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 (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.

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

“…To this end, the temporal saturation variation was simulated using the coupled heat and mass transfer model CoupModel (Jansson and Karlberg, 2011), and the same model set-up, input data, and parametrization were used at both sites. We used the same model set-up as in Wicki et al (2021), and we refer to this study for a detailed model description. To account for the different topographic and hydrogeological settings, the lower model boundary conditions were adjusted by explicitly including the elevation difference and horizontal distance to the nearest drainage point.…”

Potential improvements of landslide prediction by hydro-meteorological thresholds: an investigation based on reanalysis soil moisture data and principal component analysis

“…Lastly, Wicki et al (2021) compared the reliability of landslides forecast models based on simulated soil moisture with respect to models based on soil moisture measurements. Specifically, they assessed the potential and limitations of adopting 1D soil water transfer model for regional LEWS and disclosed the pros and cons compared of using soil moisture measurements.…”

“…Specifically, they assessed the potential and limitations of adopting 1D soil water transfer model for regional LEWS and disclosed the pros and cons compared of using soil moisture measurements. To this aim, they used plot-scale soil hydrological simulations to be able to directly compare the results to a landslide forecast model based on in situ soil moisture measurements and demonstrated a high information content of simulated soil moisture for regional landslide activity, which was even higher than when in situ soil moisture measurements were used (Wicki et al, 2021). In this regard, Wicki et al (2020b), demonstrated that the performance is strongly dependent on the distance between the soil moisture network location and landslide activity area and that the goodness increases with decreasing distances between measurement sites and landslides.…”

Evolution process and failure mechanism of a large expressway roadside landslide

“…Early detection of disaster risk is one of the most effective strategies to deal with various large-scale natural disasters. Landslide risk early warning represented by rainfall monitoring, soil water content and pore water pressure monitoring and deformation monitoring is widely used in engineering [12][13][14][15][16][17][18] . The main indicators to identify landslide risk include the abnormal increase of rainfall [19][20][21] , the increase of soil moisture content 13,[22][23][24][25][26][27] , the acceleration of slope deformation 28,29 .…”