Simulated or measured soil moisture: Which one is adding more value to regional landslide early warning?

Abstract

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.

Impact of topography on in situ soil wetness measurements for regional landslide early warning – a case study from the Swiss Alpine Foreland

“…Code and data availability. The quality-controlled hourly time series of all sensors are publicly available on the EnviDat repository (Wicki et al, 2023).…”

Using principal component analysis to incorporate multi-layer soil moisture information in hydrometeorological thresholds for landslide prediction: an investigation based on ERA5-Land reanalysis data

“…When information on non-triggering rainfall is also available, thresholds can be determined as the best classifiers based on the confusion matrix (Berti et al, 2012;Staley et al, 2013;Cancelliere, 2014, 2021;Postance et al, 2018). In the last decade, there has been an increasing interest in the development of hydrometeorological thresholds that consider rainfall characteristics and subsurface hydrological variables, such as soil moisture content and catchment storage information (Uwihirwe et al, 2022;Mirus et al, 2018a, b;Thomas et al, 2018;Segoni et al, 2018b;Wicki et al, 2020Wicki et al, , 2021Greco, 2018, 2016;Reder and Rianna, 2021;Marino et al, 2020;Palau et al, 2021;Conrad et al, 2021). These studies demonstrate improvements of the prediction performances with the hydrometeorological approach, with respect to the traditional precipitation-based thresholds, even if not all climatic areas have been explored, so further applications are still useful.…”