132398 20241125101143.0 doi 10.1016/j.agrformet.2023.109868 sideral 137621 ART-2023-137621 eng Rodrigues, Marcos Universidad de Zaragoza (orcid)0000-0002-0477-0796 Vpd-based models of dead fine fuel moisture provide best estimates in a global dataset 2023 Access copy available to the general public Unrestricted Dead fine fuel moisture content (FM) is one of the most important determinants of fire behavior. Fire scientists have attempted to effectively estimate FM for nearly a century, but we are still lacking broad scale evaluations of the different approaches for prediction. Here we tackle this problem by taking advantage or a recently compiled global fire behavior database (BONFIRE) gathering 1603 records of 1h (i.e., <6 mm diameter or thickness) dead fuel moisture content from measurements before experimental fires. We compared the results of models routinely used by different agencies worldwide, empirical models, semi-mechanistic models and also non-linear and machine learning approaches based on either temperature and relative humidity or vapor pressure deficit (VPD).A semi-mechanistic model based on VPD showed the best performance across all FM ranges and a historical model developed in Australia (MK5) was additionally recommended for low fuel moisture estimations. We also observed significant differences in FM dynamics between vegetation types with FM in grasslands more responsive to changes in atmospheric dryness than woody ecosystems. The addition of computational complexity through machine learning is not recommended since the gain in model fit is small relative to the increase in complexity. Future research efforts should concentrate on predictions at low FM (<10 %) as this is the range most significant for fire behavior and where the poorest model performance was observed. Model predictions are available fromhttps://hcfm.shinyapps.io/shinyfmd/. info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 5.6 2023 FORESTRY 3 / 89 = 0.034 2023 Q1 T1 AGRONOMY 8 / 126 = 0.063 2023 Q1 T1 METEOROLOGY & ATMOSPHERIC SCIENCES 14 / 110 = 0.127 2023 Q1 T1 1.677 2023 Agronomy and Crop Science 2023 Q1 Global and Planetary Change 2023 Q1 Forestry 2023 Q1 Atmospheric Science 2023 Q1 10.3 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Resco de Dios, Víctor Sil, Ângelo Cunill Camprubí, Àngel Fernandes, Paulo M. 3006 010 Universidad de Zaragoza Dpto. Geograf. Ordenac.Territ. Área Análisis Geográfico Regi. 346 (2023), 109868 [10 pp.] Agric. for. meteorol. AGRICULTURAL AND FOREST METEOROLOGY 0168-1923 4756936 http://zaguan.unizar.es/record/132398/files/texto_completo.pdf Versión publicada 2437502 http://zaguan.unizar.es/record/132398/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:132398 articulos driver 2024-11-22-12:03:13 ARTICLE