Seasonal forecasting of fire over Kalimantan, Indonesia

Abstract

Abstract. Large-scale fires occur frequently across Indonesia, particularly in the southern region of Kalimantan and eastern Sumatra. They have considerable impacts on carbon emissions, haze production, biodiversity, health, and economic activities.In this study, we demonstrate that severe fire and haze events in Indonesia can generally be predicted months in advance using predictions of seasonal rainfall from the ECMWF System 4 coupled ocean-atmosphere model. Based on analyses of long, up-to-date series observations on burnt area, rainfall, and tree cover, we demonstrate that fire activity is negatively correlated with rainfall and is positively associated with deforestation in Indonesia. There is a contrast between the southern region of Kalimantan (high fire activity, high tree cover loss, and strong non-linear correlation between observed rainfall and fire) and the central region of Kalimantan (low fire activity, low tree cover loss, and weak, non-linear correlation between observed rainfall and fire).The ECMWF seasonal forecast provides skilled forecasts of burnt and fire-affected area with several months lead time explaining at least 70 % of the variance between rainfall and burnt and fire-affected area. Results are strongly influenced by El Niño years which show a consistent positive bias. Overall, our findings point to a high potential for using a more physical-based method for predicting fires with several months lead time in the tropics rather than one based on indexes only. We argue that seasonal precipitation forecasts should be central to Indonesia's evolving fire management policy.

Long‐Lead Prediction of the 2015 Fire and Haze Episode in Indonesia

“…This is consistent with previous dry season precipitation forecast skill evaluations over longer time periods, which showed strong correlations at lead times of up to 4 months (Zhang et al, ) over roughly the same region. For regions within Indonesia, the high DC in September and October was seen with up to 180 days lead over Southern New Guinea and Southern Kalimantan, consistent with Spessa et al's () precipitation analysis over 1997–2010 and with a substantially lower 60 day lead time over Southern Sumatra.…”

“…Our interest was in determining how far in advance, and with how much regional detail, dangerously dry conditions in 2015 captured by the DC could have been anticipated using seasonal forecasts from a fully coupled atmosphere‐ocean model. This study builds upon previous work showing a high degree of predictability using lagged sea surface temperature indices for fire activity over Kalimantan (Ceccato et al, ; Wooster et al, ) and over the whole of Indonesia as part of a global analysis (Chen et al, ) and a retrospective study of the 1997–2010 precipitation forecast skill over Kalimantan at a 3 month lead time using the European Centre for Medium‐Range Weather Forecasts System 4 seasonal forecast model (Spessa et al, ). Operationally, the DC (and all FDRS components) can be forecast using the daily output from numerical weather prediction models at different lead times.…”

Preventing fires and haze in Southeast Asia

“…Such a fire danger rating system has already been developed for Indonesia and Malaysia 27 . This nuanced approach, and firefighting preparedness, can also be supported by fire risk forecasting, which can alert people about severe fire and haze events months in advance 28 . When the fire risk is too high, the government would need to have in place a system that allows farmers to access mechanical land clearing 29 .…”

Development of a Global Fire Weather Database

“…We note, however, that the apparently strong differences between data sets reflect a narrower DC scale and should not be over-interpreted. El-Niño-induced droughts are a recurrent feature of the region, and hence, inter-annual variability in rainfall across the regions is high (van der Werf et al, 2008;Field and Shen, 2008;Field et al, 2009;Spessa et al, 2015). As such, there is considerable variation surrounding the long-term mean monthly DC values shown in Fig.…”