000132398 001__ 132398
000132398 005__ 20241125101143.0
000132398 0247_ $$2doi$$a10.1016/j.agrformet.2023.109868
000132398 0248_ $$2sideral$$a137621
000132398 037__ $$aART-2023-137621
000132398 041__ $$aeng
000132398 100__ $$0(orcid)0000-0002-0477-0796$$aRodrigues, Marcos$$uUniversidad de Zaragoza
000132398 245__ $$aVpd-based models of dead fine fuel moisture provide best estimates in a global dataset
000132398 260__ $$c2023
000132398 5060_ $$aAccess copy available to the general public$$fUnrestricted
000132398 5203_ $$aDead 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/.
000132398 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000132398 590__ $$a5.6$$b2023
000132398 592__ $$a1.677$$b2023
000132398 591__ $$aFORESTRY$$b3 / 89 = 0.034$$c2023$$dQ1$$eT1
000132398 593__ $$aAgronomy and Crop Science$$c2023$$dQ1
000132398 591__ $$aAGRONOMY$$b8 / 126 = 0.063$$c2023$$dQ1$$eT1
000132398 593__ $$aGlobal and Planetary Change$$c2023$$dQ1
000132398 591__ $$aMETEOROLOGY & ATMOSPHERIC SCIENCES$$b14 / 110 = 0.127$$c2023$$dQ1$$eT1
000132398 593__ $$aForestry$$c2023$$dQ1
000132398 593__ $$aAtmospheric Science$$c2023$$dQ1
000132398 594__ $$a10.3$$b2023
000132398 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000132398 700__ $$aResco de Dios, Víctor
000132398 700__ $$aSil, Ângelo
000132398 700__ $$aCunill Camprubí, Àngel
000132398 700__ $$aFernandes, Paulo M.
000132398 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi.
000132398 773__ $$g346 (2023), 109868 [10 pp.]$$pAgric. for. meteorol.$$tAGRICULTURAL AND FOREST METEOROLOGY$$x0168-1923
000132398 8564_ $$s4756936$$uhttps://zaguan.unizar.es/record/132398/files/texto_completo.pdf$$yVersión publicada
000132398 8564_ $$s2437502$$uhttps://zaguan.unizar.es/record/132398/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000132398 909CO $$ooai:zaguan.unizar.es:132398$$particulos$$pdriver
000132398 951__ $$a2024-11-22-12:03:13
000132398 980__ $$aARTICLE