Journal
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
Volume 32, Issue -, Pages 55-74Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ijdrr.2017.09.036
Keywords
Rockfall; Protection forests; 3D rockfall simulation; Quantitative risk analysis; Eco-DRR
Funding
- SNF-ANR Project [310030L_156661/1]
- Swiss Federal Office for the Environment (BAFU) Project [15.0042.KP/O054-0339]
- Swiss National Science Foundation (SNF) [310030L_156661] Funding Source: Swiss National Science Foundation (SNF)
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Over the past decades, integral risk management of natural hazards has become increasingly important. Although risk analyses have been established for the design of structural protection measures against rockfalls, they have hardly been applied to ecosystem-based disaster risk reduction measures, such as protection forests. This, however, is necessary to quantify the benefits of such forests and compare them to structural measures. The goal of this study is to present a general framework which allows for integrating forests into quantitative rockfall risk analysis. The proposed framework is then applied in two case studies in Switzerland. It is based on the idea that forests influence the propagation of blocks and that they reduce their energy. It can thus be expected that the propagation probability and the intensity of a block at a given element at risk will differ between scenarios with and without forests. These effects are quantified based on rockfall simulations with and without trees and incorporated in the risk calculation. At both case study sites, current forest stands reduce risk by about 90% compared to a hypothetical non-forested situation. The forest effect can mainly be attributed to the reduction of the propagation probability of blocks. With increasing block volume, the risk reduction by the forest decreases. Based on the proposed framework, forests can be compared to other protection measures and evaluated in cost-benefit analyses. Future research on variations in the protective effect of forests due to natural dynamics or management may allow for a more long-term representation of risk.
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