View source: R/sw_Vegetation.R
estimate_PotNatVeg_biomass | R Documentation |
Adjust mean monthly biomass values of grass and shrub functional groups by climate relationships
estimate_PotNatVeg_biomass(
target_temp,
target_MAP_mm,
ref_temp,
tr_VegBiom = rSOILWAT2::sw2_tr_VegBiom,
do_adjust_phenology = FALSE,
do_adjust_biomass = FALSE,
fgrass_c3c4ann = c(1, 0, 0)
)
target_temp |
A numeric vector of length 12. Mean monthly
temperature values in degree Celsius of a target site / condition
for which |
target_MAP_mm |
A numeric value. Mean annual precipitation in millimeter of the location. |
ref_temp |
A numeric vector of length 12. Reference mean monthly
temperature values in degree Celsius under which |
tr_VegBiom |
A data.frame with 12 rows (one for each month) and columns
|
do_adjust_phenology |
A logical value. If |
do_adjust_biomass |
A logical value. If |
fgrass_c3c4ann |
A numeric vector of length 3. Relative contribution
|
A list with two elements grass
, shrub
. Each element is
a matrix with 12 rows (one for each month) and columns Biomass
,
Amount.Live
, Perc.Live
, and Litter
.
Shrubs are based on location ‘IM_USC00107648_Reynolds’ which resulted in a vegetation composition of 70 % shrubs and 30 % C3-grasses. Default monthly biomass values were estimated for MAP = 450 mm yr-1.
Grasses are based on location ‘GP_SGSLTER’ (shortgrass steppe) which resulted in 12 % shrubs, 22 % C3-grasses, and 66 % C4-grasses. Default biomass values were estimated for MAP = 340 mm yr-1.
Mean monthly reference temperature are the median values across 898 big sagebrush sites (see https://github.com/DrylandEcology/rSFSTEP2/issues/195)
If do_adjust_biomass
, then the internal function adjBiom_by_ppt()
is
used to adjust biomass by annual precipitation amount.
If do_adjust_phenology
, then the exported function
adj_phenology_by_temp()
is used to adjust the seasonal pattern of biomass
(phenology) by monthly temperature.
Bradford, J.B., Schlaepfer, D.R., Lauenroth, W.K. & Burke, I.C. (2014). Shifts in plant functional types have time-dependent and regionally variable impacts on dryland ecosystem water balance. J Ecol, 102, 1408-1418.
clim <- calc_SiteClimate(weatherList = rSOILWAT2::weatherData)
veg_cover <- rSOILWAT2::estimate_PotNatVeg_composition(
MAP_mm = 10 * clim[["MAP_cm"]],
MAT_C = clim[["MAT_C"]],
mean_monthly_ppt_mm = 10 * clim[["meanMonthlyPPTcm"]],
mean_monthly_Temp_C = clim[["meanMonthlyTempC"]],
dailyC4vars = clim[["dailyC4vars"]]
)
rSOILWAT2::estimate_PotNatVeg_biomass(
target_temp = clim[["meanMonthlyTempC"]],
target_MAP_mm = 10 * clim[["MAP_cm"]],
do_adjust_phenology = TRUE,
do_adjust_biomass = TRUE,
fgrass_c3c4ann = veg_cover[["Grasses"]]
)
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