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Fetch a single year-variable of monthly TerraClim weather

get_clim_monthly_terraclim.Rd

Wraps climateR::getTerraClim() for one (variable, year), summarises by ecoregion via zonal::execute_zonal(), and writes the long-format result as an Arrow CSV partition under path/TerraClim_Monthly/, partitioned by Variable/YEAR. Existing partitions are skipped via file.exists(), so re-runs are cheap. Unit conversions (e.g. mm -> cm for precipitation) and translation to LANDIS-II Climate Library variable names are deferred to assemble_climate_library_file_monthly_terraclim().

Usage

get_clim_monthly_terraclim(
  var,
  year,
  studyArea,
  id,
  studyArea_hash = .studyArea_hash(studyArea),
  path = .climateCachePath()
)

Arguments

var

character single TerraClim variable name (lowercase, e.g. "ppt", "tmax", "tmin", "aet").

year

integer single calendar year to fetch.

studyArea

sf polygons object delineating ecoregions.

id

character. Name of the polygon-id column in studyArea.

studyArea_hash

character or NULL. Short hash of the study-area object used as a cache subdirectory so that distinct study areas don't collide. Defaults to .studyArea_hash() of studyArea.

path

character. Directory under which the TerraClim_Monthly/ arrow dataset is written. Default uses the package climate cache (getOption("landisutils.cache.path")).

Value

Path (character) to the partition CSV for (var, year).

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

See also

assemble_climate_library_file_monthly_terraclim(), prep_monthly_weather()