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Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology
Scientific Reports, Volume: 13, Issue: 1
Swansea University Authors: Tadas Nikonovas, Allan Spessa, Stefan Doerr
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© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International LicenseDownload (7.45MB)
DOI (Published version): 10.1038/s41598-022-27075-0
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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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.
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.
Faculty of Science and Engineering
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.