. The statistical analysis of wildfire activity is a critical component of national wildfire planning, operations, and research in the United States (US). However, there are multiple federal, state, and local entities with wildfire protection and reporting responsibilities in the US, and no single, unified system of wildfire record keeping exists. To conduct even the most rudimentary interagency analyses of wildfire numbers and area burned from the authoritative systems of record, one must harvest records from dozens of disparate databases with inconsistent information content. The onus is then on the user to check for and purge redundant records of the same fire (i.e., multijurisdictional incidents with responses reported by several agencies or departments) after pooling data from different sources. Here we describe our efforts to acquire, standardize, error-check, compile, scrub, and evaluate the completeness of US federal, state, and local wildfire records from 1992–2011 for the national, interagency Fire Program Analysis (FPA) application. The resulting FPA Fire-Occurrence Database (FPA FOD) includes nearly 1.6 million records from the 20 yr period, with values for at least the following core data elements: location, at least as precise as a Public Land Survey System section (2.6 km2 grid), discovery date, and final fire size. The FPA FOD is publicly available from the Research Data Archive of the US Department of Agriculture, Forest Service ( doi:10.2737/RDS-2013-0009 ). While necessarily incomplete in some aspects, the database is intended to facilitate fairly high-resolution geospatial analysis of US wildfire activity over the past two decades, based on available information from the authoritative systems of record.

/pdf/a-spatial-database-of-wildfires-in-the-united-states-1992-2znls8x34y.pdf

A spatial database of wildfires in the United States, 1992-2011

Indigenous peoples’ detailed traditional knowledge about fire, although superficially referenced in various writings, has not for the most part been analyzed in detail or simulated by resource managers, wildlife biologists, and ecologists.... Instead, scientists have developed the principles and theories of fire ecology, fire behavior and effects models, and concepts of conservation, wildlife management and ecosystem management largely independent of native examples. (Lewis and Anderson 2002, p. 4)

https://www.fs.fed.us/psw/publications/lake/psw_2017_lake001.pdf

Returning fire to the land: celebrating traditional knowledge and fire

During active fire incidents, decisions regarding where and how to safely and effectively deploy resources to meet management objectives are often made under rapidly evolving conditions, with limited time to assess management strategies or for development of backup plans if initial efforts prove unsuccessful. Under all but the most extreme fire weather conditions, topography and fuels are significant factors affecting potential fire spread and burn severity. We leverage these relationships to quantify the effects of topography, fuel characteristics, road networks and fire suppression effort on the perimeter locations of 238 large fires, and develop a predictive model of potential fire control locations spanning a range of fuel types, topographic features and natural and anthropogenic barriers to fire spread, on a 34000km2 landscape in southern Idaho and northern Nevada. The boosted logistic regression model correctly classified final fire perimeter locations on an independent dataset with 69% accuracy without consideration of weather conditions on individual fires. The resulting fire control probability surface has potential for reducing unnecessary exposure for fire responders, coordinating pre-fire planning for operational fire response, and as a network of locations to incorporate into spatial fire planning to better align fire operations with land management objectives.

https://www.fs.fed.us/rm/pubs_journals/2017/rmrs_2017_oconnor_c002.pdf

An empirical machine learning method for predicting potential fire control locations for pre-fire planning and operational fire management

https://www.fs.fed.us/rm/pubs_other/rmrs_2013_ryan_k001.pdf

LANDFIRE - A national vegetation/fuels data base for use in fuels treatment, restoration, and suppression planning

Natural hazard risk is largely projected to increase in the future, placing growing responsibility on decision makers to proactively reduce risk. Consequently, decision support systems (DSSs) for natural hazard risk reduction (NHRR) are becoming increasingly important. In order to provide directions for future research in this growing area, a comprehensive classification system for the review of NHRR-DSSs is introduced, including scoping, problem formulation, the analysis framework, user and organisational interaction with the system, user engagement, monitoring and evaluation. A review of 101 papers based on this classification system indicates that most effort has been placed on identifying areas of risk and assessing economic consequences resulting from direct losses. However, less effort has been placed on testing risk-reduction options and considering future changes to risk. Furthermore, there was limited evidence within the reviewed papers on the success of DSSs in practice and whether stakeholders participated in DSS development and use. Classification system of different aspects of natural hazard risk-reduction DSSs.Aspects covered are scoping, analysis, user interaction, monitoring and evaluation.Review of 101 papers detailing 77 DSSs using proposed classification system.Good coverage of hydrological hazards, structural mitigation and economic impact.Limited coverage of multiple hazards, future impacts and DSS use and evaluation.

/pdf/review-of-literature-on-decision-support-systems-for-natural-3yyndftamh.pdf

Review of literature on decision support systems for natural hazard risk reduction

A new decision support tool, the Wildland Fire Decision Support System (WFDSS) has been developed to support risk-informed decision-making for individual fires in the United States. WFDSS accesses national weather data and forecasts, fire behavior prediction, economic assessment, smoke management assessment, and landscape databases to efficiently formulate and apply information to the decision making process. Risk-informed decision-making is becoming increasingly important as a means of improving fire management and offers substantial opportunities to benefit natural and community resource protection, management response effectiveness, firefighter resource use and exposure, and, possibly, suppression costs. This paper reviews the development, structure, and function of WFDSS, and how it contributes to increased flexibility and agility in decision making, leading to improved fire management program effectiveness.

/pdf/developing-the-us-wildland-fire-decision-support-system-89inu92dne.pdf

John R. Fiedler

Papers

Developing the US Wildland Fire Decision Support System