Assessing the effectiveness and measuring the performance of fuel treatments and other wildfire risk mitigation efforts are challenging endeavors. Perhaps the most complicated is quantifying avoided impacts. In this study, we show how probabilistic counterfactual analysis can help with performance evaluation.
Yellow pine and mixed-conifer (YPMC) forests of California’s Sierra Nevada have experienced widespread fire suppression for over a century, resulting in ingrowth and densification of trees, heavy fuel accumulation, and shifts in species composition.
In recent decades, there has been a significant increase in annual area burned in California's Sierra Nevada mountains. This rise in fire activity has prompted the need to understand how historical forest management practices affect fuel composition and emissions.
Background
Plant flammability is an important factor in fire behaviour and post-fire ecological responses. There is consensus about the broad attributes (or axes) of flammability but little consistency in their measurement.
The influence of forest treatments on wildfire effects is challenging to interpret. This is, in part, because the impact forest treatments have on wildfire can be slight and variable across many factors. Effectiveness of a treatment also depends on the metric considered.
Live fuel moisture content (LFMC) is a key determinant of landscape ignition potential, but quantitative estimates of its effects on wildfire are lacking. We present a causal inference framework to isolate the effect of LFMC from other drivers like fuel type, fuel amount, and meteorology.
Background: Pre-fire fuels, topography, and weather influence wildfire behaviour and fire-driven ecosystem carbon loss. However, the pre-fire characteristics that contribute to fire behaviour and effects are often understudied for wildfires because measurements are difficult to obtain.
A deeper understanding of the influence of fine-scale fuel patterns on fire behavior is essential to the design of forest treatments that aim to reduce fire hazard, enhance structural complexity, and increase ecosystem function and resilience. Of particular relevance is the impact of horizontal and vertical forest structure on potential tree torching and large-tree mortality.
Context In western US forests, the increasing frequency of large high-severity fires presents challenges for society. Quantifying how fuel conditions influence high-severity area is important for managing risks of large high-severity fires and understanding how they are changing with climate change.
Patterns of spatial heterogeneity in forests and other fire-prone ecosystems are increasingly recognized as critical for predicting fire behavior and subsequent fire effects. Given the difficulty in sampling continuous spatial patterns across scales, statistical approaches are common to scale from plot to landscapes.