Assessing the effectiveness of silvicultural treatments on fire behavior in the Hyrcanian temperate forests of northern Iran

We implemented a fire modeling approach to evaluate the effectiveness of silvicultural treatments in reducing potentiallosses to the Hyrcanian temperate forests of northern Iran, in the Siahkal National Forest (57,110 ha). We compared theeffectiveness of selection cutting, low thinning, crown thinning, and clear-cutting treatments implemented during the last tenyears (n = 241, 9500-ha) on simulated stand-scale and landscape-scale fire behavior. First, we built a set of fuel models forthe 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 ANOVAtest to analyze stand-level and landscape-scale modeling output differences between treated and untreated conditions. Theresults showed a significant reduction of stand-level fire hazard, where the average conditional flame length and crown fireprobability was reduced by about 12 and 22%, respectively. The conifer plantation patches presented the most significantreduction in the crown fire probability (>35%). On the other hand, we found a minor increase in the overall burn probabilityand 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 moderatingpotential fire risk and restoring fuel profiles in fire-sensitive temperate forests of northern Iran, where the growing persistentdroughts and fuel buildup can lead to extreme fires in the near future.