Abstract
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 to 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 a multilayer perceptron model using ECMWF SEAS5 (fifth-generation seasonal forecasting system) 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 of such integrated forecasts for Indonesia. ProbFire's event probability predictions outperformed climatology-only based fire predictions at 2- to 4-month lead times in south Kalimantan, south Sumatra and south Papua. In central Sumatra, an improvement was observed only at a 0-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.
Highlights
Recurrent severe fires in Indonesia release globally significant amounts of greenhouse gases and particulate matter into the atmosphere
Previous studies have shown that climate information from current state-of-the-art seasonal forecasting systems can be utilized for seasonal fire prediction in parts of the globe (Turco et al, 2018), including Indonesia (Spessa et al, 2015; Shawki et al, 2017)
While climate is clearly an important driver of fire activity, these climate–fire relationships are modified by human activity across a range of spatial scales, especially in regions undergoing rapid land cover changes such as Indonesia
Summary
Recurrent severe fires in Indonesia release globally significant amounts of greenhouse gases and particulate matter into the atmosphere. Anomalously severe droughts do develop, triggering catastrophic uncontrolled burning events. Two of the biggest such episodes, the 1997–1998 and 2015 events each released 0.81–2.57 (Page et al, 2002) and 0.21–0.53 Tg C (Huijnen et al, 2016; Yin et al, 2016), equivalent to 12 %–40 % and 2 %–5 % of total global carbon emissions for the year, respectively (Boden et al, 2017). Skilful seasonal climate predictions by dynamic forecasting systems (DoblasReyes et al, 2013; Johnson et al, 2019) can potentially be utilized in early warning systems, helping to prepare for and mitigate the worst of the damaging burning events. Relevant non-climatic drivers of fire occurrence have to date not been integrated with seasonal climate predictions, leaving an untapped potential for improving early fire event prediction systems. Evaluation of the potential value of such predictions for the decision makers in the region has not yet been carried out to date
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