Conference Agenda

Wildfires can sharply increase hillslope erosion and downstream sediment loads, threatening drinking-water treatability and aquatic habitat. Post-fire treatments (e.g., mulching) can reduce these impacts, but limited budgets and tight response windows require cost-effective targeting across the landscape. We develop a decision-support spatial optimization integer programming framework that links spatially explicit post-fire erosion and sediment predictions from the Water Erosion Prediction Project (WEPP) model under “no treatment” and multiple treatment scenarios to a least-cost treatment prioritization problem. Using hillslope-level outputs, the framework computes expected reductions in (i) hillslope sediment yield and (ii) watershed-scale sediment discharge or suspended-sediment indicators, and then solves a binary integer programming model to select the set of hillslope–treatment pairings that meets user-specified biophysical threshold targets at minimum cost. We refer to this integrated decision-support framework as the Post-fire Assessment of Treatments for Hillslopes (PATH) model. The framework incorporates user-defined fixed and variable treatment costs and can include practical eligibility constraints (e.g., slope limits, burn-severity-based targeting), while allowing multiple thresholds to be imposed simultaneously. We demonstrate the approach in an applied watershed setting and summarize results as cost surfaces and tradeoff curves that relate management ambition to budget requirements. Optimal portfolios concentrate treatments on the most influential hillslopes rather than treating uniformly, revealing clear cost–performance tradeoffs as thresholds tighten. When targets are infeasible, PATH provides transparent “second-best” solutions (e.g., maximizing sediment reduction subject to minimum per-hillslope standards), supporting defensible planning under binding biophysical and operational constraints.