Wildland Fire Smoke and Emissions Tradeoff Decision Support
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».