Dynamic rainfall thresholds for landslide early warning in Progo Catchment, Java, Indonesia

Ratna Satyaningsih (), Victor Jetten, Janneke Ettema, Ardhasena Sopaheluwakan, Luigi Lombardo and Danang Eko Nuryanto
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Ratna Satyaningsih: University of Twente
Victor Jetten: University of Twente
Janneke Ettema: University of Twente
Ardhasena Sopaheluwakan: Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG)
Luigi Lombardo: University of Twente
Danang Eko Nuryanto: Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG)

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 119, issue 3, No 41, 2133-2158

Abstract: Abstract This study aims to derive and evaluate new empirical rainfall thresholds as the basis for landslide early warning in Progo Catchment, Indonesia, using high-resolution rainfall datasets. Although attempts have been made to determine such thresholds for regions in Indonesia, they used coarse-resolution data and fixed rainfall duration that might not reflect the characteristics of rainfall events that induced the landslides. Therefore, we evaluated gauge-adjusted global satellite mapping of precipitation (GSMaP-GNRT) and bias-corrected climate prediction center morphing method (CMORPH-CRT) hourly rainfall estimates against measurements at rainfall stations. Based on this evaluation, a minimum rainfall of 0.2 mm/h was used to identify rain events, in addition to a minimum of 24 h of consecutive no-rain to separate two rainfall events. Rainfall thresholds were determined at various levels of non-exceedance probability, using accumulated and duration of rainfall events corresponding to 213 landslide occurrences from 2012 to 2021 compiled in this study. Receiver operating characteristics (ROC) analysis showed that thresholds based on rainfall station data, GSMaP-GNRT, and CMORPH-CRT resulted in area under ROC curve values of 0.72, 0.73, and 0.64, respectively. This result indicates that the performance of high-resolution satellite-derived data is comparable to that of ground observations in the Progo Catchment. However, GSMaP-GNRT outperformed CMORPH-CRT in discriminating the occurrence/non-occurrence of landslide-triggering rainfall events. For early warning purposes, the rainfall threshold is selected based on the probability exlevel at which the threshold maximizes the true skill score, i.e., at 10% if based on station data, or at 20% if based on GSMaP-GNRT.

Keywords: Rainfall thresholds; Landslides; Early warning; GSMaP-GNRT; CMORPH-CRT (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-023-06208-2

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