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Rainfall thresholds for landslide occurrence: systematic underestimation using coarse temporal resolution data

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  • Francesco Marra

    (Fredy & Nadine Herrmann Institute of Earth Sciences, Hebrew University of Jerusalem)

Abstract

Rainfall thresholds for landslides occurrence derived in real applications tend to be lower than the ones one would obtain using exact data. This letter shows how the use of coarse temporal resolution rainfall data causes a systematic overestimation of the duration of the triggering rainfall events that directly contributes to thresholds underestimation. A numerical experiment is devised to quantify this systematic effect for the relevant case of power-law depth/intensity–duration thresholds. In the examined conditions, i.e., the frequentist method at 5% non-exceedance probability level, ~ 70% underestimation of the scale parameter and ~ 60% overestimation of the shape parameter of the thresholds is to be expected using daily resolution rainfall data, but the exact quantification depends on the specific characteristics of each study case. The underestimation increases as the temporal resolution becomes larger than the expected minimal duration of the triggering events. Under operational conditions, sensitivity analyses based on the methods and datasets of interest are advised.

Suggested Citation

  • Francesco Marra, 2019. "Rainfall thresholds for landslide occurrence: systematic underestimation using coarse temporal resolution data," 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. 95(3), pages 883-890, February.
    • Handle: RePEc:spr:nathaz:v:95:y:2019:i:3:d:10.1007_s11069-018-3508-4
    DOI: 10.1007/s11069-018-3508-4
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    Cited by:

    1. Cong Liu & Shucai Li & Zongqing Zhou & Liping Li & Shaoshuai Shi & Meixia Wang & Chenglu Gao, 2020. "Physical model tests to determine the mechanism of submarine landslides under the effect of sea waves," 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. 102(3), pages 1451-1474, July.
    2. Alessandro C. Mondini & Fausto Guzzetti & Massimo Melillo, 2023. "Deep learning forecast of rainfall-induced shallow landslides," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    3. Stefano Luigi Gariano & Massimo Melillo & Silvia Peruccacci & Maria Teresa Brunetti, 2020. "How much does the rainfall temporal resolution affect rainfall thresholds for landslide triggering?," 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. 100(2), pages 655-670, January.
    4. 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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