Satellite rainfall products outperform ground observations for
landslide forecasting in India

Brunetti, Maria Teresa; Melillo, Massimo; Gariano, Stefano Luigi; Ciabatta, Luca; Brocca, Luca; Amarnath, Giriraj; Peruccacci, Silvia

doi:10.5194/hess-2021-42

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…However, GSMaP-GNRT outperformed CMORPH-CRT in discriminating the occurrence/non-occurrence of landslide-triggering rainfall events. Brunetti et al (2021) showed that satellite-derived rainfall products outperformed ground observations for landslide prediction in India. However, their ground observation dataset has a coarser spatiotemporal resolution compared to satellite-derived rainfall products.…”

Section: Discussionmentioning

confidence: 99%

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

Satyaningsih,

Jetten,

Ettema

et al. 2023

Nat Hazards

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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.

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Section: Discussionmentioning

confidence: 99%

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

Satyaningsih,

Jetten,

Ettema

et al. 2023

Nat Hazards

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“…For instance, Papua New Guinea used TRMM satellite precipitation estimates (Robbins, 2016). Brunetti et al (2021) showed that rainfall thresholds estimated from satellite products outperformed those derived from ground observations in India. Even Indonesia tested similar data to support its landslide EWS.…”

Section: Introductionmentioning

confidence: 99%

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

Satyaningsih¹,

Jetten²,

Ettema³

et al. 2023

Preprint

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High spatiotemporal resolution satellite data have been available to provide rainfall estimates with global coverage and relatively short latency. On the other hand, a rain gauge measures the actual rain that falls to the surface, but its network density is commonly sparse, particularly those that record at sub-daily records. These datasets are extensively used to define rainfall thresholds for landslides. This study aims to investigate the use of GSMaP-GNRT and CMORPH-CRT data along with automatic rain station data to determine rainfall thresholds for landslides in Progo Catchment, Indonesia, as the basis for landslide early warning in the area. Using the frequentist method, we derived the thresholds based on 213 landslide occurrences for 2012-2021 in the Progo Catchment. Instead of relying on a fixed time window to determine rainfall events triggering landslides, we consider a dynamic window, enabling us to adapt to the rainfall event responsible for landslides by extending or shortening its duration depending on the persistence of the rainfall signal. Results indicate that both GSMaP-GNRT and CMORPH-CRT products fail to capture high-intensity rainfall in Progo Catchment and overestimate light rainfall measured by rain gauge observations.Nevertheless, when accumulated to define the rainfall threshold, the overall performance of GSMaP-GNRT and automatic rain station data in Progo Catchment is comparable. The rainfall measured at the stations performed slightly better than the estimated rainfall from GSMaP-GNRT, particularly at a probability exceedance level below 15%. In contrast, CMORPH -CRT performed the worst for all exceedance probabilities. The suitable exceedance probability for early warning purposes in Progo Catchment is 10% if it is based on the automatic rain station data. At this exceedance probability level, the threshold can adequately discriminate triggering/non-triggering rainfall conditions and produces the minimum false alarms and missed events.

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Various Aspects of Rockfall Hazards along the Mountain Roads in India: a Systematic Review

Dahiya,

Pandit,

Sarkar

et al. 2024

Indian Geotech J

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Satellite rainfall products outperform ground observations for landslide forecasting in India

Cited by 3 publications

References 44 publications

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

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

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

Various Aspects of Rockfall Hazards along the Mountain Roads in India: a Systematic Review

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