| Title: | Simulate Evapotranspiration and Soil Moisture with the SVAT Model LWF-Brook90 |
|---|---|
| Description: | Provides a flexible and easy-to use interface for the soil vegetation atmosphere transport (SVAT) model LWF-BROOK90, written in Fortran. The model simulates daily transpiration, interception, soil and snow evaporation, streamflow and soil water fluxes through a soil profile covered with vegetation, as described in Hammel & Kennel (2001, ISBN:978-3-933506-16-0) and Federer et al. (2003) <doi:10.1175/1525-7541(2003)004%3C1276:SOAETS%3E2.0.CO;2>. A set of high-level functions for model set up, execution and parallelization provides easy access to plot-level SVAT simulations, as well as multi-run and large-scale applications. |
| Authors: | Paul Schmidt-Walter [aut, cre]
 , Volodymyr Trotsiuk
[aut] , Klaus Hammel
[aut], Martin Kennel [aut], Anthony Federer [aut], Tobias
Hohenbrink [aut] ,
Gisbert Hetkamp [aut], Robert Nuske [ctb]
, Bavarian State
Institute of Forestry (LWF) [cph, fnd], Northwest German Forest
Research Institute (NW-FVA) [cph, fnd] |
| Maintainer: | Paul Schmidt-Walter <[email protected]> |
| License: | GPL-3 |
| Version: | 0.6.1 |
| Built: | 2024-11-16 06:22:34 UTC |
| Source: | CRAN |
Uses yearly values of inter-annual vegetation development values (e.g. sai, height, densef, age) and interpolates them to a daily sequence.
approx_standprop( x_yrs, y, y_ini = NULL, xout_yrs = x_yrs, use_growthperiod = FALSE, startdoy = 121, enddoy = 279, approx.method = "constant", return_xout = FALSE )approx_standprop( x_yrs, y, y_ini = NULL, xout_yrs = x_yrs, use_growthperiod = FALSE, startdoy = 121, enddoy = 279, approx.method = "constant", return_xout = FALSE )
x_yrs |
A sequence of years or a single year. |
y |
Vector of the same length as |
y_ini |
Initial value used as a starting point for linear interpolation.
Interpreted to be valid at the 1st of January of the first year in |
xout_yrs |
Vector of years for which output is generated. May be
longer or shorter than |
use_growthperiod |
Logical: Use startdoy and enddoy for linear interpolation? If TRUE, yearly changes take place between startdoy and enddoy, othe wise from end of year to end of the year after. |
startdoy |
A single value or vector of the same length as |
enddoy |
A single value or vector of the same length as |
approx.method |
Name of interpolation method ('constant' or 'linear'). |
return_xout |
Logical: If true, daily values of |
For approx.method = 'constant', the value of y is
returned for the whole respective year in x_yrs, which results in
a yearly changing step function. If approx.method = 'linear', the
values of y are interpolated between the years in x_yrs,
and interpreted to be reached at the 31st of December of the respective
x_yrs. In this case, y_ini is required as an initial value,
from which the sequence is interpolated to the first value of y. The
linear changes are either accomplished between 31st to 31st of December of
the years in x_yrs, or during the growing season only
(use_growingperiod = TRUE).
A vector of interpolated daily values
years <- 2002:2004 height_yearly <- c(20.2,20.8,21.3) # constant 'interpolation' height_c <- approx_standprop(x_yrs = years, y = height_yearly) # linear interpolation height_ini <- 19.1 height_l <- approx_standprop(x_yrs=years, y = height_yearly, y_ini = height_ini, approx.method = 'linear') # use growthperiod height_l_gp <- approx_standprop(x_yrs = years, y = height_yearly, y_ini = height_ini, use_growthperiod = TRUE, startdoy = 121, enddoy = 279, approx.method = 'linear') dates <- seq.Date(from = as.Date(paste0(min(years),"-01-01")), to = as.Date(paste0(max(years),"-12-31")), by = "day") plot(dates, height_c, type = "l", lwd = 2, col = "black", ylim = c(19,22), ylab = "height [m]", xlab = "", xpd = TRUE) lines(dates, height_l, col = "blue", lwd = 2) lines(dates, height_l_gp, col = "green", lwd = 2) legend("topleft", legend = c("'constant'", "'linear'", "'linear', 'use_growthperiod'"), col = c("black", "blue", "green"), lwd = 2, pch = NULL, bty = "n")years <- 2002:2004 height_yearly <- c(20.2,20.8,21.3) # constant 'interpolation' height_c <- approx_standprop(x_yrs = years, y = height_yearly) # linear interpolation height_ini <- 19.1 height_l <- approx_standprop(x_yrs=years, y = height_yearly, y_ini = height_ini, approx.method = 'linear') # use growthperiod height_l_gp <- approx_standprop(x_yrs = years, y = height_yearly, y_ini = height_ini, use_growthperiod = TRUE, startdoy = 121, enddoy = 279, approx.method = 'linear') dates <- seq.Date(from = as.Date(paste0(min(years),"-01-01")), to = as.Date(paste0(max(years),"-12-31")), by = "day") plot(dates, height_c, type = "l", lwd = 2, col = "black", ylim = c(19,22), ylab = "height [m]", xlab = "", xpd = TRUE) lines(dates, height_l, col = "blue", lwd = 2) lines(dates, height_l_gp, col = "green", lwd = 2) legend("topleft", legend = c("'constant'", "'linear'", "'linear', 'use_growthperiod'"), col = c("black", "blue", "green"), lwd = 2, pch = NULL, bty = "n")
Uses functions taken from the 'sirad' package to determine astronomical daylength and extraterrestrial radiation, from which global radiation is calculated using the Angström-formula.
calc_globrad(dates, sunhours, lat, a0 = 0.25, b0 = 0.5, full_output = FALSE)calc_globrad(dates, sunhours, lat, a0 = 0.25, b0 = 0.5, full_output = FALSE)
dates |
Date vector |
sunhours |
Vector of sunshine duration hours, same length as dates. |
lat |
Latitude in decimal degrees. |
a0 |
Angström parameter a, defaults to 0.25. |
b0 |
Angström parameter b, defaults to 0.5. |
full_output |
Return extraterrestrial radiation and daylength along with global radiation? |
A sequence of global radiation in MJ/(m² d) with the length of dates,
or (if full_output = TRUE) a data.frame holding day of year,
dates, sunhours, daylength, and extraterrestrial and calculated global
solar radiation. A warning is generated if some sunshine duration hours are
higher than the expected daylength at the specified latitude.
dates <- seq.Date(as.Date("2002-01-01"), as.Date("2003-12-31"), by = 'day') calc_globrad(dates, sunhours = runif(365, 0, 7),lat = 52.8) calc_globrad(dates, sunhours = runif(365, 0, 7),lat = 52.8, full_output = TRUE)dates <- seq.Date(as.Date("2002-01-01"), as.Date("2003-12-31"), by = 'day') calc_globrad(dates, sunhours = runif(365, 0, 7),lat = 52.8) calc_globrad(dates, sunhours = runif(365, 0, 7),lat = 52.8, full_output = TRUE)
Wrapper for vegperiod
calc_vegperiod( budburst_method, leaffall_method, dates = NULL, tavg = NULL, out_yrs = NULL, budburstdoy.fixed = 121, leaffalldoy.fixed = 279, ... )calc_vegperiod( budburst_method, leaffall_method, dates = NULL, tavg = NULL, out_yrs = NULL, budburstdoy.fixed = 121, leaffalldoy.fixed = 279, ... )
budburst_method |
name of model for estimating budburst day of year.
Either 'fixed' or one of the values accepted by the 'start.method'-argument
of the function |
leaffall_method |
name of model for estimating day of year when leaffall
begin. Either 'fixed' or one of the values accepted by the
'end.method'-argument of the function |
dates |
date vector passed to |
tavg |
vector of daily mean air temperature (deg C) passed to
|
out_yrs |
integer vector of the years to be returned. If not
specified, values for the years in |
budburstdoy.fixed |
vector of values to be returned if
|
leaffalldoy.fixed |
vector of values to be returned if
|
... |
additional argument passed to |
a data.frame with columns year, start, end. If
budburst_method = 'fixed' or leaffall_method = 'fixed',
start and end contain the values specified in
budburstdoy.fixed and leaffalldoy.fixed respectively.
# fixed budburst and leaffall doy calc_vegperiod(out_yrs = 2001:2010, budburst_method = "fixed", leaffall_method = "fixed", budburstdoy.fixed = floor(runif(10, 120,130)), leaffalldoy.fixed = floor(runif(2, 260,280))) # dynamic budburst and leaffall using air temperature data(slb1_meteo) calc_vegperiod(budburst_method = "Menzel", leaffall_method = "fixed", leaffalldoy.fixed = 280, dates = slb1_meteo$dates, tavg = slb1_meteo$tmean, species = "Fagus sylvatica", est.prev = 3) calc_vegperiod(budburst_method = "Menzel", leaffall_method = "ETCCDI", dates = slb1_meteo$dates, tavg = slb1_meteo$tmean, species = "Quercus robur", est.prev = 3)# fixed budburst and leaffall doy calc_vegperiod(out_yrs = 2001:2010, budburst_method = "fixed", leaffall_method = "fixed", budburstdoy.fixed = floor(runif(10, 120,130)), leaffalldoy.fixed = floor(runif(2, 260,280))) # dynamic budburst and leaffall using air temperature data(slb1_meteo) calc_vegperiod(budburst_method = "Menzel", leaffall_method = "fixed", leaffalldoy.fixed = 280, dates = slb1_meteo$dates, tavg = slb1_meteo$tmean, species = "Fagus sylvatica", est.prev = 3) calc_vegperiod(budburst_method = "Menzel", leaffall_method = "ETCCDI", dates = slb1_meteo$dates, tavg = slb1_meteo$tmean, species = "Quercus robur", est.prev = 3)
Correct rain gauge precipitation data for wind and evaporation errors after Richter (1995)
correct_prec(month, tavg, prec, station.exposure = "mg", full_output = FALSE)correct_prec(month, tavg, prec, station.exposure = "mg", full_output = FALSE)
month |
Vector of months. |
tavg |
Vector of air temperature values (deg C). Same length as
|
prec |
Vector of measured rainfall vales (mm). Same length as
|
station.exposure |
Situation of the weather station where prec was measured: one of 'frei', 'lg', 'mg', 'sg' (corresponding to full exposure, low protected, moderate protected, strong protected situation). |
full_output |
Logical wether to return the full data set additionally including input data, correction coefficients. |
A vector of corrected rainfall data, or (if full_output ==
TRUE) a data.table containing the input objects, the month, the
precipitation type ('N4So': liquid rain, summer; 'N4Wi' liquid rain,
winter; 'N8' = sleet, 'N7' = snow), correction coefficients epsilon and b,
and the corrected rainfall.
Richter, D. (1995) Ergebnisse methodischer Untersuchungen zur Korrektur des systematischen Messfehlers des Hellmann-Niederschlagsmessers. Berichte des Deutschen Wetterdienstes, 194, 93 pp, Offenbach, Germany
clim <- slb1_meteo[as.integer(format(slb1_meteo$dates,"%Y")) %in% 2001:2005,] clim$month <- as.integer(format(clim$dates, "%m")) prec_meas <- clim$prec correct_prec_frei <- with(clim, correct_prec(month, tmean, prec, station.exposure = "frei")) correct_prec_lg <- with(clim, correct_prec(month, tmean, prec, station.exposure = "lg")) correct_prec_mg <- with(clim, correct_prec(month, tmean, prec, station.exposure = "mg")) correct_prec_sg <- with(clim, correct_prec(month, tmean, prec, station.exposure = "sg")) plot(clim$dates, cumsum(correct_prec_frei), type = "l", col = "violet", xlab = "dates", ylab = "cum. precipitation (mm)") lines(clim$dates, cumsum(correct_prec_lg), col = "blue") lines(clim$dates, cumsum(correct_prec_mg), col = "green") lines(clim$dates, cumsum(correct_prec_sg), col = "red") lines(clim$dates, cumsum(prec_meas)) legend('bottomright', c('frei', "lg", "mg", "sg"), col = c("violet", "blue", "green", "red", "black"), lty = 1, pch = NULL )clim <- slb1_meteo[as.integer(format(slb1_meteo$dates,"%Y")) %in% 2001:2005,] clim$month <- as.integer(format(clim$dates, "%m")) prec_meas <- clim$prec correct_prec_frei <- with(clim, correct_prec(month, tmean, prec, station.exposure = "frei")) correct_prec_lg <- with(clim, correct_prec(month, tmean, prec, station.exposure = "lg")) correct_prec_mg <- with(clim, correct_prec(month, tmean, prec, station.exposure = "mg")) correct_prec_sg <- with(clim, correct_prec(month, tmean, prec, station.exposure = "sg")) plot(clim$dates, cumsum(correct_prec_frei), type = "l", col = "violet", xlab = "dates", ylab = "cum. precipitation (mm)") lines(clim$dates, cumsum(correct_prec_lg), col = "blue") lines(clim$dates, cumsum(correct_prec_mg), col = "green") lines(clim$dates, cumsum(correct_prec_sg), col = "red") lines(clim$dates, cumsum(prec_meas)) legend('bottomright', c('frei', "lg", "mg", "sg"), col = c("violet", "blue", "green", "red", "black"), lty = 1, pch = NULL )
Convenience function to reorganize soil layer time series data from
layer_output list entry produced with run_LWFB90. The data is tansformed to a
wide format, by casting the variables with the layer number using data.table's
dcast-function.
extract_layer_output( x, layers = NULL, value_vars = NULL, layer_index_name = "nl", sep = "" )extract_layer_output( x, layers = NULL, value_vars = NULL, layer_index_name = "nl", sep = "" )
x |
Data.frame or data.table with layer data organized in rows and
identified by a layer index column named |
layers |
Integer vector to select a subset of layers. If not supplied, values from all layers will be returned. |
value_vars |
Character vector containing names of value-variables to be
extracted from |
layer_index_name |
Column containing layer index. Defaults to 'nl' as in
|
sep |
Separation character for constructig names from variable name and layer index. |
A data.table with the layers' values of the variables organized in columns with the names being made up of the variable name and the layer index.
# create a data.frame with monthly values # identifiers: layer number, yr and mo df <- expand.grid(nl = 1:5, yr = 2002, mo = 1:12) df #add a value variable df$var <- runif(nrow(df), -1,0) extract_layer_output(df) # add more variables df$var1 <- runif(nrow(df), 1,2) df$var2 <- runif(nrow(df), 2,3) # extract specific layers extract_layer_output(df,layers = 2:4, sep = "_layer") #extract specific variables extract_layer_output(df, layers = 2:4, value_vars = c("var1", "var2"), sep = "_layer")# create a data.frame with monthly values # identifiers: layer number, yr and mo df <- expand.grid(nl = 1:5, yr = 2002, mo = 1:12) df #add a value variable df$var <- runif(nrow(df), -1,0) extract_layer_output(df) # add more variables df$var1 <- runif(nrow(df), 1,2) df$var2 <- runif(nrow(df), 2,3) # extract specific layers extract_layer_output(df,layers = 2:4, sep = "_layer") #extract specific variables extract_layer_output(df, layers = 2:4, value_vars = c("var1", "var2"), sep = "_layer")
Generates a root density depth function for soil layers
make_rootden( soilnodes, maxrootdepth = min(soilnodes), method = "betamodel", beta = 0.97, rootdat = NULL )make_rootden( soilnodes, maxrootdepth = min(soilnodes), method = "betamodel", beta = 0.97, rootdat = NULL )
soilnodes |
Vector of soil layer depth limits (including the top and the bottom of the profile) for which the relative root distribution will be calculated (m, negative downwards). |
maxrootdepth |
The maximum rooting depth (m, negative downwards) below
which relative root length density will be set to zero (not applying when
|
method |
Method name for the root depth distribution. Possible values are 'betamodel', 'table', 'linear', 'constant'. See details. |
beta |
Parameter of the root distribution function. |
rootdat |
data.frame with a given root depth density distribution.
Columns are depth limits ('upper' and 'lower' in m, negative downwards) and
relative root densities of fine or absorbing roots ('rootden') per unit
stonefree volume. Only used when |
method = 'betamodel' calculates the relative root length
densities of the soil layers from the cumulative proportion of roots
derived by the model after Gale & Grigal (1987). method = 'table'
distributes the relative root densities provided by rootdat to the
soil layers, under preservation of total root mass. method =
'linear' returns linearly decreasing root densities with a value of 1 at
the top of the soil profile to 0 at maxrootdepth. method =
'constant' returns a uniform root distribution with a relative root length
density of 1 for all soil layers above 'maxrootdepth'.
Vector of relative root length densities for the soil layers framed
by soilnodes. Length is one less than length(soilnodes).
Gale, M.R. & Grigal D.F. (1987): "Vertical root distributions of northern tree species in relation to successional status." Canadian Journal of Forest Research, 17:829-834
depths <- c(max(slb1_soil$upper), slb1_soil$lower) roots_beta <- make_rootden(soilnodes = depths, maxrootdepth = -1,4, beta = 0.97, method = "betamodel") rootden_table <- data.frame( upper = c(0.03,0,-0.02, -0.15, -0.35, -0.5, -0.65,-0.9,-1.1,-1.3), lower = c(0,-0.02, -0.15, -0.35, -0.5, -0.65,-0.9,-1.1,-1.3,-1.6), rootden = c(10,15, 35, 15, 7.5, 4, 12, 2, 2, 0)) roots_table <- make_rootden(soilnodes = depths, method = "table", rootdat = rootden_table) roots_linear <- make_rootden(soilnodes = depths, maxrootdepth = -1.4, method = 'linear') roots_constant <- make_rootden(soilnodes = depths, maxrootdepth = -1.4, method = 'const') plot(roots_constant, slb1_soil$lower +runif(n=length(slb1_soil$lower), -0.02,0.02), type = 's', lwd = 1.5,ylab = "soil depth [m]",xlab = "relative root density", xlim = c(0,1), col = "red") lines(roots_linear, slb1_soil$lower, type = 's', col = "blue", lwd = 1.5) lines(roots_beta*10, slb1_soil$lower, type = 's', col = "brown", lwd = 1.5) lines(roots_table/100, slb1_soil$lower, type = 's', col = "green", lwd = 1.5) legend("bottomright", c("'betamodel'","'table'","'linear'", "'constant'"),seg.len = 1.5, pch = NULL, lwd =1.5, col = c("brown", "green", "blue", "red"), bty = "n")depths <- c(max(slb1_soil$upper), slb1_soil$lower) roots_beta <- make_rootden(soilnodes = depths, maxrootdepth = -1,4, beta = 0.97, method = "betamodel") rootden_table <- data.frame( upper = c(0.03,0,-0.02, -0.15, -0.35, -0.5, -0.65,-0.9,-1.1,-1.3), lower = c(0,-0.02, -0.15, -0.35, -0.5, -0.65,-0.9,-1.1,-1.3,-1.6), rootden = c(10,15, 35, 15, 7.5, 4, 12, 2, 2, 0)) roots_table <- make_rootden(soilnodes = depths, method = "table", rootdat = rootden_table) roots_linear <- make_rootden(soilnodes = depths, maxrootdepth = -1.4, method = 'linear') roots_constant <- make_rootden(soilnodes = depths, maxrootdepth = -1.4, method = 'const') plot(roots_constant, slb1_soil$lower +runif(n=length(slb1_soil$lower), -0.02,0.02), type = 's', lwd = 1.5,ylab = "soil depth [m]",xlab = "relative root density", xlim = c(0,1), col = "red") lines(roots_linear, slb1_soil$lower, type = 's', col = "blue", lwd = 1.5) lines(roots_beta*10, slb1_soil$lower, type = 's', col = "brown", lwd = 1.5) lines(roots_table/100, slb1_soil$lower, type = 's', col = "green", lwd = 1.5) legend("bottomright", c("'betamodel'","'table'","'linear'", "'constant'"),seg.len = 1.5, pch = NULL, lwd =1.5, col = c("brown", "green", "blue", "red"), bty = "n")
A daily sequence of leaf area index is derived from maximum and minimum values, dates and shape parameters using different methods.
make_seasLAI( method = "b90", year, maxlai, winlaifrac = 0, budburst_doy = 121, leaffall_doy = 279, emerge_dur = 28, leaffall_dur = 58, shp_optdoy = 220, shp_budburst = 0.5, shp_leaffall = 10, lai_doy = c(1, 121, 150, 280, 320, 365), lai_frac = c(0, 0, 0.5, 1, 0.5, 0) )make_seasLAI( method = "b90", year, maxlai, winlaifrac = 0, budburst_doy = 121, leaffall_doy = 279, emerge_dur = 28, leaffall_dur = 58, shp_optdoy = 220, shp_budburst = 0.5, shp_leaffall = 10, lai_doy = c(1, 121, 150, 280, 320, 365), lai_frac = c(0, 0, 0.5, 1, 0.5, 0) )
method |
Name of method for generating the sequence. Must be one of "b90", "linear", "Coupmodel". |
year |
Vector of years to be returned. |
maxlai |
Maximum leaf are index. |
winlaifrac |
Fraction of |
budburst_doy |
Budburst day of year (ignored when |
leaffall_doy |
Day of year when leaf fall begins (ignored when
|
emerge_dur |
Number of days from budburst until maximum leaf area index is reached. |
leaffall_dur |
Number of days until minimum leaf are index is reached. |
shp_optdoy |
Day of year when optimum value is reached (required when
|
shp_budburst |
Shape parameter for the growth phase (required when
|
shp_leaffall |
Shape parameter growth cessation (required when
|
lai_doy |
Integer vector of days of years. |
lai_frac |
Vector of values of fractional leaf area index corresponding
to lai_doy (required when |
A vector of daily lai values covering the years specified.
# Intraannual courses of leaf area index lai_b90 <- make_seasLAI(method = "b90", year = 2001, maxlai = 5, winlaifrac = 0, budburst_doy = 121, leaffall_doy = 280, emerge_dur = 15, leaffall_dur = 30) lai_doy <- c(1,110,117,135,175,220,250,290,365) lai_frac <- c(0.1,0.1,0.5,0.7,1.2,1.2,1.0,0.1,0.1) lai_linear <- make_seasLAI(method = "linear", year = 2001, maxlai = 5, lai_doy = lai_doy, lai_frac = lai_frac) lai_coupmodel <- make_seasLAI(method = "Coupmodel", year = 2001, maxlai = 5, winlaifrac = 0.1, budburst_doy = 110, leaffall_doy = 280, shp_optdoy = 180, shp_budburst = 0.5, shp_leaffall = 5) plot(lai_b90, type = "n", xlab = "doy", ylab = "lai [m²/m²]", ylim = c(0,6)) lines(lai_b90, col ="green",lwd = 2,) lines(lai_linear, col ="red",lwd = 2) lines(lai_coupmodel, col ="blue",lwd = 2) # incorparating between-year variability years <- 2001:2003 lai <- make_seasLAI(method = "Coupmodel", year = years, maxlai = c(4,6,5), budburst_doy = c(100,135,121), leaffall_doy = 280, shp_budburst = c(3,1,0.3), shp_leaffall = 3, shp_optdoy =c(210,180,240) ) dates <- seq.Date(as.Date("2001-01-01"), as.Date("2003-12-31"), by = "day") plot(dates,lai, col = "green", ylab = "lai [m²/m²]", type ="l", xlab = "", lwd = 2)# Intraannual courses of leaf area index lai_b90 <- make_seasLAI(method = "b90", year = 2001, maxlai = 5, winlaifrac = 0, budburst_doy = 121, leaffall_doy = 280, emerge_dur = 15, leaffall_dur = 30) lai_doy <- c(1,110,117,135,175,220,250,290,365) lai_frac <- c(0.1,0.1,0.5,0.7,1.2,1.2,1.0,0.1,0.1) lai_linear <- make_seasLAI(method = "linear", year = 2001, maxlai = 5, lai_doy = lai_doy, lai_frac = lai_frac) lai_coupmodel <- make_seasLAI(method = "Coupmodel", year = 2001, maxlai = 5, winlaifrac = 0.1, budburst_doy = 110, leaffall_doy = 280, shp_optdoy = 180, shp_budburst = 0.5, shp_leaffall = 5) plot(lai_b90, type = "n", xlab = "doy", ylab = "lai [m²/m²]", ylim = c(0,6)) lines(lai_b90, col ="green",lwd = 2,) lines(lai_linear, col ="red",lwd = 2) lines(lai_coupmodel, col ="blue",lwd = 2) # incorparating between-year variability years <- 2001:2003 lai <- make_seasLAI(method = "Coupmodel", year = years, maxlai = c(4,6,5), budburst_doy = c(100,135,121), leaffall_doy = 280, shp_budburst = c(3,1,0.3), shp_leaffall = 3, shp_optdoy =c(210,180,240) ) dates <- seq.Date(as.Date("2001-01-01"), as.Date("2003-12-31"), by = "day") plot(dates,lai, col = "green", ylab = "lai [m²/m²]", type ="l", xlab = "", lwd = 2)
Creates daily sequences of 'age', 'height', 'sai', 'densef', and 'lai' from
parameters and options using approx_standprop and
make_seasLAI.
make_standprop(options_b90, param_b90, out_yrs)make_standprop(options_b90, param_b90, out_yrs)
options_b90 |
A list of model control options. |
param_b90 |
A parameter list-object. |
out_yrs |
Years for which values are returned. |
A data.frame containing daily sequences of 'age', 'height', 'sai', 'densef', and 'lai'.
options_b90 <- set_optionsLWFB90() param_b90 <- set_paramLWFB90() standprop <- make_standprop(options_b90, param_b90, out_yrs = 2002:2004) plot(standprop$dates, standprop$lai, type = "l")options_b90 <- set_optionsLWFB90() param_b90 <- set_paramLWFB90() standprop <- make_standprop(options_b90, param_b90, out_yrs = 2002:2004) plot(standprop$dates, standprop$lai, type = "l")
The param vector for r_lwfbrook90 is created from model
parameters.
param_to_rlwfbrook90(param_b90, imodel)param_to_rlwfbrook90(param_b90, imodel)
param_b90 |
A named list of model parameters. |
imodel |
Name of hydraulic model ('MvG' or 'CH') |
A numerical vector with the parameters in the right order for
r_lwfbrook90.
Creates a daily sequence for one year from parameters
plant_b90(minval, maxval, doy.incr, incr.dur, doy.decr, decr.dur, maxdoy)plant_b90(minval, maxval, doy.incr, incr.dur, doy.decr, decr.dur, maxdoy)
minval |
Minimum value. |
maxval |
Maximum value. |
doy.incr |
Day of year when increasing from |
incr.dur |
Duration (number of days) since |
doy.decr |
Day of year when decreasing to |
decr.dur |
Duration (number of days) since |
maxdoy |
Length of the year, 366 for leap years, 365 for normal years. |
A numeric vector of length maxdoy.
plot(plant_b90(minval = 0,maxval=1, doy.incr = 121,incr.dur = 28, doy.decr = 280, decr.dur = 50, maxdoy = 365))plot(plant_b90(minval = 0,maxval=1, doy.incr = 121,incr.dur = 28, doy.decr = 280, decr.dur = 50, maxdoy = 365))
Creates a daily sequence for one year from parameters
plant_coupmodel( minval, maxval, doy.incr, doy.max, doy.min, shape.incr, shape.decr, maxdoy )plant_coupmodel( minval, maxval, doy.incr, doy.max, doy.min, shape.incr, shape.decr, maxdoy )
minval |
Minimum value. |
maxval |
Maximum value. |
doy.incr |
Day of year when increasing from |
doy.max |
Day of year when |
doy.min |
Day of year when |
shape.incr |
Shape parameter of the increasing phase. |
shape.decr |
Shape parameter of the decreasing phase. |
maxdoy |
Length of the year, 366 for leap years, 365 for normal years. |
A numeric vector of length maxdoy.
Jansson, P.-E. & Karlberg, L. (2004): "Coupled heat and mass transfer model for soil-plant-atmosphere systems." Royal Institute of Technolgy, Dept of Civil and Environmental Engineering Stockholm
plot(plant_coupmodel(0,5, 121, 200, 280, 0.3, 3, 365))plot(plant_coupmodel(0,5, 121, 200, 280, 0.3, 3, 365))
Creates a daily sequence for one year from doy/value pairs.
plant_linear(doys, values, maxdoy)plant_linear(doys, values, maxdoy)
doys |
Integer vector of dates (days of year). |
values |
Numeric vector of values. |
maxdoy |
Integer length of the year, 366 for leap years, 365 for normal years. |
A numeric vector of length maxdoy.
doys <- c(110,200,250,280) values <- c(0,0.8,1,0) maxdoy <- 365 plot(plant_linear(doys = doys, values = values, maxdoy = 365))doys <- c(110,200,250,280) values <- c(0,0.8,1,0) maxdoy <- 365 plot(plant_linear(doys = doys, values = values, maxdoy = 365))
Returns selected groups of variables in the chosen temporal aggregation
process_outputs_LWFB90(x, selection = set_outputLWFB90(), prec_interval = NULL)process_outputs_LWFB90(x, selection = set_outputLWFB90(), prec_interval = NULL)
x |
Named list with items |
selection |
A [7,5]-matrix with row and column names, flagging the
desired groups of variables at specified time intervals (see
|
prec_interval |
The precipitation interval of the simulation
that produced |
A named list containing the selected groups of variables in the
desired temporal resolution. The names are constructed from
selection's row names and column names, suffixed by '.ASC' as a
reminiscence to the former text file output of LWF-Brook90.
data("slb1_soil") data("slb1_meteo") opts <- set_optionsLWFB90(startdate = as.Date("2002-06-01"), enddate = as.Date("2002-06-05")) parms <- set_paramLWFB90() soil <- cbind(slb1_soil, hydpar_wessolek_tab(texture = slb1_soil$texture)) outsel <- set_outputLWFB90() outsel[,] <- 1L res <- run_LWFB90(options_b90 = opts, param_b90 = parms, climate = slb1_meteo, soil = soil) # Calculate output-aggregations using the returned object process_outputs_LWFB90(res, selection = outsel) # or calculate aggregations at run time by passing the function via output_fun-arg run_LWFB90(options_b90 = opts, param_b90 = parms, climate = slb1_meteo, soil = soil, rtrn_input = FALSE, output_fun = process_outputs_LWFB90, selection = outsel)$output_fundata("slb1_soil") data("slb1_meteo") opts <- set_optionsLWFB90(startdate = as.Date("2002-06-01"), enddate = as.Date("2002-06-05")) parms <- set_paramLWFB90() soil <- cbind(slb1_soil, hydpar_wessolek_tab(texture = slb1_soil$texture)) outsel <- set_outputLWFB90() outsel[,] <- 1L res <- run_LWFB90(options_b90 = opts, param_b90 = parms, climate = slb1_meteo, soil = soil) # Calculate output-aggregations using the returned object process_outputs_LWFB90(res, selection = outsel) # or calculate aggregations at run time by passing the function via output_fun-arg run_LWFB90(options_b90 = opts, param_b90 = parms, climate = slb1_meteo, soil = soil, rtrn_input = FALSE, output_fun = process_outputs_LWFB90, selection = outsel)$output_fun
A set of pedotransfer functions for deriving Mualem - van Genuchten parameters from soil physical properties of soil horizons, such as soil texture, bulk density and carbon content.
hydpar_puh2(clay, silt, sand, bd, oc.pct = 0.5) hydpar_hypres(clay, silt, bd, oc.pct = 0.1, topsoil = TRUE, humconv = 1.72) hydpar_hypres_tab(texture, topsoil) hydpar_wessolek_tab(texture) hydpar_ff_b90(n = 1)hydpar_puh2(clay, silt, sand, bd, oc.pct = 0.5) hydpar_hypres(clay, silt, bd, oc.pct = 0.1, topsoil = TRUE, humconv = 1.72) hydpar_hypres_tab(texture, topsoil) hydpar_wessolek_tab(texture) hydpar_ff_b90(n = 1)
clay, silt, sand
|
Numeric vectors of clay, silt, sand in mass %.
Particle size ranges for clay, silt and sand correspond to <2, 2-63, and
63-2000 |
bd |
Numeric vector of bulk density in g cm-3. |
oc.pct |
Numeric vector of organic carbon content in mass %. |
topsoil |
Logical: Is the sample from the topsoil? Used in
|
humconv |
Conversion factor from oc.pct to organic matter percent.
Default: 1.72. Only for |
texture |
Character vector of soil texture classes. For
|
n |
An integer value specifying the number of rows of the returned
data.frame (i.e. the number of repetitions of the MvG-Parameter set, only
for |
Function hydpar_puh2 derives Mualem - van Genuchten (MvG) parameters
using the regression functions developed by Puhlmann & von Wilpert (2011).
The equations of Wösten et al. (1999) are available via hydpar_hypres,
and their tabulated values for soil texture classes can be derived using the
function hydpar_hypres_tab. The table of MvG parameters from Wesselok
et al. (2009; Tab. 10) is accessible by hydpar_wessolek_tab. For this
function, soil texture classes after the German texture classification system
(KA5, AG Boden 2005) have to be provided. To derive hydraulic parameters of
forest floor horizons, the function hydpar_ff_b90 can be used. It
returns the single MvG parameter set for forest floor horizons reported by
Hammel & Kennel (2001) in their original LWF-Brook90 publication.
A data.frame with the following variables:
Saturation water content fraction
Residual water content fraction
N parameter of the van Genuchten water retention function
M parameter of the van Genuchten water retention function
Alpha parameter of the van Genuchten water retention function (1/m)
Saturated hyraulic conductivity parameter of Mualem hydraulic conductivity function (mm/d)
Tortuosity parameter of Mualem hydraulic conductivity function
AG Boden (2005) Bodenkundliche Kartieranleitung Schweizerbart'sche Verlagsbuchhandlung, Stuttgart
Food and Agriculture Organisation (FAO) (1990) Guidelines for soil description FAO/ISRIC, Rome, 3rd edition
Hammel K & Kennel M (2001) Charakterisierung und Analyse der Wasserverfügbarkeit und des Wasserhaushalts von Waldstandorten in Bayern mit dem Simulationsmodell BROOK90. Forstliche Forschungsberichte München 185
Puhlmann H, von Wilpert K (2011) Testing and development of pedotransfer functions for water retention and hydraulic conductivity of forest soils. Waldökologie, Landschaftsforschung und Naturschutz 12, pp. 61-71
Wessolek G, Kaupenjohann M and Renger H (2009) Bodenphysikalische Kennwerte und Berechnungsverfahren für die Praxis. Bodenökologie und Bodengenese 40, Berlin, Germany
Woesten JHM, Lilly A, Nemes A, Le Bas C (1999) Development and use of a database of hydraulic properties of European soils. Geoderma 90, pp. 169-185
hydpar_puh2(clay = c(10,20), silt = c(40,20), sand = c(50,60), bd = c(1.6, 1.4)) hydpar_hypres(20,20,1.5,2) hydpar_hypres_tab(texture = c("C","MF"), topsoil = c(TRUE,FALSE)) hydpar_wessolek_tab(c("Us", "Ls2", "mSfS")) hydpar_ff_b90(n = 5)hydpar_puh2(clay = c(10,20), silt = c(40,20), sand = c(50,60), bd = c(1.6, 1.4)) hydpar_hypres(20,20,1.5,2) hydpar_hypres_tab(texture = c("C","MF"), topsoil = c(TRUE,FALSE)) hydpar_wessolek_tab(c("Us", "Ls2", "mSfS")) hydpar_ff_b90(n = 5)
Passes input data matrices to the Fortran model code and returns the results
r_lwfbrook90( siteparam, climveg, param, pdur, soil_materials, soil_nodes, precdat = NULL, output_log = TRUE, chk_input = TRUE, timelimit = Inf )r_lwfbrook90( siteparam, climveg, param, pdur, soil_materials, soil_nodes, precdat = NULL, output_log = TRUE, chk_input = TRUE, timelimit = Inf )
siteparam |
A [1,9] matrix with site level information: start year, start doy, latitude, initial snow, initial groundwater, precipitation interval, a snow cover's liquid water (SNOWLQ) and cold content (CC). |
climveg |
A matrix with 15 columns of climatic and vegetation data: year, month, day, global radiation in MJ/(m² d), tmax (deg C), tmin (deg C), vappres (kPa), wind (m/s), prec (mm), mesfl (mm), densef (-), stand height (m), lai (m²/m²), sai (m²/m²), stand age (years). |
param |
A numeric vector of model input parameters (for the right order
see |
pdur |
a [1,12]-matrix of precipitation durations (hours) for each month. |
soil_materials |
A matrix of the 8 soil materials parameters. When imodel = 1 (Mualem-van Genuchten), these refer to: mat, ths, thr, alpha (1/m), npar, ksat (mm/d), tort (-), stonef (-). When imodel = 2 (Clapp-Hornberger): mat, thsat, thetaf, psif (kPa), bexp, kf (mm/d), wetinf (-), stonef (-). |
soil_nodes |
A matrix of the soil model layers with columns nl (layer number), layer midpoint (m), thickness (mm), mat, psiini (kPa), rootden (-). |
precdat |
A matrix of precipitation interval data with 6 columns: year, month, day, interval-number (1:precint), prec, mesflp. |
output_log |
Logical whether to print runtime output to console. |
chk_input |
Logical whether to check for NaNs in model inputs. |
timelimit |
Integer to set elapsed time limits for running the model. |
A list containing the daily and soil layer model outputs, along with
an error code of the simulation (see run_LWFB90.
Replace elements in a data.frame or vector of length > 1 by name
replace_vecelements(x, varnms, vals)replace_vecelements(x, varnms, vals)
x |
A vector or data.frame. |
varnms |
Variable names to match: Specify position by name and index. |
vals |
Vector of values to insert at the specified positions. |
The vector or data.frame in x with the elements 'varnms' replaced by vals.
soil_materials <- data.frame(ths = rep(0.4,3), alpha = rep(23.1, 3)) varnms = c("soil_materials.ths3", "soil_materials.ths1", "soil_materials.alpha2") vals = c(0.999, 0.001, 99) soil_materials replace_vecelements(soil_materials, varnms, vals) x <- set_paramLWFB90()[["pdur"]] varnms <- c("pdur2", "pdur12") vals <- c(0,10) x replace_vecelements(x, varnms, vals)soil_materials <- data.frame(ths = rep(0.4,3), alpha = rep(23.1, 3)) varnms = c("soil_materials.ths3", "soil_materials.ths1", "soil_materials.alpha2") vals = c(0.999, 0.001, 99) soil_materials replace_vecelements(soil_materials, varnms, vals) x <- set_paramLWFB90()[["pdur"]] varnms <- c("pdur2", "pdur12") vals <- c(0,10) x replace_vecelements(x, varnms, vals)
Sets up the input objects for the LWF-Brook90 hydrologic model, starts the model, and returns the selected results.
run_LWFB90( options_b90, param_b90, climate, precip = NULL, soil = NULL, output_fun = NULL, rtrn_input = TRUE, rtrn_output = TRUE, chk_input = TRUE, run = TRUE, timelimit = Inf, verbose = FALSE, ... )run_LWFB90( options_b90, param_b90, climate, precip = NULL, soil = NULL, output_fun = NULL, rtrn_input = TRUE, rtrn_output = TRUE, chk_input = TRUE, run = TRUE, timelimit = Inf, verbose = FALSE, ... )
options_b90 |
Named list of model control options. Use
|
param_b90 |
Named list of model input parameters. Use
|
climate |
Data.frame with daily climatic data, or a function that returns a suitable data.frame. See details for the required variables. |
precip |
Data.frame with columns 'dates' and 'prec' to supply
precipitation data separately from climate data. Can be used to provide
sub-day resolution precipitation data to LWFBrook90. For each day in dates,
1 (daily resolution) to 240 values of precipitation can be provided, with
the number of values per day defined in |
soil |
Data.frame containing the hydraulic properties of the soil layers. See section 'Soil parameters' |
output_fun |
A function or a list of functions of the form
|
rtrn_input |
Logical: append |
rtrn_output |
Logical: return the simulation results select via
|
chk_input |
Logical wether to check |
run |
Logical: run LWF-Brook90 or only return model input objects? Useful to inspect the effects of options and parameters on model input. Default is TRUE. |
timelimit |
Integer to set elapsed time limits (seconds) for running LWF-Brook90. |
verbose |
Logical: print messages to the console? Default is FALSE. |
... |
Additional arguments passed to |
A list containing the selected model output (if rtrn_output ==
TRUE), the model input (if rtrn_input == TRUE, except for
climate), and the return values of output_fun if specified.
The climate data.frame (or function) must
contain (return) the following variables in columns named 'dates' (Date),
'tmax' (deg C), 'tmin' (deg C), 'tmean' (deg C), 'windspeed' (m/s), 'prec'
(mm) , 'vappres' (kPa), and either 'globrad' ( MJ/(m²d) ) or 'sunhours' (h).
When using sunhours, please set options_b90$fornetrad =
'sunhours'.
Each row of soil represents one layer,
containing the layers' boundaries and soil hydraulic parameters. The column
names for the upper and lower layer boundaries are 'upper' and 'lower' (m,
negative downwards). When using options_b90$imodel = 'MvG', the
hydraulic parameters are 'ths', 'thr', 'alpha' (1/m), 'npar', 'ksat' (mm/d)
and 'tort'. With options_b90$imodel = 'CH', the parameters are
'thsat', 'thetaf', 'psif' (kPa), 'bexp', 'kf' (mm/d), and 'wetinf'. For both
parameterizations, the volume fraction of stones has to be named 'gravel'.
If the soil argument is not provided, list items soil_nodes
and soil_materials of param_b90 are used for the simulation.
These have to be set up in advance, see soil_to_param.
| Name | Description | Unit |
| yr | year | - |
| mo | month | - |
| da | day of month | - |
| doy | day of year | - |
| aa | average available energy above canopy | W/m2 |
| adef | available water deficit in root zone | mm |
| asubs | average available energy below canopy | W/m2 |
| awat | total available soil water in layers with roots between -6.18 kPa and param_b90$psicr |
mm |
| balerr | error in water balance (daily value, output at the day's last precipitation interval) | mm |
| byfl | total bypass flow | mm/d |
| cc | cold content of snowpack (positive) | MJ m-2 |
| dsfl | downslope flow | mm/d |
| evap | evapotranspiration | mm/d |
| flow | total streamflow | mm/d |
| gwat | groundwater storage below soil layers | mm |
| gwfl | groundwater flow | mm/d |
| intr | intercepted rain | mm |
| ints | intercepted snow | mm |
| irvp | evaporation of intercepted rain | mm/d |
| isvp | evaporation of intercepted snow | mm/d |
| lngnet | net longwave radiation | W/m2 |
| nits | total number of iterations | - |
| pint | potential interception for a canopy always wet | mm/d |
| pslvp | potential soil evaporation | mm/d |
| ptran | potential transpiration | mm/d |
| relawat | relative available soil water in layers with roots | - |
| rfal | rainfall | mm/d |
| rint | rain interception catch rate | mm/d |
| rnet | rainfall to soil surface | mm/d |
| rsno | rain on snow | mm/d |
| rthr | rain throughfall rate | mm/d |
| sthr | snow throughfall rate | mm/d |
| safrac | source area fraction | - |
| seep | seepage loss | mm/d |
| sfal | snowfall | mm/d |
| sint | snow interception catch rate | mm/d |
| slfl | input to soil surface | mm/d |
| slvp | evaporation rate from soil | mm/d |
| slrad | average solar radiation on slope over daytime | W/m2 |
| solnet | net solar radiation on slope over daytime | W/m2 |
| smlt | snowmelt | mm/d |
| snow | snowpack water equivalent | mm |
| snowlq | liquid water content of snow on the ground | mm |
| snvp | evaporation from snowpack | mm/d |
| srfl | source area flow | mm/d |
| stres | tran / ptran (daily value, output at the day's last precipitation interval) | - |
| swat | total soil water in all layers | mm |
| tran | transpiration | mm/d |
| vrfln | vertical matrix drainage from lowest layer | mm/d |
| Name | Description | Unit |
| yr | year | - |
| mo | month | - |
| da | day of month | - |
| doy | day of year | - |
| nl | index of soil layer | |
| swati | soil water volume in layer | mm |
| theta | water content of soil layer, mm water / mm soil matrix | - |
| wetnes | wetness of soil layer, fraction of saturation | - |
| psimi | matric soil water potential for soil layer | kPa |
| infl | infiltration to soil water in soil layer | mm/d |
| byfl | bypass flow from soil layer | mm/d |
| tran | transpiration from soil layer | mm/d |
| vrfl | vertical matrix drainage from soil layer | mm/d |
| dsfl | downslope drainage from layer | mm/d |
| ntfl | net flow into soil layer | mm/d |
# Set up lists containing model control options and model parameters: param_b90 <- set_paramLWFB90() options_b90 <- set_optionsLWFB90() # Set start and end Dates for the simulation options_b90$startdate <- as.Date("2003-06-01") options_b90$enddate <- as.Date("2003-06-30") # Derive soil hydraulic properties from soil physical properties # using pedotransfer functions soil <- cbind(slb1_soil, hydpar_wessolek_tab(slb1_soil$texture)) # Run LWF-Brook90 b90.result <- run_LWFB90(options_b90 = options_b90, param_b90 = param_b90, climate = slb1_meteo, soil = soil) # use a function to be performed on the output: # aggregate soil water storage down to a specific layer agg_swat <- function(x, layer) { out <- aggregate(swati~yr+doy, x$SWATDAY.ASC, FUN = sum, subset = nl <= layer) out[order(out$yr, out$doy),]} # run model without returning the selected output. b90.aggswat <- run_LWFB90(options_b90 = options_b90, param_b90 = param_b90, climate = slb1_meteo, soil = soil, output_fun = list(swat = agg_swat), rtrn_output = FALSE, layer = 10) # passed to output_fun str(b90.aggswat$output_fun$swat)# Set up lists containing model control options and model parameters: param_b90 <- set_paramLWFB90() options_b90 <- set_optionsLWFB90() # Set start and end Dates for the simulation options_b90$startdate <- as.Date("2003-06-01") options_b90$enddate <- as.Date("2003-06-30") # Derive soil hydraulic properties from soil physical properties # using pedotransfer functions soil <- cbind(slb1_soil, hydpar_wessolek_tab(slb1_soil$texture)) # Run LWF-Brook90 b90.result <- run_LWFB90(options_b90 = options_b90, param_b90 = param_b90, climate = slb1_meteo, soil = soil) # use a function to be performed on the output: # aggregate soil water storage down to a specific layer agg_swat <- function(x, layer) { out <- aggregate(swati~yr+doy, x$SWATDAY.ASC, FUN = sum, subset = nl <= layer) out[order(out$yr, out$doy),]} # run model without returning the selected output. b90.aggswat <- run_LWFB90(options_b90 = options_b90, param_b90 = param_b90, climate = slb1_meteo, soil = soil, output_fun = list(swat = agg_swat), rtrn_output = FALSE, layer = 10) # passed to output_fun str(b90.aggswat$output_fun$swat)
Wrapper function for run_LWFB90 to make multiple simulations
parallel, with varying input parameters.
run_multi_LWFB90( paramvar, param_b90, paramvar_nms = names(paramvar), cores = 2, show_progress = TRUE, ... )run_multi_LWFB90( paramvar, param_b90, paramvar_nms = names(paramvar), cores = 2, show_progress = TRUE, ... )
paramvar |
Data.frame of variable input parameters. For each row, a
simulation is performed, with the elements in |
param_b90 |
Named list of parameters, in which the parameters defined in
|
paramvar_nms |
Names of the parameters in |
cores |
Number of CPUs to use for parallel processing. Default is 2. |
show_progress |
Logical: Show progress bar? Default is TRUE. See also
section |
... |
Additional arguments passed to |
A named list with the results of the single runs as returned by
run_LWFB90. Simulation or processing errors are passed on.
The transfer of values from a row in paramvar to param_b90
before each single run simulation is done by matching names from
paramvar and param_b90. In order to address data.frame or
vector elements in param_b90 by a column name in paramvar, the
respective column name has to be set up from its name and index in
param_b90. To replace, e.g., the 2nd value of ths in the
soil_materials data.frame, the respective column name in
paramvar has to be called 'soil_materials.ths2'. In order to replace
the 3rd value of maxlai vector in param_b90, the column has to
be named 'maxlai3'.
The returned list of single run results can become very large, if many
simulations are performed and the selected output contains daily resolution
data sets, especially daily layer-wise soil moisture data. To not overload
memory, it is advised to reduce the returned simulation results to a minimum,
by carefully selecting the output, and make use of the option to pass a list
of functions to run_LWFB90 via argument output_fun.
These functions perform directly on the output of a single run simulation,
and can be used for aggregating model output on-the-fly, or for writing
results to a file or database. The regular output of
run_LWFB90 can be suppressed by setting rtrn.output =
FALSE, for exclusively returning the output of such functions.
This function provides a progress bar via the package progressr if
show_progress=TRUE. The parallel computation is then wrapped with
progressr::with_progress() to enable progress reporting from
distributed calculations. The appearance of the progress bar (including
audible notification) can be customized by the user for the entire session
using progressr::handlers() (see vignette('progressr-intro')).
data("slb1_meteo") data("slb1_soil") # Set up lists containing model control options and model parameters: parms <- set_paramLWFB90() # choose the 'Coupmodel' shape option for the annual lai dynamic, # with fixed budburst and leaf fall dates: opts <- set_optionsLWFB90(startdate = as.Date("2003-06-01"), enddate = as.Date("2003-06-30"), lai_method = 'Coupmodel', budburst_method = 'fixed', leaffall_method = 'fixed') # Derive soil hydraulic properties from soil physical properties using pedotransfer functions soil <- cbind(slb1_soil, hydpar_wessolek_tab(slb1_soil$texture)) #set up data.frame with variable parameters n <- 10 set.seed(2021) vary_parms <- data.frame(shp_optdoy = runif(n,180,240), shp_budburst = runif(n, 0.1,1), winlaifrac = runif(n, 0,0.5), budburstdoy = runif(n,100,150), soil_materials.ths3 = runif(n, 0.3,0.5), # ths of material 3 maxlai = runif(n,2,7)) # add the soil as soil_nodes and soil materials to param_b90, so ths3 can be looked up parms[c("soil_nodes", "soil_materials")] <- soil_to_param(soil) # Make a Multirun-Simulation b90.multi <- run_multi_LWFB90(paramvar = vary_parms, param_b90 = parms, options_b90 = opts, climate = slb1_meteo) names(b90.multi) # extract results evapday <- data.table::rbindlist( lapply(b90.multi, FUN = function(x) { x$output[,c("yr", "doy", "evap")] }), idcol = "srun") evapday$dates <- as.Date(paste(evapday$yr, evapday$doy),"%Y %j") srun_nms <- unique(evapday$srun) with(evapday[evapday$srun == srun_nms[1], ], plot(dates, cumsum(evap), type = "n", ylim = c(0,100)) ) for (i in 1:length(b90.multi)){ with(evapday[evapday$srun == srun_nms[i], ], lines(dates, cumsum(evap))) }data("slb1_meteo") data("slb1_soil") # Set up lists containing model control options and model parameters: parms <- set_paramLWFB90() # choose the 'Coupmodel' shape option for the annual lai dynamic, # with fixed budburst and leaf fall dates: opts <- set_optionsLWFB90(startdate = as.Date("2003-06-01"), enddate = as.Date("2003-06-30"), lai_method = 'Coupmodel', budburst_method = 'fixed', leaffall_method = 'fixed') # Derive soil hydraulic properties from soil physical properties using pedotransfer functions soil <- cbind(slb1_soil, hydpar_wessolek_tab(slb1_soil$texture)) #set up data.frame with variable parameters n <- 10 set.seed(2021) vary_parms <- data.frame(shp_optdoy = runif(n,180,240), shp_budburst = runif(n, 0.1,1), winlaifrac = runif(n, 0,0.5), budburstdoy = runif(n,100,150), soil_materials.ths3 = runif(n, 0.3,0.5), # ths of material 3 maxlai = runif(n,2,7)) # add the soil as soil_nodes and soil materials to param_b90, so ths3 can be looked up parms[c("soil_nodes", "soil_materials")] <- soil_to_param(soil) # Make a Multirun-Simulation b90.multi <- run_multi_LWFB90(paramvar = vary_parms, param_b90 = parms, options_b90 = opts, climate = slb1_meteo) names(b90.multi) # extract results evapday <- data.table::rbindlist( lapply(b90.multi, FUN = function(x) { x$output[,c("yr", "doy", "evap")] }), idcol = "srun") evapday$dates <- as.Date(paste(evapday$yr, evapday$doy),"%Y %j") srun_nms <- unique(evapday$srun) with(evapday[evapday$srun == srun_nms[1], ], plot(dates, cumsum(evap), type = "n", ylim = c(0,100)) ) for (i in 1:length(b90.multi)){ with(evapday[evapday$srun == srun_nms[i], ], lines(dates, cumsum(evap))) }
Wrapper function for run_LWFB90 to make multiple parallel
simulations of one or several parameter sets, for a series of sites with
individual climate and soil, or individual parameter sets for each
climate/soil combinations.
run_multisite_LWFB90( options_b90, param_b90, soil = NULL, climate, climate_args = NULL, all_combinations = FALSE, cores = 2, show_progress = TRUE, ... )run_multisite_LWFB90( options_b90, param_b90, soil = NULL, climate, climate_args = NULL, all_combinations = FALSE, cores = 2, show_progress = TRUE, ... )
options_b90 |
Named list of model control options to be used in all simulations |
param_b90 |
Named list of parameters to be used in all simulations, or a list of multiple parameter sets. |
soil |
Data.frame with soil properties to be used in all simulations, or a list of data.frames with different soil profiles. |
climate |
Data.frame with climate data, or a list of climate
data.frames. Alternatively, a function can be supplied that returns a
data.frame. Arguments to the function can be passed via
|
climate_args |
List of named lists of arguments passed to
|
all_combinations |
Logical: Set up and run all possible combinations of
individual |
cores |
Number of cores to use for parallel processing. |
show_progress |
Logical: Show progress bar? Default is |
... |
Further arguments passed to |
A named list with the results of the single runs as returned by
run_LWFB90. Simulation or processing errors are passed on. The
names of the returned list entries are concatenated from the names of the
input list entries in the following form: <climate> <soil> <param_b90>. If
climate is a function, the names for <climate> are taken from the
names of climate_args.
The returned list of single run results can become very large, if many
simulations are performed and the selected output contains daily resolution
data sets, especially daily layer-wise soil moisture data. To not overload
memory, it is advised to reduce the returned simulation results to a minimum,
by carefully selecting the output, and make use of the option to pass a list
of functions to run_LWFB90 via argument output_fun.
These functions perform directly on the output of a single run simulation,
and can be used for aggregating model output on-the-fly, or for writing
results to a file or database. The regular output of
run_LWFB90 can be suppressed by setting rtrn.output =
FALSE, for exclusively returning the output of such functions.
To provide full flexibility, the names of the current soil,
param_b90, and climate are automatically passed as additional
arguments (soil_nm, param_nm,clim_nm) to
run_LWFB90 and in this way become available to functions passed
via output_fun. In order to not overload the memory with climate input
data, it is advised to provide a function instead of a list of climate
data.frames, and specify its arguments for individual sites in
climate_args, in case many sites with individual climates will be
simulated.
This function provides a progress bar via the package progressr if
show_progress=TRUE. The parallel computation is then wrapped with
progressr::with_progress() to enable progress reporting from
distributed calculations. The appearance of the progress bar (including
audible notification) can be customized by the user for the entire session
using progressr::handlers() (see vignette('progressr-intro')).
data("slb1_meteo") data("slb1_soil") opts <- set_optionsLWFB90(budburst_method = "Menzel", enddate = as.Date("2002-12-31")) # define parameter sets param_l <- list(spruce = set_paramLWFB90(maxlai = 5, budburst_species = "Picea abies (frueh)", winlaifrac = 0.8), beech = set_paramLWFB90(maxlai = 6, budburst_species = "Fagus sylvatica", winlaifrac = 0)) soil <- cbind(slb1_soil, hydpar_wessolek_tab(slb1_soil$texture)) # define list of soil objects soils <- list(soil1 = soil, soil2 = soil) # define list of climate objects climates <- list(clim1 = slb1_meteo, clim2 = slb1_meteo) # run two parameter sets on a series of climate and soil-objects res <- run_multisite_LWFB90(param_b90 = param_l, options_b90 = opts, soil = soils, climate = climates) names(res) # set up and run individual parameter sets for individual locations # set up location parameters loc_parm <- data.frame(loc_id = names(climates), coords_y = c(48.0, 54.0), eslope = c(30,0), aspect = c(180,0)) # create input list of multiple param_b90 list objects param_l <- lapply(names(climates), function(x, loc_parms) { parms <- set_paramLWFB90() parms[match(names(loc_parm),names(parms), nomatch = 0)] <- loc_parm[loc_parm$loc_id == x, which(names(loc_parm) %in% names(parms))] parms }, loc_parm = loc_parm) names(param_l) <- c("locpar1", "locpar2") res <- run_multisite_LWFB90(param_b90 = param_l, options_b90 = opts, soil = soils, climate = climates) names(res)data("slb1_meteo") data("slb1_soil") opts <- set_optionsLWFB90(budburst_method = "Menzel", enddate = as.Date("2002-12-31")) # define parameter sets param_l <- list(spruce = set_paramLWFB90(maxlai = 5, budburst_species = "Picea abies (frueh)", winlaifrac = 0.8), beech = set_paramLWFB90(maxlai = 6, budburst_species = "Fagus sylvatica", winlaifrac = 0)) soil <- cbind(slb1_soil, hydpar_wessolek_tab(slb1_soil$texture)) # define list of soil objects soils <- list(soil1 = soil, soil2 = soil) # define list of climate objects climates <- list(clim1 = slb1_meteo, clim2 = slb1_meteo) # run two parameter sets on a series of climate and soil-objects res <- run_multisite_LWFB90(param_b90 = param_l, options_b90 = opts, soil = soils, climate = climates) names(res) # set up and run individual parameter sets for individual locations # set up location parameters loc_parm <- data.frame(loc_id = names(climates), coords_y = c(48.0, 54.0), eslope = c(30,0), aspect = c(180,0)) # create input list of multiple param_b90 list objects param_l <- lapply(names(climates), function(x, loc_parms) { parms <- set_paramLWFB90() parms[match(names(loc_parm),names(parms), nomatch = 0)] <- loc_parm[loc_parm$loc_id == x, which(names(loc_parm) %in% names(parms))] parms }, loc_parm = loc_parm) names(param_l) <- c("locpar1", "locpar2") res <- run_multisite_LWFB90(param_b90 = param_l, options_b90 = opts, soil = soils, climate = climates) names(res)
Create a list of model control options
set_optionsLWFB90(...)set_optionsLWFB90(...)
... |
Named values to be included in return value. |
start date of the simulation.
end date of the simulation.
use global solar radiation (='globrad') or sunshine
duration hours (='sunhours') for net radiation calculation?
number of precipitation intervals per day (default is
1). If prec_interval > 1, the precip-argument has to be
provided to run_LWFB90
correct precipitation data for wind and evaporation losses
using correct_prec?
name of method for
budburst calculation. If 'constant' or 'fixed', budburst day
of year from parameters is used. All other methods calculate budburst day
of year dynamically from airtemperatures, and the method name is passed to
the start.method-argument of vegperiod.
name of method for leaffall calculation. If
'constant' or 'fixed', beginning of leaffall (day of year)
from parameters is used. All other methods calculate budburst day of year
dynamically from temperatures, and the method name is passed to the
end.method-argument of vegperiod.
name of input for longterm (interannual) plant
development. standprop_input = 'parameters': yearly values of stand
properties height, sai, densef, lai are taken from individual parameters,
standprop_input = 'table': values from standprop_table
provided in parameters are used.
interpolation
method for aboveground stand properties. 'linear' or
'constant', see approx.method-argument of
approx_standprop.
Should
yearly changes of stand properties (growth) only take place during the
growth period? If TRUE, linear interpolation of height, sai, densef
and age are made from budburst until leaffall. During winter values are
constant. Beginning and end of the growth period are taken from parameters
budburstdoy and leaffalldoy. See use_growthperiod-argument of
approx_standprop.
name of method for
constructing seasonal course leaf area index development from parameters.
Passed to method-argument of make_seasLAI.
name of retention & conductivity model: "CH" for Clapp/Hornberger, "MvG" for Mualem/van Genuchten
method
name of the root length density depth distribution function. Any of the
names accepted by make_rootden are allowed. Additionally,
'soilvar' can be used if the root length density depth distribution
is specified in column 'rootden' in the soil-data.frame
A list of model control options for use as
options_b90-argument in run_LWFB90.
# Default options options_b90 <- set_optionsLWFB90() # Include specific options options_b90_dynamic_phenology <- set_optionsLWFB90(budburst_method = 'Menzel', leaffall_method ='vonWilpert')# Default options options_b90 <- set_optionsLWFB90() # Include specific options options_b90_dynamic_phenology <- set_optionsLWFB90(budburst_method = 'Menzel', leaffall_method ='vonWilpert')
Returns a [7,5] matrix with a default selection of LWF-Brook90 output
data sets for the use as 'output'-argument run_LWFB90.
set_outputLWFB90(output = NULL, edit = FALSE)set_outputLWFB90(output = NULL, edit = FALSE)
output |
optional |
edit |
open R's data-editor ? |
a [7,5]-matrix containing 0 and 1 for use as
output-argument in run_LWFB90
# create matrix with default selection output <- set_outputLWFB90() output # modify output[,] <- 0L output[,3] <- 1L output["Evap", c("Ann","Mon")] <- 1L output# create matrix with default selection output <- set_outputLWFB90() output # modify output[,] <- 0L output[,3] <- 1L output["Evap", c("Ann","Mon")] <- 1L output
Create the list of model parameters
set_paramLWFB90(...)set_paramLWFB90(...)
... |
Named arguments to be included in return value. |
A list with model parameters for use as param_b90-argument in
run_LWFB90.
| Name | Description | Unit | Group |
| czr | Ratio of roughness length to mean height for smooth closed canopies for heights greater than HR when LAI>LPC. Default: 0.05 | - | Canopy |
| czs | Ratio of roughness length to mean height for smooth closed canopies for heights less than HS when LAI>LPC. Default: 0.13 | m | Canopy |
| hr | Smallest height to which CZR applies. Default: 10 | m | Canopy |
| hs | Largest height to which CZS applies. Default: 1 | m | Canopy |
| lpc | Minimum leaf area index defining a closed canopy. Default: 4 | - | Canopy |
| lwidth | Average leaf width. Default: 0.1 | m | Canopy |
| nn | Eddy diffusivity extinction coefficient within canopy. Default: 2.5 | - | Canopy |
| rhotp | Ratio of total leaf area to projected area. Default: 2 | - | Canopy |
| zminh | Reference height for weather data above the canopy top height. Default: 2 | m | Canopy |
| dslope | Slope for downslope-flow. Default: 0 | deg | Flow |
| bypar | Switch to allow (1) or prevent (0) bypass flow in deeper layers. Default: 0 | - | Flow |
| drain | Switch for lower boundary condition to be free drainage (1) or no flow (0). Default: 1 | - | Flow |
| slopelen | Slope length for downslope-flow. Default: 200 | m | Flow |
| gsc | Rate constant for ground water discharge (remember that a first order groundwater reservoir is placed below the soil profile), for value 0 there is no discharge. Default: 0 | d-1 | Flow |
| gsp | Seepage fraction of groundwater discharge. Default: | - | Flow |
| ilayer | Number of layers from top to which infiltration is distributed. Default: 0 | - | Flow |
| imperv | Fraction of area which has an impermeable surface (like roads). Default: 0 | - | Flow |
| infexp | Shape parameter for distribution of infiltration in first ILayer, for value 0 infiltration is in top layer only. Default: 0 | - | Flow |
| qffc | Quickflow fraction of infiltrating water at field capacity, for value 0 there is no quickflow (bypass or surface) unless soil profile surface becomes saturated. Default: 0 | - | Flow |
| qfpar | Quickflow shape parameter. Default: 1 | - | Flow |
| qlayer | Number of layers which are considered for generation of surface or source area flow. Default: 0 | - | Flow |
| water_table_depth | Depth of the water table for a constant head boundary. -9999 means gravitational flow boundary. Default: -9999 | m | Flow |
| gwatini | Initial value of groundwater storage. Default: 0 | mm | Initial |
| snowini | Initial value of water content of snow pack. Default: 0 | mm | Initial |
| snowlqini | Initial value of liquid water content of snow pack. Default: 0 | mm | Initial |
| snowccini | Initial value of cold content of snow pack (positive). Default: 0 | MJ m-2 | Initial |
| intrainini | Initial value of intercepted rain. Default: 0 | mm | Initial |
| intsnowini | Initial value of intercepted snow. Default: 0 | - | Initial |
| psiini | Initial pressure head of soil layers. May have the same length as row.names(soil). Default: -6.3 | kPa | Initial |
| cintrl | Maximum interception storage of rain per unit LAI. Default: 0.15 | mm | Interception |
| cintrs | Maximum interception storage of rain per unit SAI. Default: 0.15 | mm | Interception |
| cintsl | Maximum interception storage of snow per unit LAI. Default: 0.6 | mm | Interception |
| cintss | Maximum interception storage of snow per unit SAI. Default: 0.6 | mm | Interception |
| frintlai | Intercepted fraction of rain per unit LAI. Default: 0.06 | - | Interception |
| frintsai | Intercepted fraction of rain per unit SAI. Default: 0.06 | - | Interception |
| fsintlai | Intercepted fraction of snow per unit LAI. Default: 0.04 | - | Interception |
| fsintsai | Intercepted fraction of snow per unit SAI. Default: 0.04 | - | Interception |
| pdur | Average duration of precipitation events for each month of the year. Default: rep(4,12) | hours | Interception |
| alb | Albedo of soil/vegetation surface without snow. Default: 0.2 | - | Meteo |
| albsn | Albedo of soil/vegetation surface with snow. Default: 0.5 | - | Meteo |
| c1 | Intercept of relation of solar radiation to sunshine duration. Default: 0.25 | - | Meteo |
| c2 | Intercept of relation of solar radiation to sunshine duration. Default: 0.5 | - | Meteo |
| c3 | Constant between 0 and 1 that determines the cloud correction to net longwave radiation from sunshine duration. Default: 0.2 | - | Meteo |
| fetch | Fetch upwind of the weather station at which wind speed was measured. Default: 5000 | m | Meteo |
| ksnvp | Correction factor for snow evaporation. Default: 0.3 | - | Meteo |
| wndrat | Average ratio of nighttime to daytime wind speed. Default: 0.3 | - | Meteo |
| z0s | Surface roughness of snow cover. Default: 0.001 | m | Meteo |
| z0w | Roughness length at the weather station at which wind speed was measured. Default: 0.005 (Grass) | m | Meteo |
| coords_x | Longitude value (decimal degrees) of the simulation location (has no effect on simulation results). Default: 9.91 | m | Meteo |
| coords_y | Latitude value (decimal degrees) of the simulation location. Default: 51.54 | m | Meteo |
| zw | Height at which wind speed was measured. Default: 2 | m | Meteo |
| eslope | Slope for evapotranspiration and snowmelt calculation. Default: 0 | deg | Meteo |
| aspect | Mean exposition of soil surface at soil profile (north: 0, east: 90, south: 180, west: 270). Default: 0 | deg | Meteo |
| obsheight | Mean height of obstacles on soil surface (grass, furrows etc.), used to calculate soil surface roughness. Default: 0.025 | m | Meteo |
| prec_corr_statexp | Station exposure situation of prec measurements (passed to correct_prec Default: 'mg' |
Meteo | |
| dpsimax | Maximum potential difference considered equal. Default: 5e-04 | kPa | Numerical |
| dswmax | Maximum change allowed in SWATI. Default: 0.05 | percent of SWATMX | Numerical |
| dtimax | Maximum iteration time step. Default: 0.5 | d | Numerical |
| budburst_species | Name of tree species for estimating budburst doy using Menzel-model (passed to vegperiod) Default: 'Fagus sylvatica' |
- | Plant |
| budburstdoy | Budburst day of year - passed to make_seasLAI. Default: 121 |
doy | Plant |
| emergedur | Leaf growth duration until maxlai is reached.. Default: 28 | d | Plant |
| height | Plant height. Default: 25 | m | Plant |
| height_ini | Initial plant height at the beginning of the simulation. Used when options_b90$standprop_interp = 'linear' (see approx_standprop). Default: 25 |
m | Plant |
| leaffalldoy | Day of year when leaf fall begins - passed to make_seasLAI Default: 279 |
doy | Plant |
| leaffalldur | Number of days until minimum lai is reached - passed to make_seasLAI Default: 58 |
d | Plant |
| sai | Steam area index. Default: 1 | - | Plant |
| sai_ini | Initial stem area index at the beginning of the simulation. Used when options_b90$standprop_interp = 'linear' (see approx_standprop), Default: 1 |
- | Plant |
| shp_leaffall | Shape parameter for leaf fall phase - passed to make_seasLAI Default: 0.3 |
- | Plant |
| shp_budburst | Shape parameter for leaf growth phase - passed to make_seasLAI Default: 3 |
- | Plant |
| shp_optdoy | Day of year when optimum value is reached - passed to make_seasLAI Default: 210 |
doy | Plant |
| lai_doy | Day of year values for lai-interpolation - passed to make_seasLAI |
doy | Plant |
| lai_frac | Fractional lai values for lai interpolation, corresponding to lai_doy - passed to make_seasLAI Default: 210 |
doy | Plant |
| winlaifrac | Minimum LAI as a fraction of maxlai. Default: 0 | - | Plant |
| standprop_table | Data.frame with yearly values of vegetation properties with columns 'year','age', 'height', 'maxlai', 'sai', 'densef' | Plant | |
| cs | Ratio of projected stem area index to canopy height. Default: 0.035 | m-1 | Plant |
| densef | Density factor for MaxLAI, CS, RtLen, RPlant, not <.001, 1 for typical stand. Default: 1 | - | Plant |
| densef_ini | Initial density factor at the beginning of the simulation. Used when options_b90$standprop_interp = 'linear' (see approx_standprop). Default: 1 |
- | Plant |
| maxlai | Maximum projected leaf area index - passed to make_seasLAI Default: 5 |
- | Plant |
| radex | Extinction coefficient for solar radiation and net radiation in the canopy. Default: 0.5 | - | Potential Transpiration |
| cvpd | Vapour pressure deficit at which leaf conductance is halved. Default: 2 | kPa | Potential Transpiration |
| glmax | Maximum leaf vapour conductance when stomata are fully open. Default: 0.0053 | m s-1 | Potential Transpiration |
| glmin | Minimum leaf vapour conductance when stomata are closed. Default: 0.0003 | m s-1 | Potential Transpiration |
| r5 | Solar radiation level at which leaf conductance is half of its value at RM. Default: 100 | W m-2 | Potential Transpiration |
| rm | Nominal maximum solar shortwave radiation possible on a leaf (to reach glmax). Default: 1000 | W m-2 | Potential Transpiration |
| t1 | Lower suboptimal temperature threshold for stomata opening - temperature relation. Default: 10 | deg C | Potential Transpiration |
| t2 | Upper suboptimal temperature threshold for stomata opening - temperature relation. Default: 30 | deg C | Potential Transpiration |
| th | Upper temperature threshold for stomata closure. Default: 40 | deg C | Potential Transpiration |
| tl | Lower temperature threshold for stomata closure. Default: 0 | deg C | Potential Transpiration |
| betaroot | Shape parameter for rootlength density depth distribution. Default: 0.97 | - | Roots |
| maxrootdepth | Maximum root depth (positive downward) - passed to make_rootden. Default: -1.5 | m | Roots |
| rootden_table | Data.frame of relative root density depth distribution with columns 'depth' and 'rootden' | Roots | |
| rstemp | Base temperature for snow-rain transition. Default: -0.5 | deg C | Snow |
| ccfac | Cold content factor. Default: 0.3 | MJ m-2 d-1 K-1 | Snow |
| grdmlt | Rate of groundmelt of snowpack. Default: 0.35 | mm d-1 | Snow |
| laimlt | Parameter for snowmelt dependence on LAI. Default: 0.2 | - | Snow |
| maxlqf | Maximum liquid water fraction of Snow. Default: 0.05 | - | Snow |
| melfac | Degree day melt factor for open. Default: 1.5 | MJ m-2 d-1 K-1 | Snow |
| saimlt | Parameter for snowmelt dependence on SAI. Default: 0.5 | - | Snow |
| snoden | Snow density. Default: 0.3 | mm mm-1 | Snow |
| rssa | Soil evaporation resistance at field capacity. Default: 100 | s m-1 | Soilevap |
| rssb | Exponent in relation of RSS to water potential. Default: 1 | - | Soilevap |
| soil_nodes | Data.frame with soil nodes discretization passed to LWF-Brook90 | - | Soil |
| soil_materials | Data.frame with soil materials (hydrualic parameters) passed to LWF-Brook90 | - | Soil |
| age_ini | Age of stand (for root development). Default: 100 | a | Water supply |
| initrdep | Initial root depth. Default: 0.25 | m | Water supply |
| initrlen | Initial water-absorbing root length per unit area. Default: 12 | m/m-2 | Water supply |
| rgroper | Period of net root growth. A value of 0 prevents root growth. Default: 0 | a | Water supply |
| rgrorate | Vertical root growth rate. Default: 0.03 | m a-1 | Water supply |
| fxylem | Fraction of internal plant resistance to water flow that is in the Xylem. Default: 0.5 | - | Water supply |
| maxrlen | Total length of fine roots per unit ground area. Default: 3000 | m m-2 | Water supply |
| mxkpl | Maximum internal conductivity for water flow through the plants. Default: 8 | mm d-1 MPa-1 | Water supply |
| nooutf | Switch that prevents outflow from the root to the soil when the soil is dry. Default: 1 | - | Water supply |
| psicr | Critical leaf water potential at which stomates close. Default: -2 | MPa | Water supply |
| rrad | Average radius of the fine or water-absorbing roots. Default: 0.35 | mm | Water supply |
# Default parameter parms <- set_paramLWFB90() # Include specific parameters parms_maxlai <- set_paramLWFB90(maxlai = c(4,6,5), height =20)# Default parameter parms <- set_paramLWFB90() # Include specific parameters parms_maxlai <- set_paramLWFB90(maxlai = c(4,6,5), height =20)
A dataset containing daily weather variables for the period 1960-2013
slb1_meteoslb1_meteo
A data.frame with 19724 rows and 9 variables
date
daily minimum temperature, deg C
daily maximum temperature, deg C
daily mean temperature, deg C
daily sum of precipitation, mm
relative Humidity, %
daily sum of global radiation, MJ/m²
daily mean wind speed measured at 10 m above ground, m/s
daily vapour pressure, kPa
Hourly precipitation data from Solling Beech experimental site 'SLB1' for year 2013
slb1_prec2013_hhslb1_prec2013_hh
A data.frame with 8760 rows and 2 variables
date
hourly sum of precipitation, mm
A dataset containing the soil horizons' physical properties
slb1_soilslb1_soil
A data.frame with 21 rows and 10 variables
horizon symbol
upper layer boundary, m
lower layer boundary, m
soil texture according to German soil texture classification system
bulk density of the fine earth, g/cm³
fraction of coarse material
sand content, mass-%
silt content, mass-%
clay content, mass-%
organic carbon content, mass-%
A dataset containing the forests's stand properties
slb1_standpropslb1_standprop
A data.frame with 49 rows and 7 variables
Year of observation
Tree species
age of the main stand
average height of the trees, m
maximum leaf area index, m²/m²
stem area index, m²/m²
stand density
Split up soil into materials and soil nodes.
soil_to_param(soil, imodel = "MvG")soil_to_param(soil, imodel = "MvG")
soil |
Data.frame with soil layer boundaries ('upper', 'lower') and hydraulic parameters. When imodel = 'MvG', columns of soil have to be named 'ths', 'thr', 'alpha', 'npar', 'ksat', 'tort', 'gravel'. When imodel = 'CH', columns have to be named thsat , 'thetaf','psif', 'bexp','kf', 'wetinf', 'gravel'. |
imodel |
Name of the hydraulic model ('MvG' or 'CH') |
a list with data.frames 'soil_nodes' and 'soil_materials'
data(slb1_soil) soil <- slb1_soil soil <- cbind(soil, hydpar_wessolek_tab(soil$texture)) str(soil) soil_layers_materials <- soil_to_param(soil) soil_layers_materialsdata(slb1_soil) soil <- slb1_soil soil <- cbind(soil, hydpar_wessolek_tab(soil$texture)) str(soil) soil_layers_materials <- soil_to_param(soil) soil_layers_materials
Takes a data.frame of yearly stand properties, trims/extends the columns height, maxlai,
sai, densef, and age for the years in out_yrs, and updates the provided parameter list.
standprop_yearly_to_param(standprop_yearly, param_b90, out_yrs)standprop_yearly_to_param(standprop_yearly, param_b90, out_yrs)
standprop_yearly |
A data.frame or data.table with columns 'year', 'height', 'maxlai', 'sai', 'densef', 'age'. |
param_b90 |
A list object to update. |
out_yrs |
Vector of years for which parameters should be updated. |
The param_b90 list-object with updated items maxlai, height, height_ini, sai, sai_ini, densef, densef_ini, age, age_ini.
param_b90 <- set_paramLWFB90() dat <- slb1_standprop years <- 2002:2005 param.new <- standprop_yearly_to_param(dat, param_b90, years) identical(param.new$maxlai, dat$maxlai[dat$year %in% years]) identical(param.new$height, dat$height[dat$year %in% years])param_b90 <- set_paramLWFB90() dat <- slb1_standprop years <- 2002:2005 param.new <- standprop_yearly_to_param(dat, param_b90, years) identical(param.new$maxlai, dat$maxlai[dat$year %in% years]) identical(param.new$height, dat$height[dat$year %in% years])