Forests can decrease the risk of shallow landslides by mechanically reinforcing the soil and positively influencing its water balance. However, little is known about the effect of different forest structures on slope stability. In the study area in St Antönien, Switzerland, we applied statistical prediction models and a physically‐based model for spatial distribution of root reinforcement in order to quantify the influence of forest structure on slope stability. We designed a generalized linear regression model and a random forest model including variables describing forest structure along with terrain parameters for a set of landslide and control points facing similar slope angle and tree coverage. The root distribution measured at regular distances from seven trees in the same study area was used to calibrate a root distribution model. The root reinforcement was calculated as a function of tree dimension and distance from tree with the root bundle model (RBMw). Based on the modelled values of root reinforcement, we introduced a proxy‐variable for root reinforcement of the nearest tree using a gamma distribution. The results of the statistical analysis show that variables related to forest structure significantly influence landslide susceptibility along with terrain parameters. Significant effects were found for gap length, the distance to the nearest trees and the proxy‐variable for root reinforcement of the nearest tree. Gaps longer than 20 m critically increased the susceptibility to landslides. Root reinforcement decreased with increasing distance from trees and is smaller in landslide plots compared to control plots. Furthermore, the influence of forest structure strongly depends on geomorphological and hydrological conditions. Our results enhance the quantitative knowledge about the influence of forest structure on root reinforcement and landslide susceptibility and support existing management recommendations for protection against gravitational natural hazards. Copyright © 2015 John Wiley & Sons, Ltd.

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Ecosystems play a potentially important role in sustainably reducing the risk of disaster events worldwide. Yet, to date, there are few comprehensive studies that summarize the state of knowledge of ecosystem services and functions for disaster risk reduction. This paper builds scientific evidence through a review of 529 English-language articles published between 2000 and 2019. It catalogues the extent of knowledge on, and confidence in, ecosystems in reducing disaster risk. The data demonstrate robust links and cost-effectiveness between certain ecosystems in reducing specific hazards, something that was revealed to be particularly true for the role of vegetation in the stabilization of steep slopes. However, the published research was limited in geographic distribution and scope, with a concentration on urban areas of the Global North, with insufficient relevant research on coastal, dryland and watershed areas, especially in the Global South. Many types of ecosystem can provide sustainable and multifunctional approaches to disaster risk reduction. Yet, if they are to play a greater role, more attention is needed to fill research gaps and develop performance standards.

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 powerlaw 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.

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Forests below rocky cliffs often play a very important role in protecting settlements against rockfall. The structure and development of these forests are expected to be substantially affected by the disturbance of the falling rocks. Knowing about this effect is important to predict the development of protection forests and consider potential effects of the falling blocks in management strategies. The goal of this study is to quantify differences in forest structure depending on rockfall activity in four different sites in the Swiss Alps. For this, we collected data on forest structure in zones of different rockfall activity and derived rockfall impact probabilities based on rockfall simulations. We assessed whether differences in forest structure and signs of rockfall disturbance could be observed between the rockfall zones. We additionally built mixed-effects models to identify the key variables explaining the forest characteristics described by diameter (DBH) and basal area (bA). The forest structure differs between the rockfall zones, however, with varying effects amongst the sites. DBH tends to decrease with increasing rockfall activity, whereas tree density appears to be little impacted by rockfall. For most sites, the number of deposited blocks and the simulated tree impact probability have a significant effect in the models along with the species, whereas for one site, hardly any effect of rockfall was found. Our results, obtained either from direct measurements or modelling, show that rockfall can locally influence the structure of forests, whereas the influence depends on the frequency and intensity of the rockfall disturbance. Impact probabilities obtained by simulations can serve as a good proxy for rockfall disturbances.

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