonics in El Salvador In: Natural hazards in El Salvador (Eds) WI Rose, JJ Bommer, DL Lopez, MJ Carr, and JJ Major. Geological Society of America, Special Paper 375Google Scholar
  13. Ehlschlaeger C (1989) Using the AT search algorithm to develop hydrologic models from digital elevation data. Proc Int Geograp Info Syst (IGIS) Sympos 89:275–281, Baltimore, MD, 18–19 March 1989 Google Scholar
  14. FAO (1974) Soil 1:5000000 volume I legend. Food and Agricultural Organization of the United Map of the World Nations Educational, Scientific, and Cultural Organization, Paris, p 59Google Scholar
  15. Georgakakos KP (1986) A generalized stochastic hydrometeorological model for flood and flash-flood forecasting 1. formulation. Water Resour Res 22(13):2083–2095CrossRefGoogle Scholar
  16. Georgakakos KP, Baumer OW (1996) Measurement and utilization of on-site soil moisture data. J Hydrol 184:131–152CrossRefGoogle Scholar
  17. Georgakakos KP, Carpenter TM (2006) Potential value of operationally available and spatially distributed ensemble soil water estimates for agriculture. J Hydrol 328:177–191CrossRefGoogle Scholar
  18. Georgakakos KP, Graham R, Jubach R, Modrick TM, Shamir E, Sperfslage JA (2013) Global flash flood guidance system, phase I. HRC technical report no. 9. Hydrologic Research Center, San Diego, 134pp Google Scholar
  19. Guzzetti F, Peruccacci S, Rossi M, Stark CP (2008) The rainfall intensity–duration control of shallow landslides and debris flows: an update. Landslides 5(1):3–17CrossRefGoogle Scholar
  20. Hastenrath S (1967) Rainfall distribution and regime in Central America. Arch. Meteor. Geophys. Bioklimatol. 15B, 201–241. Hastenrath, S. 1985. Climate and circulation of the Tropics. D. Redel, 455 pGoogle Scholar
  21. Hervas J, Van Den Eeckhaut M, Legorreta G, Trigila A (2013) Introduction. In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice volume 1: landslide inventory and susceptibility and hazard zoning. Springer-Verlag, Berlin, 607 pp Google Scholar
  22. IFRC (2013) Emergency appeal El Salvador: Tropical depression 12-E. International Federation of the Red Cross and Red Crescent Societies, Emergency appeal n° MDRSV004, GLIDE n° TC-2011-0001570SLV. 26 September 2013Google Scholar
  23. Kirschbaum DB, Adler R, Hong Y, Hill S, Lerner-Lam AL (2010) A global landslide catalog for hazard applications: method, results and limitations. Nat Hazards 52(3):561–575CrossRefGoogle Scholar
  24. Koren V, Smith M, Wang D, Zhang Z (2000) Use of soil properties data in the derivation of conceptual rainfall-runoff model parameters. In: American meteorological society 15th conference on hydrology, Long Beach, pp 103–106Google Scholar
  25. Lazzari M, Piccarreta M, Capolongo D (2013) Landslide triggering and local rainfall thresholds in Bradanic Foredeep, Basilicata Region (Southern Italy). In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice volume 2: early warning, instrumentation, and monitoring. Springer-Verlag, Berlin, 607 pp Google Scholar
  26. Moriwaki H, Inokuchi T, Hattanji T, Sassa K, Ochiai H, Wang G (2004) Failure processes in a full-scale landslide experiment using a rainfall simulator. Landslides 1(4):277–288. doi: 10.1007/s10346-004-0034-0 CrossRefGoogle Scholar
  27. National Weather Service River Forecast System (NWSRFS) (1999) User manual. National Weather Service Office of Hydrologic Development, Hydrology Laboratory, Silver Springs, MD http:// www.nws.noaa.gov/oh/hrl/nwsrfs/users_manual/htm/xrfsdocpdf.php
  28. NRC (National Research Council) (2008) Integrating multiscale observations of U.S. waters. The National Academies Press, Washington, DC, pp 74–76, http://dels.nas.edu/Report/Integrating-Multiscale-Observations/12060 Google Scholar
  29. OCHA (2010) United Nations Office for the Coordination of Humanitarian Affairs. America Central–Tormenta Tropical Agatha, Informe de Situación #3. United Nations Partnership for HumanityGoogle Scholar
  30. Pasch L, Avila A, Jiing J (1998) Atlantic tropical systems of 1994 and 1995: a comparison of a quiet season to a near record breaking one. Mon Weather Rev 126:1106–1123CrossRefGoogle Scholar
  31. Pelletier JD, Malamud BD, Blodgett T, Turcotte DL (1997) Scale-invariance of soil moisture variability and its implications for the frequency-size distribution of landslides. Eng Geol 48(3–4):255–268CrossRefGoogle Scholar
  32. Peña M, Douglas MW (2002) Characteristics of wet and dry spells over the pacific side of Central America during the rainy season. Mon Weather Rev 130:3054–3073CrossRefGoogle Scholar
  33. Ray RL, Jacobs JM (2007) Relationships among remotely soil moisture, precipitation and landslide events. Nat Hazards 43(2):211–222CrossRefGoogle Scholar
  34. Reuters (2009) Hurricane kills 124 in El Salvador. The New York Times. 08 Nov. 2009. Web. 15 July 2014Google Scholar
  35. Rose W, Bommer J, Sandoval J (2004) Natural hazards and risk mitigation in El Salvador: an introduction. In: Natural hazards in El Salvador (Eds) WI Rose, JJ Bommer, DL Lopez, MJ Carr, and JJ Major. Geological Society of America, Special Paper 375, pp. 1–4Google Scholar
  36. Sassa K, Osamu N, Solidum R, Yamazaki Y, Ohta H (2010) An integrated model simulating the initiation and motion of earthquake and rain induced rapid landslides and its application to the 2006 Leyte landslide. Landslides 7:219–236CrossRefGoogle Scholar
  37. Scofield RA, Kuligowski RJ (2003) Status and outlook of operational satellite precipitation algorithms for extreme-precipitation events. Mon Weather Rev 18:1037–1051Google Scholar
  38. Segoni S, Leoni L, Benedetti AI, Catani F, Righini G, Falorni G, Gabellani S, Rudari R, Silvestro F, Rebora N (2010) Towards a definition of a real-time forecasting network for rainfall induced shallow landslides. Nat Hazards Earth Syst Sci 9:2119–2133CrossRefGoogle Scholar
  39. Shamir E, Imam B, Gupta HV, Sorooshian S (2005) Application of temporal streamflow descriptors in hydrologic model parameter estimation. Water Resour Res 41(6):W06021. doi: 10.1029/ 2004WR003409 Google Scholar
  40. Shamir E, Carpenter TM, Fickenscher P, Georgakakos KP (2006) Evaluation of the National Weather Service operational hydrologic model and forecasts for the American River Basin. J Hydrol Eng 11(5):392–407CrossRefGoogle Scholar
  41. Shamir E, Georgakakos KP, Spencer C, Modrick TM, Murphy MJ Jr, Jubach R (2013) Evaluation of real-time flash flood forecasts for Haiti during the passage of Hurricane Tomas, November 4–6, 2010. Nat Hazards 67:459–482. doi: 10.1007/s11069-013-0573-6 CrossRefGoogle Scholar
  42. Smith MB, Seo DJ, Koren VI, Reed SM, Zhang Z (2004) The distributed model intercomparison project (DMIP): motivation and experiment design. J Hydrol 298(1–4):4–26CrossRefGoogle Scholar
  43. Temimi M, Leconte R, Chaouch N, Sukumal P, Khanbilvarde R, Brissette F (2010) A combination of remote sensing data and topographic attributes for the spatial and temporal monitoring of soil wetness. J Hydrol 388:28–40. doi: 10.1016/j.jhydrol.2010.04.021 CrossRefGoogle Scholar
  44. Townsend FC (1985) Geotechnical characteristics of residual soils. J Geotech Eng 111:77–94CrossRefGoogle Scholar
  45. UNdata. (2014) New York, NY: United Nations Statistic Division: UNCDB http://data.un.org/Default.aspx
  46. WMO (World Meteorological Organization) (2015) Flash Flood Guidance System (FFGS) with Global Coverage: http://www.wmo.int/pages/prog/hwrp/flood/ffgs/index_en.php

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Hydrologic Research CenterSan DiegoUSA
  2. 2.Scripps Institution of OceanographyUCSDLa JollaUSA

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