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Spatially distributed rainfall information and its potential for regional landslide early warning systems

Author

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  • Ekrem Canli

    (University of Vienna)

  • Bernd Loigge

    (SPL Tele GmbH & Co KG)

  • Thomas Glade

    (University of Vienna)

Abstract

Crucial to most landslide early warning system (EWS) is the precise prediction of rainfall in space and time. Researchers are aware of the importance of the spatial variability of rainfall in landslide studies. Commonly, however, it is neglected by implementing simplified approaches (e.g. representative rain gauges for an entire area). With spatially differentiated rainfall information, real-time comparison with rainfall thresholds or the implementation in process-based approaches might form the basis for improved landslide warnings. This study suggests an automated workflow from the hourly, web-based collection of rain gauge data to the generation of spatially differentiated rainfall predictions based on deterministic and geostatistical methods. With kriging usually being a labour-intensive, manual task, a simplified variogram modelling routine was applied for the automated processing of up-to-date point information data. Validation showed quite satisfactory results, yet it also revealed the drawbacks that are associated with univariate geostatistical interpolation techniques which solely rely on rain gauges (e.g. smoothing of data, difficulties in resolving small-scale, highly intermittent rainfall). In the perspective, the potential use of citizen scientific data is highlighted for the improvement of studies on landslide EWS.

Suggested Citation

  • Ekrem Canli & Bernd Loigge & Thomas Glade, 2018. "Spatially distributed rainfall information and its potential for regional landslide early warning systems," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(1), pages 103-127, April.
    • Handle: RePEc:spr:nathaz:v:91:y:2018:i:1:d:10.1007_s11069-017-2953-9
    DOI: 10.1007/s11069-017-2953-9
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    References listed on IDEAS

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    1. Yang Hong & Robert Adler & Andrew Negri & George Huffman, 2007. "Flood and landslide applications of near real-time satellite rainfall products," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 43(2), pages 285-294, November.
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    4. Lorella Montrasio & Roberto Valentino & Angela Corina & Lauro Rossi & Roberto Rudari, 2014. "A prototype system for space–time assessment of rainfall-induced shallow landslides in Italy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 1263-1290, November.
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    6. L. Lombardo & M. Cama & M. Maerker & E. Rotigliano, 2014. "A test of transferability for landslides susceptibility models under extreme climatic events: application to the Messina 2009 disaster," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 1951-1989, December.
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    Cited by:

    1. Alfonso Gutierrez-Lopez, 2021. "A Robust Gaussian variogram estimator for cartography of hydrological extreme events," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(2), pages 1469-1488, June.
    2. Zhiheng Wang & Dongchuan Wang & Qiaozhen Guo & Daikun Wang, 2020. "Regional landslide hazard assessment through integrating susceptibility index and rainfall process," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(3), pages 2153-2173, December.

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