Abstract
This work considers a two-stage stochastic integer programming (SIP) approach for optimizing fuel treatment planning under uncertainty in weather and fire occurrence for rural forests. Given a set of areas for potentially performing fuel treatment, the problem is to decide the best treatment option for each area under uncertainty in future weather and fire occurrence. A two-stage SIP model is devised whose objective is to minimize the here-and-now cost of fuel treatment in the first-stage, plus the expected future costs due to uncertain impact from potential fires in the second-stage calculated as ecosystem services losses. The model considers four fuel treatment options: no treatment, mechanical thinning, prescribed fire, and grazing. Several constraints such as budgetary and labor constraints are included in the model and a standard fire behavior model is used to estimate some of the parameters of the model such as fuel levels at the beginning of the fire season. The SIP model was applied to data for a study area in East Texas with 15 treatment areas under different weather scenarios. The results of the study show, for example, that unless the expected ecosystem services values for an area outweigh fuel treatment costs, no treatment is the best choice for the area. Thus the valuation of the area together with the probability of fire occurrence and behavior strongly drive fuel treatment choices.
Highlights
Economic and human losses have continued to increase due to the rise in the number of wildfires, which call for solutions to alleviate the risks [1,2]
This paper takes a stochastic optimization perspective and devises a model for fuel treatment planning for rural forests in East Texas to study the impact of fuel levels and values at risk on fuel treatment options
This work considers a two-stage stochastic integer programming (SIP) approach for fuel treatment planning under uncertainty in weather and fire occurrence
Summary
Economic and human losses have continued to increase due to the rise in the number of wildfires, which call for solutions to alleviate the risks [1,2]. This paper takes a stochastic optimization perspective and devises a model for fuel treatment planning for rural forests in East Texas to study the impact of fuel levels and values at risk (in terms of ecosystem services provided by the land) on fuel treatment options. This paper was motivated by the limited number of stochastic optimization models in the literature that incorporate crucial stochastic factors such as weather and vegetation growth to study the economic impact of fuel treatment planning. This work considers a two-stage stochastic integer programming (SIP) model for optimizing fuel treatment planning for rural forests and shows that fuel treatment is favorable for areas with high values at risk as well as high fuel levels that can potentially lead to large fires.
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