In mountainous terrain, rainfall-induced landslides pose a serious risk to people and infrastructure. Regional landslide early warning systems (LEWS) have proven to be a cost-efficient tool to inform the public about the imminent landslide danger. While most operational LEWS are based on rainfall exceedance thresholds only, recent studies have demonstrated an improvement of the forecast quality after the inclusion of soil hydrological information. In this study, the potential of in situ soil moisture measurements for regional landslide early warning is assessed. For the first time, a comprehensive soil moisture measurement database was compiled for Switzerland and compared with a national landslide database (Swiss flood and landslide damage database, WSL). The time series were homogenized and normalized to represent saturation values. From ensembles of sensors, the mean and standard deviation saturation were calculated and infiltration events were delimited, characterized, and classified as landslidetriggering or non-triggering based on the occurrence of landslides within a specified forecast distance. A logistic regression function was applied to model the landslide activity based on the infiltration event characteristics and several models were analysed and compared with receiver operating characteristics (ROC). A strong distance dependence becomes apparent showing a forecast goodness decrease with increasing distance between water content measurement site and landslide, and a better forecast goodness for long-lasting as opposed to short-duration precipitation events. While most variability can be explained by the two event properties antecedent saturation and change of saturation during an infiltration event, event properties that describe antecedent conditions are more important for long-lasting as opposed to shortduration precipitation events that can be better explained by properties describing event dynamics. Overall, the analysis demonstrated that in situ soil moisture data effectively contains specific information useful for landslide early warning.

Soil wetness is an important property in determining the variable disposition of hillslopes to shallow landslides. Recent studies have demonstrated the potential of in situ soil wetness information for landslide early warning. However, the spatial representativeness of in situ sensors may be affected by local heterogeneities of soil properties and hydrological processes, and their installation may be destructive. Electrical resistivity tomography (ERT) has been used in the past to estimate plot‐scale soil moisture variation and may overcome these limitations. In this study, we installed and operated an automated ERT monitoring system on a landslide‐prone hillslope in the Napf region (Switzerland). The system was operational during a period of 9 mo, and measurements were conducted at high temporal resolution and under different soil hydrological conditions. Electrical resistivity was measured along two perpendicular profile lines in Wenner–Schlumberger configuration at 0.25‐m electrode spacing. Soil saturation was calculated by the Archie's law and the parameters were fitted with colocated soil moisture sensors. Comparison of ERT‐derived soil moisture with soil wetness from in situ sensors showed a good correlation, and infiltration properties critical for landslide early warning could be reliably reproduced. Further, analysis of spatial saturation variation revealed that ERT was capable to detect heterogeneities of soil hydrological process. Under highly saturated conditions, the reliability of the saturation estimation was affected by an increased number of faulty measurements and the spatial heterogeneity of the infiltration process.

Abstract. The inclusion of soil wetness information in empirical landslide prediction models was shown to improve the forecast goodness of regional landslide early warning systems (LEWSs). However, it is still unclear which source of information – numerical models or in situ measurements – is of higher value for this purpose. In this study, soil moisture dynamics at 133 grassland sites in Switzerland were simulated for the period of 1981 to 2019, using a physically based 1D soil moisture transfer model. A common parameterization set was defined for all sites, except for site-specific soil hydrological properties, and the model performance was assessed at a subset of 14 sites where in situ soil moisture measurements were available on the same plot. A previously developed statistical framework was applied to fit an empirical landslide forecast model, and receiver operating characteristic analysis (ROC) was used to assess the forecast goodness. To assess the sensitivity of the landslide forecasts, the statistical framework was applied to different model parameterizations, to various distances between simulation sites and landslides and to measured soil moisture from a subset of 35 sites for comparison with a measurement-based forecast model. We found that (i) simulated soil moisture is a skilful predictor for regional landslide activity, (ii) that it is sensitive to the formulation of the upper and lower boundary conditions, and (iii) that the information content is strongly distance dependent. Compared to a measurement-based landslide forecast model, the model-based forecast performs better as the homogenization of hydrological processes, and the site representation can lead to a better representation of triggering event conditions. However, it is limited in reproducing critical antecedent saturation conditions due to an inadequate representation of the long-term water storage.

Abstract. The inclusion of soil wetness information in empirical landslide prediction models was shown to improve the forecast goodness of regional landslide early warning systems (LEWS). However, it is still unclear which source of information – numerical models or in-situ measurements – are of higher value for this purpose. In this study, soil moisture dynamics at 133 grassland sites in Switzerland were simulated for the period of 1981 to 2019 using a physically-based 1D soil moisture transfer model (CoupModel). A common parametrization set was defined for all sites except for site-specific soil hydrological properties, and the model performance was assessed at a subset of 14 sites where in-situ soil moisture measurements were available on the same plot. A previously developed statistical framework was applied to fit an empirical landslide forecast model, and ROC analysis was used to assess the forecast goodness. To assess the sensitivity of the landslide forecasts, the statistical framework was applied to different CoupModel parametrizations, to various distances between simulation sites and landslides, and to measured soil moisture from a subset of 35 sites for comparison with a measurement-based forecast model. We found that (i) simulated soil moisture is a skilful predictor for regional landslide activity, (ii) that it is sensitive to the formulation of the upper and lower boundary conditions, and (iii) that the information content is strongly distance-dependent. Compared to a measurement-based landslide forecast model, the model-based forecast performs better as the homogenization of hydrological processes and the site representation can lead to a better representation of triggering event conditions. However, it is limited in reproducing critical antecedent saturation conditions due to an inadequate representation of the long-term water storage.

Abstract. Recent studies have demonstrated the potential of in situ soil wetness measurements to predict regional shallow landslides. Increasing availability of monitoring data from sensor networks provides valuable information for developing future regional landslide early warning systems (LEWSs); however, most existing monitoring sites are located on flat terrain. The question arises of if the representativeness for regional landslide activity would improve if sensors were installed on a landslide-prone hillslope. To address this, two soil wetness monitoring stations were installed at close proximity on a steep slope and on a flat location in the Napf region (Northern Alpine Foreland of Switzerland), and measurements were conducted over a period of 3 years. As both sites inhibit similar lithological, vegetation, and precipitation characteristics, soil hydrological differences can be attributed to the impact of topography and hydrogeology. At the sloped site, conditions were generally wetter and less variable in time, and evidence was found for temporary lateral water transport along the slope. These differences were systematic and could be reduced by considering relative soil moisture changes. The application of a statistical landslide forecast model showed that both sites were equally able to distinguish critical from non-critical conditions for landslide triggering, which demonstrates the value of existing monitoring sites in flat areas for the application in LEWSs.

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