This paper found that local responses to climate change and fire exclusion vary with aridity at fine scales. Thus, even in mixed pine regions that have been defined as climate-limited, fire exclusion and fuel accumulation can still have local effects. Similarly, in regions that are defined as fuel-limited, climate change-driven increases in local aridity may limit fuel accumulation and its effects on the probability of fire. These finding highlight the importance of considering both spatial heterogeneity and non-stationary climate in policy and management planning.

Wildfire affects landscape ecohydrologic processes through feedbacks between fire effects, vegetation growth and water availability. Despite the links between these processes, fire is rarely incorporated dynamically into ecohydrologic models, which couple vegetation growth with water and nutrient fluxes. This omission has the potential to produce inaccurate estimates of long-term changes to carbon and water cycling in response to climate change and management. This study describes a fire-effects model that is coupled to a distributed ecohydrologic model, RHESSys, and a fire-spread model, WMFire. Fire effects of shrubland and understory vegetation varied with surface fire intensity, by design, and fire effects in forest canopies were sensitive to parameters associated with the buildup of litter and understory ladder fuels. These findings demonstrate that the fire-effects model provides an effective tool for evaluating the post-fire changes to physical and ecological processes.

This paper investigated the effect of fuels treatments on forest water use and productivity. The results showed that show that whether forest water use increases or decreases following density reduction, as well as the magnitude and rate of recovery of hydrologic changes, depends strongly on plant accessible water storage capacity within the rooting zone and the extent to which the root structures of neighboring trees interact and share water. Results highlight the importance of accounting for site-specific variation, such as soil water storage capacity, in assessing how fuel treatments may interact with ecosystem water use and drought vulnerability, and ultimately downslope impacts on streamflow.

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.

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.

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.

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.

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.

In Prep

  1. Wibbenmeyer, M., Anderson, S., Plantinga, A. (In-prep) Inequality and government responsiveness: Evidence from salient wildfire events.
  2. Kennedy, MC et al. (In-prep) Simulation of watershed-level fire regimes under a changing climate.