Assemble Social-Climate-Fire-shaped daily climate CSV from a BioSIM Arrow dataset

A thin wrapper around assemble_climate_library_file() that rewrites the output Variable column from LANDIS-II Climate Library spellings (Tmin, Tmax, windSpeed, windDirection, RH, SWR) to the lowercase Social-Climate-Fire spellings (mintemp, maxtemp, windspeed, winddirection, rh, swr). The resulting tbl_df - and the CSV it produces via writeClimateData() - is analogous to the LANDIS-II Social-Climate-Fire v4 reference input LTB_ClimateInputs_91_10_v2.csv.

Usage

assemble_climate_library_file_scf(dataset_path, vars, id_col = "EcoID")

Arguments

dataset_path: character. Path to the ClimaticEx_Daily Arrow dataset directory (containing YEAR=.../BatchID=.../part-0.csv partitions).
vars: character vector of BioSIM column names to retain (e.g. c("Prcp", "Tmin", "Tmax")).
id_col: character. Name of the ecoregion-id column in the dataset. Default "EcoID".

Value

tbl_df with the same shape as assemble_climate_library_file() but Variable values rewritten to the lowercase Social-Climate-Fire convention (precip, mintemp, maxtemp, temp, rh, windspeed, winddirection, swr).

Details

Social Climate Fire reads daily fire-weather indirectly through the LANDIS-II Climate Library (Daily_RandomYears / Daily_AverageAllYears time series), which then computes FWI internally. The Climate Library parser is case-insensitive and accepts both naming conventions, so this helper exists to produce a CSV whose Variable column visually matches the Social-Climate-Fire reference; assemble_climate_library_file() (which keeps Climate-Library-style spellings) is equally valid input to the parser.

Topographic considerations

The climate data sources used by this package (BioSIM, Daymet, TerraClim) are not PRISM-derived. PRISM (https://prism.oregonstate.edu/) applies topographically-aware interpolation (slope, aspect, elevation, coastal proximity, temperature inversions) when downscaling station observations to a grid (Daly et al. 2008); the sources wrapped here use simpler interpolation schemes that can produce large discrepancies in areas of complex topography (mountainous terrain, steep elevation gradients, rain shadows): inter-product differences of 5-60% in annual precipitation (Henn et al. 2018) and >6 \(^\circ\)C in temperature (Walton & Hall 2018) have been documented in the western US. Carefully evaluate the suitability of the chosen source for your study area, especially in topographically heterogeneous landscapes.

References

Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J., & Pasteris, P.P. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 28(15), 2031-2064. doi:10.1002/joc.1688

Henn, B., Newman, A.J., Livneh, B., Daly, C., & Lundquist, J.D. (2018). An assessment of differences in gridded precipitation datasets in complex terrain. Journal of Hydrology, 556, 1205-1219. doi:10.1016/j.jhydrol.2017.03.008