There is presently no wildfire model developed for Swedish conditions, only a
fire danger rating system (FWI) has been developed for Swedish conditions.
The demand for a wildfire model has not been great in the past in Sweden but
the climate changes now taking place increases the risk of large and intensive
wildfires in Sweden. The need for additional and better tools for sizing-up
wildfires will be in great demand in the future.
This pre-study is aimed at:
- Presenting what has been done in the wildfire modeling field during the years
and mainly the last twenty years.
- Giving recommendations on the continued work with developing a Swedish
wildfire model.
The method that was used was literature and article survey.
The study also looks into the required input data for a wildfire model and the
input data available at the moment. This issue is highly crucial as the quality
of the output of a wildfire model is depending upon the quality of the input
data.
During the study, a primitive wildfire model was constructed and refined in
order to get an insight in the complexities and problems with developing an
operational model.
The following characterization of wildfire models was used during the study:
- Statistical models: based primarily on statistics from earlier or
experimental fires. They do not explicitly consider the controlling physical
processes.
- Semi-empirical models: based on physical laws, but enhanced with some
empirical factors, often by lumping all physical mechanisms for heat transfer
together.
- Physical models: based on physical principles and distinguishing between
physical mechanisms for heat transfer.
The statistical models make no attempt to involve physical processes, as they
are merely a statistical description of test fires. Thus the lack of a physical
basis means that statistical models must be used carefully outside the test
conditions.
Semi-empirical models are often based on conservation of energy principles but
do not make any difference between conduction, convection and radiation heat
transfer.
The semi-empirical model has low computational requirements and includes
variables that are generally easy to measure in the field. So despite the issue
with limited accuracy, the speed and simplicity of these models make them
useful for operational use.
Physical models have the advantage that they are based on known relationships
and thus facilitating their scaling. Thus we can expect that physical models
would provide the most accurate predictions and have the widest applicability.
But the work on physical models is suffering of for example the lack of
understanding of several processes, such as the characterization of the
chemical processes taking place during combustion, the resulting flame
characteristics and the isolation and quantification of physical processes
governing heat transfer.
The input data available today are generally not detailed enough for physical
models. As a result, a very detailed physical model will still only give
imprecise predictions. As better and more detailed input will be available, the
use of physical models will be more justified.
A semi-empirical model is recommended being developed in Sweden. This
conclusion is based upon the following factors:
- The accuracy of a semi-empirical model is generally much better than for a
statistical model, also the use of a semi-empirical model is much wider than
the use of a statistical model.
- The amount of work required for developing a semi-empirical model will not
differ much from the amount of work required for a statistical model. In both
cases a number of test fires will have to be conducted to define and calibrate
a number of fuel models representative of Sweden.
- Presently the performance and application of physical models is not at an
acceptable level (due to for example the complexity which they are to model and
the computational capabilities of the PC’s of today) for operational use.
The semi-empirical model for Sweden is recommended to be built upon Swedish
conditions (i.e. built upon the type of vegetation found in Sweden) instead of
trying to retrofit the local Swedish conditions into an existing model. This
would most likely give the best output for Swedish conditions.
A system for better input data - weather and fuel data – should be worked on as
well. This could for example take ...
