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Assessing the impact of the Human Development Index on historical trends in the INFERNO fire model

Teixeira, João C.M. ORCID: https://orcid.org/0000-0001-7134-5653; Burton, Chantelle ORCID: https://orcid.org/0000-0003-0201-5727; Kelley, Douglas I. ORCID: https://orcid.org/0000-0003-1413-4969; Folberth, Gerd A. ORCID: https://orcid.org/0000-0002-1075-440X; O'Connor, Fiona M. ORCID: https://orcid.org/0000-0003-2893-4828; Betts, Richard A. ORCID: https://orcid.org/0000-0002-4929-0307; Voulgarakis, Apostolos ORCID: https://orcid.org/0000-0002-6656-4437. 2026 Assessing the impact of the Human Development Index on historical trends in the INFERNO fire model. Earth System Dynamics, 17 (3). 739-767. 10.5194/esd-17-739-2026

Abstract

Fire schemes within Earth System Models capture long-term historical trends in burnt area, but they struggle to reproduce the pronounced decline observed over the past two decades. This study investigates whether the observed decline in global burnt area during 1998–2016 can be better represented in the JULES-INFERNO fire model by introducing a globally uniform dependence on the Human Development Index (HDI) as a proxy for socio-economic fire controls.

This approach substantially reduces regional biases in annual burned area. In Temperate North America, model bias decreases from +735.57 % to +44.46 %, with similarly large reductions in Central America, Southern Hemisphere South America, Europe, and the Middle East. HDI also improves the representation of burned area trends in eight of the 14 GFED4s regions with significant negative trends in observations. However, correcting large positive regional biases removes compensating errors in the original model, leading to a stronger global negative bias, which shifts from −34.35 Mha in JULES-INFERNO to approximately −111 Mha in JULES-INFERNO with the HDI implementation.

Overall, while HDI improves regional performance and better captures observed downward trends in some regions, it also reduces interannual variability and underestimates larger fires. This highlights both the potential and limitations of representing socio-economic influences within fire models using a simplified globally uniform formulation.

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