Rad_Factor = np.empty([365, grid.number_of_cells])
EP30 = np.empty([365, grid.number_of_cells
]) # 30 day average PET to determine season
PET_threshold = 0 # Initializing PET_threshold to ETThresholddown
## Initializing inputs for Soil Moisture object
grid['cell']['LiveLeafAreaIndex'] = 1.6 * np.ones(grid.number_of_cells)
SM._SO = 0.59 * np.ones(grid.number_of_cells) # Initializing Soil Moisture
#TG = np.array([104, 365, 365, 365, 365, 365])#365 # Growing season in days
## Calculate current time in years such that Julian time can be calculated
current_time = 0 # Start from first day of June
for i in range(0, 365):
Rad.update(float(i) / 365.25)
PET_Tree.update(float(i) / 365.25)
PET_Shrub.update(float(i) / 365.25)
PET_Grass.update(float(i) / 365.25)
PET_[i] = [
PET_Grass._PET_value, PET_Shrub._PET_value, PET_Tree._PET_value, 0.,
PET_Shrub._PET_value, PET_Tree._PET_value
]
Rad_Factor[i] = grid['cell']['RadiationFactor']
if i < 30:
if i == 0:
EP30[0] = PET_[0]
else:
EP30[i] = np.mean(PET_[:i], axis=0)
else:
EP30[i] = np.mean(PET_[i - 30:i], axis=0)
Rad_Factor = np.empty([365,grid.number_of_cells])
EP30 = np.empty([365, grid.number_of_cells]) # 30 day average PET to determine season
PET_threshold = 0 # Initializing PET_threshold to ETThresholddown
## Initializing inputs for Soil Moisture object
grid['cell']['LiveLeafAreaIndex'] = 1.6 * np.ones( grid.number_of_cells )
SM._SO = 0.59 * np.ones(grid.number_of_cells) # Initializing Soil Moisture
Tg = 365 # Growing season in days
## Calculate current time in years such that Julian time can be calculated
current_time = 0 # Start from first day of June
for i in range(0,365):
Rad.update( float(i)/365.25)
PET_Tree.update( float(i)/365.25 )
PET_Shrub.update( float(i)/365.25 )
PET_Grass.update( float(i)/365.25 )
PET_[i] = [PET_Grass._PET_value, PET_Shrub._PET_value, PET_Tree._PET_value,
0., PET_Shrub._PET_value, PET_Tree._PET_value]
Rad_Factor[i] = grid['cell']['RadiationFactor']
if i < 30:
if i == 0:
EP30[0] = PET_[0]
else:
EP30[i] = np.mean(PET_[:i],axis = 0)
else:
EP30[i] = np.mean(PET_[i-30:i], axis = 0)
time_check = 0. # Buffer to store current_time at previous storm
# Sai Nudurupati and Erkan Istanbulluoglu- 16May2014 :
# Example to use potential_evapotranspiration_field.py
#import landlab
from landlab import RasterModelGrid
from landlab.components.radiation import Radiation
from landlab.components.pet import PotentialEvapotranspiration
import numpy as np
import matplotlib.pyplot as plt
from landlab.plot.imshow import imshow_grid
grid = RasterModelGrid( 100, 100, 20. )
elevation = np.random.rand(grid.number_of_nodes) * 1000
grid.add_zeros('node','Elevation',units = 'm')
grid['node']['Elevation'] = elevation
rad = Radiation( grid )
PET = PotentialEvapotranspiration( grid )
current_time = 0.56
rad.update( current_time )
PET.update( ConstantPotentialEvapotranspiration = 10.0 )
plt.figure(0)
imshow_grid(grid,'RadiationFactor', values_at = 'cell',
grid_units = ('m','m'))
plt.figure(1)
imshow_grid(grid,'PotentialEvapotranspiration', values_at = 'cell',
grid_units = ('m','m'))
plt.savefig('PET_test')
plt.show()
