A Physical Based Forest Fuel Moisture Scheme for High-Resolution Fire Modeling

Forest fuel moisture is one of the important factors for estimating fire weather index, fire and smoke behavior, and energy and pollutant release. A traditional technique used by major operational fire danger rating systems such as the US National Fire Danger Rating System (NFDRS) and the Canadian Forest Fire Danger Rating System (CFFDRS) is to calculate fuel moisture of live and dead vegetation based on empirical relations with meteorological conditions. The limitations with this technique include relatively small spatial resolution of observations, unavailability over part of forest regions, and uncertainties in the empirical relations. Remote sensing (RS) has emerged as a useful technique that provides high-resolution information on forest moisture conditions. RS detection however has low frequency and is currently only applicable to the live (upper) forest layer. Mesoscale meteorological models such as MM5 have been recently applied to fire research. Their ability in providing high-resolution atmospheric conditions and land-surface energy and water exchanges in both space and time makes it possible to estimate fuel moisture using physcical based schemes for various ecosystems. This study presents a fuel moisture model for high-resolution forest fire modeling. The model, called Forest Fuel Moisture Scheme (FFMS), is developed based on the energy and water conservations of the forest ecosystem and driven by the atmospheric conditions simulated by mesoscale models.