11/22/2009. Insect outbreaks and fires are nonindependent. Chad Hoffman. BB primary influence on fire is through mortality (state change)

Transcription

1 and Fire Interactions Insect and Disease effects on fire behavior: Implications for crown fire hazard Insect outbreaks and fires are nonindependent forms of disturbance Insect outbreaks may affect fire hazard Forest fires may affect tree vulnerability to insects Chad Hoffman Image by: William M. Ciesla, Forest Health Management International, ugwood.org Image by: Dale Wade, Rx Fire Doctor, ugwood.org Crown fire hazard Crown fire hazard is the combination of: Surface fire transition and Crown fire spread We are interested in the set of conditions which h lead to these two events ffect of bark beetles on fire primary influence on fire is through mortality (state change) ackground Crown fire hazard through time Why use simulation models? No studies of fire behavior in bark beetle infested stands Large expenses and risks of conducting experiments Models allow us to investigate the dynamics of combustion in a way that would be very costly as large-scale experiments 1

2 Modeling fire behavior Empirical and semi empirical models Rothermel family of models ehave, Farsite, NEXUS, FFE_FVS Surface fire rate of spread (Rothermel 1972) Fireline intensity (yram 1959) Crown fire rate of spread (Rothermel 1991) Crown fire initiation (Van Wagner 1977) Empirical and Semi-empirical Models Point process predictions, which assume homogeneous weather, fuels and topography and no fire or fuel interactions with the atmosphere Physical models Wildland fire dynamics simulator (NIST) Firetec (Los lamos) Case Study 1 ark beetle influences on fire hazard in ponderosa pine (Hoffman et al. in prep) 37 sets of paired mortality/non-mortality plots across a range of elevations, national forests Fire behavior modeling conducted with NEXUS (Scott 1999) Simulation 1 Canopy fuel load varied Simulation 2 #1 plus surface fuels adjusted based on field measurements Simulation 3 #2 plus mid-flame wind speed increased based on basal area Case Study 1 (Hoffman et al. in prep) Key findings from field data: CH increased in mortality plots Crown fuel decreased in mortality plots Case Study 1 (Hoffman et al. in prep) Case study 1 (Hoffman et al in prep) Limited to investigating the effects of on fire behavior for: Periods of time in which there are no dead fuels in the canopy Where fuels are relatively homogeneous 2

3 Physics-based models WFDS (Mell et al 25) Is a computational fluid dynamics (CFD) model of fire driven fluid flow which solves a form of the Navier-Stokes equation in time on a 3- dimensional grid WFDS Fuels Fuels are described in x,y,z space ulk density Moisture content Surface area to volume asically these types of models ensure that mass, momentum and energy are conserved through time. WFDS Effects of vegetation on wind flow are simulated WFDS Fire atmosphere interactions are simulated Case study 2 (Hoffman et al on going) How do level of mortality and spatial arrangement influence crown fire hazard 7 field locations from the Deschutes National Forest and Salmon-Challis National Forest Case study 2 (Hoffman et al. on going) Site # asal rea (m2/ha) TPH SDI RDI QMD (cm) Height (m) Mean CH 2% CH (m) (m)

4 ) 11/22/29 Results Results 12 Percent Consu umed C D DE E Max FLI (kw/m) DE E C EF F Increase in FLI (% %) C C D Example Simulations Stand is from Central Oregon on the Deschutes National Forest Stand Properties 415 TP in QMD 12.7 ft mean CH (5 FT 2% CH) Findings Case Study II The amount of canopy fuels consumed, Fire line intensity and maximum fire line intensity increase as the amount of dead trees increase Discussion The two approaches provide a range of strategies to investigate how influence fire behavior oth approaches have advantages and limitationsit ti Data requirements Computational resources User communities You need to understand the disturbance agents to understand the interactions! Where do we go from here? More work is needed! Investigating additional time periods Investigating other surface fire regimes Data sets to validate the models etter fuels data (representation and collection) etter fuels data (representation and collection) Stand responses to outbreaks Landscape-scale assessments Implication for carbon dynamics 4

5 Collaborators Questions Joel McMillian R3 FHP Carolyn Sieg USD FS RMRS Pete Fulé Northern rizona University Helen Maffei R6 FHP Penny Morgan University of Idaho Ruddy Mell NIST Russ Parsons USD FS RMRS Steve Cook University of Idaho Francisco Rego Institutor Superior de gronomia, Lisboa Funding: USD Forest Health Protection STDP program and others. 5