def test_transpiration_rate_incrases_as_vapor_pressure_deficit_increases(
leaf_local_weather=setup_leaf_local_weather()):
air_temp = 25.
leaf_temp = 25.
atmospheric_pressure = leaf_local_weather['Pa']
gb = exchange.boundary_layer_conductance(
leaf_length=0.1,
wind_speed=leaf_local_weather['u'],
atm_pressure=leaf_local_weather['Pa'],
air_temp=air_temp,
ideal_gas_cst=exchange.R)
es = utilities.saturated_air_vapor_pressure(leaf_temp)
gs = exchange.an_gs_ci(photo_params=exchange.par_photo_default(),
meteo_leaf=leaf_local_weather,
psi=0.0,
leaf_temperature=34.,
model='misson',
g0=0.019,
rbt=2. / 3.,
ca=400.,
m0=5.278,
psi0=-0.1,
d0_leuning=30.,
steepness_tuzet=1.85)[-1]
transpiration = [
exchange.transpiration_rate(leaf_temp, ea, gs, gb,
atmospheric_pressure)
for ea in linspace(es, 0, 10)
]
assert all(x <= y for x, y in zip(transpiration, transpiration[1:]))
def transpiration_rate(leaf_temperature, ea, gs, gb, atm_pressure=101.3):
"""Computes transpiration rate per unit leaf surface area.
Args:
leaf_temperature (float): [°C] leaf temperature
ea (float): [kPa] air vapor pressure
gs (float): [mol m-2 s-1] stomatal conductance to water vapor
gb (float): [mol m-2 s-1] boundary layer conductance to water vapor
atm_pressure (float): [kPa] atmospheric pressure
Returns:
(float): [mol m-2leaf s-1] transpiration rate
Warnings:
gb is calculated for both sides of a flat leaf.
"""
gv = 1. / ((2. / gb) + (1. / gs))
es_l = utils.saturated_air_vapor_pressure(leaf_temperature)
transpiration = gv * ((es_l - ea) / atm_pressure)
return transpiration
def gas_exchange_rates(g,
photo_params,
photo_n_params,
gs_params,
meteo,
E_type2,
leaf_lbl_prefix='L',
rbt=2. / 3.):
"""Computes gas exchange fluxes at the leaf scale analytically.
Args:
g: a multiscale tree graph object
photo_params (dict): values at 25 °C of Farquhar's model (cf. :func:`par_photo_default`)
photo_n_params (dict): the (slope, intercept) values of the linear relationship between photosynthetic capacity
parameters (Vcmax, Jmax, TPU, Rd) and surface-based leaf Nitrogen content
gs_params (dict): parameters of the stomatal conductance model (model, g0, m0, psi0, D0, n)
meteo (pandas.DataFrame): meteorological data
E_type2 (str): one of 'Ei' (intercepted irradiance) or 'Eabs' (absorbed irradiance)
leaf_lbl_prefix (str): prefix of the label of the leaves
rbt (float): [m2 s ubar umol-1] the combined turbulance and boundary layer resistance to CO2 transport
References:
Evers et al. 2010.
Simulation of wheat growth and development based on organ-level photosynthesis and assimilate allocation.
Journal of Experimental Botany 61, 2203 – 2216.
Notes:
This function adds to each leaf the following properties:
An (float): [umol m-2 s-1] the net CO2 assimilation
Ci (float): [umol mol] intercellular CO2 concentration
gs (float): [mol m-2 s-1] stomatal conductance to water vapor
gb (float): [mol m-2 s-1] boundary layer conductance to water vapor
E (float): [mol m-2leaf s-1] transpiration per unit leaf surface area
"""
model, g0max, m0, psi0, D0, n = [
gs_params[ikey] for ikey in ('model', 'g0', 'm0', 'psi0', 'D0', 'n')
]
meteo_leaf = deepcopy(meteo)
meteo_leaf = meteo_leaf.iloc[0]
for vid in g:
if vid > 0:
node = g.node(vid)
if node.label.startswith(leaf_lbl_prefix):
t_air = meteo_leaf.Tac
hs = meteo_leaf.hs
u = meteo_leaf.u
c_a = meteo_leaf.Ca
atm_press = meteo_leaf.Pa
node.u = u # TODO replace the meso-wind speed (u) by a micro-wind speed at the level of each leaf
psi = node.properties()['psi_head']
t_leaf = node.properties()['Tlc']
ppfd_leaf = node.properties()[E_type2]
meteo_leaf['PPFD'] = ppfd_leaf
meteo_leaf['Rg'] = ppfd_leaf / (0.48 * 4.6)
leaf_par_photo = deepcopy(photo_params)
leaf_par_photo['Vcm25'] = photo_n_params['Vcm25_N'][
0] * node.Na + photo_n_params['Vcm25_N'][1]
leaf_par_photo['Jm25'] = photo_n_params['Jm25_N'][
0] * node.Na + photo_n_params['Jm25_N'][1]
leaf_par_photo['TPU25'] = photo_n_params['TPU25_N'][
0] * node.Na + photo_n_params['TPU25_N'][1]
leaf_par_photo['Rd'] = photo_n_params['Rd_N'][
0] * node.Na + photo_n_params['Rd_N'][1]
dhd_max = leaf_par_photo['dHd']
dhd = dHd_sensibility(psi,
t_leaf,
dhd_max=dhd_max,
dhd_inhib_beg=195.,
dHd_inhib_max=180.,
psi_inhib_beg=-.75,
psi_inhib_max=-2.,
temp_inhib_beg=32,
temp_inhib_max=33)
leaf_par_photo['dHd'] = dhd
node.par_photo = leaf_par_photo
g0 = g0max # *g0_sensibility(psi, psi_crit=-1, n=4)
a_n, c_c, c_i, gs = an_gs_ci(node.par_photo, meteo_leaf, psi,
t_leaf, model, g0, rbt, c_a, m0,
psi0, D0, n)
gb = boundary_layer_conductance(node.Length, u, atm_press,
t_air, R)
# Transpiration
es_a = utils.saturated_air_vapor_pressure(t_air)
ea = es_a * hs / 100.
e = transpiration_rate(t_leaf, ea, gs, gb, atm_press)
node.An = a_n
node.Ci = c_i
node.gs = gs
node.gb = gb
node.E = max(0., e)
return