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Extreme wildfires are increasing in frequency globally, prompting new efforts to mitigate risk. The ecological appropriateness of risk mitigation strategies, however, depends on what factors are driving these increases. While regional syntheses attribute increases in fire activity to both climate change and fuel accumulation through fire exclusion, they have not disaggregated causal drivers at scales where land management is implemented. Recent advances in fire regime modeling can help us understand which drivers dominate at management-relevant scales. We conducted fire regime simulations using historical climate and fire exclusion scenarios across two watersheds in the Inland Northwestern U.S., which occur at different positions along an aridity continuum. In one watershed, climate change was the key driver increasing burn probability and the frequency of large fires; in the other, fire exclusion dominated in some locations. We also demonstrate that some areas become more fuel-limited as fire-season aridity increases due to climate change. Thus, even within watersheds, fuel management must be spatially and temporally explicit to optimize effectiveness. To guide management, we show that spatial estimates of soil aridity (or temporally averaged soil moisture) can provide a relatively simple, first-order indicator of where in a watershed fire regime is climate vs. fuel-limited and where fire regimes are most vulnerable to change.

Although natural disturbances such as wildfire, extreme weather events, and insect outbreaks play a key role in structuring ecosystems and watersheds worldwide, climate change has intensified many disturbance regimes, which can have compounding negative effects on ecosystem processes and services. Recent studies have highlighted the need to understand whether wildfire increases or decreases after large‐scale beetle outbreaks. However, observational studies have produced mixed results. To address this, we applied a coupled ecohydrologic‐fire regime‐beetle effects model (RHESSys‐WMFire‐Beetle) in a semiarid watershed in the western US. We found that in the red phase (0–5 years post‐outbreak), surface fire extent, burn probability, and surface and crown fire severity all decreased. In the gray phase (6–15 years post‐outbreak), both surface fire extent and surface and crown fire severity increased with increasing mortality. However, fire probability reached a plateau during high mortality levels (>50% in terms of carbon removed). In the old phase (one to several decades post‐outbreak), fire extent and severity still increased in all mortality levels. However, fire probability increased during low to medium mortality (≤50%) but decreased during high mortality levels (>50%). Wildfire responses also depended on the fire regime. In fuel‐limited locations, fire probability increased with increasing fuel loads, whereas in fuel‐abundant (flammability‐limited) systems, fire probability decreased due to decreases in fuel aridity from reduced plant water demand. This modeling framework can improve our understanding of the mechanisms driving wildfire responses and aid managers in predicting when and where fire hazards will increase.

Climate change has lengthened wildfire seasons and transformed fire regimes throughout the world. Thus, capturing fuel and fire dynamics is critical for projecting Earth system processes in warmer and drier future. Recent advances in fire regime modeling have linked land surface models with fire behavior models. Such models often rely on fine surface fuels to drive fire behavior and effects, and while many models can simulate processes that control how these fuels change through time (i.e., fine fuel accumulation), fuel loading estimates remain highly uncertain, largely due to uncertainties in the algorithms controlling decomposition. Uncertainties are often amplified in climate change forecasts when initial conditions and feedbacks are not well represented. The goal of this review is to highlight fine fuel decomposition as a key uncertainty in model systems. We review the current understanding of mechanisms controlling decomposition, describe how they are incorporated into models, and evaluate the uncertainties associated with different approaches. We also use three state‐of‐the‐art land surface fire regime models to demonstrate the sensitivity of decomposition and subsequent wildfire projections to both parameter and model structure uncertainty and show that sensitivity can increase substantially under future climate warming. Given that many of the governing decomposition equations are based on individual case studies from a single location, and because key parameters are often hard coded, critical uncertainties are currently ignored. It is essential to be transparent about these uncertainties as the domain of land surface models is expanded to include the evaluation of future wildfire regimes.

Climate and wildfire are closely linked. Climate regulates wildfire directly over short timescales through its effect on fuel aridity and indirectly over long timescales through vegetation productivity and the structure and abundance of fuels. Prediction of future wildfire regimes in a changing climate often uses empirical studies that presume current relationships between short‐term climate variables and wildfire activity will be stationary in the future. This is problematic because landscape‐scale wildfire dynamics exhibit non‐stationarity, with both positive and negative feedback loops that operate at different temporal and spatial scales. This requires that such feedbacks are accommodated in a model framework from which wildfire dynamics are emergent rather than pre‐specified. We use a new model, RHESSys‐WMFire, that integrates ecohydrology with fire spread and effects to simulate a 60‐yr time series of vegetation, fuel development, and wildfire in a 6572‐ha watershed in the Southern Sierra Nevada, USA, with a factorial design of increased temperature and severe drought. All climate scenarios had an initial pulse of elevated area burned associated with high temperature, low precipitation, and high fine fuel loading. There were positive correlations between annual area burned and mean annual maximum temperature and negative correlations with annual precipitation, consistent with understood direct effects of climate on wildfire in this system. Decreased vegetation productivity and increased fine fuel decomposition were predicted with increased temperature, resulting in long‐term reduced fine fuels and area burned relative to baseline. Repeated extreme drought increased area burned relative to baseline and over the long‐term had substantially reduced overstory biomass. Overstory biomass was resilient to repeat wildfire under baseline climate. The model system predicts that the short‐term direct effects of climate on wildfire can differ from long‐term indirect effects such that the simple maxim hotter/drier equals more wildfire can be both true and false, depending on scale.

Fire regimes are influenced by both exogenous drivers (e.g., increases in atmospheric CO₂and climate change) and endogenous drivers (e.g., vegetation and soil/litter moisture), which constrain fuel loads and fuel aridity. Herein, we identified how exogenous and endogenous drivers can interact to affect fuels and fire regimes in a semiarid watershed in the inland northwestern United States throughout the 21st century. We used a coupled ecohydrologic and fire regime model to examine how climate change and CO₂scenarios influence fire regimes. In this semiarid watershed, we found an increase in burned area and burn probability in the mid‐21st century (2040s) as the CO₂fertilization effect on vegetation productivity outstripped the effects of climate change‐induced fuel decreases, resulting in greater fuel loading. However, by the late‐21st century (2070s), climatic warming dominated over CO₂fertilization, thus reducing fuel loading and burned area. Fire regimes were shown to shift from flammability‐ to fuel‐limited or become increasingly fuel‐limited in response to climate change. We identified a metric to identify when fire regimes shift from flammability‐ to fuel‐limited: the ratio of the change in fuel loading to the change in its aridity. The threshold value for which this metric indicates a flammability versus fuel‐limited regime differed between grasses and woody species but remained stationary over time. Our results suggest that identifying these thresholds in other systems requires narrowing uncertainty in exogenous drivers, such as future precipitation patterns and CO₂effects on vegetation.