Towards a Fire Early Warning System for Indonesia (ToFEWSI)
Notice bibliographique
Résumé
The severe El Niño episode of 2015 led to a major and damaging increase in Indonesian peatland fire, highlighting an urgent need to develop operational systems to forecast potentially severe fire events to mitigate the impacts of fire and haze. A total of 10 ASEAN states have formally agreed to control peatland and forest fires and urgently need a fire ‘early warning’ system. An operational early warning system for forecasting dangerous burning conditions is within reach using state-of-the-art modelling tools, such as the ECMWF’s System 5 seasonal forecast model, but is currently hampered by insufficient knowledge about the influence of fluctuations in peat moisture on fire, particularly during periods of extreme drought (e.g. 1997-98 and 2015 El Niño episodes - the strongest and second strongest on record). Most present-day fires in Indonesia result from deliberate burning for land clearance, and this human factor means that burning can be influenced by policy and altered land management practice.<br/><br/>In this paper, we present an overview of the ToFEWSI project, which started in late 2017 and is funded by the UK’s National Environment Research Council (NERC) and the Indonesia Endowment for Education (LPDP). We plan to both develop a new scientific forecasting tool for fire danger and to influence policy and fire regulations – a novel combination of urgent science and policy research. ToFEWSI will develop a suite of climate-, hydrological- and agent-based modelling tools at landscape to regional-scales to predict the incidence of peat fires for the period 1997 to 2015. It builds on previously published seasonal fire forecasting and global fire weather database work. Agent-based modelling will be employed to help simulate the complex array of bio-physical, socio-economic and cultural factors that drive observed fire activity. While focusing on the tropical peatlands of Riau province, Sumatra, we will also undertake broader analysis covering Sumatra, Kalimantan and West Papua – the three main regions experiencing unsustainable burning and deforestation in Indonesia.<br/><br/>Emissions from peatland fires is a recurrent problem often causing severe environmental and health impacts at local to global scales. These problems are projected to worsen under business-as-usual policies/governance due to: i) a likely increase in the frequency of extreme El Niño events under future climate change; ii) a growing regional population, and iii) increased demand for rainforest timber, pulp and paper, palm oil and rice. Therefore, ToFEWSI will deliver a new early warning system for fires in Indonesian peatlands and a scientifically-based policy framework for the control of such fires and their atmospheric emissions. ToFEWSI will analyse and develop evidence-based policies to address non-climate factors driving fire under extreme events, as these present the most tractable means to develop sustainable mitigation actions at village and community levels. Furthermore, ToFEWSI will help Indonesia to meet its commitments under the Paris Climate Agreement on carbon emissions reduction
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 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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».