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Record W4392759699 · doi:10.5194/egusphere-egu24-14446

Wildland Fire Smoke and Emissions Tradeoff Decision Support

2024· preprint· en· W4392759699 on OpenAlexaboutno aff
Laurel Sindewald, S. P. Urbanski, Karin L. Riley, Christopher Eckerson, Alex W. Dye, Rachel Houtmann

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsSmokeEnvironmental scienceMeteorologyGeography

Abstract

fetched live from OpenAlex

In 2023, 6,551 wildfires across Canada burned 184,961 km2 of the landscape—about 5% of Canadian forests—emitting nearly 480 megatonnes of carbon, with emissions leading to air quality warnings as far away as Washington DC, USA. In early June, the air quality index in New York City was over 400, and by mid-June, smoke plumes passed above Europe. As wildland fires of increasing severity occur with increasing frequency, driven by global climate change and decades of fire suppression, societies near and far from high-risk ecosystems face increased exposure to wildfire emissions that may have both acute and long-term health impacts. Prescribed fire interventions show promise for reducing the risk of large wildfires in fire-prone ecosystems, but implementing prescribed fire can be difficult, in part due to concerns about the potential health impacts of prescribed fire smoke on nearby communities. To provide decision support for land managers aiming to reduce wildfire risk with prescribed fire treatments, we will produce a geospatial database of daily pollutant emissions and fire intensity from simulations of prescribed and wildland fires over a 20-year period for: 1) a baseline scenario of no management actions, 2) one or more scenarios of prescribed fire locations and timing based on interaction with tribes and Okanogan-Wenatchee National Forest (OWNF) managers, and 3) scenarios of prescribed fire locations and timing based on fire paths, locations of highly valued resources, areas available and suitable for treatment, determined by the research team. We can accomplish this by iterating between FSim, the Large Fire Simulator, which stochastically simulates large wildfire ignition and spread across a LANDFIRE fuels landscape, and FFE-FVS, the Forest Vegetation Simulator with the Fire and Fuels Extension, which simulates post-fire regeneration, forest growth, management actions including prescribed fire, fuel dynamics, and fuel consumption and pollutant emissions from prescribed fires and wildfires. Because FSim takes a Monte Carlo approach, simulating fires over 10,000 or more hypothetical fire seasons comprised of daily weather sequences, we will be able to estimate the probability of each landscape pixel burning in a wildfire and the conditional probability of that pixel burning at different flame lengths, allowing us to provide emissions estimates within a risk-assessment framework for managers. The framework will allow land managers to quantify the likelihood that smoke impacts from near-term prescribed fire treatments will be offset by reductions in severe smoke events from future wildfires. Additionally, the smoke event geospatial datasets may provide input into atmospheric transport models which could be used to simulate regional to national scale smoke impacts. We will pilot the project in Okanogan-Wenatchee National Forest, Washington, USA, working with the forest’s managers to design fuel treatment scenarios that will yield realistic fire occurrence trajectories and emission estimates to inform near-term prescribed fire operations. As a U.S. Federal Bipartisan Infrastructure Law Research & Development “proof of concept” project, the Wildland Fire Smoke and Emissions Tradeoff Decision Support project will inform U.S. Forest Service management policy and strategy around the use of prescribed fire in other National Forests in the U.S.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0180.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.258
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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