Understanding How Fuel Treatments Interact With Climate and Biophysical Setting to Affect Fire, Water, and Forest Health: A Process-Based Modeling Approach

Abstract

Fuel treatments are a key forest management practice used to reduce fire severity, increase water yield, and mitigate drought vulnerability. Climate change exacerbates the need for fuel treatments, with larger and more frequent wildfires, increasing water demand, and more severe drought. The effects of fuel treatments can be inconsistent and uncertain and can be altered by a variety of factors including the type of treatment, the biophysical features of the landscape, and climate. Variation in fuel treatment effects can occur even within forest stands and small watershed management units. Quantifying the likely magnitude of variation in treatment effects and identifying the dominant controls on those effects is needed to support fuel treatment planning directed at achieving specific fire, water, and forest health goals. This research aims to quantify and better understand how local differences in treatment, landscape features, and climate alter those fuel treatment effects. We address these questions using a mechanistic coupled ecohydrologic model—the Regional Hydro-Ecological Simulation System (RHESSys). We ran 13,500 scenarios covering a range of fuel treatment, biophysical, and climate conditions, for the Southern Sierra Nevada of California. Across fuel treatment type, biophysical, and climate parameters, we find nontrivial variation in fuel treatment effects on stand carbon, net primary productivity, evapotranspiration, and fire-related canopy structure variables. Response variable estimates range substantially, from increases (1–48%) to decreases (−13 to −175%) compared to untreated scenarios. The relative importance of parameters differs by response variable; however, fuel treatment method and intensity, plant accessible water storage capacity (PAWSC), and vegetation type consistently demonstrate a large influence across response variables. These parameters interact to produce non-linear effects. Results show that projections of fuel treatment effects based on singular mean parameter values (such as mean PAWSC) provide a limited picture of potential responses. Our findings emphasize the need for a more complete perspective when assessing expected fuel treatment outcomes, both in their effects and in the interacting biophysical and climatic parameters that drive them. This research also serves as a demonstration of methodology to assess the likely variation in potential effects of fuel treatments for a given planning unit.