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

Symon Mezbahuddin, Tadas Nikonovas, Alan Spessa, Robert Grant, Muhammad Imron Ali, Stefan H. Doerr, Gareth Clay

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number619
JournalScientific Reports
Issue number1
Publication statusPublished - 12 Jan 2023


  • Fires
  • Hydrology
  • Indonesia
  • Soil
  • Weather


Dive into the research topics of 'Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology'. Together they form a unique fingerprint.

Cite this