Quantification of meteorological conditions for rockfall triggers in Germany

Abstract

Abstract. 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.

A decrease in rockfall probability under climate change conditions in Germany

“…We analysed an area notedly larger than Germany, which illustrates that similar changes in the meteorological conditions affect the entire study area. In a previous study we have shown that precipitation is the most effective triggering factor for rockfall in the German lower 245 mountain ranges (Nissen et al, 2022). Even though heavy precipitation is predicted to increase as a consequence of global warming (e.g.…”

“…In contrast, a decrease in rock fall probability needs to be considered due to a potentially balancing effect of increased evapotranspiration from increased temperatures as well as temperature driven changes in soil water regimes (Dehn and Buma, 1999;Collison et al, 2000). The statistical model applied in this study therefore considers the combined effect of precipitation, freeze-thaw cycles and fissure water (Nissen et al, 2022). The model was developed for rockfall occurrences in the German lower mountain ranges, a study region that does not include areas with permafrost.…”

“…The two gridded observational data sets that were used to fit the logistic regression model in Nissen et al (2022) are used again in this study to estimate trends in climate related rockfall probability during the last decades. Daily minimum and maximum temperatures are taken from the E-OBS dataset (Cornes et al, 2018), while daily precipitation at the rockfall sites is extracted from the REGNIE data set (Rauthe et al, 2013).…”

“…In Nissen et al (2022) a logistic regression model was developed that describes the probability for rockfall as a function of meteorological predictors:…”

“…Results of two independent approaches are presented. For the first approach, the logistic regression model described by Nissen et al (2022), that was trained using meteorological observations and a rockfall data set with events observed in Germany, is applied to meteorological timeseries data. For the second approach large-scale atmospheric patterns associated with an enhanced probability for rockfall initialisation are identified and changes in the frequency of these patterns are determined.…”