An Implementation of the Rothermel Fire Spread Model in the R Programming Language

Abstract

This note describes an implementation of the Rothermel fire spread model in the R programming language. The main function, ros(), computes the forward rate of spread at the head of a surface fire according to Rothermel fire behavior model. Additional functions are described to illustrate the potential use and expansions of the package. The function rosunc() carries out uncertainty analysis of fire behavior, that has the ability of generating information-rich, probabilistic predictions, and can be coupled to spatially-explicit fire growth models using an ensemble forecasting technique. The function bestFM() estimates the fit of Standard Fuel Models to observed fire rate of spread, based on absolute bias and root mean square error. Advantages of the R implementation of Rothermel model include: open-source coding, cross-platform availability, high computational efficiency, and linking to other R packages to perform complex analyses on Rothermel fire predictions.

Appendix 1: A Primer on the R Language

A complete introduction to the R language goes beyond the scope of this paper. We will briefly illustrate the meaning of some key terms in order for the reader to understand the examples and data structures referenced in this paper. For an introduction to the R language, tutorials and working examples, refer e.g. to ’An introduction to R’ [40], from which this section is borrowed, and to the documentation available on the CRAN website (URL: http://cran.r-project.org).

The user operates R via commands entered at the prompt ’$\mathtt >$’. Elementary commands consist of either expressions or assignments. Expressions are evaluated, printed (unless specifically made invisible), and the value is lost. An assignment evaluates an expression and passes the value to an object stored in a ’workspace’ for future retrieval. The assignment operator is ’$\mathtt <-$’. R commands are case sensitive; comments can be put almost anywhere, starting with a hashmark (’#’).

R operates on named data structures. The simplest such structure is the vector, which is a one-dimensional entity consisting of an ordered collection of numeric or string elements. To set up a vector named x, say, consisting of five numbers, namely 10.4, 5.6, 3.1, 6.4 and 21.7, use the R command x $\mathtt <$ - c(10.4, 5.6, 3.1, 6.4, 21.7). An R data frame is a two-dimensional entity consisting of rows (i.e., observational units) and columns (i.e., observed variables). Vectors of the same length, for example x and y, can be concatenated to form columns in a data frame named df using the R command df $\mathtt <$ - cbind(x, y). An R list is an object consisting of an ordered collection of other objects, be them vectors, data frames, or other R data structures. List elements are numbered and may be referred to by the subsetting operator [[ ]].

Finally, functions are R objects that evaluate the result of an expression using user-defined arguments. A call to the function usually takes the form function.name (argument1, argument2). The Rothermel package for R operates mainly by some newly programmed functions.