def Em(airtemp = scipy.array([]),\
rh = scipy.array([]),\
airpress = scipy.array([]),\
Rs = scipy.array([])):
'''
Function to calculate Makkink evaporation (in mm/day). The Makkink
evaporation is a reference crop evaporation used in the Netherlands,
which is combined with a crop factor to provide an estimate of actual
crop evaporation. Source: De Bruin, H.A.R.,1987. From Penman to
Makkink', in Hooghart, C. (Ed.), Evaporation and Weather, Proceedings
and Information. Comm. Hydrological Research TNO, The Hague. pp. 5-30.
Input (measured at 2 m height):
- airtemp: (array of) daily average air temperatures [Celsius]
- rh: (array of) daily average relative humidity values[%]
- airpress: (array of) daily average air pressure data [Pa]
- Rs: (array of) average daily incoming solar radiation [J/m2/day]
Output:
- Em: (array of) Makkink evaporation values [mm]
Examples:
>>> Em_data = Em(T,RH,press,Rs)
>>> Em(21.65,67.0,101300,24200000)
4.5038304791979913
'''
# Calculate Delta and gamma constants
DELTA = meteolib.Delta_calc(airtemp)
gamma = meteolib.gamma_calc(airtemp, rh, airpress)
Lambda = meteolib.L_calc(airtemp)
# Determine length of array
l = scipy.size(airtemp)
# Check if we have a single value or an array
if l < 2: # Dealing with single value...
# calculate Em [mm/day]
Em = 0.65 * DELTA / (DELTA + gamma) * Rs / Lambda
else: # Dealing with an array
# Initiate output array
Em = scipy.zeros(l)
for i in range(0, l):
# calculate Em [mm/day]
Em[i] = 0.65 * DELTA[i] / (DELTA[i] + gamma[i]) * Rs[i] / Lambda[i]
Em = scipy.array(Em)
return Em
def Ept(airtemp = scipy.array([]),\
rh = scipy.array([]),\
airpress = scipy.array([]),\
Rn = scipy.array([]),\
G = scipy.array([])):
'''
Function to calculate daily Priestley - Taylor evaporation (in mm).
Source: Priestley, C.H.B. and R.J. Taylor, 1972. On the assessment
of surface heat flux and evaporation using large-scale parameters.
Mon. Weather Rev. 100:81-82.
Input (measured at 2 m height):
- airtemp: (array of) daily average air temperatures [Celsius]
- rh: (array of) daily average relative humidity values[%]
- airpress: (array of) daily average air pressure data [Pa]
- Rn: (array of) average daily net radiation [J/m2/day]
- G: (array of) average daily soil heat flux [J/m2/day]
Output:
- Ept: (array of) Priestley Taylor evaporation values [mm]
Examples:
>>> Ept_data = Ept(T,RH,press,Rn,G)
>>> Ept(21.65,67.0,101300,18200000,600000)
6.3494561161280778
'''
# Calculate Delta and gamma constants
DELTA = meteolib.Delta_calc(airtemp)
gamma = meteolib.gamma_calc(airtemp, rh, airpress)
Lambda = meteolib.L_calc(airtemp)
# Determine length of array
l = scipy.size(airtemp)
# Check if we have a single value or an array
if l < 2: # Dealing with single value...
# calculate Em [mm/day]
Ept = 1.26 * DELTA / (DELTA + gamma) * (Rn - G) / Lambda
else: # Dealing with an array
# Initiate output array
Ept = scipy.zeros(l)
for i in range(0, l):
# calculate Ept [mm/day]
Ept[i] = 1.26 * DELTA[i] / (DELTA[i] +
gamma[i]) * (Rn[i] - G[i]) / Lambda[i]
Ept = scipy.array(Ept)
return Ept
def ET0pm(airtemp = scipy.array([]),\
rh = scipy.array([]),\
airpress = scipy.array([]), \
Rs = scipy.array([]),\
N = scipy.array([]),\
Rext = scipy.array([]),\
u = scipy.array([]), \
Z=0.0):
'''
Function to calculate daily Penman Monteith reference evaporation
(in mm/day). Source: R.G. Allen, L.S. Pereira, D. Raes and M. Smith
(1998). Crop evapotranspiration - Guidelines for computing crop
water requirements - FAO Irrigation and drainage paper 56. FAO -
Food and Agriculture Organization of the United Nations, Rome, 1998
Input (measured at 2 m height):
- airtemp: (array of) daily average air temperatures [Celsius]
- rh: (array of) daily average relative humidity values[%]
- airpress: (array of) daily average air pressure data [hPa]
- Rs: (array of) total incoming shortwave radiation [J/m2/day]
- N: daylength [h]
- Rext: Incoming shortwave radiation at the top of the atmosphere [J/m2/day]
- u: windspeed [m/s]
- Z: elevation [m], default is 0.0 m
Output:
- ET0pm: (array of) Penman Monteith reference evaporation (short grass with optimum water supply) values [mm]
Examples:--
>>> Eref_data = ET0pm(T,RH,press,Rs,N,Rext,u)
'''
# Set constants
albedo = 0.23 # short grass albedo
sigma = 4.903E-3 # Stefan Boltzmann constant J/m2/K4/d
# Calculate Delta, gamma and lambda
DELTA = meteolib.Delta_calc(airtemp) # [Pa/K]
gamma = meteolib.gamma_calc(airtemp, rh, airpress) # [Pa/K]
Lambda = meteolib.L_calc(airtemp) # [J/kg]
# Calculate saturated and actual water vapour pressures
es = meteolib.es_calc(airtemp) # [Pa]
ea = meteolib.ea_calc(airtemp, rh) # [Pa]
# Determine length of array
l = scipy.size(airtemp)
# Check if we have a single value or an array
if l < 2: # Dealing with single value...
Rns = (1.0 - albedo) * Rs # Shortwave component [J/m2/d]
# Calculate clear sky radiation Rs0
Rs0 = (0.75 + 2E-5 * Z) * Rext # Clear sky radiation [J/m2/d]
f = 1.35 * Rs / Rs0 - 0.35
epsilom = 0.34 - 0.14 * scipy.sqrt(ea / 1000)
Rnl = f * epsilom * sigma * (airtemp +
273.15)**4 # Longwave component [J/m2/d]
Rnet = Rns - Rnl # Net radiation [J/m2/d]
ET0pm = (DELTA/1000.*Rnet/Lambda+900./(airtemp+273.16)*u*(es-ea)/1000\
*gamma/1000)/(DELTA/1000.+gamma/1000*(1.+0.34*u))
else: # Dealing with an array
# Initiate output arrays
ET0pm = scipy.zeros(l)
Rns = scipy.zeros(l)
Rs0 = scipy.zeros(l)
f = scipy.zeros(l)
epsilom = scipy.zeros(l)
Rnl = scipy.zeros(l)
Rnet = scipy.zeros(l)
for i in range(0, l):
# calculate longwave radiation component Rln (J/m2/day)
Rns[i] = (1.0 - albedo) * Rs[i] # Shortwave component [J/m2/d]
# Calculate clear sky radiation Rs0
Rs0[i] = (0.75 + 2E-5 * Z) * Rext[i]
f[i] = 1.35 * Rs[i] / Rs0[i] - 0.35
epsilom[i] = 0.34 - 0.14 * scipy.sqrt(ea[i] / 1000)
Rnl[i] = f[i] * epsilom[i] * sigma * (
airtemp[i] + 273.15)**4 # Longwave component [J/m2/d]
Rnet[i] = Rns[i] - Rnl[i] # Net radiation [J/m2/d]
ET0pm[i] = (DELTA[i]/1000.*Rnet[i]/Lambda[i]+900./(airtemp[i]+273.16)* \
u[i]*(es[i]-ea[i])/1000*gamma[i]/1000)/ \
(DELTA[i]/1000.+gamma[i]/1000*(1.+0.34*u[i]))
return ET0pm # FAO reference evaporation [mm/day]
