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Linking Fuel Moisture with Plant Physiology: Coupling a water balance model with a LFMC model to predict species-specific LFMC values.
* 1 , 1 , 2 , 3
1  Mathematical and Fluid Physics Department, Faculty of Sciences, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, Spain
2  Department of Crop and Forest Sciences, Universitat Lleida, 25198 Lleida, Spain
3  School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang, 621010, China
Academic Editor: Lotus Guo (registering DOI)

Live Fuel Moisture Content (LFMC) is a critical determinant of forest flammability and thus fire behavior and severity in many ecosystems. Currently, LFMC is monitored through satellite remote sensing or inferred from drought indices. However, remote sensing only can estimate LFMC in real-time, and cannot be used to make future flammability predictions. On the other hand, drought indices lack to reliably quantify flammability outside the ecosystems for which they are calibrated and requires species-specific calibrations.

In this context we seek to couple Medfate (Caceres et al., 2015) a water balance model which uses meteorological, edaphic and forest inventory data to predict soil moisture dynamics, with Nolan et al., (2018) LFMC model in order to determine species specific LFMC values. To achieve it LFMC simulations were calibrated and validated using field data from an independent LFMC data base (Yebra et al., 2019). In all, we have analyzed more than 2250 LFMC data from seventeen different genus in 40 sites of the Iberian Peninsula, obtaining satisfactory results.

In conclusion we have linked fuel moisture with plant physiology to estimate LFMC values in a way that allow to make future predictions and to obtain species specific values.

Keywords: WildFire; Drought; Flammability; Life Fuel Moisture Content; Modelling