ArticleScientific reports2023
Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Modelling water table depth variation in Southeast Asia forested peatlands using satellite rainfall data.Scientific reports · 2026Article
- Effective restoration can avoid peatland fires: Large scale counterfactual assessment in Kalimantan, Indonesia.iScience · 2026Article
- Major fires in Indonesian Borneo are possible under all ENSO phases.npj natural hazards · 2026Article
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7 authors.
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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.
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