Landslide probability in the German low mountain regions under climate change conditions

The influence of meteorological (pre-) condition on landslide probability in the German low mountain regions is assessed and effects arising from climate change are investigated. The landslide events analysed for this study are taken from the landslide database for Germany (Damm and Klose, 2015) and from an event inventory from the German railway company (Deutsche Bahn). We follow two different approaches in order to determine the influence of atmospheric conditions on hillslope failure.

The first approach is based on weather types. Each day is assigned one of 28 Lamb-style weather types. The meteorological variables used to classify the weather types are sea level pressure and anomalies of the atmospheric water content. We were able to identify 4 patterns associated with a statistically significant increase for landslide frequency. The climate change signal of the frequency for the occurrence for these weather types is investigated in a multi-model ensemble of regional climate simulations (EURO CORDEX). The majority of the models shows a decrease in the frequency of those relevant patterns under RCP8.5 scenario conditions. In most models this decrease is, however, not statistically significant.
 
The second approach is based on logistic regression. The logistic regression model was fitted using meteorological observations close to the landslide sites. Conditions at the day of the event as well as the pre-conditions from the days leading up to the event were considered. In order to select the best statistical model we tested a large number of physically plausible combinations of meteorological predictors. Each model was checked using cross-validation. The decision on the final model was based on the value of the logarithmic skill score and on expert judgement. As relevant predictors we identified daily precipitation, frost, and a soil moisture proxy determined from multi-day accumulated precipitation and potential evapotranspiration. 
The climate change signal is determined by applying the statistical model to the output of a multi-model ensemble of climate scenario simulations. 

Damm, B. and Klose, M. (2015): The landslide database for Germany: Closing the gap at national level, Geomorphology, 249, 82-93, https://doi.org/https://doi.org/10.1016/j.geomorph.2015.03.021.