Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology
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 fo...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-27075-0 |
| _version_ | 1797952547722362880 |
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| author | Symon Mezbahuddin Tadas Nikonovas Allan Spessa Robert F. Grant Muhammad Ali Imron Stefan H. Doerr Gareth D. Clay |
| author_facet | Symon Mezbahuddin Tadas Nikonovas Allan Spessa Robert F. Grant Muhammad Ali Imron Stefan H. Doerr Gareth D. Clay |
| author_sort | Symon Mezbahuddin |
| collection | DOAJ |
| description | 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 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. |
| first_indexed | 2024-04-10T22:49:18Z |
| format | Article |
| id | doaj.art-5b5e016ee72c47739670ade5380002ee |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-10T22:49:18Z |
| publishDate | 2023-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-5b5e016ee72c47739670ade5380002ee2023-01-15T12:08:36ZengNature PortfolioScientific Reports2045-23222023-01-0113111010.1038/s41598-022-27075-0Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrologySymon Mezbahuddin0Tadas Nikonovas1Allan Spessa2Robert F. Grant3Muhammad Ali Imron4Stefan H. Doerr5Gareth D. Clay6Department of Renewable Resources, University of AlbertaDepartment of Geography, Centre for Wildfire Research, Swansea UniversityDepartment of Geography, Centre for Wildfire Research, Swansea UniversityDepartment of Renewable Resources, University of AlbertaFaculty of Forestry, Universitas Gadjah MadaDepartment of Geography, Centre for Wildfire Research, Swansea UniversityDepartment of Geography, School of Environment, Education and Development, University of ManchesterAbstract 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.https://doi.org/10.1038/s41598-022-27075-0 |
| spellingShingle | Symon Mezbahuddin Tadas Nikonovas Allan Spessa Robert F. Grant Muhammad Ali Imron Stefan H. Doerr Gareth D. Clay Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology Scientific Reports |
| title | 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_short | 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 |
| url | https://doi.org/10.1038/s41598-022-27075-0 |
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