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The influence of meteorological (pre-) condition on landslide and rockfall probability in the German low mountain regions is assessed. 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). For each of the hazards a logistic regression model was developed which considers the combined (compound) effect of the most important meteorological triggers. The most relevant variables identified and included in the model for landslides are daily precipitation, frost, snowmelt and a soil moisture proxy determined from accumulated precipitation and potential evapotranspiration. The model for rockfall takes daily precipitation, freeze-thaw cycles and a cleft water proxy into account.The climate change signal is determined by applying the statistical models to the output of a multi-model ensemble of 23 EURO-CORDEX regional climate scenario simulations. When comparing the periods 1971-2000 and 2071-2100, it is found that the probability for rockfall would decrease under RCP8.5 scenario conditions by up to 10% while the probability for landslides would increase by up to 30%. Although the probability for both hazards peaks in summer, the projected changes are strongest in winter and related to a decrease in the number of freeze-thaw cycles and days with frost. The study demonstrates that for this type of application, it is essential to consider all climatic factors that promote or suppress hillslope failure together, as they can reinforce or cancel each other., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

A rockfall dataset for Germany is analysed with the objective of identifying the meteorological and hydrological (pre-)conditions that change the probability for such events in central Europe. The factors investigated in the analysis are precipitation amount and intensity, freeze–thaw cycles, and subsurface moisture. As there is no suitable observational dataset for all relevant subsurface moisture types (e.g. water in rock pores and cleft water) available, simulated soil moisture and a proxy for pore water are tested as substitutes. The potential triggering factors were analysed both for the day of the event and for the days leading up to it. A logistic regression model was built, which considers individual potential triggering factors and their interactions. It is found that the most important factor influencing rockfall probability in the research area is the precipitation amount at the day of the event, but the water content of the ground on that day and freeze–thaw cycles in the days prior to the event also influence the hazard probability. Comparing simulated soil moisture and the pore-water proxy as predictors for rockfall reveals that the proxy, calculated as accumulated precipitation minus potential evaporation, performs slightly better in the statistical model. Using the statistical model, the effects of meteorological conditions on rockfall probability in German low mountain ranges can be quantified. The model suggests that precipitation is most efficient when the pore-water content of the ground is high. An increase in daily precipitation from its local 50th percentile to its 90th percentile approximately doubles the probability for a rockfall event under median pore-water conditions. When the pore-water proxy is at its 95th percentile, the same increase in precipitation leads to a 4-fold increase in rockfall probability. The occurrence of a freeze–thaw cycle in the preceding days increases the rockfall hazard by about 50 %. The most critical combination can therefore be expected in winter and at the beginning of spring after a freeze–thaw transition, which is followed by a day with high precipitation amounts and takes place in a region preconditioned by a high level of subsurface moisture.

Landslide research relies on landslide inventories for a multitude of spatial, temporal, or process analyses. Generally, it takes high effort to populate a landslide inventory with relevant data. In this context, the present work investigated an effective way to handle vast amounts of automatically acquired digital data for landslide inventories by the use of machine learning algorithms and information filtering. Between July 2017 and February 2019, a keyword alert system provided 4381 documents that were automatically processed to detect landslide events in Germany. Of all those documents, 91% were automatically recognized as irrelevant or duplicates; thereby, the data volume was significantly reduced to contain only actual landslide documents. Moreover, it was shown that inclusion of the document’s images into the automated process chain for information filtering is recommended, since otherwise unobtainable important information was found in them. Compared with manual methods, the automated process chain eliminated personal idiosyncrasies and human error and replaced it with a quantifiable machine error. The applied individual algorithms for natural language processing, information retrieval, and classification have been tried and tested in their respective fields. Furthermore, the proposed method is not restricted to a specific language or region. All languages on which these algorithms are applicable can be used with the proposed method and the training of the process chain can take any geographical restriction into account. Thus, the present work introduced a method with a quantifiable error to automatically classify and filter large amounts of data during automated digital data acquisition for landslide inventories., Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659, Niedersächsische Ministerium für Wissenschaft und Kultur http://dx.doi.org/10.13039/100011937