LEADER 04132nam 2200817z- 450 001 9910557355903321 005 20231214133626.0 035 $a(CKB)5400000000042330 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76467 035 $a(EXLCZ)995400000000042330 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (222 p.) 311 $a3-0365-0930-5 311 $a3-0365-0931-3 330 $aLandslides are destructive processes causing casualties and damage worldwide. The majority of the landslides are triggered by intense and/or prolonged rainfall. Therefore, the prediction of the occurrence of rainfall-induced landslides is an important scientific and social issue. To mitigate the risk posed by rainfall-induced landslides, landslide early warning systems (LEWS) can be built and applied at different scales as effective non-structural mitigation measures. Usually, the core of a LEWS is constituted of a mathematical model that predicts landslide occurrence in the monitored areas. In recent decades, rainfall thresholds have become a widespread and well established technique for the prediction of rainfall-induced landslides, and for the setting up of prototype or operational LEWS. A rainfall threshold expresses, with a mathematic law, the rainfall amount that, when reached or exceeded, is likely to trigger one or more landslides. Rainfall thresholds can be defined with relatively few parameters and are very straightforward to operate, because their application within LEWS is usually based only on the comparison of monitored and/or forecasted rainfall. This Special Issue collects contributions on the recent research advances or well-documented applications of rainfall thresholds, as well as other innovative methods for landslide prediction and early warning. Contributions regarding the description of a LEWS or single components of LEWS (e.g., monitoring approaches, forecasting models, communication strategies, and emergency management) are also welcome. 606 $aResearch & information: general$2bicssc 610 $aloess landslide 610 $aDAN-W 610 $anumerical simulation 610 $adynamic analysis 610 $arainfall thresholds 610 $aBhutan 610 $ashallow landslides 610 $alandslides 610 $aIdukki 610 $aearly warning system 610 $alandslide hazard 610 $aantecedent rainfall threshold 610 $alandslide susceptibility 610 $asatellite-derived rainfall 610 $aTRMM Multisatellite Precipitation Analysis 3B42 (TMPA) 610 $atropical Africa 610 $arainfall 610 $athresholds 610 $aphysicallybased model 610 $ahydrological monitoring 610 $asoil water index 610 $alarge-scale landslide 610 $aSWI-D threshold 610 $ashallow landslide 610 $atemporal probability 610 $alandslide and debris flow 610 $aChina 610 $aquantile regression 610 $aWayanad 610 $aearly warning 610 $aGIS 610 $arainfall intensity 610 $aoptimization 610 $arainfall thresholds calculation 610 $amean annual rainfall 610 $alithology 610 $aSlovenia 615 7$aResearch & information: general 700 $aSegoni$b Samuele$4edt$01313327 702 $aGariano$b Stefano Luigi$4edt 702 $aRosi$b Ascanio$4edt 702 $aSegoni$b Samuele$4oth 702 $aGariano$b Stefano Luigi$4oth 702 $aRosi$b Ascanio$4oth 906 $aBOOK 912 $a9910557355903321 996 $aRainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning$93031290 997 $aUNINA