Modelling landslide hazard under global change: the case of a Pyrenean valley

作者: Séverine Bernardie , Rosalie Vandromme , Yannick Thiery , Thomas Houet , Marine Grémont

DOI: 10.5194/NHESS-2019-311

关键词: Land use, land-use change and forestryLand coverNatural hazardGlobal changeEnvironmental sciencePhysical geographyLandslideLand useSlope stabilityClimate change

摘要: Abstract. Several studies have shown that global changes important impacts in mountainous areas, since they affect natural hazards induced by hydro-meteorological events such as landslides. To estimate the capacity of valleys to cope with landslide hazard under change (climate well climate- and human-induced land use change), it is necessary evaluate evolution different components define this type hazard: topography, geology geotechnics, hydrogeology cover. The present study evaluates, through an innovative methodology, influence both vegetation cover climate on a Pyrenean valley from 2100. Once invariant features studied area, were set, we first focused assessing future construction four prospective socioeconomic scenarios their projection 2040 These inputs then used spatially model (LUCC) information produce multi-temporal LUCC maps. Then, extract water saturation uppermost layers, according two greenhouse gas emissions scenarios. based these modulate hydro-mechanical compute factor safety (FoS) levels over considered area. results demonstrate slope stability presence forest. resulting are significant despite being small dependent linked In particular, reduction human activity increase stability; contrast, anthropic leads opposite region, some stability. Climate may also impact areas because soil content; indicate FoS large part depending typology considered. Therefore, even if forest growth stabilization, groundwater conditions will lead destabilization. not uniform area particularly most extreme scenario, RCP 8.5. Compared current period, size prone deep landslides higher than (both rotational translational). On other hand, rate for typology. Interestingly, related frequency highest filling ratio. occurrences near (2021–2050 scenario 8.5) far (2071–2100 expected factors 1.5 4, respectively.

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