Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions

<p>Landslides are an impacting natural hazard in alpine regions, calling for effective forecasting and warning systems. Here we compare two methods (physically based and probabilistic) for the prediction of shallow rainfall-induced landslides in an application to Switzerland, with a specific f...

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Autores principales:	E. Leonarduzzi, B. W. McArdell, P. Molnar
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Technology T Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/1dfce7bfe57c44aba200abb3fb785d8c
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spelling	oai:doaj.org-article:1dfce7bfe57c44aba200abb3fb785d8c2021-11-15T10:19:17ZRainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions10.5194/hess-25-5937-20211027-56061607-7938https://doaj.org/article/1dfce7bfe57c44aba200abb3fb785d8c2021-11-01T00:00:00Zhttps://hess.copernicus.org/articles/25/5937/2021/hess-25-5937-2021.pdfhttps://doaj.org/toc/1027-5606https://doaj.org/toc/1607-7938<p>Landslides are an impacting natural hazard in alpine regions, calling for effective forecasting and warning systems. Here we compare two methods (physically based and probabilistic) for the prediction of shallow rainfall-induced landslides in an application to Switzerland, with a specific focus on the value of antecedent soil wetness. First, we show that landslide susceptibility predicted by the factor of safety in the infinite slope model is strongly dependent on soil data inputs, limiting the hydrologically active range where landslides can occur to only <span class="inline-formula">∼20</span> % of the country with typical soil parameters and soil depth models, not accounting for uncertainty. Second, we find the soil saturation estimate provided by a conceptual hydrological model (PREVAH) to be more informative for landslide prediction than that estimated by the physically based coarse-resolution model (TerrSysMP), which we attribute to the lack of temporal variability and coarse spatial resolution in the latter. Nevertheless, combining the soil water state estimates in TerrSysMP with the infinite slope approach improves the separation between landslide triggering and non-triggering rainfall events. Third, we demonstrate the added value of antecedent soil saturation in combination with rainfall thresholds. We propose a sequential threshold approach, where events are first split into dry and wet antecedent conditions by an <span class="inline-formula"><i>N</i></span> d (day) antecedent soil saturation threshold, and then two different total rainfall–duration threshold curves are estimated. This, among all different approaches explored, is found to be the most successful for landslide prediction.</p>E. LeonarduzziE. LeonarduzziB. W. McArdellP. MolnarCopernicus PublicationsarticleTechnologyTEnvironmental technology. Sanitary engineeringTD1-1066Geography. Anthropology. RecreationGEnvironmental sciencesGE1-350ENHydrology and Earth System Sciences, Vol 25, Pp 5937-5950 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350
spellingShingle	Technology T Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350 E. Leonarduzzi E. Leonarduzzi B. W. McArdell P. Molnar Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions
description	<p>Landslides are an impacting natural hazard in alpine regions, calling for effective forecasting and warning systems. Here we compare two methods (physically based and probabilistic) for the prediction of shallow rainfall-induced landslides in an application to Switzerland, with a specific focus on the value of antecedent soil wetness. First, we show that landslide susceptibility predicted by the factor of safety in the infinite slope model is strongly dependent on soil data inputs, limiting the hydrologically active range where landslides can occur to only <span class="inline-formula">∼20</span> % of the country with typical soil parameters and soil depth models, not accounting for uncertainty. Second, we find the soil saturation estimate provided by a conceptual hydrological model (PREVAH) to be more informative for landslide prediction than that estimated by the physically based coarse-resolution model (TerrSysMP), which we attribute to the lack of temporal variability and coarse spatial resolution in the latter. Nevertheless, combining the soil water state estimates in TerrSysMP with the infinite slope approach improves the separation between landslide triggering and non-triggering rainfall events. Third, we demonstrate the added value of antecedent soil saturation in combination with rainfall thresholds. We propose a sequential threshold approach, where events are first split into dry and wet antecedent conditions by an <span class="inline-formula"><i>N</i></span> d (day) antecedent soil saturation threshold, and then two different total rainfall–duration threshold curves are estimated. This, among all different approaches explored, is found to be the most successful for landslide prediction.</p>
format	article
author	E. Leonarduzzi E. Leonarduzzi B. W. McArdell P. Molnar
author_facet	E. Leonarduzzi E. Leonarduzzi B. W. McArdell P. Molnar
author_sort	E. Leonarduzzi
title	Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions
title_short	Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions
title_full	Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions
title_fullStr	Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions
title_full_unstemmed	Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions
title_sort	rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/1dfce7bfe57c44aba200abb3fb785d8c
work_keys_str_mv	AT eleonarduzzi rainfallinducedshallowlandslidesandsoilwetnesscomparisonofphysicallybasedandprobabilisticpredictions AT eleonarduzzi rainfallinducedshallowlandslidesandsoilwetnesscomparisonofphysicallybasedandprobabilisticpredictions AT bwmcardell rainfallinducedshallowlandslidesandsoilwetnesscomparisonofphysicallybasedandprobabilisticpredictions AT pmolnar rainfallinducedshallowlandslidesandsoilwetnesscomparisonofphysicallybasedandprobabilisticpredictions
_version_	1718428418439118848

Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions

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