Contain and Control: Wildfire Suppression Effectiveness at Incidents and Across Landscapes

The potentially large ecological, economic, and societal impacts of climate change makes it a significant problem of the 21st century. These consequences have led to tremendous development in climate change scenarios and new technologies to increase knowledge on the effect and efficiency of mitigation and adaptation measures. Large fires will occur at a higher rate than currently because of lower fuel moisture content resulting in a lower resistance to burning. This is also evidenced by more extreme fire behavior that contributes to higher economic impacts, suppression difficulties and suppression costs. The economic susceptibility concept integrates a set of economic valuation approaches for valuing timber and non-timber resources, considering the fire behavior, and as a consequence, the net value changes for each resource. Flame length increased by 4.6% to 15.69%, according to the different future climate scenarios. Climate change is expected to cause widespread changes to economic susceptibility and suppression costs because of higher flame length and fire intensity. Therefore, our outcomes show an increase in the economic susceptibility of Córdoba Province in the medium and long term (2041-2070) between 6.05% and 25.99%, respectively. In addition, we have found an increase between 65.67% and 86.73% in suppression costs in the last decade. The digital version of the economic susceptibility model using Geographic Information Systems improves its operational capabilities enhancing also its dynamism and simplicity to accept modifications and predictions revisions.

Section: Scenariosmentioning

confidence: 83%

Potential Effects of Climate Change on Fire Behavior, Economic Susceptibility and Suppression Costs in Mediterranean Ecosystems: Córdoba Province, Spain

Molina

González-Cabán

Silva

2019

“…Second, in order to improve efficiency of resource use, we must first be able to describe our response. Yet our currently limited understanding of both resource use and suppression effectiveness [48,49] challenges the utility of this management action. Further, resource movement and prepositioning is an important consideration for efficiency questions.…”

Section: Resultsmentioning

confidence: 99%

Fire Suppression Resource Scarcity: Current Metrics and Future Performance Indicators

Belval

Stonesifer

Calkin

2020

Wildland fire occurrence is highly variable in time and space, and in the United States where total area burned can vary substantially, acquiring resources (firefighters, engines, aircraft, etc.) to respond to fire demand is an important consideration. To determine the composition and scale of this set of suppression resources managers may utilize data produced by past supply and demand information. The key challenge with this approach is that there is currently no clear system of record to track suppression resource supply and demand, and there are potential pitfalls within existing systems that may provide misleading information regarding the true levels of resource scarcity. In this manuscript, we investigate the issue of resource scarcity by examining two key resources that operations personnel have identified as both high value and scarce: type 1 firefighting crews and large airtankers. We examine data from the Resource Ordering and Status System and analyze the level of resource scarcity indicated by these data over the 2014-2018 fire seasons. We focus on data metrics with potential utility for managers responsible for annual national-level decisions regarding crew and airtanker acquisition; some of these metrics are already used to inform such decisions. We examine the limitations of each metric and suggest new metrics that could more accurately reflect true resource use and scarcity. Such metrics could lead to a substantially improved decision-making process.Forests 2020, 11, 217 2 of 20 experiences at least one period of severe fire activity during which fire suppression resources are scarce, resulting in some unfilled requests for fire suppression support, e.g., [2].Balancing the demand for fire suppression resources with budgetary constraints is an ongoing Agency priority for the USFS [3,4]. Fire suppression resource use comprises a large portion of USFS fire suppression expenses, and rising fire expenditures associated with changes in wildfire occurrence and management issues continue to challenge the Agency [5]. Currently, USFS fire suppression expenditures (2.6 billion US Dollars in Fiscal Year 2018; see [6]) represent nearly two-thirds of the total Agency budget, up from 16% in 1995 [5]; this increase in fire expenditures has hindered other Agency priorities such as protection of watersheds and cultural resources, maintenance of current programs and infrastructure for a range of multiple uses, benefits, and ecosystem services provided by the National Forests, and other forestry research and technical assistance. A long-sought "fire funding fix" was approved by the US Congress in 2018 to address the negative effects of increasing wildland fire suppression expenditures on the Agency's non-fire programs. The Wildfire Suppression Funding and Forest Management Activities Act of the 2018 Omnibus Spending Bill stabilizes Agency spending across all programs, but it requires additional accountability and related efficiency metrics reporting on fire system performance in order to maintain lawmaker support ...

“…Equations (1)-(10) rely upon the crew work time equations (Equations (11)- (14) presented in the next section) to function properly; the timing and ordering set by those equations ensures that the model does not allow crews to work on nodes in stages after the crew has left the node or before the crew has entered the node.…”

Section: Spatial Crew Path Constraintsmentioning

confidence: 99%

“…Equations (11)- (14) limit the work time completed in each stage to no longer than the stage lasts. A crew may spend up to the entire stage working on a node under two circumstances: (1) if they are leaving node j for node i exactly at the start of stage m (Equation 11) or (2) if they started the stage already at node i (Equation 12and (13)).…”

Section: Crew Arrival and Work Timesmentioning

confidence: 99%

“…For example, many large fires include contingency line which is built in case primary line fails [12]. While empirical research regarding fireline effectiveness is limited [13,14], fire managers do consider expected and worst-case fire behavior when considering where suppression actions are most likely to successfully control the fire [13]. Thus, for a model to adequately account for suppression-fire interactions, such activities must be modeled over time and interact dynamically with fire behavior.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Ground Firefighting Resource Activities to Manage Risk Given Uncertain Weather

Belval

Wei

Bevers³

2019

Wildland firefighting requires managers to make decisions in complex decision environments that hold many uncertainties; these decisions need to be adapted dynamically over time as fire behavior evolves. Models used in firefighting decisions should also have the capability to adapt to changing conditions. In this paper, detailed line construction constraints are presented for use with a stochastic mixed integer fire growth and behavior program. These constraints allow suppression actions to interact dynamically with stochastic predicted fire behavior and account for many of the detailed line construction considerations. Such considerations include spatial restrictions for fire crew travel and operations. Crew safety is also addressed; crews must keep a variable safety buffer between themselves and the fire. Fireline quality issues are accounted for by comparing control line capacity with fireline intensity to determine when a fireline will hold. The model assumes crews may work at varying production rates throughout their shifts, providing flexibility to fit work assignments with the predicted fire behavior. Nonanticipativity is enforced to ensure solutions are feasible for all modeled weather scenarios. Test cases demonstrate the model’s utility and capability on a raster landscape.