grid['cell']['VegetationType'] = np.arange(0,6)
## 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
##########
grid1.number_of_cells)
# Age of the plants is randomnly generated by the constructor of vegca
# 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
##########
# 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()
"""
Creat a nodal field called 'Elevation' on the grid with units in metres and
populate it with zeros.
"""
grid.add_zeros('node', 'Elevation', units='m')
"""
This 'Elevation' field stored on the grid can be accessed as following:
"""
grid['node']['Elevation'] = elevation
"""
Instantiate an object for 'Radiation' Class. This instantiation associates
the object 'rad' with the capabilities of the class 'Radiation'. This
initiation requires an input of a grid. Creation of the object
automatically associates this grid to the object 'rad'.
"""
rad = Radiation(grid)
"""
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.
# 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
# Dummy grid for PET
grid1 = rmg(5, 4, 5)
grid1['node']['Elevation'] = 1700. * np.ones(grid1.number_of_nodes)
grid1['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)
Rad_PET = Radiation(grid1)
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(grid, data) # Cellular automaton object
##########
