Published

In western North America, wildfire is a critical component of many ecosystems and a natural hazard that can result in catastrophic losses of human lives and property. Billions of dollars are spent suppressing wildfires each year. In the past decades, academic research has made substantial contributions to the understanding of fire and its interaction with climate and land management. Most reviews of the academic literature, however, are centered in either natural or social science. We offer an integrated cross‐disciplinary guide to state‐of‐the art fire science and use this review to identify research gaps. We focus on the modern era and understanding fire in the context of a changing climate in western North America. We find that studies combining social and natural science perspectives remain limited and that interactions among coupled system components are poorly understood. For example, while natural science studies have identified how fuel treatments alter fire regimes, few social science studies examine how decisions are made about fuel treatments and how these decisions respond to changes in fire regimes. A key challenge is to better quantify the effects of actual fire management policies in a way that accounts for the complexity of coupled natural and natural–human system interactions.

Wildfire affects the ecosystem services of watersheds, and climate change will modify fire regimes and watershed dynamics. In many eco-hydrological simulations, fire is included as an exogenous force. Rarely are the bidirectional feedbacks between watersheds and fire regimes integrated in a simulation system because the eco-hydrological model predicts variables that are incompatible with the requirements of fire models. WMFire is a fire-spread model of intermediate complexity designed to be integrated with the Regional Hydro-ecological Simulation System (RHESSys). Spread in WMFire is based on four variables that (i) represent known influences on fire spread: litter load, relative moisture deficit, wind direction and topographic slope, and (ii) are derived directly from RHESSys outputs. The probability that a fire spreads from pixel to pixel depends on these variables as predicted by RHESSys. We tested a partial integration between WMFire and RHESSys on the Santa Fe (New Mexico) and the HJ Andrews (Oregon State) watersheds. Model assessment showed correspondence between expected spatial patterns of spread and seasonality in both watersheds. These results demonstrate the efficacy of an approach to link eco-hydrologic model outputs with a fire spread model. Future work will develop a fire effects module in RHESSys for a fully coupled, bidirectional model.

Low-probability, high-consequence climate change events are likely to trigger management responses that arbased on the demand for immediate action from those affected. However, these responses may be inefficient and even maladaptive in the long term.

This paper examines how behavioral biases caused by salient events affect the government provision of public goods. We develop a theory in which competing communities lobby the government for allocations of a local public good. Salient events bias community demands for the good, which results in inefficient allocations. We empirically test this theory using salient wildfires and government projects to reduce wildfire risk. Wildfires reduce risk to nearby communities, but may increase demand for fuels management projects because of biases induced by salient wildfires. We find that communities experiencing recent nearby fires are more likely to receive fuels management projects.

Ecologists often rely on computer models as virtual laboratories to evaluate alternative theories, make predictions, perform scenario analysis, and to aid in decision-making. The application of ecological models can have real-world consequences that drive ecological theory development and science-based decision and policy-making, so it is imperative that the conclusions drawn from ecological models have a strong, credible quantitative basis. In particular it is important to establish whether any predicted change in a model output has ecological and statistical significance. Ecological models may include stochastic components, using probability distributions to represent some modeled processes. An individual run of a stochastic ecological model is a random draw from an infinitely large population, requiring replicate simulations to estimate the distribution of model outcomes. An important consideration is the number of Monte Carlo replicates necessary to draw useful conclusions from the model analysis. A simple framework is presented that borrows from well-understood techniques for experimental design, including confidence interval estimation and sample size power analysis. The desired precision of interval estimates for model prediction, or the minimum desired detectable effect size between scenarios, is established by the researcher in the context of the model objectives and the ecological system. The number of replicates required to achieve that level of precision or detectable effect is computed given an estimate of the variability in the model outcomes of interest. If the number of replicates is computationally prohibitive, then the expected precision or detectable effect for that sample size should be reported. An example is given for a stochastic model of fire spread integrated with an eco-hydrological model.

In Prep

  1. Bart, R., Kennedy, M., Tague, C., McKenzie, D. (In-prep) Integrating a dynamic fire effects model with a distributed ecohydrologic model. Ecological Modelling.
  2. Bart, R., Tague, C., Kennedy, M., Hanan, E. (In-prep) A watershed-scale model for simulating natural wildfire regimes in the Western U.S. Journal of Advances in Modeling Earth Systems.