Deriving rainfall thresholds for landsliding at the regional scale: daily and hourly resolutions, normalisation, and antecedent rainfall

Leonarduzzi, Elena; Molnár, Péter

doi:10.5194/nhess-20-2905-2020

Cited by 31 publications

(23 citation statements)

References 58 publications

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“…The 10 min total of recorded lightning strikes at a distance of 3-30 km was also derived from the Montana station and used as a secondary variable for the convective character of storms (Gaál et al, 2014) in the machine learning algorithm. The local predictive power of debris flows in Illgraben was also compared with a regional prediction of slope failure using a regional data set on shallow landslides in Switzerland including associated rainfall events (Leonarduzzi et al, 2017). It is based on a gridded daily rainfall product (RhiresD) and the Swiss flood and landslide damage database of WSL (Hilker et al, 2009).…”

Section: Datamentioning

confidence: 99%

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Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment

Hirschberg

Badoux

McArdell

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. The prediction of debris flows is relevant because this type of natural hazard can pose a threat to humans and infrastructure. Debris-flow (and landslide) early warning systems often rely on rainfall intensity–duration (ID) thresholds. Multiple competing methods exist for the determination of such ID thresholds but have not been objectively and thoroughly compared at multiple scales, and a validation and uncertainty assessment is often missing in their formulation. As a consequence, updating, interpreting, generalizing and comparing rainfall thresholds is challenging. Using a 17-year record of rainfall and 67 debris flows in a Swiss Alpine catchment (Illgraben), we determined ID thresholds and associated uncertainties as a function of record duration. Furthermore, we compared two methods for rainfall definition based on linear regression and/or true-skill-statistic maximization. The main difference between these approaches and the well-known frequentist method is that non-triggering rainfall events were also considered for obtaining ID-threshold parameters. Depending on the method applied, the ID-threshold parameters and their uncertainties differed significantly. We found that 25 debris flows are sufficient to constrain uncertainties in ID-threshold parameters to ±30 % for our study site. We further demonstrated the change in predictive performance of the two methods if a regional landslide data set with a regional rainfall product was used instead of a local one with local rainfall measurements. Hence, an important finding is that the ideal method for ID-threshold determination depends on the available landslide and rainfall data sets. Furthermore, for the local data set we tested if the ID-threshold performance can be increased by considering other rainfall properties (e.g. antecedent rainfall, maximum intensity) in a multivariate statistical learning algorithm based on decision trees (random forest). The highest predictive power was reached when the peak 30 min rainfall intensity was added to the ID variables, while no improvement was achieved by considering antecedent rainfall for debris-flow predictions in Illgraben. Although the increase in predictive performance with the random forest model over the classical ID threshold was small, such a framework could be valuable for future studies if more predictors are available from measured or modelled data.

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Section: Datamentioning

confidence: 99%

“…Furthermore, we compare the uncertainties of two methods that have been used recently for determining ID-threshold parameters (e.g. Leonarduzzi et al, 2017;Leonarduzzi and Molnar, 2020;Nikolopoulos et al, 2018). These methods use linear regression and/or the true skill statistic to determine the IDthreshold parameters α and β.…”

Section: Introductionmentioning

confidence: 99%

Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment

Hirschberg

Badoux

McArdell

et al. 2021

Nat. Hazards Earth Syst. Sci.

Self Cite

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“…The magnitude-frequency distribution of the areas of the landslides in the inventory shows a characteristic shape with rollover and power law tail, with an exponent of the power tail of 2.65 and a rollover point around 190 m 2 . This exponent is higher than two landslide distributions triggered by typhoon events in Taiwan, with exponents of 1.42-1.60 (Chien-Yuan et al, 2006), though similar to earthquake-triggered landslide inventories in China (Li et al, 2013) and Haiti (Gorum et al, 2013): 2.63 and 2.71 respectively (Tanyaş et al, 2019). These numbers suggest that the small landslides are more frequent than larger ones, in comparison to other studies where the exponent of the power law tail is lower than 2 (Bennett et al, 2012;Van Den Eeck-haut et al, 2007).…”

Section: Landslide Characteristics and Landscape Predisposing Factorsmentioning

confidence: 47%

The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines

Abancó

Bennett

Matthews

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. In 2018 Typhoon Mangkhut (locally known as Typhoon Ompong) triggered thousands of landslides in the Itogon region of the Philippines. A landslide inventory of the affected region is compiled for the first time, comprising 1101 landslides over a 570 km2 area. The inventory is used to study the geomorphological characteristics and land cover more prone to landsliding as well as the hydrometeorological conditions that led to widespread failure. The results showed that landslides mostly occurred on grassland and wooded slopes of clay superficial geology, predominantly facing east-southeast. Rainfall (Integrated Multi-satellitE Retrievals for Global Precipitation Measurement, IMERG GPM) associated with Typhoon Mangkhut is compared with 33 high-intensity rainfall events that did not trigger regional landslide events in 2018. Results show that landslides occurred during high-intensity rainfall that coincided with the highest soil moisture values (estimated clays saturation point), according to Soil Moisture Active Passive level 4 (SMAP-L4) data. Our results demonstrate the potential of SMAP-L4 and GPM IMERG data for landslide hazard assessment and early warning where ground-based data are scarce. However, other rainfall events in the months leading up to Typhoon Mangkhut that had similar or higher rainfall intensities and also occurred when soils were saturated did not trigger widespread landsliding, highlighting the need for further research into the conditions that trigger landslides in typhoons.

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“…ID regional and local rainfall was derived by Geethu et al (2019) in north-eastern India for the Sikkim region and the Gangtok area, respectively. Mandal and Sarkar (2021) estimated rainfall thresholds along a vulnerable section of the NH10 road in the Darjeeling Himalayas.…”

Section: Introductionmentioning

confidence: 99%

Satellite rainfall products outperform ground observations for landslide prediction in India

Brunetti

Melillo

Gariano

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Landslides are among the most dangerous natural hazards, particularly in developing countries, where ground observations for operative early warning systems are lacking. In these areas, remote sensing can represent an important detection and monitoring process to predict landslide occurrence in space and time, particularly satellite rainfall products that have improved in terms of accuracy and resolution in recent times. Surprisingly, only a few studies have investigated the capability and effectiveness of these products in landslide prediction in reducing the impact of this hazard on the population. We have performed a comparative study of ground- and satellite-based rainfall products for landslide prediction in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides that occurred throughout India in the 13-year period between April 2007 and October 2019 has been used. Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (0.1∘ vs. 0.25∘) and temporal (hourly vs. daily) resolutions. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g. overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.

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Deriving rainfall thresholds for landsliding at the regional scale: daily and hourly resolutions, normalisation, and antecedent rainfall

Cited by 31 publications

References 58 publications

Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment

Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment

The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines

Satellite rainfall products outperform ground observations for landslide prediction in India

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