def calc_et(self, tleaf, tair, vpd, pressure, wind, par, gh, gw, rnet):
"""
Calculate transpiration following Penman-Monteith at the leaf level
accounting for effects of leaf temperature and feedback on evaporation.
For example, if leaf temperature is above the leaf temp, it can increase
vpd, but it also reduves the lw and thus the net rad availble for
evaporaiton.
Parameters:
----------
tair : float
air temperature (deg C)
tleaf : float
leaf temperature (deg C)
vpd : float
Vapour pressure deficit (kPa, needs to be in Pa, see conversion
below)
pressure : float
air pressure (using constant) (Pa)
wind : float
wind speed (m s-1)
par : float
Photosynthetically active radiation (umol m-2 s-1)
gh : float
boundary layer conductance to heat - free & forced & radiative
components (mol m-2 s-1)
gw :float
conductance to water vapour - stomatal & bdry layer components
(mol m-2 s-1)
rnet : float
Net radiation (J m-2 s-1 = W m-2)
Returns:
--------
et : float
transpiration (mol H2O m-2 s-1)
lambda_et : float
latent heat flux (W m-2)
"""
# latent heat of water vapour at air temperature (J mol-1)
lambda_et = (c.H2OLV0 - 2.365E3 * tair) * c.H2OMW
# slope of sat. water vapour pressure (e_sat) to temperature curve
# (Pa K-1), note kelvin conversion in func
slope = (calc_esat(tair + 0.1) - calc_esat(tair)) / 0.1
# psychrometric constant (Pa K-1)
gamma = c.CP * c.AIR_MASS * pressure / lambda_et
# Y cancels in eqn 10
arg1 = slope * rnet + (vpd * c.KPA_2_PA) * gh * c.CP * c.AIR_MASS
arg2 = slope + gamma * gh / gw
# W m-2
LE = max(0.0, arg1 / arg2)
# transpiration, mol H20 m-2 s-1
et = max(0.0, LE / lambda_et)
return (et, LE)
def get_values(rh, Ca, tair, par, pressure, C):
esat = calc_esat(tair)
ea = rh / 100. * esat
vpd = (esat - ea) * c.PA_2_KPA
vpd = np.where(vpd < 0.05, 0.05, vpd)
#print rh, vpd
gs_store = []
et_store = []
An_store = []
tair_store = []
Cs_store = []
Ci_store = []
et_conv = c.MOL_WATER_2_G_WATER * c.G_TO_KG * c.SEC_TO_DAY
an_conv = c.UMOL_TO_MOL * c.MOL_C_TO_GRAMS_C * c.SEC_TO_DAY
for i,ta in enumerate(tair):
# We can't vary VPD independent of Tair ...
Td = get_dewpoint(ta, rh)
if Td > 0.0:
(An, gsw, et, LE,
Cs, Ci) = C.main(ta, par, vpd[i], wind, pressure, Ca)
gs_store.append(gsw) # mol H20 m-2 s-1
et_store.append(et * et_conv) # mm d-1
An_store.append(An * an_conv) # g C m-2 d-1
Cs_store.append(Cs)
Ci_store.append(Ci)
tair_store.append(ta)
return gs_store, et_store, An_store, tair_store, Cs_store, Ci_store
def get_values(rh, Ca, tair, par, pressure, C):
kpa_2_pa = 1000.
pa_2_kpa = 1.0 / kpa_2_pa
esat = calc_esat(tair, pressure)
ea = rh / 100. * esat
vpd = (esat - ea) * pa_2_kpa
#print rh, vpd
gs_store = []
et_store = []
An_store = []
tair_store = []
for i, ta in enumerate(tair):
#Td = get_dewpoint(ta, rh)
#if Td > 0.0:
(An, gsw, et, LE) = C.main(ta, par, vpd[i], wind, pressure, Ca)
gs_store.append(gsw) # mol H20 m-2 s-1
et_store.append(et * 18 * 0.001 * 86400.) # mm d-1
#et_store.append(LE)
An_store.append(An) #umol m-2 s-1
#An_store.append(An*12.*0.000001*86400.) # g C m-2 d-1
tair_store.append(ta)
return gs_store, et_store, An_store, tair_store
def calc_rnet(self, par, tair, tair_k, vpd, pressure):
"""
Net isothermal radaiation (Rnet, W m-2), i.e. the net radiation that
would be recieved if leaf and air temperature were the same.
References:
----------
Jarvis and McNaughton (1986)
Parameters:
----------
par : float
Photosynthetically active radiation (umol m-2 s-1)
tair : float
air temperature (deg C)
tair_k : float
air temperature (K)
vpd : float
Vapour pressure deficit (kPa, needs to be in Pa, see conversion
below)
pressure : float
air pressure (using constant) (Pa)
Returns:
--------
rnet : float
Net radiation (J m-2 s-1 = W m-2)
"""
print("**", par)
# Short wave radiation (W m-2)
#sw_rad = par * c.PAR_2_SW
# this matches CABLE's logic ... which means they are halving the
# SW_down that is used to compute the direct/diffuse terms and presumbly
# all other calcs
sw_rad = par / 4.6 # W m-2
# absorbed short-wave radiation
#SW_abs = self.SW_abs * math.cos(math.radians(self.angle)) * SW_rad
# atmospheric water vapour pressure (Pa)
ea = max(0.0, calc_esat(tair) - (vpd * c.KPA_2_PA))
# apparent emissivity for a hemisphere radiating at air temperature
# eqn D4
emissivity_atm = 0.642 * (ea / tair_k)**(1.0 / 7.0)
# isothermal net LW radiaiton at top of canopy, assuming emissivity of
# the canopy is 1
net_lw_rad = (1.0 - emissivity_atm) * c.SIGMA * tair_k**4
# black leaves, table 1, Leuning 1995
#kd = 0.8
# isothermal net radiation (W m-2)
rnet = p.SW_abs * sw_rad - net_lw_rad #* kd * exp(-kd * lai)
return rnet
def calc_rnet(self, par, tair, tair_k, tleaf_k, vpd, pressure):
"""
Net isothermal radaiation (Rnet, W m-2), i.e. the net radiation that
would be recieved if leaf and air temperature were the same.
References:
----------
Jarvis and McNaughton (1986)
Parameters:
----------
par : float
Photosynthetically active radiation (umol m-2 s-1)
tair : float
air temperature (deg C)
tair_k : float
air temperature (K)
tleaf_k : float
leaf temperature (K)
vpd : float
Vapour pressure deficit (kPa, needs to be in Pa, see conversion
below)
pressure : float
air pressure (using constant) (Pa)
Returns:
--------
rnet : float
Net radiation (J m-2 s-1 = W m-2)
"""
# Short wave radiation (W m-2)
SW_rad = par * self.PAR_2_SW
# absorbed short-wave radiation
SW_abs = self.SW_abs * math.cos(math.radians(self.angle)) * SW_rad
# atmospheric water vapour pressure (Pa)
ea = max(0.0, calc_esat(tair, pressure) - (vpd * self.kpa_2_pa))
# apparent emissivity for a hemisphere radiating at air temperature
# eqn D4
emissivity_atm = 0.642 * (ea / tair_k)**(1.0 / 7.0)
# downward long-wave radiation from the sky (W m-2)
lw_dw = emissivity_atm * self.sigma * tair_k**4
# outgoing long-wave radiation at the top of the canopy (leaf) (W m-2)
lw_up = self.emissivity_leaf * self.sigma * tleaf_k**4
# isothermal net radiation (W m-2), note W m-2 = J m-2 s-1
rnet = SW_abs + (lw_up - lw_dw)
return rnet
def calc_rnet(self, par, tair, tair_k, tleaf_k, vpd, pressure):
"""
Net isothermal radaiation (Rnet, W m-2), i.e. the net radiation that
would be recieved if leaf and air temperature were the same.
References:
----------
Jarvis and McNaughton (1986)
Parameters:
----------
par : float
Photosynthetically active radiation (umol m-2 s-1)
tair : float
air temperature (deg C)
tair_k : float
air temperature (K)
tleaf_k : float
leaf temperature (K)
vpd : float
Vapour pressure deficit (kPa, needs to be in Pa, see conversion
below)
pressure : float
air pressure (using constant) (Pa)
Returns:
--------
rnet : float
Net radiation (J m-2 s-1 = W m-2)
"""
# Short wave radiation (W m-2)
SW_rad = par * self.PAR_2_SW
# absorbed short-wave radiation
#SW_abs = self.SW_abs * math.cos(math.radians(self.angle)) * SW_rad
# atmospheric water vapour pressure (Pa)
ea = max(0.0, calc_esat(tair, pressure) - (vpd * self.kpa_2_pa))
# apparent emissivity for a hemisphere radiating at air temperature
# eqn D4
emissivity_atm = 0.642 * (ea / tair_k)**(1.0 / 7.0)
# isothermal net LW radiaiton at top of canopy, assuming emissivity of
# the canopy is 1
net_lw_rad = (1.0 - emissivity_atm) * self.sigma * tair_k**4
# isothermal net radiation (W m-2)
rnet = self.SW_abs * SW_rad - net_lw_rad #* kd * exp(-kd * s->lai)
return rnet
def calc_rnet(self, par, tair, tair_k, tleaf_k, vpd, pressure):
"""
Net isothermal radaiation (Rnet, W m-2), i.e. the net radiation that
would be recieved if leaf and air temperature were the same.
References:
----------
Jarvis and McNaughton (1986)
Parameters:
----------
par : float
Photosynthetically active radiation (umol m-2 s-1)
tair : float
air temperature (deg C)
tair_k : float
air temperature (K)
tleaf_k : float
leaf temperature (K)
vpd : float
Vapour pressure deficit (kPa, needs to be in Pa, see conversion
below)
pressure : float
air pressure (using constant) (Pa)
Returns:
--------
rnet : float
Net radiation (J m-2 s-1 = W m-2)
"""
leaf_abs = 0.5
# Short wave radiation (W m-2)
SW_rad = par * self.PAR_2_SW
# absorbed short-wave radiation
#SW_abs = self.SW_abs * math.cos(math.radians(self.angle)) * SW_rad
# atmospheric water vapour pressure (Pa)
ea = max(0.0, calc_esat(tair, pressure) - (vpd * self.kpa_2_pa))
# apparent emissivity for a hemisphere radiating at air temperature
# eqn D4
emissivity_atm = 0.642 * (ea / tair_k)**(1.0 / 7.0)
# isothermal net LW radiaiton at top of canopy, assuming emissivity of
# the canopy is 1
net_lw_rad = (1.0 - emissivity_atm) * self.sigma * tair_k**4
# isothermal net radiation (W m-2)
rnet = self.SW_abs * SW_rad - net_lw_rad #* kd * exp(-kd * s->lai)
return rnet
def get_values(rh, Ca, tair, par, pressure, C):
kpa_2_pa = 1000.
pa_2_kpa = 1.0 / kpa_2_pa
esat = calc_esat(tair, pressure)
ea = rh / 100. * esat
vpd = (esat - ea) * pa_2_kpa
#print rh, vpd
gs_store = []
et_store = []
An_store = []
tair_store = []
for i,ta in enumerate(tair):
#Td = get_dewpoint(ta, rh)
#if Td > 0.0:
(An, gsw, et) = C.main(ta, par, vpd[i], wind, pressure, Ca)
gs_store.append(gsw)
et_store.append(et*18*0.001*86400.)
An_store.append(An*12.*0.000001*86400.)
tair_store.append(ta)
return gs_store, et_store, An_store, tair_store
def calc_et(self, tleaf, tair, vpd, pressure, wind, par, gh, gw,
rnet):
"""
Calculate transpiration following Penman-Monteith at the leaf level
accounting for effects of leaf temperature and feedback on evaporation.
For example, if leaf temperature is above the leaf temp, it can increase
vpd, but it also reduves the lw and thus the net rad availble for
evaporaiton.
Parameters:
----------
tair : float
air temperature (deg C)
tleaf : float
leaf temperature (deg C)
vpd : float
Vapour pressure deficit (kPa, needs to be in Pa, see conversion
below)
pressure : float
air pressure (using constant) (Pa)
wind : float
wind speed (m s-1)
par : float
Photosynthetically active radiation (umol m-2 s-1)
gh : float
boundary layer conductance to heat (mol m-2 s-1)
gw :float
conductance to water vapour (mol m-2 s-1)
rnet : float
Net radiation (J m-2 s-1 = W m-2)
Returns:
--------
et : float
transpiration (mol H2O m-2 s-1)
lambda_et : float
latent heat flux (W m-2)
"""
# latent heat of water vapour at air temperature (j mol-1)
lambda_et = (self.h2olv0 - 2.365E3 * tair) * self.h2omw
# slope of sat. water vapour pressure (e_sat) to temperature curve
# (pa K-1), note kelvin conversion in func
slope = ((calc_esat(tair + 0.1, pressure) -
calc_esat(tair, pressure)) / 0.1)
#slope = self.calc_slope_of_saturation_vapour_pressure_curve(tair)
# psychrometric constant
gamma = self.cp * self.air_mass * pressure / lambda_et
# Y cancels in eqn 10
arg1 = (slope * rnet + (vpd * self.kpa_2_pa) * gh * self.cp *
self.air_mass)
arg2 = slope + gamma * gh / gw
# W m-2
LE = arg1 / arg2
# transpiration, mol H20 m-2 s-1
# multiply by 18 (grams)* 0.001 (grams to kg) * 86400.
# to get to kg m2 d-1 or mm d-1
et = LE / lambda_et
# et units = mol H20 m-2 s-1,
return (et, LE)
def main(fname):
wtc = read_file(fname)
# create hourly values for subsequent averaging of fluxes
wtc["DateTime_hr"] = wtc["DateTime_hr"].apply(\
lambda x: pd.to_datetime(x).round('30min'))
wtc_m = wtc.groupby(
['DateTime_hr', 'chamber', 'T_treatment', 'HWtrt', 'combotrt'],
as_index=False).mean()
wtc_m = wtc_m.set_index('DateTime_hr')
st = pd.datetime.strptime('2016-10-20 00:00:00', '%Y-%m-%d %H:%M:%S')
en = pd.datetime.strptime('2016-11-11 20:00:00', '%Y-%m-%d %H:%M:%S')
wtc_m = wtc_m[(wtc_m.index >= st) & (wtc_m.index <= en)]
df_ct = wtc_m[(wtc_m["HWtrt"] == "C") & (wtc_m["PAR"] > 10.0)]
df_hw = wtc_m[(wtc_m["HWtrt"] == "HW") & (wtc_m["PAR"] > 10.0)]
PARlimit = 600
df_ct = df_ct[df_ct["PAR"] > PARlimit]
df_hw = df_hw[df_hw["PAR"] > PARlimit]
# Parameters
# A stuff
Vcmax25 = 34.0
Jmax25 = 60.0
Rd25 = 0.92
Eaj = 21640.
Eav = 51780.
deltaSj = 633.0
deltaSv = 640.0
Hdv = 200000.0
Hdj = 200000.0
Q10 = 1.92
# Misc stuff
leaf_width = 0.01
SW_abs = 0.86 # absorptance to short_wave rad [0,1], typically 0.4-0.6
lat = -33.611111
lon = 150.740694
# variables though obviously fixed here.
wind = 8.0
pressure = 100.0 * c.KPA_2_PA
g0 = 0.003
g1 = 2.9
Ca = 400.
D0 = 1.5 # Not used
gamma = 0.0 # Not used
C = CoupledModel(g0,
g1,
D0,
gamma,
Vcmax25,
Jmax25,
Rd25,
Eaj,
Eav,
deltaSj,
deltaSv,
Hdv,
Hdj,
Q10,
leaf_width,
SW_abs,
gs_model="medlyn")
Et_hw = []
An_hw = []
for i in range(len(df_hw)):
(An, gsw, et, LE) = C.main(df_hw.Tair_al[i],
df_hw.PAR[i],
df_hw.VPD[i],
wind,
pressure,
Ca,
rnet=None)
Et_hw.append(et * c.MOL_2_MMOL) # mmol m-2 s-1
An_hw.append(An) # umol m-2 s-1
Et_hw2 = []
An_hw2 = []
for i in range(len(df_hw)):
ea = max(
0.0,
calc_esat(df_hw.Tair_al[i], pressure) -
(df_hw.VPD[i] * c.KPA_2_PA))
rnet = calc_net_radiation(df_hw.index[i].dayofyear,
df_hw.index[i].hour, lat, lon,
df_hw.PAR[i] * c.PAR_2_SW, df_hw.Tair_al[i],
ea)
(An, gsw, et, LE) = C.main(df_hw.Tair_al[i],
df_hw.PAR[i],
df_hw.VPD[i],
wind,
pressure,
Ca,
rnet=rnet)
Et_hw2.append(et * c.MOL_2_MMOL) # mmol m-2 s-1
An_hw2.append(An) # umol m-2 s-1
width = 9
height = width / 1.618
fig = plt.figure(figsize=(width, height))
fig.subplots_adjust(hspace=0.1)
fig.subplots_adjust(wspace=0.05)
plt.rcParams['text.usetex'] = False
plt.rcParams['font.family'] = "sans-serif"
plt.rcParams['font.sans-serif'] = "Helvetica"
plt.rcParams['axes.labelsize'] = 20
plt.rcParams['font.size'] = 18
plt.rcParams['legend.fontsize'] = 18
plt.rcParams['xtick.labelsize'] = 18
plt.rcParams['ytick.labelsize'] = 18
ax1 = fig.add_subplot(111)
x = df_hw.TargTempC_Avg
y = df_hw.Trans
x = np.nan_to_num(x)
y = np.nan_to_num(y)
xy = np.vstack([x, y])
z = gaussian_kde(xy)(xy)
ax1.scatter(x,
y,
c=z,
s=25,
edgecolor='',
cmap='Reds',
alpha=0.7,
label="HW")
ax1.scatter(x, Et_hw, color='black', s=5, alpha=0.7, label="Model")
ax1.scatter(x,
Et_hw2,
color='green',
s=5,
alpha=0.7,
label="Varying R$_{net}$")
ax1.legend(scatterpoints=1,
loc="upper right",
frameon=False,
handletextpad=0.1)
legend = ax1.get_legend()
legend.legendHandles[0].set_color("red")
legend.legendHandles[0]._sizes = [60]
legend.legendHandles[1]._sizes = [60]
legend.legendHandles[2]._sizes = [60]
ax1.locator_params(nbins=4, axis='x')
ax1.locator_params(nbins=4, axis='y')
ax1.set_ylim(0, 4)
ax1.set_xlim(15, 50)
ax1.set_ylabel('E$_{canopy}$ (mmol m$^{-2}$ s$^{-1}$)')
ax1.set_xlabel('Canopy temperature ($^\circ$C)')
#fig.savefig("plots/increasing_g0.pdf", bbox_inches='tight',
# pad_inches=0.1)
fig.savefig("plots/varying_rnet.png",
dpi=300,
bbox_inches='tight',
pad_inches=0.1)
return (new_Tleaf, et, le_et, gbH, gw)
if __name__ == "__main__":
from get_days_met_forcing import get_met_data
doy = 180.
#
## Met data ...
#
(par, tair, vpd) = get_met_data(p.lat, p.lon, doy)
# more realistic VPD
rh = 40.
esat = calc_esat(tair)
ea = rh / 100. * esat
vpd = (esat - ea) * c.PA_2_KPA
vpd = np.where(vpd < 0.05, 0.05, vpd)
#
## Fixed met stuff
#
wind = 2.5
pressure = 101325.0
Ca = 400.0
lai = p.LAI
##
### Run Two-leaf
##
def main():
doy = 180.
#
## Met data ...
#
(par, tair, vpd) = get_met_data(p.lat, p.lon, doy)
# more realistic VPD
rh = 40.
esat = calc_esat(tair)
ea = rh / 100. * esat
vpd = (esat - ea) * c.PA_2_KPA
vpd = np.where(vpd < 0.05, 0.05, vpd)
#
## Fixed met stuff
#
wind = 2.5
pressure = 101325.0
Ca = 400.0
LAI = p.LAI
##
### Run Big-leaf
##
B = BigLeaf(p, gs_model="medlyn")
An_bl = np.zeros(48)
gsw_bl = np.zeros(48)
et_bl = np.zeros(48)
tcan_bl = np.zeros(48)
for i in range(len(par)):
hod = float(i) / 2. + 1800. / 3600. / 2.
(An, gsw, et, Tcan) = B.main(p, tair[i], par[i], vpd[i], wind,
pressure, Ca, doy, hod, LAI)
An_bl[i] = An
et_bl[i] = et
tcan_bl[i] = Tcan
##
### Run 2-leaf
##
T = TwoLeaf(p, gs_model="medlyn")
An_tl = np.zeros(48)
et_tl = np.zeros(48)
tcan_tl = np.zeros(48)
hod = 0
for i in range(len(par)):
(An, et, Tcan, apar, lai_leaf) = T.main(p, tair[i], par[i], vpd[i],
wind, pressure, Ca, doy,
hod / 2., LAI)
sun_frac = lai_leaf[c.SUNLIT] / np.sum(lai_leaf)
sha_frac = lai_leaf[c.SHADED] / np.sum(lai_leaf)
An_tl[i] = np.sum(An)
et_tl[i] = np.sum(et)
tcan_tl[i] = (Tcan[c.SUNLIT] * sun_frac) + (Tcan[c.SHADED] * sha_frac)
hod += 1
fig = plt.figure(figsize=(16, 4))
fig.subplots_adjust(hspace=0.1)
fig.subplots_adjust(wspace=0.2)
plt.rcParams['text.usetex'] = False
plt.rcParams['font.family'] = "sans-serif"
plt.rcParams['font.sans-serif'] = "Helvetica"
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['font.size'] = 14
plt.rcParams['legend.fontsize'] = 10
plt.rcParams['xtick.labelsize'] = 14
plt.rcParams['ytick.labelsize'] = 14
almost_black = '#262626'
# change the tick colors also to the almost black
plt.rcParams['ytick.color'] = almost_black
plt.rcParams['xtick.color'] = almost_black
# change the text colors also to the almost black
plt.rcParams['text.color'] = almost_black
# Change the default axis colors from black to a slightly lighter black,
# and a little thinner (0.5 instead of 1)
plt.rcParams['axes.edgecolor'] = almost_black
plt.rcParams['axes.labelcolor'] = almost_black
ax1 = fig.add_subplot(131)
ax2 = fig.add_subplot(132)
ax3 = fig.add_subplot(133)
ax1.plot(np.arange(48) / 2., An_bl, label="Big leaf")
ax1.plot(np.arange(48) / 2., An_tl, label="Two leaf")
ax1.legend(numpoints=1, loc="best")
ax1.set_ylabel("$A_{\mathrm{n}}$ ($\mathrm{\mu}$mol m$^{-2}$ s$^{-1}$)")
ax2.plot(np.arange(48) / 2., et_bl * c.MOL_TO_MMOL, label="Big leaf")
ax2.plot(np.arange(48) / 2., et_tl * c.MOL_TO_MMOL, label="Two leaf")
ax2.set_ylabel("E (mmol m$^{-2}$ s$^{-1}$)")
ax2.set_xlabel("Hour of day")
ax3.plot(np.arange(48) / 2., tcan_bl, label="Tcanopy$_{1leaf}$")
ax3.plot(np.arange(48) / 2., tcan_tl, label="Tcanopy$_{2leaf}$")
ax3.plot(np.arange(48) / 2., tair, label="Tair")
ax3.set_ylabel("Temperature (deg C)")
ax3.legend(numpoints=1, loc="best")
ax1.locator_params(nbins=6, axis="y")
ax2.locator_params(nbins=6, axis="y")
plt.show()
fig.savefig("/Users/%s/Desktop/A_E_Tcan.pdf" % (os.getlogin()),
bbox_inches='tight',
pad_inches=0.1)
def calc_et(self, tleaf, tair, vpd, pressure, wind, par, gh, gw,
rnet):
"""
Calculate transpiration following Penman-Monteith at the leaf level
accounting for effects of leaf temperature and feedback on evaporation.
For example, if leaf temperature is above the leaf temp, it can increase
vpd, but it also reduves the lw and thus the net rad availble for
evaporaiton.
Parameters:
----------
tair : float
air temperature (deg C)
tleaf : float
leaf temperature (deg C)
vpd : float
Vapour pressure deficit (kPa, needs to be in Pa, see conversion
below)
pressure : float
air pressure (using constant) (Pa)
wind : float
wind speed (m s-1)
par : float
Photosynthetically active radiation (umol m-2 s-1)
gh : float
boundary layer conductance to heat (mol m-2 s-1)
gw :float
conductance to water vapour (mol m-2 s-1)
rnet : float
Net radiation (J m-2 s-1 = W m-2)
Returns:
--------
et : float
transpiration (mol H2O m-2 s-1)
lambda_et : float
latent heat flux (W m-2)
"""
# latent heat of water vapour at air temperature (j mol-1)
lambda_et = (self.h2olv0 - 2.365E3 * tair) * self.h2omw
# slope of sat. water vapour pressure (e_sat) to temperature curve
# (pa K-1), note kelvin conversion in func
slope = ((calc_esat(tair + 0.1, pressure) -
calc_esat(tair, pressure)) / 0.1)
#slope = self.calc_slope_of_saturation_vapour_pressure_curve(tair)
# psychrometric constant
gamma = self.cp * self.air_mass * pressure / lambda_et
# Y cancels in eqn 10
arg1 = (slope * rnet + (vpd * self.kpa_2_pa) * gh * self.cp *
self.air_mass)
arg2 = slope + gamma * gh / gw
# W m-2
LE = arg1 / arg2
# transpiration, mol H20 m-2 s-1
# multiply by 18 (grams)* 0.001 (grams to kg) * 86400.
# to get to kg m2 d-1 or mm d-1
et = LE / lambda_et
# et units = mol H20 m-2 s-1,
return (et, LE)
def main():
lat = -23.575001
lon = 152.524994
doy = 180.0
#
## Met data ...
#
(par, tair, vpd) = get_met_data(lat, lon, doy)
wind = 2.5
pressure = 101325.0
Ca = 400.0
# more realistic VPD
rh = 40.
esat = calc_esat(tair)
ea = rh / 100. * esat
vpd = (esat - ea) * c.PA_2_KPA
vpd = np.where(vpd < 0.05, 0.05, vpd)
#plt.plot(vpd)
#plt.show()
#sys.exit()
## Parameters
#
g0 = 0.001
g1 = 1.5635 # Puechabon
D0 = 1.5 # kpa
Vcmax25 = 60.0
Jmax25 = Vcmax25 * 1.67
Rd25 = 2.0
Eaj = 30000.0
Eav = 60000.0
deltaSj = 650.0
deltaSv = 650.0
Hdv = 200000.0
Hdj = 200000.0
Q10 = 2.0
gamma = 0.0
leaf_width = 0.02
LAI = 3.
# Cambell & Norman, 11.5, pg 178
# The solar absorptivities of leaves (-0.5) from Table 11.4 (Gates, 1980)
# with canopies (~0.8) from Table 11.2 reveals a surprising difference.
# The higher absorptivityof canopies arises because of multiple reflections
# among leaves in a canopy and depends on the architecture of the canopy.
SW_abs = 0.8 # use canopy absorptance of solar radiation
##
### Run Big-leaf
##
B = BigLeaf(g0,
g1,
D0,
gamma,
Vcmax25,
Jmax25,
Rd25,
Eaj,
Eav,
deltaSj,
deltaSv,
Hdv,
Hdj,
Q10,
leaf_width,
SW_abs,
gs_model="medlyn")
An_bl = np.zeros(48)
gsw_bl = np.zeros(48)
et_bl = np.zeros(48)
tcan_bl = np.zeros(48)
hod = 0
for i in range(len(par)):
(An_bl[i], gsw_bl[i], et_bl[i],
tcan_bl[i]) = B.main(tair[i], par[i], vpd[i], wind, pressure, Ca, doy,
hod, lat, lon, LAI)
hod += 1
##
### Run 2-leaf
##
T = TwoLeaf(g0,
g1,
D0,
gamma,
Vcmax25,
Jmax25,
Rd25,
Eaj,
Eav,
deltaSj,
deltaSv,
Hdv,
Hdj,
Q10,
leaf_width,
SW_abs,
gs_model="medlyn")
An_tl = np.zeros(48)
gsw_tl = np.zeros(48)
et_tl = np.zeros(48)
tcan_tl = np.zeros(48)
hod = 0
for i in range(len(par)):
(An_tl[i], gsw_tl[i], et_tl[i],
tcan_tl[i]) = T.main(tair[i], par[i], vpd[i], wind, pressure, Ca, doy,
hod, lat, lon, LAI)
hod += 1
##
### Run 2-leaf opt
##
T = TwoLeafOpt(g0,
g1,
D0,
gamma,
Vcmax25,
Jmax25,
Rd25,
Eaj,
Eav,
deltaSj,
deltaSv,
Hdv,
Hdj,
Q10,
leaf_width,
SW_abs,
gs_model="medlyn")
An_tlo = np.zeros(48)
gsw_tlo = np.zeros(48)
et_tlo = np.zeros(48)
tcan_tlo = np.zeros(48)
hod = 0
for i in range(len(par)):
(An_tlo[i], gsw_tlo[i], et_tlo[i],
tcan_tlo[i]) = T.main(tair[i], par[i], vpd[i], wind, pressure, Ca,
doy, hod, lat, lon, LAI)
hod += 1
##
### Run 2-leaf Manon
##
Ao = np.zeros(48)
gso = np.zeros(48)
Eo = np.zeros(48)
Aob = np.zeros(48)
gsob = np.zeros(48)
Eob = np.zeros(48)
hod = 0
for i in range(len(par)):
cos_zenith = calculate_solar_geometry(doy, hod, lat, lon)
zenith_angle = np.rad2deg(np.arccos(cos_zenith))
elevation = 90.0 - zenith_angle
if elevation > 0.0 and par[i] > 50.0:
p = declared_params()
p.PPFD = par[i]
p.sw_rad_day = par[i] * c.PAR_2_SW
p.LAI = LAI
p.coszen = cos_zenith
p.VPD = vpd[i]
p.precip = 0
p.Tair = tair[i]
p.Vmax25 = Vcmax25
p.g1 = g1
p.CO2 = Ca / 1000 * 101.325
p.JV = 1.67
p.Rlref = Rd25
p.Ej = Eaj
p.Ev = Eav
p.deltaSv = deltaSv
p.deltaSj = deltaSj
p.max_leaf_width = leaf_width
p.gamstar25 = 4.33 # 42.75 / 101.25 umol m-2 s-1
p.Kc25 = 41.0264925 # 404.9 umol m-2 s-1
p.Ko25 = 28208.88 # 278.4 mmol mol-1
p.O2 = 21.27825 # 210 *1000. / 101.25 210 mmol mol-1
p.alpha = 0.3
p.albedo = 0.2
p.Egamstar = 37830.0
p.Ec = 79430.0
p.Eo = 36380.0
p.P50 = 2.02 # Puechabon
p.P88 = 4.17
p.kmax = 0.862457122856143
_, _, fscale2can = absorbed_radiation_2_leaves(p)
p = p.append(pd.Series([np.nansum(fscale2can)], index=['fscale']))
Eo[i], gso[i], Ao[i], __, __ = solve_std(p, p.fc, photo='Farquhar')
#"""
try:
fstom_opt_psi, Eo[i], gso[i], Ao[i], _, _ = profit_psi(
p, photo='Farquhar', res='med', case=2)
p = p.append(
pd.Series([fstom_opt_psi], index=['fstom_opt_psi']))
except (ValueError, AttributeError):
(Eo[i], gso[i], Ao[i]) = (0., 0., 0.)
#"""
hod += 1
fig = plt.figure(figsize=(16, 4))
fig.subplots_adjust(hspace=0.1)
fig.subplots_adjust(wspace=0.2)
plt.rcParams['text.usetex'] = False
plt.rcParams['font.family'] = "sans-serif"
plt.rcParams['font.sans-serif'] = "Helvetica"
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['font.size'] = 14
plt.rcParams['legend.fontsize'] = 10
plt.rcParams['xtick.labelsize'] = 14
plt.rcParams['ytick.labelsize'] = 14
almost_black = '#262626'
# change the tick colors also to the almost black
plt.rcParams['ytick.color'] = almost_black
plt.rcParams['xtick.color'] = almost_black
# change the text colors also to the almost black
plt.rcParams['text.color'] = almost_black
# Change the default axis colors from black to a slightly lighter black,
# and a little thinner (0.5 instead of 1)
plt.rcParams['axes.edgecolor'] = almost_black
plt.rcParams['axes.labelcolor'] = almost_black
ax1 = fig.add_subplot(131)
ax2 = fig.add_subplot(132)
ax3 = fig.add_subplot(133)
#ax1.plot(np.arange(48)/2., An_bl, label="Big leaf")
ax1.plot(np.arange(48) / 2., An_tl, label="Two leaf")
ax1.plot(np.arange(48) / 2., An_tlo, label="Two leaf Opt")
ax1.plot(np.arange(48) / 2., Ao, label="Two leaf Manon")
ax1.legend(numpoints=1, loc="best")
ax1.set_ylabel("$A_{\mathrm{n}}$ ($\mathrm{\mu}$mol m$^{-2}$ s$^{-1}$)")
#ax2.plot(np.arange(48)/2., et_bl * c.MOL_TO_MMOL, label="Big leaf")
ax2.plot(np.arange(48) / 2., et_tl * c.MOL_TO_MMOL, label="Two leaf")
ax2.plot(np.arange(48) / 2., et_tlo * c.MOL_TO_MMOL, label="Two leaf opt")
ax2.plot(np.arange(48) / 2., Eo, label="Two leaf Manon")
ax2.set_ylabel("E (mmol m$^{-2}$ s$^{-1}$)")
ax2.set_xlabel("Hour of day")
#ax3.plot(np.arange(48)/2., tcan_bl, label="Tcanopy$_{1leaf}$")
ax3.plot(np.arange(48) / 2., tcan_tl, label="Tcanopy$_{2leaf}$")
ax3.plot(np.arange(48) / 2., tair, label="Tair")
ax3.set_ylabel("Temperature (deg C)")
ax3.legend(numpoints=1, loc="best")
ax1.locator_params(nbins=6, axis="y")
ax2.locator_params(nbins=6, axis="y")
plt.show()
fig.savefig("/Users/%s/Desktop/A_E_Tcan.pdf" % (os.getlogin()),
bbox_inches='tight',
pad_inches=0.1)
