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The Norwegian national landslide early warning system (LEWS), operational since 2013, is managed by the Norwegian Water Resources and Energy Directorate and was designed for monitoring and forecasting the hydrometeorological conditions potentially triggering slope failures. Decision-making in the LEWS is based upon rainfall thresholds, hydrometeorological and real-time landslide observations as well as on landslide inventory and susceptibility maps. Daily alerts are issued throughout the country considering variable size warning zones. Warnings are issued once per day for the following 3 days and can be updated according to weather forecasts and information gathered by the monitoring network. The performance of the LEWS operational in Norway has been evaluated applying the EDuMaP method, which is based on the computation of a duration matrix relating number of landslides and warning levels issued in a warning zone. In the past, this method has been exclusively employed to analyse the performance of regional early warning models considering fixed warning zones. Herein, an original approach is proposed for the computation of the elements of the duration matrix in the case of early warning models issuing alerts on variable size areas. The approach has been used to evaluate the warnings issued in Western Norway, in the period 2013-2014, considering two datasets of landslides. The results indicate that the landslide datasets do not significantly influence the performance evaluation, although a slightly better performance is registered for the smallest dataset. Different performance results are observed as a function of the values adopted for one of the most important input parameters of EDuMaP, the landslide density criterion (i.e. setting the thresholds to differentiate among classesof landslide events). To investigate this issue, a parametric analysis has been conducted; the results of the analysis show significant differences among computed performances when absolute or relative landslide density criteria are considered. [ABSTRACT FROM AUTHOR]

The Norwegian national landslide early warning system (LEWS), operational since 2013, is managed by the Norwegian Water Resources and Energy Directorate and has been designed for monitoring and forecasting the hydro-meteorological conditions potentially triggering slope failures. Decision-making in the EWS is based upon hazard threshold levels, hydro-meteorological and real-time landslide observations as well as on landslide inventory and susceptibility maps. In the development phase of the EWS, hazard threshold levels have been obtained through statistical analyses of historical landslides and modelled hydro-meteorological parameters. Daily hydro-meteorological conditions such as rainfall, snowmelt, runoff, soil saturation, groundwater level and frost depth have been derived from a distributed version of the hydrological HBV-model. Two different landslide susceptibility maps are used as supportive data in deciding daily warning levels. Daily alerts are issued throughout the country considering variable warning zones. Warnings are issued once per day for the following 3 days with the possibility to update them according to the information gathered by the monitoring network. The performance of the LEWS operational in Norway has been evaluated applying the EDuMaP method, which is based on the computation of a duration matrix relating landslide and warning events. This method has been principally employed to analyse the performance of regional early warning model considering fixed warning zones for issuing alerts. The original approach proposed herein allows the computation of the elements of the duration matrix in the case of early warning models issuing alerts on variable warning zones. The approach has been used to evaluate the warnings issued in Western Norway, in the period 2013–2014, considering two datasets of landslides. The results indicate that the landslide datasets do not significantly influence the performance evaluation, although a slightly better performance is registered for the smallest and more accurate dataset. Different performance results are observed as a function of the values adopted for one of the most important input parameters of EDuMaP, the landslide density criterion (i.e. setting the thresholds to differentiate among classes of landslide events). To investigate this issue, a parametric analysis has been conducted; the results of the analysis show significant differences among computed performances when absolute or relative landslide density criteria are considered. [ABSTRACT FROM AUTHOR]

Weather-induced shallow landslides in coarse-grained soils are first failure phenomena often occurring as multiple slides in natural slopes over wide areas. Diffuse monitoring over the area of interest is required if one wants to predict the possible location of future landslides or the soil conditions leading to triggering for early warning purposes. This study aims at highlighting the role played by ground-based monitoring devices when they are used, singularly or combined together, as tools to assess the conditions of slopes in granular soils leading to shallow first-failure slides. Monitoring is discussed in relation to expectations coming from analytical and numerical models within an "observational modelling" framework, i.e. methods and procedures that use inverse analysis techniques to update the numerical model of a boundary value problem using monitoring data. The influence of the location of the monitoring instruments is assessed in different stratigraphic conditions and considering the saturation degree of the soil layers. The time-dependent soil parameters considered are the following: positive pore water pressure, suction, soil water content, stress, surface and subsurface displacements. All these parameters can be monitored employing static permanently deployed point sensors and, potentially, using low-cost instruments providing high resolution data in time and space. The focus of the contribution is on the meaning of the measurements as potential detectors of soil conditions leading to failure within a slope. To this aim, a series of parametric analyses employing analytical and numerical methods, in 1D and 2D conditions, will be presented. The analyses investigate the soil response to applied boundary conditions. When only hydraulic modelling is performed, the prediction of the groundwater regime in a slope in relation to time-dependent atmospheric conditions is used as input in limit equilibrium slope stability analyses to derive the variation with time of the factors of safety along potential slip surfaces. When hydromechanical coupling is explicitly considered, the numerical simulations are aimed at reproducing the stress-strain evolution associated with the transient seepage processes induced by changing hydraulic boundary conditions. In both cases, the results of the parametric analyses are used to evaluate the modelled response of a slope at the onset of failure, in different locations and in relation to many monitoring parameters (e.g., pore water pressures, soil water contents, stresses, displacements). The presented results should be seen as a contribution towards the aim of defining and adopting an economically sustainable and technically reliable monitoring strategy for early warning purposes, by predicting the conditions leading to the triggering of shallow slides in coarse-grained soils over wide areas. [ABSTRACT FROM AUTHOR]

Landslide early warning systems (LEWS) represent a significant option for addressing weather-induced landslides worldwide. The structure of LEWS can be schematized as an interrelation of three main modules: landslide model, warning model, and warning system. Warning models developed for weather-induced landslides are mainly based on correlation laws, for which thresholds are defined considering one or more combinations of measured rainfall that have led (or not led) to slope movements. However, the distinction between triggering and non-triggering rainfall conditions is not trivial, thus conventional methods adopted to this aim are often highly subjective. Moreover, data on landslide occurrences and rainfall measurements are often either not available or accessible only to a restricted number of scientists. In this study information from an open-access non-conventional landslide inventory and rainfall data from satellite monitoring have been employed within a conceptual framework for the development of a probabilistic warning model. The main steps of the proposed approach are: collection of input data, correlation between landslides and rainfall combinations, and probabilistic analysis. Data on landslide occurrences are derived from the FraneItalia catalog (https://data.mendeley.com/datasets/zygb8jygrw/1), a landslide inventory based on information retrieved from online Italian news. The database currently spans from 2010 to 2017 and contains 8931 landslides, most of them triggered by rainfall. Rainfall measurements are derived from the satellite-based NASA Global Precipitation Measurement (GPM) database, which contains gridded precipitation and precipitation-error estimates, with a half-hour temporal resolution and a 0.10-degree spatial resolution, covering most of the earth starting from March 2014. Successively, an algorithm approach based on a reduced set of parameters has been applied to reconstruct the rainfall events, to identify the rainfall conditions that resulted in landslides and to measure the duration and the cumulated rainfall for the events. Finally, a Bayesian methodology has been developed to determine landslide conditional probabilities associated to each rainfall combination.The proposed procedure has been tested by analysing the reported landslides from March 2014 to December 2017 within the eight warning zones defined for hydrogeological risk management in the Campania region (Italy). Although this study is only in a preliminary phase, the results achieved herein demonstrates the potential of the proposed probabilistic method to define objective and reproducible rainfall thresholds for landslide early warning purposes. [ABSTRACT FROM AUTHOR]