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ProbFire: a probabilistic fire early warning system for Indonesia

Tadas Nikonovas, Allan Spessa, Stefan Doerr Orcid Logo, Gareth D. Clay, Symon Mezbahuddin Orcid Logo

Natural Hazards and Earth System Sciences, Volume: 22, Issue: 2, Pages: 303 - 322

Swansea University Authors: Tadas Nikonovas, Allan Spessa, Stefan Doerr Orcid Logo

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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...

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Published in: Natural Hazards and Earth System Sciences
ISSN: 1684-9981
Published: Copernicus GmbH 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa59304
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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&#x2013;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. 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spelling 2022-08-05T11:05:08.1222099 v2 59304 2022-02-04 ProbFire: a probabilistic fire early warning system for Indonesia 940b37dbdcb6896884af0887808b089c Tadas Nikonovas Tadas Nikonovas true false 4cd392e40ebc82bd13c0117d07b28d81 Allan Spessa Allan Spessa true false 575eb5094f2328249328b3e43deb5088 0000-0002-8700-9002 Stefan Doerr Stefan Doerr true false 2022-02-04 SGE 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 Journal Article Natural Hazards and Earth System Sciences 22 2 303 322 Copernicus GmbH 1684-9981 Fire, Extreme Landscape, Indonesia, Early warning system 4 2 2022 2022-02-04 10.5194/nhess-22-303-2022 COLLEGE NANME Geography COLLEGE CODE SGE Swansea University External research funder(s) paid the OA fee (includes OA grants disbursed by the Library) This research has been supported by the UK’s National Environment Research Council – Newton Fund on behalf of UK Research & Innovation (grant no. NE/P014801/1). NE/P014801/1 2022-08-05T11:05:08.1222099 2022-02-04T14:17:49.4665825 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Tadas Nikonovas 1 Allan Spessa 2 Stefan Doerr 0000-0002-8700-9002 3 Gareth D. Clay 4 Symon Mezbahuddin 0000-0001-9341-4023 5 59304__22303__292a253b9090471290eeb83c68043a80.pdf 2022_Nikonovas etal_ProbFire_Indonesia_NHESS.pdf 2022-02-04T14:22:25.6824120 Output 7338083 application/pdf Version of Record true © Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License. true eng https://creativecommons.org/licenses/by/4.0/
title ProbFire: a probabilistic fire early warning system for Indonesia
spellingShingle ProbFire: a probabilistic fire early warning system for Indonesia
Tadas Nikonovas
Allan Spessa
Stefan Doerr
title_short ProbFire: a probabilistic fire early warning system for Indonesia
title_full ProbFire: a probabilistic fire early warning system for Indonesia
title_fullStr ProbFire: a probabilistic fire early warning system for Indonesia
title_full_unstemmed ProbFire: a probabilistic fire early warning system for Indonesia
title_sort ProbFire: a probabilistic fire early warning system for Indonesia
author_id_str_mv 940b37dbdcb6896884af0887808b089c
4cd392e40ebc82bd13c0117d07b28d81
575eb5094f2328249328b3e43deb5088
author_id_fullname_str_mv 940b37dbdcb6896884af0887808b089c_***_Tadas Nikonovas
4cd392e40ebc82bd13c0117d07b28d81_***_Allan Spessa
575eb5094f2328249328b3e43deb5088_***_Stefan Doerr
author Tadas Nikonovas
Allan Spessa
Stefan Doerr
author2 Tadas Nikonovas
Allan Spessa
Stefan Doerr
Gareth D. Clay
Symon Mezbahuddin
format Journal article
container_title Natural Hazards and Earth System Sciences
container_volume 22
container_issue 2
container_start_page 303
publishDate 2022
institution Swansea University
issn 1684-9981
doi_str_mv 10.5194/nhess-22-303-2022
publisher Copernicus GmbH
college_str Faculty of Science and Engineering
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hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography
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description 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
published_date 2022-02-04T04:16:30Z
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