A decrease in rockfall probability under climate change conditions in Germany

The effect of climate change on rockfalls in the German low mountain regions is investigated following two different approaches. The first approach uses a logistic regression model that describes the combined effect of precipitation, freeze–thaw cycles, and fissure water on rockfall probability. The climate change signal for the past 6 decades is analysed by applying the model to meteorological observations. The possible effect of climate change until the end of the century is explored by applying the statistical model to the output of a multi-model ensemble of 23 regional climate scenario simulations. It is found that the number of days per year exhibiting an above-average probability for rockfalls has mostly been decreasing during the last few decades. Statistical significance is, however, present at only a few sites. A robust and statistically significant decrease can be seen in the Representative Concentration Pathway (RCP) climate scenario 8.5 (RCP8.5) simulations for Germany and neighbouring regions, locally falling below −10 % when comparing the last 30 years of the 20th century to the last 30 years of the 21st century. The most important factor determining the projected decrease in rockfall probability is a reduction in the number of freeze–thaw cycles expected under future climate conditions. For the second approach four large-scale meteorological patterns that are associated with enhanced rockfall probability are identified from reanalysis data. The frequency of all four patterns exhibits a seasonal cycle that maximises in the cold half of the year (winter and spring). Trends in the number of days that can be assigned to these patterns are determined both in meteorological reanalysis data and in climate simulations. In the reanalysis no statistically significant trend is found. For the future scenario simulations all climate models show a statistically significant decrease in the number of rockfall-promoting weather situations

Rockfall Vulnerability of a Rural Road Network—A Methodological Approach in the Harz Mountains, Germany

Mass movements are linked to increasing amounts of damage and disruptions to transportation infrastructures. A valid risk assessment in order to reduce future costs is not always appropriate, as adequate information on landslide data is missing. The presented study estimates the rockfall susceptibility on a rural road network in the Harz mountains using a bivariate statistical method (information value method). The model is validated using a receiver operating characteristic (ROC) analysis. In addition, the vulnerability of the road network is estimated using vulnerability indicators. The susceptibility model assigns a high or very high susceptibility to 23% of the area in the road network corridor. The relevant road sections are linked to high slope values, NE orientations of road sections, and low-to-moderate vulnerability values. The highest vulnerability values can be found on marginal road sections with high average daily traffic volumes. The combination of the presented methods proposes an easily applicable estimate of vulnerability where conventional methods (i.e., vulnerability curves, matrices) cannot be implemented

Rockfall Vulnerability of a Rural Road Network—A Methodological Approach in the Harz Mountains, Germany

Mass movements are linked to increasing amounts of damage and disruptions to transportation infrastructures. A valid risk assessment in order to reduce future costs is not always appropriate, as adequate information on landslide data is missing. The presented study estimates the rockfall susceptibility on a rural road network in the Harz mountains using a bivariate statistical method (information value method). The model is validated using a receiver operating characteristic (ROC) analysis. In addition, the vulnerability of the road network is estimated using vulnerability indicators. The susceptibility model assigns a high or very high susceptibility to 23% of the area in the road network corridor. The relevant road sections are linked to high slope values, NE orientations of road sections, and low-to-moderate vulnerability values. The highest vulnerability values can be found on marginal road sections with high average daily traffic volumes. The combination of the presented methods proposes an easily applicable estimate of vulnerability where conventional methods (i.e., vulnerability curves, matrices) cannot be implemented

Elevated temperature alters proteomic responses of individual organisms within a biofilm community.

Microbial communities that underpin global biogeochemical cycles will likely be influenced by elevated temperature associated with environmental change. Here, we test an approach to measure how elevated temperature impacts the physiology of individual microbial groups in a community context, using a model microbial-based ecosystem. The study is the first application of tandem mass tag (TMT)-based proteomics to a microbial community. We accurately, precisely and reproducibly quantified thousands of proteins in biofilms growing at 40, 43 and 46 °C. Elevated temperature led to upregulation of proteins involved in amino-acid metabolism at the level of individual organisms and the entire community. Proteins from related organisms differed in their relative abundance and functional responses to temperature. Elevated temperature repressed carbon fixation proteins from two Leptospirillum genotypes, whereas carbon fixation proteins were significantly upregulated at higher temperature by a third member of this genus. Leptospirillum group III bacteria may have been subject to viral stress at elevated temperature, which could lead to greater carbon turnover in the microbial food web through the release of viral lysate. Overall, these findings highlight the utility of proteomics-enabled community-based physiology studies, and provide a methodological framework for possible extension to additional mixed culture and environmental sample analyses