Fuel treatment effectiveness at the landscape scale: a systematic review of simulation studies comparing treatment scenarios in North America
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Notice bibliographique
Résumé
Abstract Background The risk of destructive wildfire on fire-prone landscapes with excessive fuel buildup has prompted the use of fuel reduction treatments to protect valued resources from wildfire damage. The question of how to maximize the effectiveness of fuel reduction treatments at landscape scales is important because treating an entire landscape may be undesirable or unfeasible. We reviewed 86 simulation studies that examined landscape-scale fuel reduction treatment effectiveness for landscapes of the USA or Canada. Each of these studies tested effects of fuel reduction treatments on wildfire through comparisons of landscape scenarios differing by treatment design or other attributes. Results from these studies were summarized to assess what they reveal about factors determining fuel treatment effectiveness at landscape scales. Results Qualifying studies focused primarily but not exclusively on forested landscapes of the western USA and ranged in size from 200 to 3,400,000 ha. Most studies showed that scenarios with fuel reduction treatments had lower levels of wildfire compared to untreated scenarios. Damaging wildfire types decreased while beneficial wildfire increased as a result of treatments in most cases where these were differentiated. Wildfire outcomes were influenced by five dimensions of treatment design (extent, placement, size, prescription, and timing) and other factors beyond the treatments (weather, climate, fire/fuel attributes, and other management inputs). Studies testing factorial combinations showed that the relative importance of these factors varied across landscapes and contexts. Conclusions Simulation studies have highlighted general principles of effective fuel treatment design at landscape scales, including the desirability of treating extensive areas with appropriate prescriptions at sufficient frequency to reduce wildfire impacts even under extreme conditions that may be more prevalent in the future. More specific, context-dependent strategies have also been provided, such as a variety of placement schemes prioritizing the protection of different resources. Optimization algorithms were shown to be helpful for determining treatment placement and timing to achieve desired objectives under given constraints. Additional work is needed to expand the geographical scope of these studies, further examine the importance and interactions of driving factors, and assess longer-term effects of fuel reduction treatments under projected climate change.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,005 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle