Assemble LANDIS-II Climate Library wide-format table from a TerraClim Arrow dataset

Reads the partitioned Arrow CSV dataset written by get_clim_monthly_terraclim(), applies unit conversions, translates raw TerraClim variable names to LANDIS-II Climate Library names via var_landis(), and pivots to wide format keyed by ecoregion id. The result is the same shape that prep_monthly_weather() returns and is suitable for writeClimateData().

Usage

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

Arguments

dataset_path: character. Path to the TerraClim_Monthly Arrow dataset directory (containing Variable=.../YEAR=.../part-0.csv partitions).
vars: character vector of TerraClim variable names to retain (lowercase, e.g. c("ppt", "tmax", "tmin")).
id_col: character. Name of the ecoregion-id column in the dataset. Default "EcoID".

Value

tbl_df with columns Year, Month, Variable, and one column per ecoregion id.

Details

Unit conversions applied:

ppt (mm) \(\to\) cm (\(\div 10\))

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