The relationship between large fire occurrence and drought has important implications for fire prediction under current and future climates. This study’s primary objective was to evaluate correlations between drought and fire-danger-rating indices representing short- and long-term drought, to determine which had the strongest relationships with large fire occurrence at the scale of the western United States during the years 1984–2008. We combined 4–8-km gridded drought and fire-danger-rating indices with information on fires greater than 404.7 ha (1000 acres). To account for differences in indices across climate and vegetation assemblages, indices were converted to percentile conditions for each pixel. Correlations between area burned and short-term indices Energy Release Component and monthly precipitation percentile were strong (R2 = 0.92 and 0.89), as were correlations between number of fires and these indices (R2 = 0.94 and 0.93). As the period of time tabulated by indices lengthened, correlations with fire occurrence weakened: Palmer Drought Severity Index and 24-month Standardised Precipitation Index percentile showed weak correlations with area burned (R2 = 0.25 and –0.01) and number of large fires (R2 = 0.3 and 0.01). These results indicate associations between short-term indices and moisture content of dead fuels, the primary carriers of surface fire.

Climate changes are expected to increase fire frequency, fire season length, and cumulative area burned in the western United States. We focus on the potential impact of mid‐21st‐century climate changes on annual burn probability, fire season length, and large fire characteristics including number and size for a study area in the Northern Rocky Mountains. Although large fires are rare they account for most of the area burned in western North America, burn under extreme weather conditions, and exhibit behaviors that preclude methods of direct control. Allocation of resources, development of management plans, and assessment of fire effects on ecosystems all require an understanding of when and where fires are likely to burn, particularly under altered climate regimes that may increase large fire occurrence. We used the large fire simulation modelFSim to model ignition, growth, and containment of wildfires under two climate scenarios: contemporary (based on instrumental weather) and mid‐century (based on an ensemble average of global climate models driven by the A1BSRESemissions scenario). Modeled changes in fire patterns include increased annual burn probability, particularly in areas of the study region with relatively short contemporary fire return intervals; increased individual fire size and annual area burned; and fewer years without large fires. High fire danger days, represented by threshold values of Energy Release Component (ERC), are projected to increase in number, especially in spring and fall, lengthening the climatic fire season. For fire managers,ERCis an indicator of fire intensity potential and fire economics, with higherERCthresholds often associated with larger, more expensive fires. Longer periods of elevatedERCmay significantly increase the cost and complexity of fire management activities, requiring new strategies to maintain desired ecological conditions and limit fire risk. Increased fire activity (within the historical range of frequency and severity, and depending on the extent to which ecosystems are adapted) may maintain or restore ecosystem functionality; however, in areas that are highly departed from historical fire regimes or where there is disequilibrium between climate and vegetation, ecosystems may be rapidly and persistently altered by wildfires, especially those that burn under extreme conditions.

The primary theme of this study is the cost-effectiveness of fuel treatments at multiple scales of investment. We focused on the nexus of fuel management and suppression response planning, designing spatial fuel treatment strategies to incorporate landscape features that provide control opportunities that are relevant to fire operations. Our analysis explored the frequency and magnitude of fire-treatment encounters, which are critical determinants of treatment efficacy. Additionally, we examined avoided area burned, avoided suppression costs, and avoided damages, and combined all three under the umbrella of leverage to explore multiple dimensions with which to characterize return on investment. We chose the Sierra National Forest, California, USA, as our study site, due to previous work providing relevant data and analytical products, and because it has the potential for large, long-duration fires and corresponding potential for high suppression expenditures. Modeling results generally confirmed that fire-treatment encounters are rare, such that median suppression cost savings are zero, but in extreme years, savings can more than offset upfront investments. Further, reductions in risk can expand areas where moderated suppression response would be appropriate, and these areas can be mapped in relation to fire control opportunities.

The complexity and demands of wildland firefighting in the western U.S. have increased over recent decades due to factors including the expansion of the wildland-urban interface, lengthening fire seasons associated with climate change, and changes in vegetation due to past fire suppression and timber harvest. In light of these changes, the use of more wildland fire on the landscape could reduce fuels and form barriers to the spread of future fires while performing forest restoration in some areas. However, the risks, costs and benefits of changing fire response strategy have not been quantified. Here, we identify gaps regarding the ability to simulate alternative wildfire suppression strategies, due to a number of factors including limited data collected on fireline construction, as well as synergies between firefighting resources and resource effectiveness. We present a fire management continuum: at one end lies full suppression of all fires under all circumstances, and at the opposite end lies no suppression of any fires regardless of location or time in season, with a wide array of managed fire options falling in between. Next, we demonstrate the proof-of-concept using a stochastic fire simulation model, FSim, to simulate two alternative fire suppression strategies close to opposite ends of this continuum for the Sierra National Forest of California: (1) business-as-usual, which equates to nearly full fire suppression; and (2) full suppression of human-caused fires and no suppression actions on lightning-caused fires. Results indicate that fire management strategy can substantially affect the number of large fires and landscape burn probabilities, both of which were shown to increase under the second scenario. However, temporal feedbacks are expected to play an important role: we show that increases in burned area substantially limit ignition potential and the extent of subsequent fires within the first five to ten years, especially under the second scenario. While subject to current data gaps and limitations in fire modeling, the methodology presented here can be used to simulate a number of alternative fire suppression strategies, including decisions to suppress or not suppress fires based on location, time of season or other factors. This method also provides basic inputs needed to estimate risks, costs and benefits of various alternative suppression strategies in future work. In future work, uncertainties resulting from current limitations in knowledge can be addressed using techniques such as scenario planning in order to provide land managers with a set of possible fire outcomes.

Maps of the number, size, and species of trees in forests across the western United States are desirable for many applications such as estimating terrestrial carbon resources, predicting tree mortality following wildfires, and for forest inventory. However, detailed mapping of trees for large areas is not feasible with current technologies, but statistical methods for matching the forest plot data with biophysical characteristics of the landscape offer a practical means to populate landscapes with a limited set of forest plot inventory data. We used a modified random forests approach with Landscape Fire and Resource Management Planning Tools (LANDFIRE) vegetation and biophysical predictors to impute plot data collected by theUSForest Service's Forest Inventory Analysis (FIA) to the landscape at 30‐m grid resolution. This method imputes the plot with the best statistical match, according to a “forest” of decision trees, to each pixel of gridded landscape data. In this work, we used theLANDFIREdata set for gridded input because it is publicly available, offers seamless coverage of variables needed for fire models, and is consistent with other data sets, including burn probabilities and flame length probabilities generated for the continental United States. The main output of this project is a map of imputed plot identifiers at 30 × 30 m spatial resolution for the western United States that can be linked to theFIAdatabases to produce tree‐level maps or to map other plot attributes. In addition, we used the imputed inventory data to generate maps of forest cover, forest height, and vegetation group at 30 × 30 m resolution for all forested pixels in the western United States, as a means of assessing the accuracy of our methodology. The results showed good correspondence between the targetLANDFIREdata and the imputed plot data, with an overall within‐class agreement of 79% for forest cover, 96% for forest height, and 92% for vegetation group.

A 30 × 30m-resolution gridded dataset of forest plot identifiers was developed for the conterminous United States (CONUS) using a random forests machine-learning imputation approach. Forest plots from the US Forest Service Forest Inventory and Analysis program (FIA) were imputed to gridded c2014 landscape data provided by the LANDFIRE project using topographic, biophysical, and disturbance variables. The output consisted of a raster map of plot identifiers. From the plot identifiers, users of the dataset can link to a number of tree- and plot-level attributes stored in the accompanying tables and in the publicly available FIA DataMart, and then produce maps of any of these attributes, including number of trees per acre, tree species, and forest type. Of 67,141 FIA plots available, 62,758 of these (93.5%) were utilized at least once in imputation to 2,841,601,981 forested pixels in CONUS. Continuous high-resolution forest structure data at a national scale will be invaluable for analyzing carbon dynamics, habitat distributions, and fire effects.