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The absence of a comprehensive and accurate database on fire occurrence means that some proportion of wildfires is not reported. This paper examines wildfires reported in the National Fire Incident Reporting System (NFIRS) database and compares it with the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) fire detection data and the CAL FIRE Fire and Resource Assessment Program (FRAP) geodatabase to understand underreporting of wildland fires in the NFIRS system. The paper discusses a series of large wildland fires and uses a Generalised Linear Model to identify the conditions where large wildfires go unreported. This paper shows that the NFIRS database, which is the primary fire database in the US, significantly underreports wildland fires. Evidence from California suggests that 32% of fires within local jurisdictions detected via MODIS satellite were identified in the NFIRS database; thus, there could be a significant number of fires going unreported nationwide. Examining eight large fires in California, only 16% of the structures damaged or destroyed, as reported in CAL FIRE, were reported in NFIRS. Areas with underreporting tend to have a higher level of poverty, higher population density, higher level of people without vehicles, lower income and a higher level of single parents with children under 18, as well as a higher level of people under the age of 18.

Research shows that some categories of human-ignited wildfires may be forecastable, owing to their temporal clustering, with the possibility that resources could be predeployed to help reduce the incidence of such wildfires. We estimated several kinds of incendiary and other human-ignited wildfire forecast models at the weekly time step for tribal land units in the United States, evaluating their forecast skill out of sample. Analyses show that an autoregressive conditional Poisson model of both incendiary and non-incendiary human-ignited wildfires is more accurate out of sample compared with alternatives, and the simplest of the autoregressive conditional Poisson models performed the best. Additionally, an ensemble of these and simpler, less analytically intensive approaches performed even better. Wildfire hotspot forecast models using all model types were evaluated in a simulation mode to assess the net benefits of forecasts in the context of law-enforcement resource reallocations. Our analyses show that such hotspot tools could yield large positive net benefits for the tribes in terms of suppression expenditures averted for incendiary wildfires but that the hotspot tools were less likely to be beneficial for addressing outbreaks of non-incendiary human-ignited wildfires.

Humans cause more than 55% of wildfires on lands managed by the USDA Forest Service and US Department of the Interior, contributing to both suppression expenditures and damages. One means to reduce the expenditures and damages associated with these wildfires is through fire prevention activities, which can include burn permits, public service programs or announcements, outreach efforts to schools, youth groups and equipment operators, and law enforcement. Using data from 17 US Bureau of Indian Affairs tribal units, we modelled the effect of prevention programs and law enforcement on the number of human-caused ignitions. We also included weather and lagged burned area in our estimation of fixed-effects count models. The results show that prevention activities led to significant reductions in wildfires caused by escaped campfires, juveniles, fire-use (e.g. escaped debris burns) and equipment. Increased law enforcement resulted in fewer incendiary- and equipment-caused wildfires. Using average suppression expenditures by wildfire and our estimate of avoided wildfires per additional year of prevention, we estimate partial benefit–cost ratios of greater than 4.5 for all Bureau of Indian Affairs regions for the continuation of the prevention program.

The number of smoking-caused wildfires has been falling nationwide. In national forests in 2011, smoking-caused wildfires represented only 10% of their 1980 level. No other cause of wildfire has experienced this level of decline. For 12 states, we evaluate the rate of smoking-caused wildfires and find it is a function of weather, other ignitions, the number of adult smokers, the presence of improved wildfire cause-determination methods, and whether a state required the sale of less fire-prone cigarettes. We find the decline in adult smoking rates has led to a reduction of smoking-caused fires by 9%. The finding that less fire-prone cigarettes appear successful at limiting wildfire starts – by 23% – is a likely unintended benefit of a technology aimed at reducing fire fatalities in residences. We also find that the improvements in wildfire cause determination have resulted in a reduction in smoking-classified fires by 48%. Although improved wildfire cause-determination methods do not necessarily reduce the number of wildfires, they ensure that the causes of wildfire are accurately tracked. Accurate wildfire cause determination can, however, result in targeting wildfire-prevention programs to specific fire-cause categories, which can lead to a reduction in the overall number of wildfires.

We describe how two important tools of wildfire management, wildfire prevention education and prescribed fire for fuels management, can be coordinated to minimise the combination of management costs and expected societal losses resulting from wildland fire. We present a long-run model that accounts for the dynamics of wildfire, the effects of fuels management on wildfire ignition risk and area burned, and the effects of wildfire prevention education on the ignition risk of human-caused, unintentional wildfires. Based on wildfire management activities in Florida from 2002 to 2007, we find that although wildfire prevention education and prescribed fire have different effects on timing and types of fires, the optimal solution is to increase both interventions. Prescribed fire affects whole landscapes and therefore reduces losses from all wildfire types (including lightning), whereas wildfire prevention education reduces only human-caused ignitions. However, prescribed fire offers a longer-term solution with little short-term flexibility. Wildfire prevention education programs, by comparison, are more flexible, both in time and space, and can respond to unexpected outbreaks, but with limited mitigation longevity. Only when used together in a coordinated effort do we find the costs and losses from unintentional wildfires are minimised.

Wildfires that spread into wildland–urban interface (WUI) communities present significant challenges on several fronts. In the United States, the WUI accounts for a significant portion of wildland fire suppression and wildland fuel treatment costs. Methods to reduce structure losses are focussed on fuel treatments in either wildland fuels or residential fuels. There is a need for a well-characterised, systematic testing of these approaches across a range of community and structure types and fire conditions. Laboratory experiments, field measurements and fire behaviour models can be used to better determine the exposure conditions faced by communities and structures. The outcome of such an effort would be proven fuel treatment techniques for wildland and residential fuels, risk assessment strategies, economic cost analysis models, and test methods with representative exposure conditions for fire-resistant building designs and materials.

We analyse the spatio-temporal structure of wildfire ignitions in the St Johns River Water Management District in north-eastern Florida. We show, using tools to analyse point patterns (e.g. the L-function), that wildfire events occur in clusters. Clustering of these events correlates with irregular distribution of fire ignitions, including lightning and human sources, and fuels on the landscape. In addition, we define a relative clustering index that summarizes the amount of clustering over various spatial scales. We carry our analysis in three steps: purely temporal, purely spatial, and spatio-temporal. Our results show that arson and lightning are the leading causes of wildfires in this region and that ignitions by railroad, lightning, and arson are spatially more clustered than ignitions by other accidental causes.