# component. Seedlings of shrubs and trees are determined by their age
# within the constructor as well. Hence the random vegetation type
# field is called for types GRASS, SHRUB, TREE and BARE only. -SN 10Mar15
grid['cell']['VegetationType'] = np.arange(0, 6)
# Create radiation, soil moisture and Vegetation objects
PD_D = PrecipitationDistribution(mean_storm = data['mean_storm_dry'], \
mean_interstorm = data['mean_interstorm_dry'],
mean_storm_depth = data['mean_storm_depth_dry'])
PD_W = PrecipitationDistribution(mean_storm = data['mean_storm_wet'], \
mean_interstorm = data['mean_interstorm_wet'],
mean_storm_depth = data['mean_storm_depth_wet'])
Rad = Radiation(grid)
PET_Tree = PotentialEvapotranspiration( grid1, method = data['PET_method'], \
MeanTmaxF = data['MeanTmaxF_tree'],
DeltaD = data['DeltaD'] )
PET_Shrub = PotentialEvapotranspiration( grid1, method = data['PET_method'], \
MeanTmaxF = data['MeanTmaxF_shrub'],
DeltaD = data['DeltaD'] )
PET_Grass = PotentialEvapotranspiration( grid1, method = data['PET_method'], \
MeanTmaxF = data['MeanTmaxF_grass'],
DeltaD = data['DeltaD'] )
SM = SoilMoisture(grid, data) # Soil Moisture object
VEG = Vegetation(grid, data) # Vegetation object
vegca = VegCA(grid1, data) # Cellular automaton object
##########
n = data['n_short'] # Defining number of storms the model will be run
##########
## Create input dictionary from text file
InputFile = 'Inputs_Vegetation_CA.txt'
data = txt_data_dict( InputFile ) # Create dictionary that holds the inputs
# Create rainfall, radiation, potential evapotranspiration,
# soil moisture and Vegetation objects
# Assign parameters to the components
PD_D = PrecipitationDistribution(mean_storm = data['mean_storm_dry'], \
mean_interstorm = data['mean_interstorm_dry'],
mean_storm_depth = data['mean_storm_depth_dry'])
PD_W = PrecipitationDistribution(mean_storm = data['mean_storm_wet'], \
mean_interstorm = data['mean_interstorm_wet'],
mean_storm_depth = data['mean_storm_depth_wet'])
Rad = Radiation( grid )
PET_Tree = PotentialEvapotranspiration( grid1, method = data['PET_method'], \
MeanTmaxF = data['MeanTmaxF_tree'],
DeltaD = data['DeltaD'] )
PET_Shrub = PotentialEvapotranspiration( grid1, method = data['PET_method'], \
MeanTmaxF = data['MeanTmaxF_shrub'],
DeltaD = data['DeltaD'] )
PET_Grass = PotentialEvapotranspiration( grid1, method = data['PET_method'], \
MeanTmaxF = data['MeanTmaxF_grass'],
DeltaD = data['DeltaD'] )
SM = SoilMoisture( grid, data ) # Soil Moisture object
VEG = Vegetation( grid, data ) # Vegetation object
vegca = VegCA( grid1, data ) # Cellular automaton object
##########
n = 6600 # Defining number of storms the model will be run
##########
# 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()
