PET_ = np.zeros([365,grid.number_of_cells])
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)
Set random time for our example. Time is in years.
"""
current_time = 0.56
"""
'Radiation' class has an update function (like CSDMS BMI component). This
function takes current_time as an input argument and calculates Total Short
Wave Radiation incident on each cell at noon of the julian day represented
by current_time input. It also calculates the Radiation Factor which
represents the ratio of total short wave radiation incident on a grid cell
and flat surface. Hence Radiation Factor will be 1.0 if the grid cell has a
slope of 0.0 . Therefore, two cellular fields are created on the grid
(which means that the two arrays of length equivalent to number of cells
that the grid has are created and stored in conjunction with the grid.
Whenever this grid is transferred, these two cellular fields go with them.
"""
rad.update(current_time)
"""
Create a figure window available from pyplot library. This allows separating
figures while plotting multiple figures.
"""
plt.figure(0)
"""
Plot the cellular field 'TotalShortWaveRadiation' that is available on the
grid. imshow_grid is a Landlab plotting tool that reads the input of
grid, the variable name that needs to be plotted, and type of field (whether
it is 'cell' or 'node', etc... It also reads optional inputs (keywords),
grid_units (units of grid X and Y axes , e.g. 'm'). For more options, please refer
documentation for landlab.plot.imshow.
"""
imshow_grid(grid,
'TotalShortWaveRadiation',
PET_ = np.zeros([365, grid.number_of_cells])
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)
# 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()
