def ligin_nratio(self):
""" Estimate Lignin/N ratio, as this dictates the how plant litter is
seperated between metabolic and structural pools.
Returns:
--------
lnleaf : float
lignin:N ratio of leaf
lnroot : float
lignin:N ratio of fine root
"""
nceleaf = self.ratio_of_litternc_to_live_leafnc()
nceroot = self.ratio_of_litternc_to_live_rootnc()
if float_eq(nceleaf, 0.0):
# This is effectively a hack that results in fluxes being turned
# off into the metabolic pool. It seems that the century code
# would effectively result in no metabolic fluxes. Might be worth
# looking at the latest century code to see how they get around
# this?
lnleaf = 1E20
else:
lnleaf = self.params.ligshoot / self.params.cfracts / nceleaf
if float_eq(nceroot, 0.0):
# This is effectively a hack that results in fluxes being turned
# off into the metabolic pool. It seems that the century code
# would effectively result in no metabolic fluxes. Might be worth
# looking at the latest century code to see how they get around
# this?
lnroot = 1E20
else:
lnroot = self.params.ligroot / self.params.cfracts / nceroot
return (lnleaf, lnroot)
def ligin_nratio(self):
""" Estimate Lignin/N ratio, as this dictates the how plant litter is
seperated between metabolic and structural pools.
Returns:
--------
lnleaf : float
lignin:N ratio of leaf
lnroot : float
lignin:N ratio of fine root
"""
nc_leaf_litter = self.ratio_of_litternc_to_live_leafnc()
nc_root_litter = self.ratio_of_litternc_to_live_rootnc()
if float_eq(nc_leaf_litter, 0.0):
# catch divide by zero if we have no leaves
lnleaf = 0.0
else:
lnleaf = self.params.ligshoot / self.params.cfracts / nc_leaf_litter
#print self.params.ligshoot
if float_eq(nc_root_litter, 0.0):
# catch divide by zero if we have no roots
lnroot = 0.0
else:
lnroot = self.params.ligroot / self.params.cfracts / nc_root_litter
return (lnleaf, lnroot)
def partition_plant_litter_n(self, nsurf, nsoil):
""" Partition litter N from the plant (surface) and roots into metabolic
and structural pools
Parameters:
-----------
nsurf : float
N input from surface pool
nsoil : float
N input from soil pool
"""
# constant structural input n:c as per century
if not self.control.strfloat:
# dead plant litter -> structural pool
# n flux -> surface structural pool
self.fluxes.n_surf_struct_litter = (self.fluxes.surf_struct_litter /
self.params.structcn)
# n flux -> soil structural pool
self.fluxes.n_soil_struct_litter = (self.fluxes.soil_struct_litter /
self.params.structcn)
# if not enough N for structural, all available N goes to structural
if float_gt( self.fluxes.n_surf_struct_litter, nsurf):
self.fluxes.n_surf_struct_litter = nsurf
if float_gt(self.fluxes.n_soil_struct_litter, nsoil):
self.fluxes.n_soil_struct_litter = nsoil
# structural input n:c is a fraction of metabolic
else:
c_surf_struct_litter = (self.fluxes.surf_struct_litter *
self.params.structrat +
self.fluxes.surf_metab_litter)
if float_eq(c_surf_struct_litter, 0.0):
self.fluxes.n_surf_struct_litter = 0.0
else:
self.fluxes.n_surf_struct_litter = (nsurf *
self.fluxes.surf_struct_litter *
self.params.structrat /
c_surf_struct_litter)
c_soil_struct_litter = (self.fluxes.soil_struct_litter *
self.params.structrat +
self.fluxes.soil_metab_litter)
if float_eq(c_soil_struct_litter, 0.0):
self.fluxes.n_soil_struct_litter = 0.
else:
self.fluxes.n_soil_struct_litter = (nsurf *
self.fluxes.soil_struct_litter *
self.params.structrat /
c_soil_struct_litter)
# remaining N goes to metabolic pools
self.fluxes.n_surf_metab_litter = (nsurf -
self.fluxes.n_surf_struct_litter)
self.fluxes.n_soil_metab_litter = (nsoil -
self.fluxes.n_soil_struct_litter)
def ligin_nratio(self):
""" Estimate Lignin/N ratio, as this dictates the how plant litter is
seperated between metabolic and structural pools.
Returns:
--------
lnleaf : float
lignin:N ratio of leaf
lnroot : float
lignin:N ratio of fine root
"""
nc_leaf_litter = self.ratio_of_litternc_to_live_leafnc()
nc_root_litter = self.ratio_of_litternc_to_live_rootnc()
if float_eq(nc_leaf_litter, 0.0):
# catch divide by zero if we have no leaves
lnleaf = 0.0
else:
lnleaf = self.params.ligshoot / self.params.cfracts / nc_leaf_litter
#print self.params.ligshoot
if float_eq(nc_root_litter, 0.0):
# catch divide by zero if we have no roots
lnroot = 0.0
else:
lnroot = self.params.ligroot / self.params.cfracts / nc_root_litter
return (lnleaf, lnroot)
def partition_plant_litter_n(self, nsurf, nsoil):
""" Partition litter N from the plant (surface) and roots into metabolic
and structural pools
Parameters:
-----------
nsurf : float
N input from surface pool
nsoil : float
N input from soil pool
"""
# constant structural input n:c as per century
if not self.control.strfloat:
# dead plant litter -> structural pool
# n flux -> surface structural pool
self.fluxes.n_surf_struct_litter = (self.fluxes.surf_struct_litter /
self.params.structcn)
# n flux -> soil structural pool
self.fluxes.n_soil_struct_litter = (self.fluxes.soil_struct_litter /
self.params.structcn)
# if not enough N for structural, all available N goes to structural
if float_gt( self.fluxes.n_surf_struct_litter, nsurf):
self.fluxes.n_surf_struct_litter = nsurf
if float_gt(self.fluxes.n_soil_struct_litter, nsoil):
self.fluxes.n_soil_struct_litter = nsoil
# structural input n:c is a fraction of metabolic
else:
c_surf_struct_litter = (self.fluxes.surf_struct_litter *
self.params.structrat +
self.fluxes.surf_metab_litter)
if float_eq(c_surf_struct_litter, 0.0):
self.fluxes.n_surf_struct_litter = 0.0
else:
self.fluxes.n_surf_struct_litter = (nsurf *
self.fluxes.surf_struct_litter *
self.params.structrat /
c_surf_struct_litter)
c_soil_struct_litter = (self.fluxes.soil_struct_litter *
self.params.structrat +
self.fluxes.soil_metab_litter)
if float_eq(c_soil_struct_litter, 0.0):
self.fluxes.n_soil_struct_litter = 0.
else:
self.fluxes.n_soil_struct_litter = (nsurf *
self.fluxes.soil_struct_litter *
self.params.structrat /
c_soil_struct_litter)
# remaining N goes to metabolic pools
self.fluxes.n_surf_metab_litter = (nsurf -
self.fluxes.n_surf_struct_litter)
self.fluxes.n_soil_metab_litter = (nsoil -
self.fluxes.n_soil_struct_litter)
def day_end_calculations(self, days_in_year=None, INIT=False):
"""Calculate derived values from state variables.
Parameters:
-----------
day : integer
day of simulation
INIT : logical
logical defining whether it is the first day of the simulation
"""
# update N:C of plant pool
if float_eq(self.state.shoot, 0.0):
self.state.shootnc = 0.0
else:
self.state.shootnc = self.state.shootn / self.state.shoot
#print self.state.rootn , self.state.roo
if float_eq(self.state.root, 0.0):
self.state.rootnc = 0.0
else:
self.state.rootnc = max(0.0, self.state.rootn / self.state.root)
# total plant, soil & litter nitrogen
self.state.soiln = (self.state.inorgn + self.state.activesoiln +
self.state.slowsoiln + self.state.passivesoiln)
self.state.litternag = self.state.structsurfn + self.state.metabsurfn
self.state.litternbg = self.state.structsoiln + self.state.metabsoiln
self.state.littern = self.state.litternag + self.state.litternbg
self.state.plantn = (self.state.shootn + self.state.rootn +
self.state.crootn + self.state.branchn +
self.state.stemn)
self.state.totaln = (self.state.plantn + self.state.littern +
self.state.soiln)
# total plant, soil, litter and system carbon
self.state.soilc = (self.state.activesoil + self.state.slowsoil +
self.state.passivesoil)
self.state.littercag = self.state.structsurf + self.state.metabsurf
self.state.littercbg = self.state.structsoil + self.state.metabsoil
self.state.litterc = self.state.littercag + self.state.littercbg
self.state.plantc = (self.state.root + self.state.croot +
self.state.shoot + self.state.stem +
self.state.branch)
self.state.totalc = (self.state.soilc + self.state.litterc +
self.state.plantc)
#self.state.plantnc = self.state.plantn / self.state.plantc
#print self.state.plantnc
# optional constant passive pool
if self.control.passiveconst == True:
self.state.passivesoil = self.params.passivesoilz
self.state.passivesoiln = self.params.passivesoilnz
if INIT == False:
#Required so max leaf & root N:C can depend on Age
self.state.age += 1.0 / days_in_year
def hurricane(self):
""" Specifically for the florida simulations - reduce LAI by 40% """
# Reduce LAI by 40%
self.state.lai -= (self.state.lai * 0.4)
# adjust C in the foliage
orig_shoot_c = self.state.shoot
self.state.shoot = (self.state.lai / (self.params.sla *
const.M2_AS_HA /
const.KG_AS_TONNES /
self.params.cfracts))
lost_c = orig_shoot_c - self.state.shoot
lost_n = self.state.shootnc * lost_c
self.state.shootn -= lost_n
# Drop straight to floor, no retranslocation
# C -> structural
if float_eq(lost_c, 0.0):
nc_leaf_litter = 0.0
else:
nc_leaf_litter = lost_n / lost_c
if float_eq(nc_leaf_litter, 0.0):
# catch divide by zero if we have no leaves
lnleaf = 0.0
else:
lnleaf = self.params.ligshoot / self.params.cfracts / nc_leaf_litter
fmleaf = max(0.0, 0.85 - (0.018 * lnleaf))
self.fluxes.surf_struct_litter += lost_c * (1.0 - fmleaf)
# C -> metabolic
self.fluxes.surf_metab_litter += lost_c * fmleaf
# N -> structural
if float_eq(self.fluxes.surf_struct_litter, 0.0):
self.fluxes.n_surf_struct_litter += 0.0
else:
self.fluxes.n_surf_struct_litter += (lost_n *
self.fluxes.surf_struct_litter *
self.params.structrat /
self.fluxes.surf_struct_litter)
# N -> metabolic pools
self.fluxes.n_surf_metab_litter += (lost_n -
self.fluxes.n_surf_struct_litter)
#self.state.structsurf += lost_c
#self.state.structsurfn += lost_n
def carbon_allocation(self, nitfac, doy, days_in_yr):
""" C distribution - allocate available C through system
Parameters:
-----------
nitfac : float
leaf N:C as a fraction of 'Ncmaxfyoung' (max 1.0)
"""
if self.control.deciduous_model:
days_left = self.state.growing_days[doy]
self.fluxes.cpleaf = self.fluxes.lrate * days_left
self.fluxes.cpbranch = self.fluxes.brate * days_left
self.fluxes.cpstem = self.fluxes.wrate * days_left
self.fluxes.cproot = self.state.c_to_alloc_root * 1.0 / days_in_yr
else:
self.fluxes.cpleaf = self.fluxes.npp * self.state.alleaf
self.fluxes.cproot = self.fluxes.npp * self.state.alroot
self.fluxes.cpbranch = self.fluxes.npp * self.state.albranch
self.fluxes.cpstem = self.fluxes.npp * self.state.alstem
#print self.fluxes.cpleaf, self.fluxes.npp, self.state.alleaf
# evaluate SLA of new foliage accounting for variation in SLA
# with tree and leaf age (Sands and Landsberg, 2002). Assume
# SLA of new foliage is linearly related to leaf N:C ratio
# via nitfac. Based on date from two E.globulus stands in SW Aus, see
# Corbeels et al (2005) Ecological Modelling, 187, 449-474.
# (m2 onesided/kg DW)
self.state.sla = (self.params.slazero + nitfac *
(self.params.slamax - self.params.slazero))
if self.control.deciduous_model:
if float_eq(self.state.shoot, 0.0):
self.state.lai = 0.0
elif self.state.leaf_out_days[doy] > 0.0:
self.state.lai += (self.fluxes.cpleaf *
(self.state.sla * const.M2_AS_HA /
(const.KG_AS_TONNES * self.params.cfracts)) -
(self.fluxes.deadleaves +
self.fluxes.ceaten) *
self.state.lai / self.state.shoot)
else:
self.state.lai = 0.0
else:
# update leaf area [m2 m-2]
if float_eq(self.state.shoot, 0.0):
self.state.lai = 0.0
else:
self.state.lai += (self.fluxes.cpleaf *
(self.state.sla * const.M2_AS_HA /
(const.KG_AS_TONNES * self.params.cfracts)) -
(self.fluxes.deadleaves +
self.fluxes.ceaten) *
self.state.lai / self.state.shoot)
def carbon_allocation(self, nitfac, doy, days_in_yr):
""" C distribution - allocate available C through system
Parameters:
-----------
nitfac : float
leaf N:C as a fraction of 'Ncmaxfyoung' (max 1.0)
"""
if self.control.deciduous_model:
days_left = self.state.growing_days[doy]
self.fluxes.cpleaf = self.fluxes.lrate * days_left
self.fluxes.cpbranch = self.fluxes.brate * days_left
self.fluxes.cpstem = self.fluxes.wrate * days_left
self.fluxes.cproot = self.state.c_to_alloc_root * 1.0 / days_in_yr
else:
self.fluxes.cpleaf = self.fluxes.npp * self.state.alleaf
self.fluxes.cproot = self.fluxes.npp * self.state.alroot
self.fluxes.cpbranch = self.fluxes.npp * self.state.albranch
self.fluxes.cpstem = self.fluxes.npp * self.state.alstem
#print self.fluxes.cpleaf, self.fluxes.npp, self.state.alleaf
# evaluate SLA of new foliage accounting for variation in SLA
# with tree and leaf age (Sands and Landsberg, 2002). Assume
# SLA of new foliage is linearly related to leaf N:C ratio
# via nitfac. Based on date from two E.globulus stands in SW Aus, see
# Corbeels et al (2005) Ecological Modelling, 187, 449-474.
# (m2 onesided/kg DW)
self.state.sla = (self.params.slazero + nitfac *
(self.params.slamax - self.params.slazero))
if self.control.deciduous_model:
if float_eq(self.state.shoot, 0.0):
self.state.lai = 0.0
elif self.state.leaf_out_days[doy] > 0.0:
self.state.lai += (
self.fluxes.cpleaf *
(self.state.sla * const.M2_AS_HA /
(const.KG_AS_TONNES * self.params.cfracts)) -
(self.fluxes.deadleaves + self.fluxes.ceaten) *
self.state.lai / self.state.shoot)
else:
self.state.lai = 0.0
else:
# update leaf area [m2 m-2]
if float_eq(self.state.shoot, 0.0):
self.state.lai = 0.0
else:
self.state.lai += (
self.fluxes.cpleaf *
(self.state.sla * const.M2_AS_HA /
(const.KG_AS_TONNES * self.params.cfracts)) -
(self.fluxes.deadleaves + self.fluxes.ceaten) *
self.state.lai / self.state.shoot)
def day_end_calculations(self, prjday, days_in_year=None, INIT=False):
"""Calculate derived values from state variables.
Parameters:
-----------
day : integer
day of simulation
INIT : logical
logical defining whether it is the first day of the simulation
"""
# update N:C of plant pools
if float_eq(self.state.shoot, 0.0):
self.state.shootnc = 0.0
else:
self.state.shootnc = self.state.shootn / self.state.shoot
#print self.state.rootn , self.state.root
if float_eq(self.state.root, 0.0):
self.state.rootnc = 0.0
else:
self.state.rootnc = max(0.0, self.state.rootn / self.state.root)
# total plant, soil & litter nitrogen
self.state.soiln = (self.state.inorgn + self.state.activesoiln +
self.state.slowsoiln + self.state.passivesoiln)
self.state.litternag = self.state.structsurfn + self.state.metabsurfn
self.state.litternbg = self.state.structsoiln + self.state.metabsoiln
self.state.littern = self.state.litternag + self.state.litternbg
self.state.plantn = (self.state.shootn + self.state.rootn +
self.state.branchn + self.state.stemn)
self.state.totaln = (self.state.plantn + self.state.littern +
self.state.soiln)
# total plant, soil, litter and system carbon
self.state.soilc = (self.state.activesoil + self.state.slowsoil +
self.state.passivesoil)
self.state.littercag = self.state.structsurf + self.state.metabsurf
self.state.littercbg = self.state.structsoil + self.state.metabsoil
self.state.litterc = self.state.littercag + self.state.littercbg
self.state.plantc = (self.state.root + self.state.shoot +
self.state.stem + self.state.branch)
self.state.totalc = (self.state.soilc + self.state.litterc +
self.state.plantc)
# optional constant passive pool
if self.control.passiveconst == True:
self.state.passivesoil = self.params.passivesoilz
self.state.passivesoiln = self.params.passivesoilnz
if INIT == False:
#Required so max leaf & root N:C can depend on Age
self.state.age += 1.0 / days_in_year
def print_output_file(self):
""" Either print the daily output file (at the end of the year) or
print the final state + param file. """
# print the daily output file, this is done once at the end of each yr
if self.control.print_options == "DAILY":
if self.control.output_ascii:
self.pr.write_daily_outputs_file(self.day_output)
else:
self.pr.write_daily_outputs_file_to_binary(self.day_output)
# print the final state
elif self.control.print_options == "END":
if not self.control.deciduous_model:
# This shouldn't do anything, but I will leave it here for
# potential applications. SLA has a N dependancy, though I
# always turn this off...because of this we require this step.
# However this won't work for tress if all the leaves have
# gone! Need to stop that happening! So I guess the sensible
# thing would be to do nothing.
if float_eq(self.state.shoot, 0.0):
pass #
#self.params.slainit = 0.01
else:
# need to save initial SLA to current one!
self.params.sla = (self.state.lai / const.M2_AS_HA *
const.KG_AS_TONNES *
self.params.cfracts /self.state.shoot)
self.correct_rate_constants(output=True)
self.pr.save_state()
def print_output_file(self):
""" Either print the daily output file (at the end of the year) or
print the final state + param file. """
# print the daily output file, this is done once at the end of each yr
if self.control.print_options == "DAILY":
if self.control.output_ascii:
self.pr.write_daily_outputs_file(self.day_output)
else:
self.pr.write_daily_outputs_file_to_binary(self.day_output)
# print the final state
elif self.control.print_options == "END":
if not self.control.deciduous_model:
# This shouldn't do anything, but I will leave it here for
# potential applications. SLA has a N dependancy, though I
# always turn this off...because of this we require this step.
# However this won't work for tress if all the leaves have
# gone! Need to stop that happening! So I guess the sensible
# thing would be to do nothing.
if float_eq(self.state.shoot, 0.0):
pass #
#self.params.slainit = 0.01
else:
# need to save initial SLA to current one!
self.params.sla = (self.state.lai / const.M2_AS_HA *
const.KG_AS_TONNES *
self.params.cfracts / self.state.shoot)
self.correct_rate_constants(output=True)
self.pr.save_state()
def model_zero(self, day):
"""
References:
-----------
* Kirschbaum et al 1994 pc & e
Parameters:
-----------
day : integer
simulation day
Returns:
--------
npp : float
net primary productivity
"""
(sw_rad, ca, temp) = self.get_met_data(day)
if float_eq(sw_rad, 0.0):
sw_rad = 0.000001
gpp_max = ((self.params.cfracts * sw_rad / const.M2_AS_HA *
self.params.epsilon * const.G_AS_TONNES) * temp_dep(temp) /
temp_dep(14.0))
lue = (1.21) * self.state.shootnc / (0.0076 + self.state.shootnc)
return lue * gpp_max * self.state.light_interception
def adjust_residence_time_of_slow_pool(self):
""" Priming simulations the residence time of the slow pool is flexible,
as the flux out of the active pool (factive) increases the residence
time of the slow pool decreases.
"""
# total flux out of the factive pool
self.fluxes.factive = (self.fluxes.active_to_slow +
self.fluxes.active_to_passive +
self.fluxes.co2_to_air[4])
if float_eq(self.fluxes.factive, 0.0):
# Need to correct units of rate constant
residence_time_slow_pool = (1.0 / (self.params.kdec6 *
const.NDAYS_IN_YR))
else:
residence_time_slow_pool = (1.0 / self.params.prime_y *
(self.fluxes.factive /
(self.fluxes.factive +
self.params.prime_z)))
# GDAY uses decay rates rather than residence times...
self.params.kdec6 = 1.0 / residence_time_slow_pool
# rate constant needs to be per day inside GDAY
self.params.kdec6 /= const.NDAYS_IN_YR
# Save for outputting purposes only
self.fluxes.rtslow = residence_time_slow_pool
def are_we_dead(self):
""" Simplistic scheme to allow GDAY to die and re-establish the
following year """
if float_eq(self.state.lai, 0.0):
print "DEAD"
# i.e. we have just died put stem C into struct litter
# works for grasses as this would be zero anyway
if not self.dead:
self.state.structsurf = self.state.stem
self.state.structsurfn = self.state.stemn
# Need to zero stuff for output state and fluxes.
self.state.age = 0.0
self.state.branch = 0.0
self.state.branchn = 0.0
self.state.cstore = 0.0
self.state.nstore = 0.0
self.state.root = 0.0
self.state.rootn = 0.0
self.state.sapwood = 0.0
self.state.shoot = 0.0
self.state.shootn = 0.0
self.state.stem = 0.0
self.state.stemn = 0.0
self.state.stemnimm = 0.0
self.state.stemnmob = 0.0
self.dead = True # johnny 5 is dead
print "dead"
def calc_root_exudation_uptake_of_C(self):
""" The amount of C which enters the active pool varies according to the
CUE of SOM in response to root exudation (REXCUE). REXCUE determines
the fraction of REXC that enters the active pool as C. The remaining
flux is respired.
REXCUE determines which fraction of REXC enters the active pool as C
(delta_Cact). The remaining fraction of REXC is respired as CO2.
"""
active_CN = self.state.activesoil / self.state.activesoiln
if self.params.root_exu_CUE == -1.0: # flexible CUE
# flexible cue
# The constraint of 0.3<=REXCUE<=0.6 is based on observations of the
# physical limits of microbes
if float_eq(self.fluxes.root_exc, 0.0):
rex_NC = 0.0
else:
rex_NC = self.fluxes.root_exn / self.fluxes.root_exc
self.fluxes.rexc_cue = max(0.3, min(0.6, rex_NC * active_CN))
else:
self.fluxes.rexc_cue = self.params.root_exu_CUE
C_to_active_pool = self.fluxes.root_exc * self.fluxes.rexc_cue
self.state.activesoil += C_to_active_pool
# update respiration fluxes.
self.fluxes.co2_released_exud = (self.fluxes.root_exc *
(1.0 - self.fluxes.rexc_cue))
self.fluxes.hetero_resp += self.fluxes.co2_released_exud
def are_we_dead(self):
""" Simplistic scheme to allow GDAY to die and re-establish the
following year """
if float_eq(self.state.lai, 0.0):
print "DEAD"
# i.e. we have just died put stem C into struct litter
# works for grasses as this would be zero anyway
if not self.dead:
self.state.structsurf = self.state.stem
self.state.structsurfn = self.state.stemn
# Need to zero stuff for output state and fluxes.
self.state.age = 0.0
self.state.branch = 0.0
self.state.branchn = 0.0
self.state.cstore = 0.0
self.state.nstore = 0.0
self.state.root = 0.0
self.state.rootn = 0.0
self.state.sapwood = 0.0
self.state.shoot = 0.0
self.state.shootn = 0.0
self.state.stem = 0.0
self.state.stemn = 0.0
self.state.stemnimm = 0.0
self.state.stemnmob = 0.0
self.dead = True # johnny 5 is dead
print "dead"
def hurricane(self):
""" Specifically for the florida simulations - reduce LAI by 40% """
# Reduce LAI by 40%
self.state.lai -= (self.state.lai * 0.4)
# adjust C in the foliage
orig_shoot_c = self.state.shoot
self.state.shoot = (self.state.lai /
(self.params.sla * const.M2_AS_HA /
const.KG_AS_TONNES / self.params.cfracts))
lost_c = orig_shoot_c - self.state.shoot
lost_n = self.state.shootnc * lost_c
self.state.shootn -= lost_n
# Drop straight to floor, no retranslocation
# C -> structural
if float_eq(lost_c, 0.0):
nc_leaf_litter = 0.0
else:
nc_leaf_litter = lost_n / lost_c
if float_eq(nc_leaf_litter, 0.0):
# catch divide by zero if we have no leaves
lnleaf = 0.0
else:
lnleaf = self.params.ligshoot / self.params.cfracts / nc_leaf_litter
fmleaf = max(0.0, 0.85 - (0.018 * lnleaf))
self.fluxes.surf_struct_litter += lost_c * (1.0 - fmleaf)
# C -> metabolic
self.fluxes.surf_metab_litter += lost_c * fmleaf
# N -> structural
if float_eq(self.fluxes.surf_struct_litter, 0.0):
self.fluxes.n_surf_struct_litter += 0.0
else:
self.fluxes.n_surf_struct_litter += (
lost_n * self.fluxes.surf_struct_litter *
self.params.structrat / self.fluxes.surf_struct_litter)
# N -> metabolic pools
self.fluxes.n_surf_metab_litter += (lost_n -
self.fluxes.n_surf_struct_litter)
def inputs_from_structrual_pool(self, nsurf, nsoil):
"""structural pool input fluxes
Parameters:
-----------
nsurf : float
N input from surface pool
nsoil : float
N input from soil pool
"""
# constant structural input n:c as per century
if not self.control.strfloat:
# dead plant -> structural
# surface
self.fluxes.nresid[0] = self.fluxes.cresid[0] / self.params.structcn
# soil
self.fluxes.nresid[1] = self.fluxes.cresid[1] / self.params.structcn
# if not enough N for structural, all available N goes to structural
if float_gt(self.fluxes.nresid[0], nsurf):
self.fluxes.nresid[0] = nsurf
if float_gt(self.fluxes.nresid[1], nsoil):
self.fluxes.nresid[1] = nsoil
else:
# structural input n:c is a fraction of metabolic
cwgtsu = (self.fluxes.cresid[0] * self.params.structrat +
self.fluxes.cresid[2])
if float_eq(cwgtsu, 0.0):
self.fluxes.nresid[0] = 0.0
else:
self.fluxes.nresid[0] = (nsurf * self.fluxes.cresid[0] *
self.params.structrat / cwgtsu)
cwgtsl = (self.fluxes.cresid[1] * self.params.structrat +
self.fluxes.cresid[3])
if float_eq(cwgtsl, 0.0):
self.fluxes.nresid[1] = 0.
else:
self.fluxes.nresid[1] = (nsurf * self.fluxes.cresid[1] *
self.params.structrat / cwgtsl)
def model_one(self, day):
""" old g'day n:c and temperature factors
References:
-----------
* mcmurtrie et al 1992 aust j bot
Parameters:
-----------
day : integer
simulation day
Returns:
--------
npp : float
net primary productivity
"""
(sw_rad, ca, temp) = self.get_met_data(day)
if float_eq(sw_rad, 0.0):
sw_rad = 0.000001
rco2 = 1.632 * (ca - 60.9) / (ca + 121.8)
gpp_max = ((self.params.cfracts * sw_rad / const.M2_AS_HA *
self.params.epsilon * const.G_AS_TONNES) * rco2)
# leaf n:c effect on photosynthetic efficiency (theta = 1).
if float_gt(self.state.shootnc, self.params.n_crit):
lue = 1.0
else:
rat = self.state.shootnc / self.params.n_crit
if float_eq(self.params.ncpower, 1.0):
lue = rat
else:
lue = pow(rat, self.params.ncpower)
return lue * gpp_max * self.state.light_interception
def calc_wue(self, vpd, ca, amb_co2):
"""water use efficiency
Not sure of units conversions here, have to ask BM
Parameters:
-----------
vpd : float
average daily vpd [kPa]
ca : float
atmospheric co2, depending on flag set in param file this will be
ambient or elevated. [umol mol-1]
"""
if self.control.wue_model == 0:
# Gday original implementation
# (gC / kg H20)
if float_gt(vpd, 0.0):
# WUE Power law dependence on co2, Pepper et al 2005.
co2_ratio = (ca / amb_co2)
co2_adjustment = co2_ratio**self.params.co2_effect_on_wue
# wue inversely proportional to daily mean vpd
self.fluxes.wue = self.params.wue0 * co2_adjustment / vpd
else:
self.fluxes.wue = 0.0
elif self.control.wue_model == 1 and self.control.assim_model == 7:
conv = const.MOL_C_TO_GRAMS_C / const.MOL_WATER_TO_GRAMS_WATER
self.fluxes.wue = (conv * 1000.0 * (ca * const.UMOL_TO_MOL *
(1.0 - self.fluxes.cica_avg) /
(1.6 * vpd / 101.0)))
#if self.fluxes.wue > 20.0: self.fluxes.wue = 20.0 # FIX THIS!!!
# what is this? ask BM
elif self.control.wue_model == 2:
self.fluxes.wue = (self.params.wue0 * 0.27273 / vpd *
ca / amb_co2)
elif self.control.wue_model == 3 :
if float_eq(self.fluxes.transpiration, 0.0):
self.fluxes.wue = 0.0
else:
self.fluxes.wue = (self.fluxes.gpp_gCm2 /
self.fluxes.transpiration)
else:
raise AttributeError('Unknown WUE calculation option')
def ratio_of_litternc_to_live_leafnc(self):
"""ratio of litter N:C to live leaf N:C
Returns:
--------
nc_leaf_litter : float
N:C ratio of litter to foliage
"""
if self.control.use_eff_nc:
nc_leaf_litter = self.params.liteffnc * (1.0 - self.params.fretrans)
else:
if float_eq(self.fluxes.deadleaves, 0.0):
nc_leaf_litter = 0.0
else:
nc_leaf_litter = self.fluxes.deadleafn / self.fluxes.deadleaves
return nc_leaf_litter
def ratio_of_litternc_to_live_leafnc(self):
"""ratio of litter N:C to live leaf N:C
Returns:
--------
nc_leaf_litter : float
N:C ratio of litter to foliage
"""
if self.control.use_eff_nc:
nc_leaf_litter = self.params.liteffnc * (1.0 - self.params.fretrans)
else:
if float_eq(self.fluxes.deadleaves, 0.0):
nc_leaf_litter = 0.0
else:
nc_leaf_litter = self.fluxes.deadleafn / self.fluxes.deadleaves
return nc_leaf_litter
def ratio_of_litternc_to_live_rootnc(self):
"""ratio of litter N:C to live root N:C
Returns:
--------
nc_root_litter : float
N:C ratio of litter to live root
"""
if self.control.use_eff_nc:
nc_root_litter = (self.params.liteffnc * self.params.ncrfac *
(1.0 - self.params.rretrans))
else:
if float_eq(self.fluxes.deadroots, 0.0):
nc_root_litter = 0.0
else:
nc_root_litter = self.fluxes.deadrootn / self.fluxes.deadroots
return nc_root_litter
def ratio_of_litternc_to_live_rootnc(self):
"""ratio of litter N:C to live root N:C
Returns:
--------
nc_root_litter : float
N:C ratio of litter to live root
"""
if self.control.use_eff_nc:
nc_root_litter = (self.params.liteffnc * self.params.ncrfac *
(1.0 - self.params.rretrans))
else:
if float_eq(self.fluxes.deadroots, 0.0):
nc_root_litter = 0.0
else:
nc_root_litter = self.fluxes.deadrootn / self.fluxes.deadroots
return nc_root_litter
def model_three(self, day):
"""
Parameters:
-----------
day : integer
simulation day
Returns:
--------
npp : float
net primary productivity
"""
(sw_rad, ca, temp) = self.get_met_data(day)
if float_eq(sw_rad, 0.0):
sw_rad = 0.000001
rco2 = 1.82 * (ca - 50.0) / (ca + 197.0)
lue = (1.45 * self.state.ncontent) / (self.state.ncontent + 2.21) * rco2
gpp_max = sw_rad / const.M2_AS_HA * const.G_AS_TONNES
return lue * gpp_max * self.state.light_interception
def print_output_file(self):
""" Either print the daily output file (at the end of the year) or
print the final state + param file. """
# print the daily output file, this is done once at the end of each yr
if self.control.print_options == "DAILY":
self.pr.write_daily_outputs_file(self.day_output)
# print the final state
elif self.control.print_options == "END":
if not self.control.deciduous_model:
if float_eq(self.state.shoot, 0.0):
self.params.slainit = 0.01
else:
# need to save initial SLA to current one!
conv = const.M2_AS_HA * const.KG_AS_TONNES
self.params.slainit = (
self.state.lai / const.M2_AS_HA * const.KG_AS_TONNES * self.params.cfracts / self.state.shoot
)
self.correct_rate_constants(output=True)
self.pr.save_state()
def print_output_file(self):
""" Either print the daily output file (at the end of the year) or
print the final state + param file. """
# print the daily output file, this is done once at the end of each yr
if self.control.print_options == "DAILY":
self.pr.write_daily_outputs_file(self.day_output)
# print the final state
elif self.control.print_options == "END":
if not self.control.deciduous_model:
if float_eq(self.state.shoot, 0.0):
self.params.slainit = 0.01
else:
# need to save initial SLA to current one!
conv = const.M2_AS_HA * const.KG_AS_TONNES
self.params.slainit = (self.state.lai / const.M2_AS_HA *
const.KG_AS_TONNES *
self.params.cfracts /
self.state.shoot)
self.correct_rate_constants(output=True)
self.pr.save_state()
def calculate_photosynthesis(self, day, daylen):
""" Photosynthesis is calculated assuming GPP is proportional to APAR,
a commonly assumed reln (e.g. Potter 1993, Myneni 2002). The slope of
relationship btw GPP and APAR, i.e. LUE is modelled using the
photosynthesis eqns from Sands.
Assumptions:
------------
(1) photosynthetic light response is a non-rectangular hyperbolic func
of photon-flux density with a light-saturatred photosynthetic rate
(Amax), quantum yield (alpha) and curvature (theta).
(2) the canopy is horizontally uniform.
(3) PAR distribution within the canopy obeys Beer's law.
(4) light-saturated photosynthetic rate declines with canopy depth in
proportion to decline in PAR
(5) alpha + theta do not vary within the canopy
(6) dirunal variation of PAR is sinusoidal.
(7) The model makes no assumption about N within the canopy, however
this version assumes N declines exponentially through the cnaopy.
(8) Leaf temperature is the same as the air temperature.
Parameters:
----------
day : int
project day.
daylen : float
length of day in hours.
Returns:
-------
Nothing
Method calculates GPP, NPP and Ra.
"""
# local var for tidyness
(Tk_am, Tk_pm, par, vpd_am, vpd_pm, ca) = self.get_met_data(day)
# calculate mate params & account for temperature dependencies
N0 = self.calculate_top_of_canopy_n()
gamma_star_am = self.calculate_co2_compensation_point(Tk_am)
gamma_star_pm = self.calculate_co2_compensation_point(Tk_pm)
Km_am = self.calculate_michaelis_menten_parameter(Tk_am)
Km_pm = self.calculate_michaelis_menten_parameter(Tk_pm)
(jmax_am, vcmax_am) = self.calculate_jmax_and_vcmax(Tk_am, N0)
(jmax_pm, vcmax_pm) = self.calculate_jmax_and_vcmax(Tk_pm, N0)
ci_am = self.calculate_ci(vpd_am, ca)
ci_pm = self.calculate_ci(vpd_pm, ca)
# quantum efficiency calculated for C3 plants
alpha_am = self.calculate_quantum_efficiency(ci_am, gamma_star_am)
alpha_pm = self.calculate_quantum_efficiency(ci_pm, gamma_star_pm)
# Reducing assimilation if we encounter frost. Frost is assumed to
# impact on the maximum photosynthetic capacity and alpha_j
# So there is only an indirect effect on LAI, this could be changed...
if self.control.frost:
Tmax = self.met_data['tmax'][day]
Tmin = self.met_data['tmin'][day]
Thard = self.calc_frost_hardiness(daylen, Tmin, Tmax)
(total_alpha_limf,
total_amax_limf) = self.calc_frost_impact_factors(
Thard, Tmin, Tmax)
alpha_am *= total_alpha_limf
alpha_pm *= total_alpha_limf
# Rubisco carboxylation limited rate of photosynthesis
ac_am = self.assim(ci_am, gamma_star_am, a1=vcmax_am, a2=Km_am)
ac_pm = self.assim(ci_pm, gamma_star_pm, a1=vcmax_pm, a2=Km_pm)
# Light-limited rate of photosynthesis allowed by RuBP regeneration
aj_am = self.assim(ci_am,
gamma_star_am,
a1=jmax_am / 4.0,
a2=2.0 * gamma_star_am)
aj_pm = self.assim(ci_pm,
gamma_star_pm,
a1=jmax_pm / 4.0,
a2=2.0 * gamma_star_pm)
# light-saturated photosynthesis rate at the top of the canopy (gross)
asat_am = min(aj_am, ac_am)
asat_pm = min(aj_pm, ac_pm)
if self.control.frost:
asat_am *= total_amax_limf
asat_pm *= total_amax_limf
# LUE (umol C umol-1 PAR)
lue_am = self.epsilon(asat_am, par, daylen, alpha_am)
lue_pm = self.epsilon(asat_pm, par, daylen, alpha_pm)
# use average to simulate canopy photosynthesis
lue_avg = (lue_am + lue_pm) / 2.0
if float_eq(self.state.lai, 0.0):
self.fluxes.apar = 0.0
else:
# absorbed photosynthetically active radiation (umol m-2 s-1)
self.fluxes.apar = par * self.state.fipar
apar_half_day = self.fluxes.apar / 2.0
# convert umol m-2 d-1 -> gC m-2 d-1
self.fluxes.gpp_gCm2 = self.fluxes.apar * lue_avg * const.UMOL_2_GRAMS_C
self.fluxes.gpp_am = apar_half_day * lue_am * const.UMOL_2_GRAMS_C
self.fluxes.gpp_pm = apar_half_day * lue_pm * const.UMOL_2_GRAMS_C
# g C m-2 to tonnes hectare-1 day-1
self.fluxes.gpp = self.fluxes.gpp_gCm2 * const.GRAM_C_2_TONNES_HA
if self.control.nuptake_model == 3:
self.fluxes.gpp_gCm2 *= self.params.ac
self.fluxes.gpp_am *= self.params.ac
self.fluxes.gpp_pm *= self.params.ac
def fire(self, growth_obj):
"""
Fire...
* 100 percent of aboveground biomass
* 100 percent of surface litter
* 50 percent of N volatilized to the atmosphere
* 50 percent of N returned to inorgn pool"
* Coarse roots are not damaged by fire!
vaguely following ...
http://treephys.oxfordjournals.org/content/24/7/765.full.pdf
"""
totaln = (self.state.branchn + self.state.shootn + self.state.stemn +
self.state.structsurfn)
self.state.inorgn += totaln / 2.0
# re-establish everything with C/N ~ 25.
if self.control.alloc_model == "GRASSES":
self.state.branch = 0.0
self.state.branchn = 0.0
self.state.sapwood = 0.0
self.state.stem = 0.0
self.state.stemn = 0.0
self.state.stemnimm = 0.0
self.state.stemnmob = 0.0
else:
self.state.branch = 0.001
self.state.branchn = 0.00004
self.state.sapwood = 0.001
self.state.stem = 0.001
self.state.stemn = 0.00004
self.state.stemnimm = 0.00004
self.state.stemnmob = 0.0
self.state.age = 0.0
self.state.lai = 0.01
self.state.metabsurf = 0.0
self.state.metabsurfn = 0.0
self.state.prev_sma = 1.0
self.state.root = 0.001
self.state.rootn = 0.00004
self.state.shoot = 0.001
self.state.shootn = 0.00004
self.state.structsurf = 0.001
self.state.structsurfn = 0.00004
# reset litter flows
self.fluxes.deadroots = 0.0
self.fluxes.deadstems = 0.0
self.fluxes.deadbranch = 0.0
self.fluxes.deadsapwood = 0.0
self.fluxes.deadleafn = 0.0
self.fluxes.deadrootn = 0.0
self.fluxes.deadbranchn = 0.0
self.fluxes.deadstemn = 0.0
# update N:C of plant pools
if float_eq(self.state.shoot, 0.0):
self.state.shootnc = 0.0
else:
self.state.shootnc = self.state.shootn / self.state.shoot
#print self.state.rootn , self.state.root
if float_eq(self.state.root, 0.0):
self.state.rootnc = 0.0
else:
self.state.rootnc = max(0.0, self.state.rootn / self.state.root)
growth_obj.sma.reset_stream() # reset any stress limitation
self.state.prev_sma = 1.0
def calculate_photosynthesis(self, day, daylen):
""" Photosynthesis is calculated assuming GPP is proportional to APAR,
a commonly assumed reln (e.g. Potter 1993, Myneni 2002). The slope of
relationship btw GPP and APAR, i.e. LUE is modelled using the
photosynthesis eqns from Sands.
Assumptions:
------------
(1) photosynthetic light response is a non-rectangular hyperbolic func
of photon-flux density with a light-saturatred photosynthetic rate
(Amax), quantum yield (alpha) and curvature (theta).
(2) the canopy is horizontally uniform.
(3) PAR distribution within the canopy obeys Beer's law.
(4) light-saturated photosynthetic rate declines with canopy depth in
proportion to decline in PAR
(5) alpha + theta do not vary within the canopy
(6) dirunal variation of PAR is sinusoidal.
(7) The model makes no assumption about N within the canopy, however
this version assumes N declines exponentially through the cnaopy.
(8) Leaf temperature is the same as the air temperature.
Parameters:
----------
day : int
project day.
daylen : float
length of day in hours.
Returns:
-------
Nothing
Method calculates GPP, NPP and Ra.
"""
# local var for tidyness
(am, pm) = self.am, self.pm # morning/afternoon
(Tair_K, par, vpd, ca) = self.get_met_data(day)
# calculate mate params & account for temperature dependencies
gamma_star = self.calculate_co2_compensation_point(Tair_K)
Km = self.calculate_michaelis_menten_parameter(Tair_K)
N0 = self.calculate_top_of_canopy_n()
(jmax, vcmax) = self.calculate_jmax_and_vcmax(Tair_K, N0)
ci = [self.calculate_ci(vpd[k], ca) for k in am, pm]
alpha = self.calculate_quantum_efficiency(ci, gamma_star)
# Rubisco carboxylation limited rate of photosynthesis
ac = [self.assim(ci[k], gamma_star[k], a1=vcmax[k], a2=Km[k]) \
for k in am, pm]
# Light-limited rate of photosynthesis allowed by RuBP regeneration
aj = [self.assim(ci[k], gamma_star[k], a1=jmax[k]/4.0, \
a2=2.0*gamma_star[k]) for k in am, pm]
# light-saturated photosynthesis rate at the top of the canopy (gross)
asat = [min(aj[k], ac[k]) for k in am, pm]
# Assumption that the integral is symmetric about noon, so we average
# the LUE accounting for variability in temperature, but importantly
# not PAR
lue = [self.epsilon(asat[k], par, daylen, alpha[k]) for k in am, pm]
# mol C mol-1 PAR - use average to simulate canopy photosynthesis
lue_avg = sum(lue) / 2.0
if float_eq(self.state.lai, 0.0):
self.fluxes.apar = 0.0
else:
par_mol = par * const.UMOL_TO_MOL
# absorbed photosynthetically active radiation
self.fluxes.apar = par_mol * self.state.fipar
# gC m-2 d-1
self.fluxes.gpp_gCm2 = (self.fluxes.apar * lue_avg *
const.MOL_C_TO_GRAMS_C)
self.fluxes.gpp_am_pm[am] = ((self.fluxes.apar / 2.0) * lue[am] *
const.MOL_C_TO_GRAMS_C)
self.fluxes.gpp_am_pm[pm] = ((self.fluxes.apar / 2.0) * lue[pm] *
const.MOL_C_TO_GRAMS_C)
self.fluxes.npp_gCm2 = self.fluxes.gpp_gCm2 * self.params.cue
if self.control.nuptake_model == 3:
self.fluxes.gpp_gCm2 *= self.params.ac
self.fluxes.gpp_am_pm[am] *= self.params.ac
self.fluxes.gpp_am_pm[pm] *= self.params.ac
self.fluxes.npp_gCm2 = self.fluxes.gpp_gCm2 * self.params.cue
# g C m-2 to tonnes hectare-1 day-1
conv = const.G_AS_TONNES / const.M2_AS_HA
self.fluxes.gpp = self.fluxes.gpp_gCm2 * conv
self.fluxes.npp = self.fluxes.npp_gCm2 * conv
# Plant respiration assuming carbon-use efficiency.
self.fluxes.auto_resp = self.fluxes.gpp - self.fluxes.npp
def calculate_photosynthesis(self, day, daylen):
""" Photosynthesis is calculated assuming GPP is proportional to APAR,
a commonly assumed reln (e.g. Potter 1993, Myneni 2002). The slope of
relationship btw GPP and APAR, i.e. LUE is modelled using the
photosynthesis eqns from Sands.
Assumptions:
------------
(1) photosynthetic light response is a non-rectangular hyperbolic func
of photon-flux density with a light-saturatred photosynthetic rate
(Amax), quantum yield (alpha) and curvature (theta).
(2) the canopy is horizontally uniform.
(3) PAR distribution within the canopy obeys Beer's law.
(4) light-saturated photosynthetic rate declines with canopy depth in
proportion to decline in PAR
(5) alpha + theta do not vary within the canopy
(6) dirunal variation of PAR is sinusoidal.
(7) The model makes no assumption about N within the canopy, however
this version assumes N declines exponentially through the cnaopy.
(8) Leaf temperature is the same as the air temperature.
Parameters:
----------
day : int
project day.
daylen : float
length of day in hours.
Returns:
-------
Nothing
Method calculates GPP, NPP and Ra.
"""
# local var for tidyness
(am, pm) = self.am, self.pm # morning/afternoon
(Tair_K, par, vpd, ca) = self.get_met_data(day)
ci = [self.calculate_ci(vpd[k], ca) for k in am, pm]
N0 = self.calculate_top_of_canopy_n()
# Quantum efficiency (umol mol-1), no Ci or temp dependancey in c4
# plants
# Ehleringer, J. R., 1978: Implications of quantum yield differences
# on the distributions of C3 and C4 grasses. Oecologia, 31, 255-267.
alpha = 0.04
# C4 assimilation following from Collatz et al. 1992
#?? Exponential factor in the equation defining kt
#alpharf = 0.067 # mol/mol
kslope = 0.7 # initial slope of photosynthetic CO2 response (mol m-2 s-1), Collatz table 2
theta = 0.83 # curvature parameter, Collatz table 2
beta = 0.93 # curvature parameter, Collatz table 2
# http://www.cesm.ucar.edu/models/cesm1.0/clm/CLM4_Tech_Note.pdf
# Table 8.2 has PFT values...
vcmax25 = self.params.vcmaxna * N0 + self.params.vcmaxnb
# Massad et al. 2007
Eav = 67294.0
Hdv = 144568.0
deltaSv = 472.0
vcmax = [self.peaked_arrh(vcmax25, Eav, Tair_K[k], deltaSv, Hdv) \
for k in am, pm]
# reduce photosynthetic capacity with moisture stress
vcmax = [self.state.wtfac_root * vcmax[k] for k in am, pm]
#Je = vcmax
#Jc = k / vmax * vcmax * ci
#Ji = alpha * I
# Rubisco and light limited capacity
par_per_sec = par / (60.0 * 60.0 * daylen)
M = [self.quadratic(theta, -(alpha*par_per_sec+vcmax[k]), alpha*par_per_sec*vcmax[k]) for k in am, pm]
# M and CO2 limitation
A = [self.quadratic(beta, -(M[k]+kslope*ci[k]), M[k]*kslope*ci[k]) for k in am, pm]
# These respiration terms are just for assimilation calculations,
# autotrophic respiration is stil assumed to be half of GPP
(Rd, Rm) = self.calc_respiration(Tair_K, vcmax)
# Net (saturated) photosynthetic rate, not sure if this
# makes sense.
Asat = [A[k] - Rd[k] for k in am, pm]
"""
# calculate mate parameters, e.g. accounting for temp dependancy
(Km, Kc, Ko, Kp) = self.calculate_michaelis_menten_parameter(Tair_K)
gamma_star = self.calculate_co2_compensation_point(Tair_K)
# Currently i dont have any information on how these depednancies
# vary with N, nor do I have site parameters so going to use
# values at 25 degrees from the literature, hardwired till this is
# resolved.
# Values from table 4.1, in von Caemmerer 2000, pg 100.
vcmax25 = 60.0
vpmax25 = 120.0
jmax25 = 400.0
if self.control.modeljm == True:
jmax = self.calculate_jmax_parameter(Tair_K, jmax25)
vcmax = self.calculate_vcmax_parameter(Tair_K, vcmax25)
vpmax = self.calculate_vpmax_parameter(Tair_K, vpmax25)
else:
jmax = [self.params.jmax, self.params.jmax]
vcmax = [self.params.vcmax, self.params.vcmax]
vpmax = [self.params.vpmax, self.params.vpmax]
# reduce photosynthetic capacity with moisture stress
#jmax = [self.state.wtfac_root * jmax[k] for k in am, pm]
#vcmax = [self.state.wtfac_root * vcmax[k] for k in am, pm]
#vpmax = [self.state.wtfac_root * vpmax[k] for k in am, pm]
# These respiration terms are just for assimilation calculations,
# autotrophic respiration is stil assumed to be half of GPP
(Rd, Rm) = self.calc_respiration(Tair_K, vcmax)
# Calculate assimilation rates.
Ac = self.calc_enzyme_limited_assim(Km, Kc, Ko, Kp, ci, vpmax, vcmax,
Rd, Rm)
par_per_sec = par / (60.0 * 60.0 * daylen)
Aj = self.calc_light_limited_assim(par_per_sec, jmax, ci, Rd, Rm)
Asat = [min(Aj[k], Ac[k]) for k in am, pm]
"""
# Assumption that the integral is symmetric about noon, so we average
# the LUE accounting for variability in temperature, but importantly
# not PAR
lue = [self.epsilon(Asat[k], par, daylen, alpha) for k in am, pm]
# mol C mol-1 PAR - use average to simulate canopy photosynthesis
lue_avg = sum(lue) / 2.0
if float_eq(self.state.lai, 0.0):
self.fluxes.apar = 0.0
else:
par_mol = par * const.UMOL_TO_MOL
# absorbed photosynthetically active radiation
self.fluxes.apar = par_mol * self.state.fipar
# gC m-2 d-1
self.fluxes.gpp_gCm2 = (self.fluxes.apar * lue_avg *
const.MOL_C_TO_GRAMS_C)
self.fluxes.gpp_am_pm[am] = ((self.fluxes.apar / 2.0) * lue[am] *
const.MOL_C_TO_GRAMS_C)
self.fluxes.gpp_am_pm[pm] = ((self.fluxes.apar / 2.0) * lue[pm] *
const.MOL_C_TO_GRAMS_C)
#print self.fluxes.gpp_gCm2
self.fluxes.npp_gCm2 = self.fluxes.gpp_gCm2 * self.params.cue
if self.control.nuptake_model == 3:
self.fluxes.gpp_gCm2 *= self.params.ac
self.fluxes.gpp_am_pm[am] *= self.params.ac
self.fluxes.gpp_am_pm[pm] *= self.params.ac
self.fluxes.npp_gCm2 = self.fluxes.gpp_gCm2 * self.params.cue
# g C m-2 to tonnes hectare-1 day-1
conv = const.G_AS_TONNES / const.M2_AS_HA
self.fluxes.gpp = self.fluxes.gpp_gCm2 * conv
self.fluxes.npp = self.fluxes.npp_gCm2 * conv
# Plant respiration assuming carbon-use efficiency.
self.fluxes.auto_resp = self.fluxes.gpp - self.fluxes.npp
def calculate_photosynthesis(self, day, daylen):
""" Photosynthesis is calculated assuming GPP is proportional to APAR,
a commonly assumed reln (e.g. Potter 1993, Myneni 2002). The slope of
relationship btw GPP and APAR, i.e. LUE is modelled using the
photosynthesis eqns from Sands.
Assumptions:
------------
(1) photosynthetic light response is a non-rectangular hyperbolic func
of photon-flux density with a light-saturatred photosynthetic rate
(Amax), quantum yield (alpha) and curvature (theta).
(2) the canopy is horizontally uniform.
(3) PAR distribution within the canopy obeys Beer's law.
(4) light-saturated photosynthetic rate declines with canopy depth in
proportion to decline in PAR
(5) alpha + theta do not vary within the canopy
(6) dirunal variation of PAR is sinusoidal.
(7) The model makes no assumption about N within the canopy, however
this version assumes N declines exponentially through the cnaopy.
(8) Leaf temperature is the same as the air temperature.
Parameters:
----------
day : int
project day.
daylen : float
length of day in hours.
Returns:
-------
Nothing
Method calculates GPP, NPP and Ra.
"""
# local var for tidyness
(am, pm) = self.am, self.pm # morning/afternoon
(temp, par, vpd, ca) = self.get_met_data(day)
Tk = [temp[k] + const.DEG_TO_KELVIN for k in am, pm]
# calculate mate parameters, e.g. accounting for temp dependancy
gamma_star = self.calculate_co2_compensation_point(Tk)
km = self.calculate_michaelis_menten_parameter(Tk)
N0 = self.calculate_leafn()
jmax = self.calculate_jmax_parameter(Tk, N0)
vcmax = self.calculate_vcmax_parameter(Tk, N0)
alpha = self.calculate_quantum_efficiency(temp)
# calculate ratio of intercellular to atmospheric CO2 concentration.
# Also allows productivity to be water limited through stomatal opening.
cica = [self.calculate_ci_ca_ratio(vpd[k]) for k in am, pm]
ci = [i * ca for i in cica]
# store value as needed in water balance calculation
self.fluxes.cica_avg = sum(cica) / len(cica)
# Rubisco-limited rate of photosynthesis
ac = [self.aclim(ci[k], gamma_star[k], km[k], vcmax[k]) for k in am, pm]
# Light-limited rate of photosynthesis allowed by RuBP regeneration
aj = [self.ajlim(jmax[k], ci[k], gamma_star[k]) for k in am, pm]
# Note that these are gross photosynthetic rates. Response to elevated
# [CO2] is reduced if N declines, but increases as gs declines.
asat = [min(aj[k], ac[k]) for k in am, pm]
# GPP is assumed to be proportional to APAR, where the LUE defines the
# slope of this relationship. LUE, calculation is performed for morning
# and afternnon periods.
lue = [self.epsilon(asat[k], par, daylen, alpha[k]) for k in am, pm]
# mol C mol-1 PAR - use average to simulate canopy photosynthesis
lue_avg = sum(lue) / len(lue)
if float_eq(self.state.lai, 0.0):
self.fluxes.apar = 0.0
else:
par_mol = par * const.UMOL_TO_MOL
self.fluxes.apar = par_mol * self.state.light_interception
# gC m-2 d-1
self.fluxes.gpp_gCm2 = (self.fluxes.apar * lue_avg *
const.MOL_C_TO_GRAMS_C)
self.fluxes.npp_gCm2 = self.fluxes.gpp_gCm2 * self.params.cue
self.fluxes.gpp_am_gCm2 = ((self.fluxes.apar / 2.0) * lue[am] *
const.MOL_C_TO_GRAMS_C)
self.fluxes.gpp_pm_gCm2 = ((self.fluxes.apar / 2.0) * lue[pm] *
const.MOL_C_TO_GRAMS_C)
# tonnes hectare-1 day-1
conv = const.G_AS_TONNES / const.M2_AS_HA
self.fluxes.gpp = self.fluxes.gpp_gCm2 * conv
self.fluxes.npp = self.fluxes.npp_gCm2 * conv
# Plant respiration assuming carbon-use efficiency.
self.fluxes.auto_resp = self.fluxes.gpp - self.fluxes.npp
def __init__(self, fname=None, DUMP=False, spin_up=False, met_header=4):
""" Set up model
* Read meterological forcing file
* Read user config file and adjust the model parameters, control or
initial state attributes that are used within the code.
* Setup all class instances...perhaps this isn't the tidyest place for
this?
* Initialise things, zero stores etc.
Parameters:
----------
fname : string
filename of model parameters, including path
chk_cmd_line : logical
parse the cmd line?
DUMP : logical
dump a the default parameters to a file
met_header : in
row number of met file header with variable name
Returns:
-------
Nothing
Controlling class of the model, runs things.
"""
self.day_output = [] # store daily output
# initialise model structures and read met data
(self.control, self.params, self.state, self.files, self.fluxes,
self.met_data, self.print_opts) = initialise_model_data(fname,
met_header,
DUMP=DUMP)
# params are defined in per year, needs to be per day
# Important this is done here as rate constants elsewhere in the code
# are assumed to be in units of days not years!
self.correct_rate_constants(output=False)
# class instance
self.cs = CarbonSoilFlows(self.control, self.params, self.state,
self.fluxes, self.met_data)
self.ns = NitrogenSoilFlows(self.control, self.params, self.state,
self.fluxes, self.met_data)
self.lf = Litter(self.control, self.params, self.state, self.fluxes)
self.pg = PlantGrowth(self.control, self.params, self.state,
self.fluxes, self.met_data)
self.cb = CheckBalance(self.control, self.params, self.state,
self.fluxes, self.met_data)
self.db = Disturbance(self.control, self.params, self.state,
self.fluxes, self.met_data)
if self.control.deciduous_model:
if self.state.max_lai is None:
self.state.max_lai = 0.01 # initialise to something really low
self.state.max_shoot = 0.01 # initialise to something really low
# Are we reading in last years average growing season?
if (float_eq(self.state.avg_alleaf, 0.0)
and float_eq(self.state.avg_alstem, 0.0)
and float_eq(self.state.avg_albranch, 0.0)
and float_eq(self.state.avg_alleaf, 0.0)
and float_eq(self.state.avg_alroot, 0.0)
and float_eq(self.state.avg_alcroot, 0.0)):
self.pg.calc_carbon_allocation_fracs(0.0) #comment this!!
else:
self.fluxes.alleaf = self.state.avg_alleaf
self.fluxes.alstem = self.state.avg_alstem
self.fluxes.albranch = self.state.avg_albranch
self.fluxes.alroot = self.state.avg_alroot
self.fluxes.alcroot = self.state.avg_alcroot
self.pg.allocate_stored_c_and_n(init=True)
#self.pg.enforce_sensible_nstore()
self.P = Phenology(
self.fluxes,
self.state,
self.control,
self.params.previous_ncd,
store_transfer_len=self.params.store_transfer_len)
self.pr = PrintOutput(self.params, self.state, self.fluxes,
self.control, self.files, self.print_opts)
# build list of variables to prin
(self.print_state, self.print_fluxes) = self.pr.get_vars_to_print()
# print model defaul
if DUMP == True:
self.pr.save_default_parameters()
sys.exit(0)
self.dead = False # johnny 5 is alive
# calculate initial stuff, e.g. C:N ratios and zero annual flux sum
self.day_end_calculations(INIT=True)
self.state.pawater_root = self.params.wcapac_root
self.state.pawater_topsoil = self.params.wcapac_topsoil
self.spin_up = spin_up
self.state.lai = max(
0.01, (self.params.sla * const.M2_AS_HA / const.KG_AS_TONNES /
self.params.cfracts * self.state.shoot))
# figure out the number of years for simulation and the number of
# days in each year
self.years = uniq(self.met_data["year"])
self.days_in_year = [
self.met_data["year"].count(yr) for yr in self.years
]
if self.control.water_stress == False:
sys.stderr.write("**** You have turned off the drought stress")
sys.stderr.write(", I assume you're debugging??!\n")
def day_end_calculations(self, prjday, days_in_year=None, INIT=False):
"""Calculate derived values from state variables.
Parameters:
-----------
day : integer
day of simulation
INIT : logical
logical defining whether it is the first day of the simulation
"""
self.fluxes.ninflow = self.met_data['ndep'][prjday]
# update N:C of plant pools
if float_eq(self.state.shoot, 0.0):
self.state.shootnc = 0.0
else:
self.state.shootnc = self.state.shootn / self.state.shoot
self.state.rootnc = max(0.0, self.state.rootn / self.state.root)
# total plant, soil & litter nitrogen
self.state.soiln = (self.state.inorgn + self.state.activesoiln +
self.state.slowsoiln + self.state.passivesoiln)
self.state.litternag = self.state.structsurfn + self.state.metabsurfn
self.state.litternbg = self.state.structsoiln + self.state.metabsoiln
self.state.littern = self.state.litternag + self.state.litternbg
self.state.plantn = (self.state.shootn + self.state.rootn +
self.state.branchn + self.state.stemn)
self.state.totaln = (self.state.plantn + self.state.littern +
self.state.soiln)
# total plant, soil, litter and system carbon
self.state.soilc = (self.state.activesoil + self.state.slowsoil +
self.state.passivesoil)
self.state.littercag = self.state.structsurf + self.state.metabsurf
self.state.littercbg = self.state.structsoil + self.state.metabsoil
self.state.litterc = self.state.littercag + self.state.littercbg
self.state.plantc = (self.state.root + self.state.shoot +
self.state.stem + self.state.branch)
self.state.totalc = (self.state.soilc + self.state.litterc +
self.state.plantc)
# optional constant passive pool
if self.control.passiveconst == True:
self.state.passivesoil = self.params.passivesoilz
self.state.passivesoiln = self.params.passivesoilnz
if INIT == False:
#Required so max leaf & root N:C can depend on Age
self.state.age += 1.0 / days_in_year
# N Net mineralisation, i.e. excess of N outflows over inflows
self.fluxes.nmineralisation = (self.fluxes.ninflow +
self.fluxes.ngross +
self.fluxes.nrootexudate -
self.fluxes.nimmob +
self.fluxes.nlittrelease)
# calculate NEP
self.fluxes.nep = (self.fluxes.npp - self.fluxes.hetero_resp -
self.fluxes.ceaten *
(1. - self.params.fracfaeces))
def fire(self, growth_obj):
"""
Fire...
* 100 percent of aboveground biomass
* 100 percent of surface litter
* 50 percent of N volatilized to the atmosphere
* 50 percent of N returned to inorgn pool"
* Coarse roots are not damaged by fire!
vaguely following ...
http://treephys.oxfordjournals.org/content/24/7/765.full.pdf
"""
totaln = (self.state.branchn + self.state.shootn + self.state.stemn +
self.state.structsurfn)
self.state.inorgn += totaln / 2.0
# re-establish everything with C/N ~ 25.
if self.control.alloc_model == "GRASSES":
self.state.branch = 0.0
self.state.branchn = 0.0
self.state.sapwood = 0.0
self.state.stem = 0.0
self.state.stemn = 0.0
self.state.stemnimm = 0.0
self.state.stemnmob = 0.0
else:
self.state.branch = 0.001
self.state.branchn = 0.00004
self.state.sapwood = 0.001
self.state.stem = 0.001
self.state.stemn = 0.00004
self.state.stemnimm = 0.00004
self.state.stemnmob = 0.0
self.state.age = 0.0
self.state.metabsurf = 0.0
self.state.metabsurfn = 0.0
self.state.prev_sma = 1.0
self.state.root = 0.001
self.state.rootn = 0.00004
self.state.shoot = 0.001
self.state.lai = (self.params.sla * const.M2_AS_HA /
const.KG_AS_TONNES / self.params.cfracts *
self.state.shoot)
self.state.shootn = 0.00004
self.state.structsurf = 0.001
self.state.structsurfn = 0.00004
# reset litter flows
self.fluxes.deadroots = 0.0
self.fluxes.deadstems = 0.0
self.fluxes.deadbranch = 0.0
self.fluxes.deadsapwood = 0.0
self.fluxes.deadleafn = 0.0
self.fluxes.deadrootn = 0.0
self.fluxes.deadbranchn = 0.0
self.fluxes.deadstemn = 0.0
# update N:C of plant pools
if float_eq(self.state.shoot, 0.0):
self.state.shootnc = 0.0
else:
self.state.shootnc = self.state.shootn / self.state.shoot
if self.control.ncycle == False:
self.state.shootnc = self.params.prescribed_leaf_NC
#print self.state.rootn , self.state.root
if float_eq(self.state.root, 0.0):
self.state.rootnc = 0.0
else:
self.state.rootnc = max(0.0, self.state.rootn / self.state.root)
growth_obj.sma.reset_stream() # reset any stress limitation
self.state.prev_sma = 1.0
def carbon_allocation(self, nitfac, doy, days_in_yr):
""" C distribution - allocate available C through system
Parameters:
-----------
nitfac : float
leaf N:C as a fraction of 'Ncmaxfyoung' (max 1.0)
References:
-----------
* Hale, M. G. et al. (1981) Factors affecting root exudation and
significance for the rhizosphere ecoystems. Biological and chemical
interactions in the rhizosphere. Stockholm. Sweden: Ecological
Research Committee of NFR. pg. 43--71.
* Lambers, J. T. and Poot, P. (2003) Structure and Functioning of
Cluster Roots and Plant Responses to Phosphate Deficiency.
* Martin, J. K. and Puckjeridge, D. W. (1982) Carbon flow through the
rhizosphere of wheat crops in South Australia. The cyclcing of carbon,
nitrogen, sulpher and phosphorous in terrestrial and aquatic
ecosystems. Canberra: Australian Academy of Science. pg 77--82.
* McMurtrie, R. E. et al (2000) Plant and Soil, 224, 135-152.
Also see:
* Rovira, A. D. (1969) Plant Root Exudates. Botanical Review, 35,
pg 35--57.
"""
if self.control.deciduous_model:
days_left = self.state.growing_days[doy]
self.fluxes.cpleaf = self.fluxes.lrate * days_left
self.fluxes.cpbranch = self.fluxes.brate * days_left
self.fluxes.cpstem = self.fluxes.wrate * days_left
self.fluxes.cproot = self.state.c_to_alloc_root * 1.0 / days_in_yr
else:
self.fluxes.cpleaf = self.fluxes.npp * self.state.alleaf
self.fluxes.cproot = self.fluxes.npp * self.state.alroot
self.fluxes.cpbranch = self.fluxes.npp * self.state.albranch
self.fluxes.cpstem = self.fluxes.npp * self.state.alstem
# C flux into root exudation, see McMurtrie et al. 2000. There is no
# reference given for the 0.15 in McM, however 14c work by Hale et al and
# Martin and Puckeridge suggest values range between 10-20% of NPP. So
# presumably this is where this values of 0.15 (i.e. the average) comes
# from
self.fluxes.cprootexudate = self.fluxes.npp * self.state.alroot_exudate
#self.fluxes.cprootexudate = 0.0
# evaluate SLA of new foliage accounting for variation in SLA
# with tree and leaf age (Sands and Landsberg, 2002). Assume
# SLA of new foliage is linearly related to leaf N:C ratio
# via nitfac
# (m2 onesided/kg DW)
self.state.sla = (self.params.slazero + nitfac *
(self.params.slamax - self.params.slazero))
if self.control.deciduous_model:
if float_eq(self.state.shoot, 0.0):
self.state.lai = 0.0
elif self.state.leaf_out_days[doy] > 0.0:
self.state.lai += (self.fluxes.cpleaf *
(self.state.sla * const.M2_AS_HA /
(const.KG_AS_TONNES * self.params.cfracts)) -
(self.fluxes.deadleaves +
self.fluxes.ceaten) *
self.state.lai / self.state.shoot)
else:
self.state.lai = 0.0
else:
# update leaf area [m2 m-2]
self.state.lai += (self.fluxes.cpleaf *
(self.state.sla * const.M2_AS_HA /
(const.KG_AS_TONNES * self.params.cfracts)) -
(self.fluxes.deadleaves +
self.fluxes.ceaten) *
self.state.lai / self.state.shoot)
def calculate_soil_water_fac(self):
""" Estimate a relative water availability factor [0..1]
A drying soil results in physiological stress that can induce stomatal
closure and reduce transpiration. Further, N mineralisation depends on
top soil moisture.
self.params.qs = 0.2 in SDGVM
References:
-----------
* Landsberg and Waring (1997) Forest Ecology and Management, 95, 209-228.
See --> Figure 2.
* Egea et al. (2011) Agricultural Forest Meteorology, 151, 1370-1384.
But similarly see:
* van Genuchten (1981) Soil Sci. Soc. Am. J, 44, 892--898.
* Wang and Leuning (1998) Ag Forest Met, 91, 89-111.
* Pepper et al. (2008) Functional Change Biology, 35, 493-508
Returns:
--------
wtfac_topsoil : float
water availability factor for the top soil [0,1]
wtfac_root : float
water availability factor for the root zone [0,1]
"""
# turn into fraction...
smc_topsoil = self.state.pawater_topsoil / self.params.wcapac_topsoil
smc_root = self.state.pawater_root / self.params.wcapac_root
if self.control.sw_stress_model == 0:
wtfac_topsoil = smc_topsoil**self.params.qs
wtfac_root = smc_root**self.params.qs
elif self.control.sw_stress_model == 1:
wtfac_topsoil = self.calc_sw_modifier(smc_topsoil,
self.params.ctheta_topsoil,
self.params.ntheta_topsoil)
wtfac_root = self.calc_sw_modifier(smc_root,
self.params.ctheta_root,
self.params.ntheta_root)
elif self.control.sw_stress_model == 2:
# Stomatal limitaiton
# Exponetial function to reduce g1 with soil water limitation
# based on Zhou et al. 2013, AFM, following Makela et al 1996.
# For the moment I have hardwired the PFT parameter as I am still
# testing.
# Because the model is a daily model we are assuming that LWP is
# well approximated by the night SWP.
if float_eq(smc_topsoil, 0.0):
psi_swp_topsoil = -1.5
else:
arg1 = self.params.psi_sat_topsoil
arg2 = smc_topsoil /self.params.theta_sat_topsoil
arg3 = -self.params.b_topsoil
psi_swp_topsoil = arg1 * arg2**arg3
if float_eq(smc_root, 0.0):
psi_swp_root = -1.5
else:
arg1 = self.params.psi_sat_root
arg2 = smc_root/self.params.theta_sat_root
arg3 = -self.params.b_root
psi_swp_root = arg1 * arg2**arg3
# multipliy these by g1, same as eqn 3 in Zhou et al. 2013.
b = 0.66
wtfac_topsoil = exp(b * psi_swp_topsoil)
wtfac_root = exp(b * psi_swp_root)
#print self.state.pawater_root,wtfac_root
return (wtfac_topsoil, wtfac_root)
def update_plant_state(self, fdecay, rdecay):
""" Daily change in C content
Parameters:
-----------
fdecay : float
foliage decay rate
rdecay : float
fine root decay rate
"""
self.state.shoot += (self.fluxes.cpleaf - self.fluxes.deadleaves -
self.fluxes.ceaten)
self.state.root += self.fluxes.cproot - self.fluxes.deadroots
self.state.branch += self.fluxes.cpbranch - self.fluxes.deadbranch
self.state.stem += self.fluxes.cpstem - self.fluxes.deadstems
if self.control.deciduous_model:
self.state.shootn += (self.fluxes.npleaf -
(self.fluxes.deadleafn - self.fluxes.neaten))
else:
self.state.shootn += (self.fluxes.npleaf -
fdecay * self.state.shootn -
self.fluxes.neaten)
self.state.shootn = max(0.0, self.state.shootn)
self.state.rootn += self.fluxes.nproot - rdecay * self.state.rootn
self.state.branchn += (self.fluxes.npbranch - self.params.bdecay *
self.state.branchn)
self.state.stemnimm += (self.fluxes.npstemimm - self.params.wdecay *
self.state.stemnimm)
self.state.stemnmob += (self.fluxes.npstemmob - self.params.wdecay *
self.state.stemnmob -
self.params.retransmob * self.state.stemnmob)
self.state.stemn = self.state.stemnimm + self.state.stemnmob
# maximum leaf n:c ratio is function of stand age
# - switch off age effect by setting ncmaxfyoung = ncmaxfold
age_effect = ((self.state.age - self.params.ageyoung) /
(self.params.ageold - self.params.ageyoung))
ncmaxf = (self.params.ncmaxfyoung - (self.params.ncmaxfyoung -
self.params.ncmaxfold) * age_effect)
if float_lt(ncmaxf, self.params.ncmaxfold):
ncmaxf = self.params.ncmaxfold
if float_gt(ncmaxf, self.params.ncmaxfyoung):
ncmaxf = self.params.ncmaxfyoung
# if foliage or root n:c ratio exceeds its max, then nitrogen uptake is
# cut back n.b. new ring n/c max is already set because it is related
# to leaf n:c
extrar = 0.
extras = 0.
if float_gt(self.state.shootn, (self.state.shoot * ncmaxf)):
extras = self.state.shootn - self.state.shoot * ncmaxf
#n uptake cannot be reduced below zero.
if float_gt(extras, self.fluxes.nuptake):
extras = self.fluxes.nuptake
self.state.shootn -= extras
self.fluxes.nuptake -= extras
ncmaxr = ncmaxf * self.params.ncrfac # max root n:c
if float_gt(self.state.rootn, (self.state.root * ncmaxr)):
extrar = self.state.rootn - self.state.root * ncmaxr
#n uptake cannot be reduced below zero.
if float_gt((extras + extrar), self.fluxes.nuptake):
extrar = self.fluxes.nuptake - extras
self.state.rootn -= extrar
self.fluxes.nuptake -= extrar
if self.control.deciduous_model:
# update annual fluxes - store for next year
self.state.clabile_store += self.fluxes.npp
self.state.aroot_uptake += self.fluxes.nuptake
self.state.aretrans += self.fluxes.retrans
self.state.anloss += self.fluxes.nloss
# update N:C of plant pools
if float_eq(self.state.shoot, 0.0):
self.state.shootnc = 0.0
else:
self.state.shootnc = max(self.state.shootn / self.state.shoot,
self.params.ncfmin)
# N:C of stuctural wood as function of N:C of foliage
# (kgN kg-1C)(Medlyn et al 2000)
self.state.nc_ws = (self.params.nc_wsa + self.params.nc_wsb *
self.state.shootnc)
# N:C of new wood as function of N:C of foliage
self.state.nc_wnew = (self.params.nc_wnewa + self.params.nc_wnewb *
self.state.shootnc)
def calculate_photosynthesis(self, day, daylen):
""" Photosynthesis is calculated assuming GPP is proportional to APAR,
a commonly assumed reln (e.g. Potter 1993, Myneni 2002). The slope of
relationship btw GPP and APAR, i.e. LUE is modelled using the
photosynthesis eqns from Sands.
Assumptions:
------------
(1) photosynthetic light response is a non-rectangular hyperbolic func
of photon-flux density with a light-saturatred photosynthetic rate
(Amax), quantum yield (alpha) and curvature (theta).
(2) the canopy is horizontally uniform.
(3) PAR distribution within the canopy obeys Beer's law.
(4) light-saturated photosynthetic rate declines with canopy depth in
proportion to decline in PAR
(5) alpha + theta do not vary within the canopy
(6) dirunal variation of PAR is sinusoidal.
(7) The model makes no assumption about N within the canopy, however
this version assumes N declines exponentially through the cnaopy.
(8) Leaf temperature is the same as the air temperature.
Parameters:
----------
day : int
project day.
daylen : float
length of day in hours.
Returns:
-------
Nothing
Method calculates GPP, NPP and Ra.
"""
# local var for tidyness
(Tair_K, par, vpd, ca) = self.get_met_data(day)
ci = [self.calculate_ci(vpd[k], ca) for k in self.am, self.pm]
N0 = self.calculate_top_of_canopy_n()
alpha = self.params.alpha_c4
# Temp dependancies from Massad et al. 2007
(vcmax, vcmax25) = self.calculate_vcmax_parameter(Tair_K, N0)
# Rubisco and light-limited capacity (Appendix, 2B)
par_per_sec = par / (60.0 * 60.0 * daylen)
M = [
self.quadratic(a=self.beta1, b=-(vcmax[k] + alpha * par_per_sec), c=(vcmax[k] * alpha * par_per_sec))
for k in self.am, self.pm
]
# The limitation of the overall rate by M and CO2 limited flux:
A = [
self.quadratic(a=self.beta2, b=-(M[k] + self.kslope * ci[k]), c=(M[k] * self.kslope * ci[k]))
for k in self.am, self.pm
]
# These respiration terms are just for assimilation calculations,
# autotrophic respiration is stil assumed to be half of GPP
(Rd) = self.calc_respiration(Tair_K, vcmax25)
# Net (saturated) photosynthetic rate, not sure if this
# makes sense.
asat = [A[k] - Rd[k] for k in self.am, self.pm]
# LUE (umol C umol-1 PAR)
lue = [self.epsilon(asat[k], par, daylen, alpha) for k in self.am, self.pm]
lue_avg = sum(lue) / 2.0 # use average to simulate canopy photosynthesis
if float_eq(self.state.lai, 0.0):
self.fluxes.apar = 0.0
else:
# absorbed photosynthetically active radiation (umol m-2 s-1)
self.fluxes.apar = par * self.state.fipar
# gC m-2 d-1
self.fluxes.gpp_gCm2 = self.fluxes.apar * lue_avg * const.UMOL_TO_MOL * const.MOL_C_TO_GRAMS_C
self.fluxes.gpp_am_pm[self.am] = (
(self.fluxes.apar / 2.0) * lue[self.am] * const.UMOL_TO_MOL * const.MOL_C_TO_GRAMS_C
)
self.fluxes.gpp_am_pm[self.pm] = (
(self.fluxes.apar / 2.0) * lue[self.pm] * const.UMOL_TO_MOL * const.MOL_C_TO_GRAMS_C
)
self.fluxes.npp_gCm2 = self.fluxes.gpp_gCm2 * self.params.cue
if self.control.nuptake_model == 3:
self.fluxes.gpp_gCm2 *= self.params.ac
self.fluxes.gpp_am_pm[am] *= self.params.ac
self.fluxes.gpp_am_pm[pm] *= self.params.ac
self.fluxes.npp_gCm2 = self.fluxes.gpp_gCm2 * self.params.cue
# g C m-2 to tonnes hectare-1 day-1
conv = const.G_AS_TONNES / const.M2_AS_HA
self.fluxes.gpp = self.fluxes.gpp_gCm2 * conv
self.fluxes.npp = self.fluxes.npp_gCm2 * conv
# Plant respiration assuming carbon-use efficiency.
self.fluxes.auto_resp = self.fluxes.gpp - self.fluxes.npp
def __init__(self, fname=None, DUMP=False, spin_up=False, met_header=4):
""" Set up model
* Read meterological forcing file
* Read user config file and adjust the model parameters, control or
initial state attributes that are used within the code.
* Setup all class instances...perhaps this isn't the tidyest place for
this?
* Initialise things, zero stores etc.
Parameters:
----------
fname : string
filename of model parameters, including path
chk_cmd_line : logical
parse the cmd line?
DUMP : logical
dump a the default parameters to a file
met_header : in
row number of met file header with variable name
Returns:
-------
Nothing
Controlling class of the model, runs things.
"""
self.day_output = [] # store daily output
# initialise model structures and read met data
(self.control, self.params,
self.state, self.files,
self.fluxes, self.met_data,
self.print_opts) = initialise_model_data(fname, met_header, DUMP=DUMP)
# params are defined in per year, needs to be per day
# Important this is done here as rate constants elsewhere in the code
# are assumed to be in units of days not years!
self.correct_rate_constants(output=False)
# class instance
self.cs = CarbonSoilFlows(self.control, self.params, self.state,
self.fluxes, self.met_data)
self.ns = NitrogenSoilFlows(self.control, self.params, self.state,
self.fluxes, self.met_data)
self.lf = Litter(self.control, self.params, self.state, self.fluxes)
self.pg = PlantGrowth(self.control, self.params, self.state,
self.fluxes, self.met_data)
self.cb = CheckBalance(self.control, self.params, self.state,
self.fluxes, self.met_data)
self.db = Disturbance(self.control, self.params, self.state,
self.fluxes, self.met_data)
if self.control.deciduous_model:
if self.state.max_lai is None:
self.state.max_lai = 0.01 # initialise to something really low
self.state.max_shoot = 0.01 # initialise to something really low
# Are we reading in last years average growing season?
if (float_eq(self.state.avg_alleaf, 0.0) and
float_eq(self.state.avg_alstem, 0.0) and
float_eq(self.state.avg_albranch, 0.0) and
float_eq(self.state.avg_alleaf, 0.0) and
float_eq(self.state.avg_alroot, 0.0) and
float_eq(self.state.avg_alcroot, 0.0)):
self.pg.calc_carbon_allocation_fracs(0.0) #comment this!!
else:
self.fluxes.alleaf = self.state.avg_alleaf
self.fluxes.alstem = self.state.avg_alstem
self.fluxes.albranch = self.state.avg_albranch
self.fluxes.alroot = self.state.avg_alroot
self.fluxes.alcroot = self.state.avg_alcroot
self.pg.allocate_stored_c_and_n(init=True)
#self.pg.enforce_sensible_nstore()
self.P = Phenology(self.fluxes, self.state, self.control,
self.params.previous_ncd,
store_transfer_len=self.params.store_transfer_len)
self.pr = PrintOutput(self.params, self.state, self.fluxes,
self.control, self.files, self.print_opts)
# build list of variables to prin
(self.print_state, self.print_fluxes) = self.pr.get_vars_to_print()
# print model defaul
if DUMP == True:
self.pr.save_default_parameters()
sys.exit(0)
self.dead = False # johnny 5 is alive
# calculate initial stuff, e.g. C:N ratios and zero annual flux sum
self.day_end_calculations(INIT=True)
self.state.pawater_root = self.params.wcapac_root
self.state.pawater_topsoil = self.params.wcapac_topsoil
self.spin_up = spin_up
self.state.lai = max(0.01, (self.params.sla * const.M2_AS_HA /
const.KG_AS_TONNES / self.params.cfracts *
self.state.shoot))
# figure out the number of years for simulation and the number of
# days in each year
self.years = uniq(self.met_data["year"])
self.days_in_year = [self.met_data["year"].count(yr)
for yr in self.years]
if self.control.water_stress == False:
sys.stderr.write("**** You have turned off the drought stress")
sys.stderr.write(", I assume you're debugging??!\n")
def calculate_photosynthesis(self, day, daylen):
""" Photosynthesis is calculated assuming GPP is proportional to APAR,
a commonly assumed reln (e.g. Potter 1993, Myneni 2002). The slope of
relationship btw GPP and APAR, i.e. LUE is modelled using the
photosynthesis eqns from Sands.
Assumptions:
------------
(1) photosynthetic light response is a non-rectangular hyperbolic func
of photon-flux density with a light-saturatred photosynthetic rate
(Amax), quantum yield (alpha) and curvature (theta).
(2) the canopy is horizontally uniform.
(3) PAR distribution within the canopy obeys Beer's law.
(4) light-saturated photosynthetic rate declines with canopy depth in
proportion to decline in PAR
(5) alpha + theta do not vary within the canopy
(6) dirunal variation of PAR is sinusoidal.
(7) The model makes no assumption about N within the canopy, however
this version assumes N declines exponentially through the cnaopy.
(8) Leaf temperature is the same as the air temperature.
Parameters:
----------
day : int
project day.
daylen : float
length of day in hours.
Returns:
-------
Nothing
Method calculates GPP, NPP and Ra.
"""
# local var for tidyness
(Tk_am, Tk_pm, par, vpd_am, vpd_pm, ca) = self.get_met_data(day)
# calculate mate params & account for temperature dependencies
N0 = self.calculate_top_of_canopy_n()
ci_am = self.calculate_ci(vpd_am, ca)
ci_pm = self.calculate_ci(vpd_pm, ca)
alpha = self.params.alpha_c4
# Temp dependancies from Massad et al. 2007
(vcmax_am, vcmax25_am) = self.calculate_vcmax_parameter(Tk_am, N0)
(vcmax_pm, vcmax25_pm) = self.calculate_vcmax_parameter(Tk_pm, N0)
# Rubisco and light-limited capacity (Appendix, 2B)
par_per_sec = par / (60.0 * 60.0 * daylen)
M_am = self.quadratic(a=self.beta1,
b=-(vcmax_am + alpha * par_per_sec),
c=(vcmax_am * alpha * par_per_sec))
M_pm = self.quadratic(a=self.beta1,
b=-(vcmax_pm + alpha * par_per_sec),
c=(vcmax_pm * alpha * par_per_sec))
# The limitation of the overall rate by M and CO2 limited flux:
A_am = self.quadratic(a=self.beta2,
b=-(M_am + self.kslope * ci_am),
c=(M_am * self.kslope * ci_am))
A_pm = self.quadratic(a=self.beta2,
b=-(M_pm + self.kslope * ci_pm),
c=(M_pm * self.kslope * ci_pm))
# These respiration terms are just for assimilation calculations,
# autotrophic respiration is stil assumed to be half of GPP
Rd_am = self.calc_respiration(Tk_am, vcmax25_am)
Rd_pm = self.calc_respiration(Tk_pm, vcmax25_pm)
# Net (saturated) photosynthetic rate, not sure if this
# makes sense.
asat_am = A_am - Rd_am
asat_pm = A_pm - Rd_pm
# LUE (umol C umol-1 PAR)
lue_am = self.epsilon(asat_am, par, daylen, alpha)
lue_pm = self.epsilon(asat_pm, par, daylen, alpha)
# use average to simulate canopy photosynthesis
lue_avg = (lue_am + lue_pm) / 2.0
if float_eq(self.state.lai, 0.0):
self.fluxes.apar = 0.0
else:
# absorbed photosynthetically active radiation (umol m-2 s-1)
self.fluxes.apar = par * self.state.fipar
apar_half_day = self.fluxes.apar / 2.0
# convert umol m-2 d-1 -> gC m-2 d-1
self.fluxes.gpp_gCm2 = self.fluxes.apar * lue_avg * const.UMOL_2_GRAMS_C
self.fluxes.gpp_am = apar_half_day * lue_am * const.UMOL_2_GRAMS_C
self.fluxes.gpp_pm = apar_half_day * lue_pm * const.UMOL_2_GRAMS_C
self.fluxes.npp_gCm2 = self.fluxes.gpp_gCm2 * self.params.cue
if self.control.nuptake_model == 3:
self.fluxes.gpp_gCm2 *= self.params.ac
self.fluxes.gpp_am *= self.params.ac
self.fluxes.gpp_pm *= self.params.ac
self.fluxes.npp_gCm2 = self.fluxes.gpp_gCm2 * self.params.cue
# g C m-2 to tonnes hectare-1 day-1
self.fluxes.gpp = self.fluxes.gpp_gCm2 * const.GRAM_C_2_TONNES_HA
self.fluxes.npp = self.fluxes.npp_gCm2 * const.GRAM_C_2_TONNES_HA
# Plant respiration assuming carbon-use efficiency.
self.fluxes.auto_resp = self.fluxes.gpp - self.fluxes.npp
def calculate_photosynthesis(self, day, daylen):
""" Photosynthesis is calculated assuming GPP is proportional to APAR,
a commonly assumed reln (e.g. Potter 1993, Myneni 2002). The slope of
relationship btw GPP and APAR, i.e. LUE is modelled using the
photosynthesis eqns from Sands.
Assumptions:
------------
(1) photosynthetic light response is a non-rectangular hyperbolic func
of photon-flux density with a light-saturatred photosynthetic rate
(Amax), quantum yield (alpha) and curvature (theta).
(2) the canopy is horizontally uniform.
(3) PAR distribution within the canopy obeys Beer's law.
(4) light-saturated photosynthetic rate declines with canopy depth in
proportion to decline in PAR
(5) alpha + theta do not vary within the canopy
(6) dirunal variation of PAR is sinusoidal.
(7) The model makes no assumption about N within the canopy, however
this version assumes N declines exponentially through the cnaopy.
(8) Leaf temperature is the same as the air temperature.
Parameters:
----------
day : int
project day.
daylen : float
length of day in hours.
Returns:
-------
Nothing
Method calculates GPP, NPP and Ra.
"""
# local var for tidyness
(am, pm) = self.am, self.pm # morning/afternoon
(Tair_K, par, vpd, ca) = self.get_met_data(day)
ci = [self.calculate_ci(vpd[k], ca) for k in am, pm]
N0 = self.calculate_top_of_canopy_n()
alpha = self.params.alpha_c4
# Temp dependancies from Massad et al. 2007
(vcmax, vcmax25) = self.calculate_vcmax_parameter(Tair_K, N0)
# Rubisco and light-limited capacity (Appendix, 2B)
par_per_sec = par / (60.0 * 60.0 * daylen)
M = [
self.quadratic(a=self.beta1,
b=-(vcmax[k] + alpha * par_per_sec),
c=(vcmax[k] * alpha * par_per_sec)) for k in am, pm
]
# The limitation of the overall rate by M and CO2 limited flux:
A = [
self.quadratic(a=self.beta2,
b=-(M[k] + self.kslope * ci[k]),
c=(M[k] * self.kslope * ci[k])) for k in am, pm
]
# These respiration terms are just for assimilation calculations,
# autotrophic respiration is stil assumed to be half of GPP
(Rd) = self.calc_respiration(Tair_K, vcmax25)
# Net (saturated) photosynthetic rate, not sure if this
# makes sense.
Asat = [A[k] - Rd[k] for k in am, pm]
# Assumption that the integral is symmetric about noon, so we average
# the LUE accounting for variability in temperature, but importantly
# not PAR
lue = [self.epsilon(Asat[k], par, daylen, alpha) for k in am, pm]
# mol C mol-1 PAR - use average to simulate canopy photosynthesis
lue_avg = sum(lue) / 2.0
if float_eq(self.state.lai, 0.0):
self.fluxes.apar = 0.0
else:
par_mol = par * const.UMOL_TO_MOL
# absorbed photosynthetically active radiation
self.fluxes.apar = par_mol * self.state.fipar
# gC m-2 d-1
self.fluxes.gpp_gCm2 = (self.fluxes.apar * lue_avg *
const.MOL_C_TO_GRAMS_C)
self.fluxes.gpp_am_pm[am] = ((self.fluxes.apar / 2.0) * lue[am] *
const.MOL_C_TO_GRAMS_C)
self.fluxes.gpp_am_pm[pm] = ((self.fluxes.apar / 2.0) * lue[pm] *
const.MOL_C_TO_GRAMS_C)
#print self.fluxes.gpp_gCm2
self.fluxes.npp_gCm2 = self.fluxes.gpp_gCm2 * self.params.cue
if self.control.nuptake_model == 3:
self.fluxes.gpp_gCm2 *= self.params.ac
self.fluxes.gpp_am_pm[am] *= self.params.ac
self.fluxes.gpp_am_pm[pm] *= self.params.ac
self.fluxes.npp_gCm2 = self.fluxes.gpp_gCm2 * self.params.cue
# g C m-2 to tonnes hectare-1 day-1
conv = const.G_AS_TONNES / const.M2_AS_HA
self.fluxes.gpp = self.fluxes.gpp_gCm2 * conv
self.fluxes.npp = self.fluxes.npp_gCm2 * conv
# Plant respiration assuming carbon-use efficiency.
self.fluxes.auto_resp = self.fluxes.gpp - self.fluxes.npp
def calculate_photosynthesis(self, day, daylen):
""" Photosynthesis is calculated assuming GPP is proportional to APAR,
a commonly assumed reln (e.g. Potter 1993, Myneni 2002). The slope of
relationship btw GPP and APAR, i.e. LUE is modelled using the
photosynthesis eqns from Sands.
Assumptions:
------------
(1) photosynthetic light response is a non-rectangular hyperbolic func
of photon-flux density with a light-saturatred photosynthetic rate
(Amax), quantum yield (alpha) and curvature (theta).
(2) the canopy is horizontally uniform.
(3) PAR distribution within the canopy obeys Beer's law.
(4) light-saturated photosynthetic rate declines with canopy depth in
proportion to decline in PAR
(5) alpha + theta do not vary within the canopy
(6) dirunal variation of PAR is sinusoidal.
(7) The model makes no assumption about N within the canopy, however
this version assumes N declines exponentially through the cnaopy.
(8) Leaf temperature is the same as the air temperature.
Parameters:
----------
day : int
project day.
daylen : float
length of day in hours.
Returns:
-------
Nothing
Method calculates GPP, NPP and Ra.
"""
# local var for tidyness
(Tk_am, Tk_pm, par, vpd_am, vpd_pm, ca) = self.get_met_data(day)
# calculate mate params & account for temperature dependencies
N0 = self.calculate_top_of_canopy_n()
gamma_star_am = self.calculate_co2_compensation_point(Tk_am)
gamma_star_pm = self.calculate_co2_compensation_point(Tk_pm)
Km_am = self.calculate_michaelis_menten_parameter(Tk_am)
Km_pm = self.calculate_michaelis_menten_parameter(Tk_pm)
(jmax_am, vcmax_am) = self.calculate_jmax_and_vcmax(Tk_am, N0)
(jmax_pm, vcmax_pm) = self.calculate_jmax_and_vcmax(Tk_pm, N0)
ci_am = self.calculate_ci(vpd_am, ca)
ci_pm = self.calculate_ci(vpd_pm, ca)
# quantum efficiency calculated for C3 plants
alpha_am = self.calculate_quantum_efficiency(ci_am, gamma_star_am)
alpha_pm = self.calculate_quantum_efficiency(ci_pm, gamma_star_pm)
# Reducing assimilation if we encounter frost. Frost is assumed to
# impact on the maximum photosynthetic capacity and alpha_j
# So there is only an indirect effect on LAI, this could be changed...
if self.control.frost:
Tmax = self.met_data['tmax'][day]
Tmin = self.met_data['tmin'][day]
Thard = self.calc_frost_hardiness(daylen, Tmin, Tmax)
(total_alpha_limf,
total_amax_limf) = self.calc_frost_impact_factors(Thard, Tmin, Tmax)
alpha_am *= total_alpha_limf
alpha_pm *= total_alpha_limf
# Rubisco carboxylation limited rate of photosynthesis
ac_am = self.assim(ci_am, gamma_star_am, a1=vcmax_am, a2=Km_am)
ac_pm = self.assim(ci_pm, gamma_star_pm, a1=vcmax_pm, a2=Km_pm)
# Light-limited rate of photosynthesis allowed by RuBP regeneration
aj_am = self.assim(ci_am, gamma_star_am, a1=jmax_am/4.0,
a2=2.0*gamma_star_am)
aj_pm = self.assim(ci_pm, gamma_star_pm, a1=jmax_pm/4.0,
a2=2.0*gamma_star_pm)
# light-saturated photosynthesis rate at the top of the canopy (gross)
asat_am = min(aj_am, ac_am)
asat_pm = min(aj_pm, ac_pm)
if self.control.frost:
asat_am *= total_amax_limf
asat_pm *= total_amax_limf
# LUE (umol C umol-1 PAR)
lue_am = self.epsilon(asat_am, par, daylen, alpha_am)
lue_pm = self.epsilon(asat_pm, par, daylen, alpha_pm)
# use average to simulate canopy photosynthesis
lue_avg = (lue_am + lue_pm) / 2.0
if float_eq(self.state.lai, 0.0):
self.fluxes.apar = 0.0
else:
# absorbed photosynthetically active radiation (umol m-2 s-1)
self.fluxes.apar = par * self.state.fipar
apar_half_day = self.fluxes.apar / 2.0
# convert umol m-2 d-1 -> gC m-2 d-1
self.fluxes.gpp_gCm2 = self.fluxes.apar * lue_avg * const.UMOL_2_GRAMS_C
self.fluxes.gpp_am = apar_half_day * lue_am * const.UMOL_2_GRAMS_C
self.fluxes.gpp_pm = apar_half_day * lue_pm * const.UMOL_2_GRAMS_C
# g C m-2 to tonnes hectare-1 day-1
self.fluxes.gpp = self.fluxes.gpp_gCm2 * const.GRAM_C_2_TONNES_HA
if self.control.nuptake_model == 3:
self.fluxes.gpp_gCm2 *= self.params.ac
self.fluxes.gpp_am *= self.params.ac
self.fluxes.gpp_pm *= self.params.ac
def calculate_photosynthesis(self, day, daylen):
""" Photosynthesis is calculated assuming GPP is proportional to APAR,
a commonly assumed reln (e.g. Potter 1993, Myneni 2002). The slope of
relationship btw GPP and APAR, i.e. LUE is modelled using the
photosynthesis eqns from Sands.
Assumptions:
------------
(1) photosynthetic light response is a non-rectangular hyperbolic func
of photon-flux density with a light-saturatred photosynthetic rate
(Amax), quantum yield (alpha) and curvature (theta).
(2) the canopy is horizontally uniform.
(3) PAR distribution within the canopy obeys Beer's law.
(4) light-saturated photosynthetic rate declines with canopy depth in
proportion to decline in PAR
(5) alpha + theta do not vary within the canopy
(6) dirunal variation of PAR is sinusoidal.
(7) The model makes no assumption about N within the canopy, however
this version assumes N declines exponentially through the cnaopy.
(8) Leaf temperature is the same as the air temperature.
Parameters:
----------
day : int
project day.
daylen : float
length of day in hours.
Returns:
-------
Nothing
Method calculates GPP, NPP and Ra.
"""
# local var for tidyness
(Tair_K, par, vpd, ca) = self.get_met_data(day)
# calculate mate params & account for temperature dependencies
gamma_star = self.calculate_co2_compensation_point(Tair_K)
Km = self.calculate_michaelis_menten_parameter(Tair_K)
N0 = self.calculate_top_of_canopy_n()
(jmax, vcmax) = self.calculate_jmax_and_vcmax(Tair_K, N0)
ci = [self.calculate_ci(vpd[k], ca) for k in self.am, self.pm]
# quantum efficiency calculated for C3 plants
alpha = self.calculate_quantum_efficiency(ci, gamma_star)
# Rubisco carboxylation limited rate of photosynthesis
ac = [self.assim(ci[k], gamma_star[k], a1=vcmax[k], a2=Km[k]) for k in self.am, self.pm]
# Light-limited rate of photosynthesis allowed by RuBP regeneration
aj = [self.assim(ci[k], gamma_star[k], a1=jmax[k] / 4.0, a2=2.0 * gamma_star[k]) for k in self.am, self.pm]
# light-saturated photosynthesis rate at the top of the canopy (gross)
asat = [min(aj[k], ac[k]) for k in self.am, self.pm]
# LUE (umol C umol-1 PAR)
lue = [self.epsilon(asat[k], par, daylen, alpha[k]) for k in self.am, self.pm]
lue_avg = sum(lue) / 2.0 # use average to simulate canopy photosynthesis
if float_eq(self.state.lai, 0.0):
self.fluxes.apar = 0.0
else:
# absorbed photosynthetically active radiation (umol m-2 s-1)
self.fluxes.apar = par * self.state.fipar
# gC m-2 d-1
self.fluxes.gpp_gCm2 = self.fluxes.apar * lue_avg * const.UMOL_TO_MOL * const.MOL_C_TO_GRAMS_C
self.fluxes.gpp_am_pm[self.am] = (
(self.fluxes.apar / 2.0) * lue[self.am] * const.UMOL_TO_MOL * const.MOL_C_TO_GRAMS_C
)
self.fluxes.gpp_am_pm[self.pm] = (
(self.fluxes.apar / 2.0) * lue[self.pm] * const.UMOL_TO_MOL * const.MOL_C_TO_GRAMS_C
)
self.fluxes.npp_gCm2 = self.fluxes.gpp_gCm2 * self.params.cue
if self.control.nuptake_model == 3:
self.fluxes.gpp_gCm2 *= self.params.ac
self.fluxes.gpp_am_pm[self.am] *= self.params.ac
self.fluxes.gpp_am_pm[self.pm] *= self.params.ac
self.fluxes.npp_gCm2 = self.fluxes.gpp_gCm2 * self.params.cue
# g C m-2 to tonnes hectare-1 day-1
conv = const.G_AS_TONNES / const.M2_AS_HA
self.fluxes.gpp = self.fluxes.gpp_gCm2 * conv
self.fluxes.npp = self.fluxes.npp_gCm2 * conv
# Plant respiration assuming carbon-use efficiency.
self.fluxes.auto_resp = self.fluxes.gpp - self.fluxes.npp
def calculate_soil_water_fac(self):
""" Estimate a relative water availability factor [0..1]
A drying soil results in physiological stress that can induce stomatal
closure and reduce transpiration. Further, N mineralisation depends on
top soil moisture.
self.params.qs = 0.2 in SDGVM
References:
-----------
* Landsberg and Waring (1997) Forest Ecology and Management, 95, 209-228.
See --> Figure 2.
* Egea et al. (2011) Agricultural Forest Meteorology, 151, 1370-1384.
But similarly see:
* van Genuchten (1981) Soil Sci. Soc. Am. J, 44, 892--898.
* Wang and Leuning (1998) Ag Forest Met, 91, 89-111.
* Pepper et al. (2008) Functional Change Biology, 35, 493-508
Returns:
--------
wtfac_topsoil : float
water availability factor for the top soil [0,1]
wtfac_root : float
water availability factor for the root zone [0,1]
"""
# turn into fraction...
smc_topsoil = self.state.pawater_topsoil / self.params.wcapac_topsoil
smc_root = self.state.pawater_root / self.params.wcapac_root
if self.control.sw_stress_model == 0:
wtfac_topsoil = smc_topsoil**self.params.qs
wtfac_root = smc_root**self.params.qs
elif self.control.sw_stress_model == 1:
wtfac_topsoil = self.calc_sw_modifier(smc_topsoil,
self.params.ctheta_topsoil,
self.params.ntheta_topsoil)
wtfac_root = self.calc_sw_modifier(smc_root,
self.params.ctheta_root,
self.params.ntheta_root)
elif self.control.sw_stress_model == 2:
# Stomatal limitaiton
# Exponetial function to reduce g1 with soil water limitation
# based on Zhou et al. 2013, AFM, following Makela et al 1996.
# For the moment I have hardwired the PFT parameter as I am still
# testing.
# Because the model is a daily model we are assuming that LWP is
# well approximated by the night SWP.
if float_eq(smc_topsoil, 0.0):
psi_swp_topsoil = -1.5
else:
arg1 = self.params.psi_sat_topsoil
arg2 = smc_topsoil /self.params.theta_sat_topsoil
arg3 = -self.params.b_topsoil
psi_swp_topsoil = arg1 * arg2**arg3
if float_eq(smc_root, 0.0):
psi_swp_root = -1.5
else:
arg1 = self.params.psi_sat_root
arg2 = smc_root/self.params.theta_sat_root
arg3 = -self.params.b_root
psi_swp_root = arg1 * arg2**arg3
# multipliy these by g1, same as eqn 3 in Zhou et al. 2013.
b = 0.66
wtfac_topsoil = exp(b * psi_swp_topsoil)
wtfac_root = exp(b * psi_swp_root)
#print self.state.pawater_root,wtfac_root
return (wtfac_topsoil, wtfac_root)
