A review of the relationships between drought and forest fire in the United States.

The historical and presettlement relationships between drought and wildfire are well documented in North America, with forest fire occurrence and area clearly increasing in response to drought. There is also evidence that drought interacts with other controls (forest productivity, topography, fire weather, management activities) to affect fire intensity, severity, extent, and frequency. Fire regime characteristics arise across many individual fires at a variety of spatial and temporal scales, so both weather and climate - including short- and long-term droughts - are important and influence several, but not all, aspects of fire regimes. We review relationships between drought and fire regimes in United States forests, fire-related drought metrics and expected changes in fire risk, and implications for fire management under climate change. Collectively, this points to a conceptual model of fire on real landscapes: fire regimes, and how they change through time, are products of fuels and how other factors affect their availability (abundance, arrangement, continuity) and flammability (moisture, chemical composition). Climate, management, and land use all affect availability, flammability, and probability of ignition differently in different parts of North America. From a fire ecology perspective, the concept of drought varies with scale, application, scientific or management objective, and ecosystem.

Carbon, climate, and natural disturbance: a review of mechanisms, challenges, and tools for understanding forest carbon stability in an uncertain future.

In this review, we discuss current research on forest carbon risk from natural disturbance under climate change for the United States, with emphasis on advancements in analytical mapping and modeling tools that have potential to drive research for managing future long-term stability of forest carbon. As a natural mechanism for carbon storage, forests are a critical component of meeting climate mitigation strategies designed to combat anthropogenic emissions. Forests consist of long-lived organisms (trees) that can store carbon for centuries or more. However, trees have finite lifespans, and disturbances such as wildfire, insect and disease outbreaks, and drought can hasten tree mortality or reduce tree growth, thereby slowing carbon sequestration, driving carbon emissions, and reducing forest carbon storage in stable pools, particularly the live and standing dead portions that are counted in many carbon offset programs. Many forests have natural disturbance regimes, but climate change and human activities disrupt the frequency and severity of disturbances in ways that are likely to have consequences for the long-term stability of forest carbon. To minimize negative effects and maximize resilience of forest carbon, disturbance risks must be accounted for in carbon offset protocols, carbon management practices, and carbon mapping and modeling techniques. This requires detailed mapping and modeling of the quantities and distribution of forest carbon across the United States and hopefully one day globally; the frequency, severity, and timing of disturbances; the mechanisms by which disturbances affect carbon storage; and how climate change may alter each of these elements. Several tools (e.g. fire spread models, imputed forest inventory models, and forest growth simulators) exist to address one or more of the aforementioned items and can help inform management strategies that reduce forest carbon risk, maintain long-term stability of forest carbon, and further explore challenges, uncertainties, and opportunities for evaluating the continued potential of, and threats to, forests as viable mechanisms for forest carbon storage, including carbon offsets. A growing collective body of research and technological improvements have advanced the science, but we highlight and discuss key limitations, uncertainties, and gaps that remain.

TreeMap, a tree-level model of conterminous US forests circa 2014 produced by imputation of FIA plot data.

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.

Spatial heterogeneity of winds during Santa Ana and non-Santa Ana wildfires in Southern California with implications for fire risk modeling.

In Southern California, the Santa Ana winds are famous for their role in spreading large wildfires during the fall/winter season. Combined with Southern California's complex topography, Santa Anas create challenges for modeling wind-fire relationships in this region. Here, we assess heterogeneity of winds during Santa Ana and non-Santa Ana days, on days with and without large-fire ignitions, across a modern high-density observational network of 30 meteorological stations. Wind speeds on Santa Ana days with a large fire ignition (mean windspeed = 5.19 m/s) are significantly higher than on Santa Ana days without large fire ignitions (3.96 m/s), while on non-Santa Ana days winds are generally weaker, during both fire (2.30 m/s) and non-fire (2.38 m/s) days. Hierarchical clustering of meteorological stations during both Santa Ana and non-Santa Ana days reveals groups of stations with consistently similar wind speed and directions. All stations clearly exhibit high wind speeds on Santa Ana days, and most record contrasting wind characteristics during Santa Ana versus non-Santa Ana ignitions. Additionally, our analysis revealed that key geographic siting traits are not represented in the network, including few stations with northwest aspect and upper slope in the southern mountains.

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 US's 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.