Function spwb() is a water balance model that determines changes in soil moisture,
soil water potentials, plant transpiration and drought stress at daily steps for a given forest stand
during a period specified in the input climatic data. Function pwb() performs plant water balance
only (i.e. soil moisture dynamics is an input) at daily steps for a given forest stand
during a period specified in the input climatic data. On both simulation functions plant transpiration
and photosynthesis processes are conducted with different level of detail depending on the transpiration mode.
spwb(
x,
meteo,
latitude,
elevation,
slope = NA_real_,
aspect = NA_real_,
CO2ByYear = numeric(0),
waterTableDepth = NA_real_
)pwb(
x,
meteo,
W,
latitude,
elevation,
slope = NA_real_,
aspect = NA_real_,
canopyEvaporation = numeric(0),
snowMelt = numeric(0),
soilEvaporation = numeric(0),
herbTranspiration = numeric(0),
CO2ByYear = numeric(0)
)
Function spwb returns a list of class 'spwb' whereas function pwb returns a list of class 'pwb'.
There are many elements in common in these lists, so they are listed here together:
"latitude": Latitude (in degrees) given as input.
"topography": Vector with elevation, slope and aspect given as input.
"weather": A copy of the input weather data frame.
"spwbInput": An copy of the object x of class spwbInput given as input.
"spwbOutput": An copy of the final state of the object x of class spwbInput.
"WaterBalance": A data frame where different variables (in columns) are given for each simulated day (in rows):
"PET": Potential evapotranspiration (in mm).
"Precipitation": Input precipitation (in mm).
"Rain": Precipitation as rainfall (in mm).
"Snow": Precipitation as snowfall (in mm).
"NetRain": Net rain, after accounting for interception (in mm).
"Infiltration": The amount of water infiltrating into the soil (in mm).
"InfiltrationExcess": Excess infiltration in the topmost layer leading to an increase in runoff (in mm).
"SaturationExcess": Excess saturation in the topmost layer leading to an increase in runoff (in mm).
"CapillarityRise": Water entering the soil via capillarity rise (mm) from the water table, if waterTableDepth is supplied.
"Runoff": The amount of water exported via surface runoff (in mm).
"DeepDrainage": The amount of water exported via deep drainage (in mm).
"Evapotranspiration": Evapotranspiration (in mm).
"SoilEvaporation": Bare soil evaporation (in mm).
"HerbTranspiration": Transpiration due to the herbaceous layer (in mm).
"PlantExtraction": Amount of water extracted from soil by woody plants (in mm).
"Transpiration": Woody plant transpiration (in mm).
"HydraulicRedistribution": Water redistributed among soil layers, transported through the plant hydraulic network.
"EnergyBalance": A data frame with the daily values of energy balance components for the soil and the canopy (only for transpirationMode = "Sperry" or transpirationMode = "Sureau").
"Temperature": A data frame with the daily values of minimum/mean/maximum temperatures for the atmosphere (input), canopy and soil (only for transpirationMode = "Sperry" or transpirationMode = "Sureau").
"Soil": A list with the following subelements:
"SWC": Soil water content (percent of soil volume) in each soil layer (and overall).
"RWC": Relative soil moisture content (relative to field capacity) in each soil layer (and overall).
"REW": Relative extractable water (min. psi = -5 MPa) in each soil layer (and overall).
"ML": Soil water volume in each soil layer (in L/m2) (and overall).
"Psi": Soil water potential in each soil layer (in MPa) (and overall).
"PlantExt": Plant extraction from each soil layer (in mm) (and overall).
"HydraulicInput": Water that entered the layer coming from other layers and transported via the plant hydraulic network (in mm) (and overall).
"Snow": A data frame where the following variable (in columns) is given for each simulated day (in rows):
"SWE": Snow water equivalent (mm) of the snow pack.
"Stand": A data frame where different variables (in columns) are given for each simulated day (in rows):
"LAI": LAI of the stand (including the herbaceous layer and live + dead leaves of woody plants) (in m2/m2).
"LAIherb": LAI of the herbaceous layer (in m2/m2).
"LAIlive": LAI of the woody plants assuming all leaves are unfolded (in m2/m2).
"LAIexpanded": LAI of the woody plants with leaves actually unfolded (in m2/m2).
"LAIdead": LAI of the woody plants corresponding to dead leaves (in m2/m2).
"Cm": Water retention capacity of the canopy (in mm) (accounting for leaf phenology).
"LgroundPAR": The percentage of PAR that reaches the ground (accounting for leaf phenology).
"LgroundSWR": The percentage of SWR that reaches the ground (accounting for leaf phenology).
"Plants": A list of daily results for plant cohorts (see below).
"subdaily": A list of objects of class spwb_day, one per day simulated (only if required in control parameters, see defaultControl).
When transpirationMode = "Granier", element "Plants" is a list with the following subelements:
"LAI": A data frame with the daily leaf area index for each plant cohort.
"LAIlive": A data frame with the daily leaf area index for each plant cohort, assuming all leaves are unfolded (in m2/m2).
"FPAR": A data frame with the fraction of PAR at the canopy level of each plant cohort.
"AbsorbedSWRFraction": A data frame with the fraction of SWR absorbed by each plant cohort.
"Transpiration": A data frame with the amount of daily transpiration (in mm) for each plant cohort.
"GrossPhotosynthesis": A data frame with the amount of daily gross photosynthesis (in g C·m-2) for each plant cohort.
"PlantPsi": A data frame with the average daily water potential of each plant (in MPa).
"LeafPLC": A data frame with the average daily proportion of leaf conductance loss of each plant ([0-1]).
"StemPLC": A data frame with the average daily proportion of stem conductance loss of each plant ([0-1]).
"PlantWaterBalance": A data frame with the daily balance between transpiration and soil water extraction for each plant cohort.
"LeafRWC": A data frame with the average daily leaf relative water content of each plant (in percent).
"StemRWC": A data frame with the average daily stem relative water content of each plant (in percent).
"LFMC": A data frame with the daily live fuel moisture content (in percent of dry weight).
"PlantStress": A data frame with the amount of daily stress [0-1] suffered by each plant cohort (relative whole-plant conductance).
If transpirationMode="Sperry" or transpirationMode="Sureau", element "Plants" is a list with the following subelements:
"LAI": A data frame with the daily leaf area index for each plant cohort.
"AbsorbedSWR": A data frame with the daily SWR absorbed by each plant cohort.
"NetLWR": A data frame with the daily net LWR by each plant cohort.
"Transpiration": A data frame with the amount of daily transpiration (in mm) for each plant cohorts.
"GrossPhotosynthesis": A data frame with the amount of daily gross photosynthesis (in g C·m-2) for each plant cohort.
"NetPhotosynthesis": A data frame with the amount of daily net photosynthesis (in g C·m-2) for each plant cohort.
"dEdP": A data frame with mean daily values of soil-plant conductance (derivative of the supply function) for each plant cohort.
"PlantWaterBalance": A data frame with the daily balance between transpiration and soil water extraction for each plant cohort.
"SunlitLeaves" and "ShadeLeaves": A list with daily results for sunlit and shade leaves:
"PsiMin": A data frame with the minimum (midday) daily sunlit or shade leaf water potential (in MPa).
"PsiMax": A data frame with the maximum (predawn) daily sunlit or shade leaf water potential (in MPa).
"LeafPsiMin": A data frame with the minimum (midday) daily (average) leaf water potential of each plant (in MPa).
"LeafPsiMax": A data frame with the maximum (predawn) daily (average) leaf water potential of each plant (in MPa).
"LeafRWC": A data frame with the average daily leaf relative water content of each plant (in percent).
"StemRWC": A data frame with the average daily stem relative water content of each plant (in percent).
"LFMC": A data frame with the daily live fuel moisture content (in percent of dry weight).
"StemPsi": A data frame with the minimum daily stem water potential of each plant (in MPa).
"LeafPLC": A data frame with the average daily proportion of leaf conductance loss of each plant ([0-1]).
"StemPLC": A data frame with the average daily proportion of stem conductance loss of each plant ([0-1]).
"RootPsi": A data frame with the minimum daily root water potential of each plant (in MPa).
"RhizoPsi": A list of data frames (one per plant cohort) with the minimum daily root water potential of each plant (in MPa).
"PlantStress": A data frame with the amount of daily stress [0-1] suffered by each plant cohort (relative whole-plant conductance).
An object of class spwbInput.
A data frame with daily meteorological data series.
Row names of the data frame should correspond to date strings with format "yyyy-mm-dd" (see Date). Alternatively,
a column called "dates" or "Dates" can contain Date or POSIXct classes.
The following columns are required and cannot have missing values:
MinTemperature: Minimum temperature (in degrees Celsius).
MaxTemperature: Maximum temperature (in degrees Celsius).
Precipitation: Precipitation (in mm).
The following columns are required but can contain missing values (NOTE: missing values will raise warnings):
MinRelativeHumidity: Minimum relative humidity (in percent).
MaxRelativeHumidity: Maximum relative humidity (in percent).
Radiation: Solar radiation (in MJ/m2/day).
The following columns are optional:
WindSpeed: Above-canopy wind speed (in m/s). This column may not exist, or can be left with NA values. In both cases simulations will assume a constant value specified in defaultControl.
CO2: Atmospheric (above-canopy) CO2 concentration (in ppm). This column may not exist, or can be left with NA values. In both cases simulations will assume a constant value specified in defaultControl.
Patm: Atmospheric pressure (in kPa). This column may not exist, or can be left with NA values. In both cases, a value is estimated from elevation.
Latitude (in degrees).
Elevation above sea level (in m), slope (in degrees) and aspect (in degrees from North).
A named numeric vector with years as names and atmospheric CO2 concentration (in ppm) as values. Used to specify annual changes in CO2 concentration along the simulation (as an alternative to specifying daily values in meteo).
Water table depth (in mm). When not missing, capillarity rise will be allowed if lower than total soil depth.
A matrix with the same number of rows as meteo and as many columns as soil layers, containing the soil moisture of each layer as proportion of field capacity.
A vector of daily canopy evaporation (from interception) values (mm). The length should match the number of rows in meteo.
A vector of daily snow melt values (mm). The length should match the number of rows in meteo.
A vector of daily bare soil evaporation values (mm). The length should match the number of rows in meteo.
A vector of daily herbaceous transpiration values (mm). The length should match the number of rows in meteo.
Miquel De Cáceres Ainsa, CREAF
Nicolas Martin-StPaul, URFM-INRAE
The simulation functions allow using three different sub-models of transpiration and photosynthesis:
The sub-model corresponding to 'Granier' transpiration mode is illustrated by function transp_transpirationGranier and was described in De Caceres et al. (2015),
and implements an approach originally described in Granier et al. (1999).
The sub-model corresponding to 'Sperry' transpiration mode is illustrated by function transp_transpirationSperry and was described in De Caceres et al. (2021), and
implements a modelling approach originally described in Sperry et al. (2017).
The sub-model corresponding to 'Sureau' transpiration mode is illustrated by function transp_transpirationSureau and was described for model SurEau-Ecos v2.0 in Ruffault et al. (2022).
Simulations using the 'Sperry' or 'Sureau' transpiration mode are computationally much more expensive than 'Granier'.
De Cáceres M, Martínez-Vilalta J, Coll L, Llorens P, Casals P, Poyatos R, Pausas JG, Brotons L. (2015) Coupling a water balance model with forest inventory data to predict drought stress: the role of forest structural changes vs. climate changes. Agricultural and Forest Meteorology 213: 77-90 (doi:10.1016/j.agrformet.2015.06.012).
De Cáceres M, Mencuccini M, Martin-StPaul N, Limousin JM, Coll L, Poyatos R, Cabon A, Granda V, Forner A, Valladares F, Martínez-Vilalta J (2021) Unravelling the effect of species mixing on water use and drought stress in holm oak forests: a modelling approach. Agricultural and Forest Meteorology 296 (doi:10.1016/j.agrformet.2020.108233).
Granier A, Bréda N, Biron P, Villette S (1999) A lumped water balance model to evaluate duration and intensity of drought constraints in forest stands. Ecol Modell 116:269–283. https://doi.org/10.1016/S0304-3800(98)00205-1.
Ruffault J, Pimont F, Cochard H, Dupuy JL, Martin-StPaul N (2022) SurEau-Ecos v2.0: a trait-based plant hydraulics model for simulations of plant water status and drought-induced mortality at the ecosystem level. Geoscientific Model Development 15, 5593-5626 (doi:10.5194/gmd-15-5593-2022).
Sperry, J. S., M. D. Venturas, W. R. L. Anderegg, M. Mencuccini, D. S. Mackay, Y. Wang, and D. M. Love. 2017. Predicting stomatal responses to the environment from the optimization of photosynthetic gain and hydraulic cost. Plant Cell and Environment 40, 816-830 (doi: 10.1111/pce.12852).
spwbInput, spwb_day, plot.spwb,
extract, summary.spwb, forest, aspwb
# \donttest{
#Load example daily meteorological data
data(examplemeteo)
#Load example plot plant data
data(exampleforest)
#Default species parameterization
data(SpParamsMED)
#Define soil with default soil params (4 layers)
examplesoil <- defaultSoilParams(4)
#Initialize control parameters
control <- defaultControl("Granier")
#Initialize input
x1 <- spwbInput(exampleforest,examplesoil, SpParamsMED, control)
#Call simulation function
S1 <- spwb(x1, examplemeteo, latitude = 41.82592, elevation = 100)
#Switch to 'Sperry' transpiration mode
control <- defaultControl("Sperry")
#Initialize input
x2 <- spwbInput(exampleforest,examplesoil, SpParamsMED, control)
#Call simulation function
S2 <- spwb(x2, examplemeteo, latitude = 41.82592, elevation = 100)
#Switch to 'Sureau' transpiration mode
control <- defaultControl("Sureau")
#Initialize input
x3 <- spwbInput(exampleforest,examplesoil, SpParamsMED, control)
#Call simulation function
S3 <- spwb(x3, examplemeteo, latitude = 41.82592, elevation = 100)
# }
Run the code above in your browser using DataLab