Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology

Mezbahuddin, Symon; Nikonovas, Tadas; Spessa, Allan; Grant, Robert F.; Imron, Muhammad Ali; Doerr, Stefan; Clay, Gareth D.

doi:10.1038/s41598-022-27075-0

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Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology

Symon Mezbahuddin, Tadas Nikonovas, Allan Spessa, Robert F. Grant, Muhammad Ali Imron, Stefan Doerr

, Gareth D. Clay

Scientific Reports, Volume: 13, Issue: 1

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

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DOI (Published version): 10.1038/s41598-022-27075-0

Abstract

Soil moisture deficits and water table dynamics are major biophysical controls on peat and non-peat fires in Indonesia. Development of modern fire forecasting models in Indonesia is hampered by the lack of scalable hydrologic datasets or scalable hydrology models that can inform the fire forecasting...

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Published in:	Scientific Reports
ISSN:	2045-2322
Published:	Springer Science and Business Media LLC 2023
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URI:	https://cronfa.swan.ac.uk/Record/cronfa62302
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first_indexed	2023-01-12T14:01:05Z
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spelling	2023-01-30T13:39:22.2701020 v2 62302 2023-01-11 Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology 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 2023-01-11 SGE Soil moisture deficits and water table dynamics are major biophysical controls on peat and non-peat fires in Indonesia. Development of modern fire forecasting models in Indonesia is hampered by the lack of scalable hydrologic datasets or scalable hydrology models that can inform the fire forecasting models on soil hydrologic behaviour. Existing fire forecasting models in Indonesia use weather data-derived fire probability indices, which often do not adequately proxy the sub-surface hydrologic dynamics. Here we demonstrate that soil moisture and water table dynamics can be simulated successfully across tropical peatlands and non-peatland areas by using a process-based eco-hydrology model (ecosys) and publicly available data for weather, soil, and management. Inclusion of these modelled water table depth and soil moisture contents significantly improves the accuracy of a neural network model in predicting active fires at two-weekly time scale. This constitutes an important step towards devising an operational fire early warning system for Indonesia. Journal Article Scientific Reports 13 1 Springer Science and Business Media LLC 2045-2322 12 1 2023 2023-01-12 10.1038/s41598-022-27075-0 Data availability:All model inputs and validation data are publicly available and can be downloaded from data sources (web links and/or published paper) cited within the main manuscript and the supplementary materials. COLLEGE NANME Geography COLLEGE CODE SGE Swansea University External research funder(s) paid the OA fee (includes OA grants disbursed by the Library) Tis study forms part of the Towards an Operational Fire Early Warning System for Indonesia (TOFEWSI) project, which was funded through the UK’s National Environment Research Council—Newton Fund (Grant NE/P014801/1) on behalf of UK Research & Innovation as well as through the Indonesia Endowment Fund for Education and the Indonesian Science Fund. 2023-01-30T13:39:22.2701020 2023-01-11T10:02:42.7792766 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Symon Mezbahuddin 1 Tadas Nikonovas 2 Allan Spessa 3 Robert F. Grant 4 Muhammad Ali Imron 5 Stefan Doerr 0000-0002-8700-9002 6 Gareth D. Clay 7 62302__26270__94cd1e37d60342d9b7ed1af67edb99ae.pdf 62302.pdf 2023-01-12T14:01:53.0084682 Output 7808226 application/pdf Version of Record true © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
title	Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology
spellingShingle	Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology Tadas Nikonovas Allan Spessa Stefan Doerr
title_short	Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology
title_full	Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology
title_fullStr	Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology
title_full_unstemmed	Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology
title_sort	Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology
author_id_str_mv	940b37dbdcb6896884af0887808b089c 4cd392e40ebc82bd13c0117d07b28d81 575eb5094f2328249328b3e43deb5088
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author	Tadas Nikonovas Allan Spessa Stefan Doerr
author2	Symon Mezbahuddin Tadas Nikonovas Allan Spessa Robert F. Grant Muhammad Ali Imron Stefan Doerr Gareth D. Clay
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description	Soil moisture deficits and water table dynamics are major biophysical controls on peat and non-peat fires in Indonesia. Development of modern fire forecasting models in Indonesia is hampered by the lack of scalable hydrologic datasets or scalable hydrology models that can inform the fire forecasting models on soil hydrologic behaviour. Existing fire forecasting models in Indonesia use weather data-derived fire probability indices, which often do not adequately proxy the sub-surface hydrologic dynamics. Here we demonstrate that soil moisture and water table dynamics can be simulated successfully across tropical peatlands and non-peatland areas by using a process-based eco-hydrology model (ecosys) and publicly available data for weather, soil, and management. Inclusion of these modelled water table depth and soil moisture contents significantly improves the accuracy of a neural network model in predicting active fires at two-weekly time scale. This constitutes an important step towards devising an operational fire early warning system for Indonesia.
published_date	2023-01-12T04:21:48Z
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Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology

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