Module simsoil.transpiration
Functions related to calculating (evapo)transpiration. These are largely taken from the MODIS MOD16 framework, but the goal is not to reproduce MOD16; rather, they facilitate the calculation of transpiration as one of the three components of evapotranspiration. Various primary sources are cited in the MOD16 code:
- Monteith, J. L., and M. Unsworth. 2001. "Principles of Environmental Physics", Second Ed.
- Mu, Q., Zhao, M., & Running, S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8), 1781–1800.
NOTE: Currently, canopy evaporation and potential transpiration are denominated in seconds (i.e., kg m-2 sec-1) instead of in days, as in MOD16. This is for compatibility with soil water infiltration models that run in with sub-daily time steps. As such, net radiation to the land surface or canopy should be in units of J m-2 s-1.
Expand source code
'''
Functions related to calculating (evapo)transpiration. These are largely taken
from the MODIS MOD16 framework, but the goal is not to reproduce MOD16;
rather, they facilitate the calculation of transpiration as one of the three
components of evapotranspiration. Various primary sources are cited in the
MOD16 code:
- Monteith, J. L., and M. Unsworth. 2001. "Principles of Environmental
Physics", Second Ed.
- Mu, Q., Zhao, M., & Running, S. W. (2011). Improvements to a MODIS
global terrestrial evapotranspiration algorithm. *Remote Sensing of
Environment*, 115(8), 1781–1800.
**NOTE:** Currently, canopy evaporation and potential transpiration are
denominated in seconds (i.e., kg m-2 sec-1) instead of in days, as in MOD16.
This is for compatibility with soil water infiltration models that run in
with sub-daily time steps. As such, net radiation to the land surface or
canopy should be in units of J m-2 s-1.
'''
import numpy as np
STEFAN_BOLTZMANN = 5.67e-8 # Stefan-Boltzmann constant, W m-2 K-4
SPECIFIC_HEAT_CAPACITY_AIR = 1013 # J kg-1 K-1, Monteith & Unsworth (2001)
# Ratio of molecular weight of water vapor to that of dry air (ibid.)
MOL_WEIGHT_WET_DRY_RATIO_AIR = 0.622
# Calculate the latent heat of vaporization (J kg-1)
latent_heat_vapor = lambda temp_k: (2.501 - 0.002361 * (temp_k - 273.15)) * 1e6
def canopy_evaporation(
pressure, temp_k, rhumidity, vpd, lai, fpar, rad_canopy, f_wet = None,
g_h = 0.01, g_e = 0.01):
r'''
Wet canopy evaporation calculated according to the MODIS MOD16 framework,
with air density calculation from the [
National Physical Laboratory (2021),
"Buoyancy Correction and Air Density Measurement."
](http://resource.npl.co.uk/docs/science_technology/mass_force_pressure/
clubs_groups/instmc_weighing_panel/buoycornote.pdf)
$$
\lambda E = \frac{(s\, A_c\, F_c + \rho\, C_p\, D\, (F_c / r_a))F_{wet}}{s +
(P_a\, C_p\, r_c)(\lambda\, \varepsilon\, r_a)^{-1}}
$$
Parameters
----------
pressure : float or numpy.ndarray
The air pressure in Pascals
temp_k : float or numpy.ndarray
The air temperature in degrees K
rhumidity : float or numpy.ndarray
Relative humidity, as a proportion on [0,1]
vpd : float or numpy.ndarray
The vapor pressure deficit in Pascals
lai : float or numpy.ndarray
The leaf area index (LAI)
fpar : float or numpy.ndarray
Fraction of photosynthetically active radiation (PAR) absorbed by
the vegetation canopy
rad_canopy : float or numpy.ndarray
Net radiation to the canopy (J m-2 s-1)
f_wet : float or numpy.ndarray or None
(Optional) Fraction of the land surface that is saturated/ covered
with standing water; if None, calculates this fraction like MOD16,
based on the relative humidity
g_h : float
Leaf conductance to sensible heat per unit LAI
(Default: 0.01 m s-1 LAI-1)
g_e : float
Leaf conductance to evaporated water per unit LAI
(Default: 0.01 m s-1 LAI-1)
Returns
-------
float or numpy.ndarray
Evaporation from the wet canopy surface (kg m-2 s-1)
'''
assert np.logical_and(0 <= rhumidity, rhumidity <= 1),\
'Relative humidity (rhumidity) must be on the interval [0,1]'
if f_wet is None:
f_wet = np.where(rhumidity < 0.7, 0, np.power(rhumidity, 4))
# NIST simplified air density formula with buoyancy correction (NPL 2021)
rho = np.divide( # Convert Pa to mbar, RH to RH% (percentage)
0.348444 * (pressure / 100) - (rhumidity * 100) *\
(0.00252 * temp_k - 273.15 - 0.020582),
temp_k) # kg m-3
# Wet canopy resistance to sensible heat ("rhc")
r_h = 1 / (g_h * lai * f_wet)
# Wet canopy resistance to evaporated water on the surface ("rvc")
r_c = 1 / (g_e * lai * f_wet)
# Resistance to radiative heat transfer through air ("rrc")
r_r = np.divide(
rho * SPECIFIC_HEAT_CAPACITY_AIR,
4 * STEFAN_BOLTZMANN * np.power(temp_k, 3))
# Aerodynamic resistance to evaporated water on the wet canopy surface
r_a = np.divide(r_h * r_r, r_h + r_r) # (s m-1)
# Slope of saturation vapor pressure curve
s = svp_slope(temp_k)
# Latent heat of vaporization (J kg-1)
lhv = latent_heat_vapor(temp_k)
# Mu et al. (2011), Equation 17; PET (J m-2 s-1) is divided by the latent
# heat of vaporization (J kg-1) to obtain mass flux (kg m-2 s-1)
return np.divide(
f_wet * ((s * rad_canopy) +\
(rho * SPECIFIC_HEAT_CAPACITY_AIR * vpd * (fpar * 1/r_a))),
s + ((pressure * SPECIFIC_HEAT_CAPACITY_AIR * r_c) *\
1/(lhv * MOL_WEIGHT_WET_DRY_RATIO_AIR * r_a))) / lhv
def psychrometric_constant(pressure, temp_k):
r'''
The psychrometric constant, which relates the vapor pressure to the air
temperature. Calculation derives from the "Handbook of Hydrology" by D.R.
Maidment, Section 4.2.28.
$$
\gamma = \frac{C_p \times P}{\lambda\times 0.622}
$$
Where `C_p` is the specific heat capacity of air, `P` is air pressure, and
`lambda` is the latent heat of vaporization.
Parameters
----------
pressure : float or numpy.ndarray
The air pressure in Pascals
temp_k : float or numpy.ndarray
The air temperature in degrees K
Returns
-------
float or numpy.ndarray
The psychrometric constant at this pressure, temperature (Pa K-1)
'''
lhv = latent_heat_vapor(temp_k) # Latent heat of vaporization (J kg-1)
return (SPECIFIC_HEAT_CAPACITY_AIR * pressure) /\
(lhv * MOL_WEIGHT_WET_DRY_RATIO_AIR)
def radiation_net(sw_rad, sw_albedo, temp_k):
r'''
Net incoming radiation to the land surface, calculated according to the
MOD16 algorithm and Cleugh et al. (2007); see Equation 7 in the MODIS
MOD16 Collection 6.1 User's Guide.
- Cleugh, H. A., Leuning, R., Mu, Q., & Running, S. W. (2007).
Regional evaporation estimates from flux tower and MODIS satellite data.
*Remote Sensing of Environment*, 106(3), 285–304.
$$
R_{net} = (1 - \alpha)\times R_{S\downarrow} +
(\varepsilon_a - \varepsilon_s) \times \sigma \times T^4
\quad\mbox{where}\quad \varepsilon_s = 0.97
$$
Where `alpha` is the MODIS albedo, `R_S` is down-welling short-wave
radiation, `sigma` is the Stefan-Boltzmann constant, and:
$$
\varepsilon_a = 1 - 0.26\,\mathrm{exp}\left(
-7.77\times 10^{-4}\times (T - 273.15)^2
\right)
$$
Parameters
----------
sw_rad : float or numpy.ndarray
Incoming short-wave radiation (W m-2)
sw_albedo : float or numpy.ndarray
White-sky albedo, e.g., from MODIS MCD43A3
temp_k : float or numpy.ndarray
Air temperature in degrees K
Returns
-------
float or numpy.ndarray
Net incoming radiation to the land surface (W m-2)
'''
# Mu et al. (2011), Equation 5
emis_surface = 0.97
emis_atmos = 1 - 0.26 * np.exp(-7.77e-4 * np.power(temp_k - 273.15, 2))
return sw_rad * (1 - sw_albedo) +\
STEFAN_BOLTZMANN * (emis_atmos - emis_surface) * np.power(temp_k, 4)
def svp(temp_k):
r'''
The saturation vapor pressure, based on [
the Food and Agriculture Organization's (FAO) formula, Equation 13
](http://www.fao.org/3/X0490E/x0490e07.htm).
$$
\mathrm{SVP} = 1\times 10^3\left(
0.6108\,\mathrm{exp}\left(
\frac{17.27 (T - 273.15)}{T - 273.15 + 237.3}
\right)
\right)
$$
Parameters
----------
temp_k : float or numpy.ndarray
The air temperature in degrees K
Returns
-------
float or numpy.ndarray
'''
temp_c = temp_k - 273.15
# And convert from kPa to Pa
return 1e3 * 0.6108 * np.exp((17.27 * temp_c) / (temp_c + 237.3))
def svp_slope(temp_k):
r'''
The slope of the saturation vapour pressure curve, which describes the
relationship between saturation vapor pressure and temperature. Based on [
the Food and Agriculture Organization's (FAO) formula, Equation 13
](http://www.fao.org/3/X0490E/x0490e07.htm).
$$
\Delta = 4098\times [\mathrm{SVP}]\times (T - 273.15 + 237.3)^{-2}
$$
Parameters
----------
temp_k : float or numpy.ndarray
The air temperature in degrees K
Returns
-------
float or numpy.ndarray
The slope of the saturation vapor pressure curve in Pascals per
degree K (Pa degK-1)
'''
temp_c = temp_k - 273.15
svp = 0.6108 * np.exp((17.27 * temp_c) / (temp_c + 237.3))
# With conversion of SVP from kPa to Pa
return (4098 * (svp * 1e3)) / np.power(temp_c + 237.3, 2)
def transpiration_potential(
air_temp_k, pressure, fpar, sw_rad, sw_albedo, rhumidity = None,
f_wet = None):
r'''
Estimates potential transpiration in each soil layer. Transpiration is
calculated using the Priestly-Taylor method for "potential transpiration,"
as described in the MODIS MOD16 framework.
$$
\lambda T = \frac{\alpha\, s\, A_c\, (1 - F_{wet})}{\gamma\, s}
$$
Where `alpha` is the MODIS albedo, `s` is the slope of the saturation
vapor pressure curve or `svp_slope()`, `A_c` is the net radiation
intercepted by the canopy, `F_wet` is the fraction of the land surface
that is saturated, and `gamma` is the `psychrometric_constant()`.
Parameters
----------
air_temp_k : numpy.ndarray
Air temperature (deg K)
pressure : numpy.ndarray
Air pressure (Pa)
fpar : numpy.ndarray
Fraction of photosynthetically active radiation (PAR) absorbed by
the vegetation canopy
sw_rad : numpy.ndarray
Incoming short-wave radiation, (W m-2) or (J s-1 m-2)
sw_albedo : numpy.ndarray
White-sky albedo for short-wave radiation
rhumidity: numpy.ndarray or None
(Optional) The relative humidity, expressed as a proportion on the
interval [0,1]; if not provided, f_wet must be provided.
f_wet : numpy.ndarray or None
(Optional) Fraction of the land surface that is saturated/ covered
with standing water; if None, calculates this fraction like MOD16,
based on the relative humidity (rhumidity).
Returns
-------
numpy.ndarray
A (Z x 1) array of transpiration in each soil layer (kg m-2 s-1)
'''
assert rhumidity is not None or f_wet is not None,\
'One must be provided: "rhumidity" or "f_wet"'
if rhumidity is not None:
assert np.logical_or(np.isnan(rhumidity),
np.logical_and(0 <= rhumidity, rhumidity <= 1)).all(),\
'Relative humidity (rhumidity) must be on the interval [0,1]'
if f_wet is None:
f_wet = np.where(rhumidity < 0.7, 0, np.power(rhumidity, 4))
alpha = 1.26 # Equation 18, MOD16 Collection 6.1 User's Guide
s = svp_slope(air_temp_k) # Slope of saturation vapor pressure curve
gamma = psychrometric_constant(pressure, air_temp_k)
# Net radiation to the land surface
net_rad = radiation_net(sw_rad, sw_albedo, air_temp_k)
# Net radiation to the canopy
canopy_rad = np.multiply(fpar, net_rad)
pt = np.divide(alpha * s * canopy_rad * (1 - f_wet), s + gamma)
# Divide by latent heat of vaporization to convert to a mass flux
return pt / latent_heat_vapor(air_temp_k)Functions
def canopy_evaporation(pressure, temp_k, rhumidity, vpd, lai, fpar, rad_canopy, f_wet=None, g_h=0.01, g_e=0.01)-
Wet canopy evaporation calculated according to the MODIS MOD16 framework, with air density calculation from the National Physical Laboratory (2021), "Buoyancy Correction and Air Density Measurement."
\lambda E = \frac{(s\, A_c\, F_c + \rho\, C_p\, D\, (F_c / r_a))F_{wet}}{s + (P_a\, C_p\, r_c)(\lambda\, \varepsilon\, r_a)^{-1}}
Parameters
pressure:floatornumpy.ndarray- The air pressure in Pascals
temp_k:floatornumpy.ndarray- The air temperature in degrees K
rhumidity:floatornumpy.ndarray- Relative humidity, as a proportion on [0,1]
vpd:floatornumpy.ndarray- The vapor pressure deficit in Pascals
lai:floatornumpy.ndarray- The leaf area index (LAI)
fpar:floatornumpy.ndarray- Fraction of photosynthetically active radiation (PAR) absorbed by the vegetation canopy
rad_canopy:floatornumpy.ndarray- Net radiation to the canopy (J m-2 s-1)
f_wet:floatornumpy.ndarrayorNone- (Optional) Fraction of the land surface that is saturated/ covered with standing water; if None, calculates this fraction like MOD16, based on the relative humidity
g_h:float- Leaf conductance to sensible heat per unit LAI (Default: 0.01 m s-1 LAI-1)
g_e:float- Leaf conductance to evaporated water per unit LAI (Default: 0.01 m s-1 LAI-1)
Returns
floatornumpy.ndarray- Evaporation from the wet canopy surface (kg m-2 s-1)
Expand source code
def canopy_evaporation( pressure, temp_k, rhumidity, vpd, lai, fpar, rad_canopy, f_wet = None, g_h = 0.01, g_e = 0.01): r''' Wet canopy evaporation calculated according to the MODIS MOD16 framework, with air density calculation from the [ National Physical Laboratory (2021), "Buoyancy Correction and Air Density Measurement." ](http://resource.npl.co.uk/docs/science_technology/mass_force_pressure/ clubs_groups/instmc_weighing_panel/buoycornote.pdf) $$ \lambda E = \frac{(s\, A_c\, F_c + \rho\, C_p\, D\, (F_c / r_a))F_{wet}}{s + (P_a\, C_p\, r_c)(\lambda\, \varepsilon\, r_a)^{-1}} $$ Parameters ---------- pressure : float or numpy.ndarray The air pressure in Pascals temp_k : float or numpy.ndarray The air temperature in degrees K rhumidity : float or numpy.ndarray Relative humidity, as a proportion on [0,1] vpd : float or numpy.ndarray The vapor pressure deficit in Pascals lai : float or numpy.ndarray The leaf area index (LAI) fpar : float or numpy.ndarray Fraction of photosynthetically active radiation (PAR) absorbed by the vegetation canopy rad_canopy : float or numpy.ndarray Net radiation to the canopy (J m-2 s-1) f_wet : float or numpy.ndarray or None (Optional) Fraction of the land surface that is saturated/ covered with standing water; if None, calculates this fraction like MOD16, based on the relative humidity g_h : float Leaf conductance to sensible heat per unit LAI (Default: 0.01 m s-1 LAI-1) g_e : float Leaf conductance to evaporated water per unit LAI (Default: 0.01 m s-1 LAI-1) Returns ------- float or numpy.ndarray Evaporation from the wet canopy surface (kg m-2 s-1) ''' assert np.logical_and(0 <= rhumidity, rhumidity <= 1),\ 'Relative humidity (rhumidity) must be on the interval [0,1]' if f_wet is None: f_wet = np.where(rhumidity < 0.7, 0, np.power(rhumidity, 4)) # NIST simplified air density formula with buoyancy correction (NPL 2021) rho = np.divide( # Convert Pa to mbar, RH to RH% (percentage) 0.348444 * (pressure / 100) - (rhumidity * 100) *\ (0.00252 * temp_k - 273.15 - 0.020582), temp_k) # kg m-3 # Wet canopy resistance to sensible heat ("rhc") r_h = 1 / (g_h * lai * f_wet) # Wet canopy resistance to evaporated water on the surface ("rvc") r_c = 1 / (g_e * lai * f_wet) # Resistance to radiative heat transfer through air ("rrc") r_r = np.divide( rho * SPECIFIC_HEAT_CAPACITY_AIR, 4 * STEFAN_BOLTZMANN * np.power(temp_k, 3)) # Aerodynamic resistance to evaporated water on the wet canopy surface r_a = np.divide(r_h * r_r, r_h + r_r) # (s m-1) # Slope of saturation vapor pressure curve s = svp_slope(temp_k) # Latent heat of vaporization (J kg-1) lhv = latent_heat_vapor(temp_k) # Mu et al. (2011), Equation 17; PET (J m-2 s-1) is divided by the latent # heat of vaporization (J kg-1) to obtain mass flux (kg m-2 s-1) return np.divide( f_wet * ((s * rad_canopy) +\ (rho * SPECIFIC_HEAT_CAPACITY_AIR * vpd * (fpar * 1/r_a))), s + ((pressure * SPECIFIC_HEAT_CAPACITY_AIR * r_c) *\ 1/(lhv * MOL_WEIGHT_WET_DRY_RATIO_AIR * r_a))) / lhv def latent_heat_vapor(temp_k)-
Expand source code
latent_heat_vapor = lambda temp_k: (2.501 - 0.002361 * (temp_k - 273.15)) * 1e6 def psychrometric_constant(pressure, temp_k)-
The psychrometric constant, which relates the vapor pressure to the air temperature. Calculation derives from the "Handbook of Hydrology" by D.R. Maidment, Section 4.2.28.
\gamma = \frac{C_p \times P}{\lambda\times 0.622}
Where
C_pis the specific heat capacity of air,Pis air pressure, andlambdais the latent heat of vaporization.Parameters
pressure:floatornumpy.ndarray- The air pressure in Pascals
temp_k:floatornumpy.ndarray- The air temperature in degrees K
Returns
floatornumpy.ndarray- The psychrometric constant at this pressure, temperature (Pa K-1)
Expand source code
def psychrometric_constant(pressure, temp_k): r''' The psychrometric constant, which relates the vapor pressure to the air temperature. Calculation derives from the "Handbook of Hydrology" by D.R. Maidment, Section 4.2.28. $$ \gamma = \frac{C_p \times P}{\lambda\times 0.622} $$ Where `C_p` is the specific heat capacity of air, `P` is air pressure, and `lambda` is the latent heat of vaporization. Parameters ---------- pressure : float or numpy.ndarray The air pressure in Pascals temp_k : float or numpy.ndarray The air temperature in degrees K Returns ------- float or numpy.ndarray The psychrometric constant at this pressure, temperature (Pa K-1) ''' lhv = latent_heat_vapor(temp_k) # Latent heat of vaporization (J kg-1) return (SPECIFIC_HEAT_CAPACITY_AIR * pressure) /\ (lhv * MOL_WEIGHT_WET_DRY_RATIO_AIR) def radiation_net(sw_rad, sw_albedo, temp_k)-
Net incoming radiation to the land surface, calculated according to the MOD16 algorithm and Cleugh et al. (2007); see Equation 7 in the MODIS MOD16 Collection 6.1 User's Guide.
- Cleugh, H. A., Leuning, R., Mu, Q., & Running, S. W. (2007). Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of Environment, 106(3), 285–304.
R_{net} = (1 - \alpha)\times R_{S\downarrow} + (\varepsilon_a - \varepsilon_s) \times \sigma \times T^4 \quad\mbox{where}\quad \varepsilon_s = 0.97
Where
alphais the MODIS albedo,R_Sis down-welling short-wave radiation,sigmais the Stefan-Boltzmann constant, and:\varepsilon_a = 1 - 0.26\,\mathrm{exp}\left( -7.77\times 10^{-4}\times (T - 273.15)^2 \right)
Parameters
sw_rad:floatornumpy.ndarray- Incoming short-wave radiation (W m-2)
sw_albedo:floatornumpy.ndarray- White-sky albedo, e.g., from MODIS MCD43A3
temp_k:floatornumpy.ndarray- Air temperature in degrees K
Returns
floatornumpy.ndarray- Net incoming radiation to the land surface (W m-2)
Expand source code
def radiation_net(sw_rad, sw_albedo, temp_k): r''' Net incoming radiation to the land surface, calculated according to the MOD16 algorithm and Cleugh et al. (2007); see Equation 7 in the MODIS MOD16 Collection 6.1 User's Guide. - Cleugh, H. A., Leuning, R., Mu, Q., & Running, S. W. (2007). Regional evaporation estimates from flux tower and MODIS satellite data. *Remote Sensing of Environment*, 106(3), 285–304. $$ R_{net} = (1 - \alpha)\times R_{S\downarrow} + (\varepsilon_a - \varepsilon_s) \times \sigma \times T^4 \quad\mbox{where}\quad \varepsilon_s = 0.97 $$ Where `alpha` is the MODIS albedo, `R_S` is down-welling short-wave radiation, `sigma` is the Stefan-Boltzmann constant, and: $$ \varepsilon_a = 1 - 0.26\,\mathrm{exp}\left( -7.77\times 10^{-4}\times (T - 273.15)^2 \right) $$ Parameters ---------- sw_rad : float or numpy.ndarray Incoming short-wave radiation (W m-2) sw_albedo : float or numpy.ndarray White-sky albedo, e.g., from MODIS MCD43A3 temp_k : float or numpy.ndarray Air temperature in degrees K Returns ------- float or numpy.ndarray Net incoming radiation to the land surface (W m-2) ''' # Mu et al. (2011), Equation 5 emis_surface = 0.97 emis_atmos = 1 - 0.26 * np.exp(-7.77e-4 * np.power(temp_k - 273.15, 2)) return sw_rad * (1 - sw_albedo) +\ STEFAN_BOLTZMANN * (emis_atmos - emis_surface) * np.power(temp_k, 4) def svp(temp_k)-
The saturation vapor pressure, based on the Food and Agriculture Organization's (FAO) formula, Equation 13 .
\mathrm{SVP} = 1\times 10^3\left( 0.6108\,\mathrm{exp}\left( \frac{17.27 (T - 273.15)}{T - 273.15 + 237.3} \right) \right)
Parameters
temp_k:floatornumpy.ndarray- The air temperature in degrees K
Returns
floatornumpy.ndarray
Expand source code
def svp(temp_k): r''' The saturation vapor pressure, based on [ the Food and Agriculture Organization's (FAO) formula, Equation 13 ](http://www.fao.org/3/X0490E/x0490e07.htm). $$ \mathrm{SVP} = 1\times 10^3\left( 0.6108\,\mathrm{exp}\left( \frac{17.27 (T - 273.15)}{T - 273.15 + 237.3} \right) \right) $$ Parameters ---------- temp_k : float or numpy.ndarray The air temperature in degrees K Returns ------- float or numpy.ndarray ''' temp_c = temp_k - 273.15 # And convert from kPa to Pa return 1e3 * 0.6108 * np.exp((17.27 * temp_c) / (temp_c + 237.3)) def svp_slope(temp_k)-
The slope of the saturation vapour pressure curve, which describes the relationship between saturation vapor pressure and temperature. Based on the Food and Agriculture Organization's (FAO) formula, Equation 13 .
\Delta = 4098\times [\mathrm{SVP}]\times (T - 273.15 + 237.3)^{-2}
Parameters
temp_k:floatornumpy.ndarray- The air temperature in degrees K
Returns
floatornumpy.ndarray- The slope of the saturation vapor pressure curve in Pascals per degree K (Pa degK-1)
Expand source code
def svp_slope(temp_k): r''' The slope of the saturation vapour pressure curve, which describes the relationship between saturation vapor pressure and temperature. Based on [ the Food and Agriculture Organization's (FAO) formula, Equation 13 ](http://www.fao.org/3/X0490E/x0490e07.htm). $$ \Delta = 4098\times [\mathrm{SVP}]\times (T - 273.15 + 237.3)^{-2} $$ Parameters ---------- temp_k : float or numpy.ndarray The air temperature in degrees K Returns ------- float or numpy.ndarray The slope of the saturation vapor pressure curve in Pascals per degree K (Pa degK-1) ''' temp_c = temp_k - 273.15 svp = 0.6108 * np.exp((17.27 * temp_c) / (temp_c + 237.3)) # With conversion of SVP from kPa to Pa return (4098 * (svp * 1e3)) / np.power(temp_c + 237.3, 2) def transpiration_potential(air_temp_k, pressure, fpar, sw_rad, sw_albedo, rhumidity=None, f_wet=None)-
Estimates potential transpiration in each soil layer. Transpiration is calculated using the Priestly-Taylor method for "potential transpiration," as described in the MODIS MOD16 framework.
\lambda T = \frac{\alpha\, s\, A_c\, (1 - F_{wet})}{\gamma\, s}
Where
alphais the MODIS albedo,sis the slope of the saturation vapor pressure curve orsvp_slope(),A_cis the net radiation intercepted by the canopy,F_wetis the fraction of the land surface that is saturated, andgammais thepsychrometric_constant().Parameters
air_temp_k:numpy.ndarray- Air temperature (deg K)
pressure:numpy.ndarray- Air pressure (Pa)
fpar:numpy.ndarray- Fraction of photosynthetically active radiation (PAR) absorbed by the vegetation canopy
sw_rad:numpy.ndarray- Incoming short-wave radiation, (W m-2) or (J s-1 m-2)
sw_albedo:numpy.ndarray- White-sky albedo for short-wave radiation
rhumidity:numpy.ndarrayorNone- (Optional) The relative humidity, expressed as a proportion on the interval [0,1]; if not provided, f_wet must be provided.
f_wet:numpy.ndarrayorNone- (Optional) Fraction of the land surface that is saturated/ covered with standing water; if None, calculates this fraction like MOD16, based on the relative humidity (rhumidity).
Returns
numpy.ndarray- A (Z x 1) array of transpiration in each soil layer (kg m-2 s-1)
Expand source code
def transpiration_potential( air_temp_k, pressure, fpar, sw_rad, sw_albedo, rhumidity = None, f_wet = None): r''' Estimates potential transpiration in each soil layer. Transpiration is calculated using the Priestly-Taylor method for "potential transpiration," as described in the MODIS MOD16 framework. $$ \lambda T = \frac{\alpha\, s\, A_c\, (1 - F_{wet})}{\gamma\, s} $$ Where `alpha` is the MODIS albedo, `s` is the slope of the saturation vapor pressure curve or `svp_slope()`, `A_c` is the net radiation intercepted by the canopy, `F_wet` is the fraction of the land surface that is saturated, and `gamma` is the `psychrometric_constant()`. Parameters ---------- air_temp_k : numpy.ndarray Air temperature (deg K) pressure : numpy.ndarray Air pressure (Pa) fpar : numpy.ndarray Fraction of photosynthetically active radiation (PAR) absorbed by the vegetation canopy sw_rad : numpy.ndarray Incoming short-wave radiation, (W m-2) or (J s-1 m-2) sw_albedo : numpy.ndarray White-sky albedo for short-wave radiation rhumidity: numpy.ndarray or None (Optional) The relative humidity, expressed as a proportion on the interval [0,1]; if not provided, f_wet must be provided. f_wet : numpy.ndarray or None (Optional) Fraction of the land surface that is saturated/ covered with standing water; if None, calculates this fraction like MOD16, based on the relative humidity (rhumidity). Returns ------- numpy.ndarray A (Z x 1) array of transpiration in each soil layer (kg m-2 s-1) ''' assert rhumidity is not None or f_wet is not None,\ 'One must be provided: "rhumidity" or "f_wet"' if rhumidity is not None: assert np.logical_or(np.isnan(rhumidity), np.logical_and(0 <= rhumidity, rhumidity <= 1)).all(),\ 'Relative humidity (rhumidity) must be on the interval [0,1]' if f_wet is None: f_wet = np.where(rhumidity < 0.7, 0, np.power(rhumidity, 4)) alpha = 1.26 # Equation 18, MOD16 Collection 6.1 User's Guide s = svp_slope(air_temp_k) # Slope of saturation vapor pressure curve gamma = psychrometric_constant(pressure, air_temp_k) # Net radiation to the land surface net_rad = radiation_net(sw_rad, sw_albedo, air_temp_k) # Net radiation to the canopy canopy_rad = np.multiply(fpar, net_rad) pt = np.divide(alpha * s * canopy_rad * (1 - f_wet), s + gamma) # Divide by latent heat of vaporization to convert to a mass flux return pt / latent_heat_vapor(air_temp_k)