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Abstract
Recurrent extreme landscape fire episodes associated with drought events in Indonesia pose severe environmental, societal and economic threats. The ability to predict severe fire episodes months in advance would enable relevant agencies and communities to more effectively initiate fire-preventative measures and mitigate fire impacts. While dynamic seasonal climate predictions are increasingly skilful at predicting fire-favourable conditions months in advance in Indonesia, there is little evidence that such information is widely used yet by decision makers.
In this study, we move beyond forecasting fire risk based on drought predictions at seasonal timescales and (i) develop a probabilistic early fire warning system for Indonesia (ProbFire) based on a multilayer perceptron model using ECMWF SEAS5 (fifth-generation seasonal forecasting system) dynamic climate forecasts together with forest cover, peatland extent and active-fire datasets that can be operated on a standard computer; (ii) benchmark the performance of this new system for the 2002–2019 period; and (iii) evaluate the potential economic benefit of such integrated forecasts for Indonesia.
ProbFire's event probability predictions outperformed climatology-only based fire predictions at 2- to 4-month lead times in south Kalimantan, south Sumatra and south Papua. In central Sumatra, an improvement was observed only at a 0-month lead time, while in west Kalimantan seasonal predictions did not offer any additional benefit over climatology-only-based predictions. We (i) find that seasonal climate forecasts coupled with the fire probability prediction model confer substantial benefits to a wide range of stakeholders involved in fire management in Indonesia and (ii) provide a blueprint for future operational fire warning systems that integrate climate predictions with non-climate features.
In this study, we move beyond forecasting fire risk based on drought predictions at seasonal timescales and (i) develop a probabilistic early fire warning system for Indonesia (ProbFire) based on a multilayer perceptron model using ECMWF SEAS5 (fifth-generation seasonal forecasting system) dynamic climate forecasts together with forest cover, peatland extent and active-fire datasets that can be operated on a standard computer; (ii) benchmark the performance of this new system for the 2002–2019 period; and (iii) evaluate the potential economic benefit of such integrated forecasts for Indonesia.
ProbFire's event probability predictions outperformed climatology-only based fire predictions at 2- to 4-month lead times in south Kalimantan, south Sumatra and south Papua. In central Sumatra, an improvement was observed only at a 0-month lead time, while in west Kalimantan seasonal predictions did not offer any additional benefit over climatology-only-based predictions. We (i) find that seasonal climate forecasts coupled with the fire probability prediction model confer substantial benefits to a wide range of stakeholders involved in fire management in Indonesia and (ii) provide a blueprint for future operational fire warning systems that integrate climate predictions with non-climate features.
Original language | English |
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Pages (from-to) | 303 - 322 |
Journal | Natural Hazards and Earth System Sciences |
Volume | 22 |
DOIs | |
Publication status | Published - 4 Feb 2022 |
Fingerprint
Dive into the research topics of 'ProbFire: a probabilistic fire early warning system for Indonesia'. Together they form a unique fingerprint.Projects
- 1 Finished
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Towards a Fire Early Warning systm for Indonesia (ToFEWSI)
Clay, G. (PI)
1/10/17 → 30/09/21
Project: Research
Research output
- 2 Article
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Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology
Mezbahuddin, S., Nikonovas, T., Spessa, A., Grant, R., Imron Ali, M., Doerr, S. H. & Clay, G., 12 Jan 2023, In: Scientific Reports. 13, 1, 619.Research output: Contribution to journal › Article › peer-review
Open Access -
Near-complete loss of fire-resistant primary tropical forest cover in Sumatra and Kalimantan
Nikonovas, T., Spessa, A., Doerr, S., Clay, G. & Mezbahuddin, S., 18 Dec 2020, In: Communications Earth & Environment. 1, 65.Research output: Contribution to journal › Article › peer-review
Open Access