Evaluating Basin-Scale Forest Adaptation Scenarios: Wildfire, Streamflow, Biomass, and Economic Recovery Synergies and Trade-Offs

Abstract

Active forest management is applied in many parts of the western United States to reduce wildfire severity, mitigate vulnerability to drought and bark beetle mortality, and more recently, to increase snow retention and late-season streamflow. A rapidly warming climate accelerates the need for these restorative treatments, but the treatment priority among forest patches varies considerably. We simulated four treatment scenarios across the 3,450 km2 Wenatchee River basin in eastern Washington, United States. We used a decision support tool (DST) to assess trade-offs and synergies within and among treatments on wildfire risk and smoke emissions, water yield and snow retention, biomass production, and economic return. Treatment scenarios emphasized prescribed burning (BurnOnly), biomass production (MaxBiomass), gap-based thinning to optimize water yield (IdealWater), and a principle-based restoration scenario (RA1). Fire hazard, smoke emissions, and biomass production metrics were evaluated across scenarios using the Forest Vegetation Simulator, and water yields were modeled using the Distributed Hydrology Soil Vegetation Model. Simulations were summarized to both patch- (101–102 ha) and subwatershed- (103–104 ha) scales, and treatment effects were evaluated against an untreated baseline landscape. We used logic models to rank effect sizes by scenario across metrics along a continuum between −1 (no or weak effect) to +1 (large effect). All treatments produced benefits across one or more ecosystem services and led to synergistic benefits to water yield and wildfire hazard reduction. Tradeoffs among resource benefits were clear in wilderness where reliance on prescribed burning without mechanical treatment increased costs and eliminated the potential for biomass recovery. The BurnOnly scenario improved fire risk metrics and streamflow, but effect sizes were lower compared to other treatments. IdealWater showed the strongest benefits overall, demonstrating the ability to capture multiple resource benefits through spatially explicit thinning. Our study provides a framework for integrating strategic and tactical models that evaluate tradeoffs and synergies gained through varied management approaches. We demonstrate the utility of decision support modeling to enhance management synergies across large landscapes.

Trade‐offs among management objectives in mature Douglas‐fir forests of the Pacific Northwest

“…These findings are broadly consistent with previous research. At the stand scale, many prior studies have found that moderate‐intensity harvest strategies like our thinned management condition minimized pairwise trade‐offs between one or more biodiversity‐ and/or climate‐related objectives (e.g., D'Amato et al, 2011; Henneron et al, 2015; Krcmar et al, 2005; Lucash et al, 2023), and at landscape scales, uniform application of individual treatments involving intermediate levels of overstory retention commonly emerge as the multiobjective compromise when all objectives are weighted equally important (Carpentier et al, 2017; Fürstenau et al, 2007; Povak et al, 2022; Schwenk et al, 2012). However, it is also clear that for most realistic multiobjective portfolios, individual silvicultural treatment regimes are unlikely to be optimal for all objectives (e.g., Carpentier et al, 2017; Eyvindson et al, 2018; Seidl et al, 2007).…”

“…However, it is also clear that for most realistic multiobjective portfolios, individual silvicultural treatment regimes are unlikely to be optimal for all objectives (e.g., Carpentier et al, 2017; Eyvindson et al, 2018; Seidl et al, 2007). At larger spatial scales, optimal multiobjective strategies typically involve allocation of different stand‐scale treatments across the landscape, such that landscape‐scale trade‐offs are minimized by efficient application of a range of silvicultural treatments that each maximize benefits for individual objectives at the stand scale (Eyvindson et al, 2018; Povak et al, 2022; Schwenk et al, 2012; Triviño et al, 2017).…”

Informing climate adaptation strategies using ecological simulation models and spatial decision support tools

“…We assessed the degree of alignment between the seven Primary Topics using Nonmetric Multidimensional Scaling (NMDS) ordinations. We used the metaMDS() function from the vegan R package (Oksanen et al, 2022), with Bray-Curtis distance and 99 maximum random starts. In each patch, we used the SOE scores for current conditions, future trend, and treatment efficacy among each of the Primary Topic areas to generate the dissimilarity matrix used in The plus/minus symbol following the units for each metric indicates the directionality of the premise used to generate strength-of-evidence (SOE) scores.…”

Quantitative methods for integrating climate adaptation strategies into spatial decision support models

“…Decision support systems (DSS) have a long history in forest management planning across the world Marto et al, 2019). Relevant to our context, DSSs are knowledge-based systems that are commonly used to evaluate ecosystem conditions and facilitate prioritizing spatial management treatments (Reynolds and Hessburg, 2005;Povak et al, 2022). PROMOTE uses fuzzy logic to evaluate current ecosystem resource conditions across a landscape as well as the future potential and stability of these resources under climate change and natural disturbances.…”