We implemented a fire modeling approach to evaluate the effectiveness of silvicultural treatments in reducing potential losses to the Hyrcanian temperate forests of northern Iran, in the Siahkal National Forest (57,110 ha). We compared the effectiveness of selection cutting, low thinning, crown thinning, and clear-cutting treatments implemented during the last ten years (n = 241, 9500-ha) on simulated stand-scale and landscape-scale fire behavior. First, we built a set of fuel models for the different treatment prescriptions. We then modeled 10,000 fires at the 30-m resolution, assuming low, moderate, high, very high, and extreme weather scenarios and human-caused ignition patterns. Finally, we implemented a One-way ANOVA test to analyze stand-level and landscape-scale modeling output differences between treated and untreated conditions. The results showed a significant reduction of stand-level fire hazard, where the average conditional flame length and crown fire probability was reduced by about 12 and 22%, respectively. The conifer plantation patches presented the most significant reduction in the crown fire probability (>35%). On the other hand, we found a minor increase in the overall burn probability and fire size at the landscape scale. Stochastic fire modeling captured the complex interactions among terrain, vegetation, ignition locations, and weather conditions in the study area. Our findings highlight fuel treatment efficacy for moderating potential fire risk and restoring fuel profiles in fire-sensitive temperate forests of northern Iran, where the growing persistent droughts and fuel buildup can lead to extreme fires in the near future.
Keywords: Fire behavior; Fire modeling; Siahkal National Forest; Silvicultural treatments; Temperate forests.
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.