Karin L. Riley

The relationship of large fire occurrence with drought and fire danger indices in the western USA, 1984–2008: the role of temporal scale

The relationship between large fire occurrence and drought has important implications for fire prediction under current and future climates. This studys 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.

A comparative risk assessment framework for wildland fire management: the 2010 cohesive strategy science report

The FLAME Act of 2009 requires the U.S. Department of Agriculture Forest Service and the U.S. Department of Interior to submit to Congress a Cohesive Wildfire Management Strategy. In this report, we explore the general science available for a risk-based approach to fire and fuels management and suggest analyses that may be applied at multiple scales to inform decisionmaking and tradeoff analysis. We discuss scientific strengths and limitations of wildfire risk assessment frameworks, including the benefit of broad scalability as demonstrated by four recent case studies. We further highlight the role of comparative risk assessment, which extends the analysis to include the decision space available to managers and stakeholders to allow them to explore the tradeoffs between alternative courses of action. We identify scientific limitations of the analytical protocol and discuss questions of how to better address climate change, smoke modeling issues, and socioeconomic vulnerability, and how to better quantify treatment effectiveness. Key challenges are: achieving a balance between retaining analytical flexibility at regional and sub-regional planning scales while simultaneously retaining data and methodological consistency at the national scale, and identifying and aligning regional and national priorities to inform multi-objective strategy development. As implementation proceeds, the analytical protocol will no doubt be modified, but the contents of this report comprise a rigorous and transparent framework for comparative risk assessment built from the best available science.

A Framework for Assessing Global Change Risks to Forest Carbon Stocks in the United States

Among terrestrial environments, forests are not only the largest long-term sink of atmospheric carbon (C), but are also susceptible to global change themselves, with potential consequences including alterations of C cycles and potential C emission. To inform global change risk assessment of forest C across large spatial/temporal scales, this study constructed and evaluated a basic risk framework which combined the magnitude of C stocks and their associated probability of stock change in the context of global change across the US. For the purposes of this analysis, forest C was divided into five pools, two live (aboveground and belowground biomass) and three dead (dead wood, soil organic matter, and forest floor) with a risk framework parameterized using the USs national greenhouse gas inventory and associated forest inventory data across current and projected future Köppen-Geiger climate zones (A1F1 scenario). Results suggest that an initial forest C risk matrix may be constructed to focus attention on short- and long-term risks to forest C stocks (as opposed to implementation in decision making) using inventory-based estimates of total stocks and associated estimates of variability (i.e., coefficient of variation) among climate zones. The empirical parameterization of such a risk matrix highlighted numerous knowledge gaps: 1) robust measures of the likelihood of forest C stock change under climate change scenarios, 2) projections of forest C stocks given unforeseen socioeconomic conditions (i.e., land-use change), and 3) appropriate social responses to global change events for which there is no contemporary climate/disturbance analog (e.g., severe droughts in the Lake States). Coupling these current technical/social limits of developing a risk matrix to the biological processes of forest ecosystems (i.e., disturbance events and interaction among diverse forest C pools, potential positive feedbacks, and forest resiliency/recovery) suggests an operational forest C risk matrix remains elusive.

Near-term probabilistic forecast of significant wildfire events for the Western United States

Fire danger and potential for large fires in the United States (US) is currently indicated via several forecasted qualitative indices. However, landscape-level quantitative forecasts of the probability of a large fire are currently lacking. In this study, we present a framework for forecasting large fire occurrence – an extreme value event – and evaluating measures of uncertainties that do not rely on distributional assumptions. The statistical model presented here incorporates qualitative fire danger indices along with other location and seasonal specific explanatory variables to produce maps of forecasted probability of an ignition becoming a large fire, as well as numbers of large fires with measures of uncertainties. As an example, 6 years of fire occurrence data from the Western US were used to study the utility of two fire danger indices: the 7-Day Significant Fire Potential Outlook issued by Predictive Services in the US and the National Fire Danger Rating’s Energy Release Component. This exercise highlights the potential utility of the quantitative risk index as a real-time decision support tool that can enhance managers’ abilities to discriminate among planning areas in terms of the likelihood and range of expected significant fire events. The approach is applicable wherever there are archived historical data from both observed fires and fire danger indices.

Utilizing random forests imputation of forest plot data for landscape-level wildfire analyses

Maps of the number, size, and species of trees in forests across the United States are desirable for a number of applications. For landscape-level fire and forest simulations that use the Forest Vegetation Simulator (FVS), a spatial tree-level dataset, or “tree list”, is a necessity. FVS is widely used at the stand level for simulating fire effects on tree mortality, carbon, and biomass, but uses at the landscape level are limited by lack of availability of forest inventory data for large contiguous areas. Detailed mapping of trees across large areas is not feasible with current technologies, but statistical methods for matching 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 Landfire vegetation and biophysical predictors at 30m grid resolution. In essence, the random forests method creates a “forest” of decision trees in order to choose the forest plot with the best statistical match for each grid cell in the landscape. Landfire data was used in this project because is publicly available, offers seamless coverage of variables needed for fire models, and is consistent with other datasets, including burn probabilities and flame length probabilities generated for the continental US by Fire Program Analysis (FPA). We used the imputed forest plot data to generate a map of forest cover and height as well as existing vegetation group for a study area in eastern Oregon, and examined correlations with Landfire data. The results showed good correspondence between the two data sets (84-97% within-class agreement, depending on the variable). In future research, the new imputed grid of inventory data will be used for landscape simulation studies to determine risk to terrestrial carbon resources from wildfire as well as to investigate the effect of fuel treatments on burn probability and fire sizes.

Predicting wildfire ignitions, escapes, and large fire activity using Predictive Service’s 7-Day Fire Potential Outlook in the western USA

Can fire potential forecasts assist with pre-positioning of fire suppression resources, which could result in a cost savings to the United States government? Here, we present a preliminary assessment of the 7-Day Fire Potential Outlook forecasts made by the Predictive Services program. We utilized historical fire occurrence data and archived forecasts to assess how well the 7-Day Outlook predicts wildfire ignitions and escaped fires, ultimately to help characterize the effectiveness of this tool for prepositioning national firefighting resources. The historical fire occurrence data track ignitions on all land ownerships; from this dataset, we established number and location of ignitions and final fire size for the years 2009-2011 for Predictive Service Areas (PSAs) within the Northwest and Southwest Geographic Areas. These data were then matched to the corresponding PSA and appropriate forecast for each of the seven days prior to the ignition date. Final fire size was used as a metric to establish whether an ignition escaped initial attack, with fires greater than 121.4 hectares (300 acres) considered escaped. Our results show that 7-Day Outlook values yield better-than-random prediction of large fire activity, although there is wide variation in this relationship among individual PSAs. In addition, the number of escaped fires increased with the number of ignitions, with this relationship showing a distinct regional pattern. Fires were more likely to escape during certain times of the year, with this season being earlier in the Southwest than in the Northwest. Significantly higher numbers of escaped fires per ignition occurred during days considered to be high risk by the meteorologist than on lower-risk days.