20 Sep 2021

20 Sep 2021

Review status: this preprint is currently under review for the journal NHESS.

ProbFire: a probabilistic fire early warning system for Indonesia

Tadas Nikonovas1, Allan C. Spessa1, Stefan H. Doerr1, Gareth D. Clay2, and Symon Mezbahuddin3 Tadas Nikonovas et al.
  • 1Department of Geography, Swansea University, Swansea, SA2 8PP, UK
  • 2Department of Geography, University of Manchester, Manchester, M13 9Pl, UK
  • 3Department of Renewable Resources, University of Alberta, Edmonton, T6G 2E3, Canada

Abstract. Recurrent extreme landscape fire episodes associated with drought events in Indonesia pose severe environmental, societal and economic threats. The ability to predict severe fire episodes months in advance would enable relevant agencies and communities more effectively initiate fire preventative measures and mitigate fire impacts. While dynamic seasonal climate predictions are increasingly skilful at predicting fire-favourable conditions months in advance in Indonesia, there is little evidence that such information is widely used yet by decision makers.

In this study, we move beyond forecasting fire risk based on drought predictions at seasonal timescales, and (i) develop a probabilistic early fire warning system for Indonesia (ProbFire) based on multilayer perceptron model using ECMWF SEAS5 dynamic climate forecasts together with forest cover, peatland extent and active fire datasets that can be operated on a standard computer, (ii) benchmark the performance of this new system for the 2002–2019 period, and (iii) evaluate the potential economic benefit such integrated forecasts for Indonesia.

ProbFire's event probability predictions outperformed climatology-only based fire predictions at three to five-month lead times in south Kalimantan, south Sumatra and south Papua. In central Sumatra, an improvement was observed only at one month lead time, while in west Kalimantan seasonal predictions did not offer any additional benefit over climatology only-based predictions. We (i) find that seasonal climate forecasts coupled with the fire probability prediction model confer substantial benefits to a wide range of stakeholders involved in fire management in Indonesia and (ii) provide a blueprint for future operational fire warning systems that integrate climate predictions with non-climate features.

Tadas Nikonovas et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-245', Anonymous Referee #1, 26 Sep 2021
    • AC1: 'Reply on RC1', Tadas Nikonovas, 24 Nov 2021
  • RC2: 'Comment on nhess-2021-245', Anonymous Referee #2, 15 Oct 2021
    • AC2: 'Reply on RC2', Tadas Nikonovas, 24 Nov 2021

Tadas Nikonovas et al.

Tadas Nikonovas et al.


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Short summary
Extreme fire episodes in Indonesia emit large amounts of green house gasses and have negative effects on human health in the region. In this study we show that such burning events can be predicted several month in advance in large parts of Indonesia using existing seasonal climate forecasts and forest cover change datasets. A reliable early fire warning system would enable local agencies to prepare and mitigate the worst of the effects.