Biomass production and fire regimes


Forest age distribution can be seen as a footprint of catastrophic disturbance history. Because of the large variation in annual area burned in Canadian forests, a stable age distribution is unlikely. This is consistent with observations reported in the literature and with forest inventory data.

The results of model investigations into the dynamics of forest age distribution are largely determined by model assumptions, modeling approach, and the inclusion of random effects in the models. Various assumptions made in fire models reflect different definitions of fire processes and different levels of simplification in fire incidents, and thus can lead to different model behavior.

A stable age distribution can be achieved only if age-specific mortality rates are fixed; a small variation in age-specific mortality could lead to much slower or even lack of convergence to a stable age distribution.

Different definitions of fire frequency and fire cycle are interrelated, except for the fire frequency definition based on fire number. The point-based definitions can be seen as special cases of area-based definitions in which the area is reduced to a single site.

Biased estimates of fire frequency and fire cycle could arise from existing methods of estimation. The biased estimates can be a result of sampling design, sample size, and the disturbance history of the study area under investigation.

Two types of spatial models for simulating a fire-spread process can be distinguished: fire event simulators and fire regime simulators.

Fire event simulators are mainly deterministic models that aim to reproduce a fire event in every detail by linking available dynamic weather variables to the formula for calculating fire perimeter locations continuously. The main methods are the Dijkstra labeling algorithm and the application of Huygens' principle.

Fire regime simulators aim to estimate long-term dynamics of fire disturbances. The temporal resolutions were usually coarser than those in the fire event simulators. Major methods include cellular automata, percolation, and a special case of general epidemic processes.

The simulation results are consistent with the general understanding that fire suppression has reduced the number of large fires (defined as larger than 200 hectares), while increasing the mean size of large fires.

Project status

  • On-going

Team members