Quantifying the effect of forests on frequency and intensity of rockfalls

Abstract

Forests serve as a natural means of protection against small rockfalls. Due to their barrier effect, they reduce the intensity and the propagation probability of falling rocks and thus reduce the occurrence frequency of a rockfall event for a given element at risk. However, despite established knowledge on the protective effect of forests, they are generally neglected in quantitative rockfall risk analyses. Their inclusion in quantitative rockfall risk assessment would, however, be necessary to express their efficiency in monetary terms and to allow comparison of forests with other protective measures, such as nets and dams. The goal of this study is to quantify the effect of forests on the occurrence frequency and intensity of rockfalls. We therefore defined an onset frequency of blocks based on a power-law magnitude–frequency distribution and determined their propagation probabilities on a virtual slope based on rockfall simulations. Simulations were run for different forest and non-forest scenarios under varying forest stand and terrain conditions. We analysed rockfall frequencies and intensities at five different distances from the release area. Based on two multivariate statistical prediction models, we investigated which of the terrain and forest characteristics predominantly drive the role of forest in reducing rockfall occurrence frequency and intensity and whether they are able to predict the effect of forest on rockfall risk. The rockfall occurrence frequency below forested slopes is reduced between approximately 10 and 90 % compared to non-forested slope conditions; whereas rockfall intensity is reduced by 10 to 70 %. This reduction increases with increasing slope length and decreases with decreasing tree density, tree diameter and increasing rock volume, as well as in cases of clustered or gappy forest structures. The statistical prediction models reveal that the cumulative basal area of trees, block volume and horizontal forest structure represent key variables for the prediction of the protective effect of forests. In order to validate these results, models have to be tested on real slopes with a wide variation of terrain and forest conditions.