In tom-locatelli/fgr: R Version of the ForestGALES wind risk model

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UNDERGOING BETA-TESTING. PLEASE DO NOT USE

fgr

fgr is the R release of the process-based wind risk model ForestGALES (see Hale et al., 2015 for a thorough description of the model). ForestGALES was initially developed to calculate the risk of wind damage to unthinned or lightly thinned monospecific conifer plantations in the UK (Nicoll et al., 2006). The model has found favour in the scientific forestry community, and has been subsequently extended to allow wind risk modelling to other species not commercially grown in the UK (e.g. see Kamimura et al., 2008; Moore and Somerville, 1998; Ruel et al., 2000). It has been recently parametrised for Eucalyptus globulus (Locatelli et al., 2016). Current model development projects include incorporating other broadleaves (e.g. Beech, Birch, Oak). ForestGALES was submitted to a Global Sensitivity Analysis (Locatelli et al., 2017). The major recent development of ForestGALES, included in this fgr release, stems from the work of Hale et al. (2012), who developed a method to assess the risk to individual trees within a stand. This approach extends the application of ForestGALES to include heterogeneous stands, the effect of thinning practices on tree spacing, and between-trees competition with the use of competition indices.

The goal of fgr is to provide forest scientists, landscape ecologists, modellers, and forest managers/planners with the ability to assess the vulnerability of forest stands to wind damage as part of their R workflow, and within a FOSS environment. Functions for the calculation of components of airflow modelling over canopies that are used in fgr are made accessible to the user.

The core science and rationale of the ForestGALES wind risk model is thoroughly detailed in Hale et al. (2015). The vignette documentation for the fgr package is provided in html_vignette format to describe, and occasionally clarify, new model developments and some of the science upon which the model is built (tree stability and airflow near forest canopies), detail the mathematical formulas adopted in the simulations, provide examples of fgr applications using the package datasets, and include a small bibliography of the field of forest wind damage. To facilitate reading LaTeX mathematical notation used in the package documentation, the vignettes are also provided as a PDF manual.

In the spirit of cooperation towards model application and development, fgr users are encouraged to provide suggestions for model improvement. We hope you enjoy using the package as much as we do developing ForestGALES and fgr!

Installation

You can install fgr from github with:

# install.packages("devtools")
devtools::install_github("tom-locatelli/fgr", build_vignettes = TRUE)

Note the use of build_vignettes to make sure all the package documentation is installed, as vignette are not automatically built on GitHub.

Example

This is a basic example which shows you how to calculate wind risk through the rotation of a Sitka spruce stand, using the df_rou dataset included in the package. Note that the wrapper fgr function fg_rou is Vectorized to compute multiple lines of input.

## basic example code
library("fgr")
fg_rou_v <- Vectorize(fg_rou)

stand_id <- as.character(df_rou$stand_id)
species <- as.character(df_rou$species)
mean_ht <- df_rou$mean_ht
mean_dbh <- df_rou$mean_dbh
spacing <- df_rou$spacing
full_output = 0
weib_a <- df_rou$weib_a
weib_k <- 1.85

out_rou <- as.data.frame(t(fg_rou_v(stand_id, species, mean_ht, mean_dbh, spacing, full_output, weib_a, weib_k)))
#Note again the use of the vectorised function ("fg_rou_v""), and the transpose ("t") and data frame ("as.data.frame") functions to convert the list outputs of the fg_rou function to a data.frame object suitable for extracting a CSV file.

Bibliography

Hale SE, Gardiner BA, Wellpott A, Nicoll BC and Achim A, 2012. Wind loading of trees: influence of tree size and competition. Eur J For Res 131, 203-217. doi 10.1007/s10342-010-0448-2
Hale, S.A., Gardiner, B.A., Peace, A., Nicoll, B., Taylor, P., Pizzirani, S. 2015. Comparison and validation of three versions of a forest wind risk model. Environmental Modelling & Software, 68, 27-41.
Kamimura, K., Gardiner, B.A., Kato, A., Hiroshima, T., Shiraishi, N. 2008. Developing a decision support approach to reduce wind damage risk - a case study on sugi (Cryptomeria japonica (L.f.) D.Don) forests in Japan. Forestry, 81, 429-445.
Locatelli, T., Gardiner, B.A., Tarantola, S., Nicoll, B., Bonnefond, J.-M., Garrigou, D., Kamimura, K., Patenaude, G. 2016. Modelling wind risk to Eucalyptus globulus (Labill.) stands. Forest Ecology and Management, 365, 159-173.
Locatelli, T., Tarantola, S., Gardiner, B. and Patenaude, G., 2017. Variance-based sensitivity analysis of a wind risk model-Model behaviour and lessons for forest modelling. Environmental Modelling & Software, 87, pp.84-109.
Moore, J. and Somerville, A. 1998. Assessing the risk of wind damage to plantation forests in New Zealand. New Zealand Forestry, 43, 25-29.
Nicoll, B.C., Gardiner, B.A., Rayner, B., Peace, A.J. 2006. Anchorage of coniferous trees in relation to species, soil type, and rooting depth. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere, 36, 1871-1883.
Ruel, J.C., Quine, C.P., Meunier, S., Suarez, J. 2000. Estimating windthrow risk in balsam fir stands with the ForestGALES model. Forestry Chronicle, 76, 329-337.