Robust strategies for forest wildfire mitigation under uncertainty

Abstract

Modern forest management is often focused on minimizing wildfire risk by regaining fire-resiliency from historical conditions. This process is labor and resource intensive and it is usually infeasible to treat entire forests. Therefore, forest treatment allocation optimization is applied to help identify the best mitigation plan, with a key objective being the minimization of departure from historical landscape conditions. However, the vagueness and imprecision of historical landscape condition models and their corresponding departure indicators due to data limitations often necessitate the use of simulated substitutes. Since spatial optimization, integral for identifying optimal mitigation plans, relies on simulation outputs, addressing uncertainty becomes a crucial concern. We analyze optimization outputs for different historical condition scenarios and propose strategies to identify robust alternatives in two steps. First, we conduct a series of spatial optimizations using a weighted-sum method, systematically varying the importance assigned to different objectives for each scenario of historical conditions. This process helps us understand how changes in the assumptions about past forest conditions and the relative weights assigned to each assumption affect the optimal management plans. Second, we analyze the resulting solutions to identify patterns and overlaps among the optimal spatial configurations across scenarios. By focusing on the identification of common patterns, we develop a strategy to select solutions that are robust to uncertainty in historical condition models. We find that the strategy based on identifying commonalities among optimal solutions yields management plans that are, on average, 8.9% more robust compared to plans derived without explicit consideration of robustness. We conclude that the commonality seeking strategy in solution space alleviates risk in decision making. These insights highlight the importance of developing strategies for applying robust strategies in spatially explicit optimization problems.