def Initialize_(data, grid, grid1, elevation):
# Plant types are defined as following:
# GRASS = 0; SHRUB = 1; TREE = 2; BARE = 3;
# SHRUBSEEDLING = 4; TREESEEDLING = 5
# Initialize random plant type field
grid['cell']['vegetation__plant_functional_type'] = compose_veg_grid(
grid,
percent_bare=data['percent_bare_initial'],
percent_grass=data['percent_grass_initial'],
percent_shrub=data['percent_shrub_initial'],
percent_tree=data['percent_tree_initial'])
# Assign plant type for representative ecohydrologic simulations
grid1['cell']['vegetation__plant_functional_type'] = np.arange(0, 6)
grid1['node']['topographic__elevation'] = (1700. *
np.ones(grid1.number_of_nodes))
grid['node']['topographic__elevation'] = elevation
PD_D = PrecipitationDistribution(
mean_storm_duration=data['mean_storm_dry'],
mean_interstorm_duration=data['mean_interstorm_dry'],
mean_storm_depth=data['mean_storm_depth_dry'])
PD_W = PrecipitationDistribution(
mean_storm_duration=data['mean_storm_wet'],
mean_interstorm_duration=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'],
delta_d=data['DeltaD'])
PET_Shrub = PotentialEvapotranspiration(grid1,
method=data['PET_method'],
MeanTmaxF=data['MeanTmaxF_shrub'],
delta_d=data['DeltaD'])
PET_Grass = PotentialEvapotranspiration(grid1,
method=data['PET_method'],
MeanTmaxF=data['MeanTmaxF_grass'],
delta_d=data['DeltaD'])
SM = SoilMoisture(grid, **data) # Soil Moisture object
VEG = Vegetation(grid, **data) # Vegetation object
vegca = VegCA(grid, **data) # Cellular automaton object
# # Initializing inputs for Soil Moisture object
grid['cell']['vegetation__live_leaf_area_index'] = (
1.6 * np.ones(grid.number_of_cells))
grid['cell']['soil_moisture__initial_saturation_fraction'] = (
0.59 * np.ones(grid.number_of_cells))
# Initializing Soil Moisture
return PD_D, PD_W, Rad, Rad_PET, PET_Tree, PET_Shrub, PET_Grass, SM, \
VEG, vegca
def initialize(data, grid, grid1):
"""Initialize random plant type field.
Plant types are defined as the following:
* GRASS = 0
* SHRUB = 1
* TREE = 2
* BARE = 3
* SHRUBSEEDLING = 4
* TREESEEDLING = 5
"""
grid1.at_cell['vegetation__plant_functional_type'] = compose_veg_grid(
grid1,
percent_bare=data['percent_bare_initial'],
percent_grass=data['percent_grass_initial'],
percent_shrub=data['percent_shrub_initial'],
percent_tree=data['percent_tree_initial'])
# Assign plant type for representative ecohydrologic simulations
grid.at_cell['vegetation__plant_functional_type'] = np.arange(6)
grid1.at_node['topographic__elevation'] = np.full(grid1.number_of_nodes,
1700.)
grid.at_node['topographic__elevation'] = np.full(grid.number_of_nodes,
1700.)
precip_dry = PrecipitationDistribution(
mean_storm_duration=data['mean_storm_dry'],
mean_interstorm_duration=data['mean_interstorm_dry'],
mean_storm_depth=data['mean_storm_depth_dry'])
precip_wet = PrecipitationDistribution(
mean_storm_duration=data['mean_storm_wet'],
mean_interstorm_duration=data['mean_interstorm_wet'],
mean_storm_depth=data['mean_storm_depth_wet'])
radiation = Radiation(grid)
pet_tree = PotentialEvapotranspiration(grid,
method=data['PET_method'],
MeanTmaxF=data['MeanTmaxF_tree'],
delta_d=data['DeltaD'])
pet_shrub = PotentialEvapotranspiration(grid,
method=data['PET_method'],
MeanTmaxF=data['MeanTmaxF_shrub'],
delta_d=data['DeltaD'])
pet_grass = PotentialEvapotranspiration(grid,
method=data['PET_method'],
MeanTmaxF=data['MeanTmaxF_grass'],
delta_d=data['DeltaD'])
soil_moisture = SoilMoisture(grid, **data) # Soil Moisture object
vegetation = Vegetation(grid, **data) # Vegetation object
vegca = VegCA(grid1, **data) # Cellular automaton object
# Initializing inputs for Soil Moisture object
grid.at_cell['vegetation__live_leaf_area_index'] = (
1.6 * np.ones(grid.number_of_cells))
grid.at_cell['soil_moisture__initial_saturation_fraction'] = (
0.59 * np.ones(grid.number_of_cells))
return (precip_dry, precip_wet, radiation, pet_tree, pet_shrub, pet_grass,
soil_moisture, vegetation, vegca)
def initialize_components(data, grid_veg=None, grid=None, pet_method='Cosine'):
# Plant types are defined as following:
# GRASS = 0; SHRUB = 1; TREE = 2; BARE = 3;
# SHRUBSEEDLING = 4; TREESEEDLING = 5
# Initialize random plant type field
grid.at_cell['vegetation__plant_functional_type'] = compose_veg_grid(
grid,
percent_bare=data['percent_bare_initial'],
percent_grass=data['percent_grass_initial'],
percent_shrub=data['percent_shrub_initial'],
percent_tree=data['percent_tree_initial'])
# Assign plant type for representative ecohydrologic simulations
grid_veg.at_cell['vegetation__plant_functional_type'] = np.arange(0, 6)
grid.at_node['topographic__elevation'] = np.full(grid.number_of_nodes,
1700.)
grid_veg.at_node['topographic__elevation'] = np.full(
grid_veg.number_of_nodes, 1700.)
precip_dry = PrecipitationDistribution(
mean_storm_duration=data['mean_storm_dry'],
mean_interstorm_duration=data['mean_interstorm_dry'],
mean_storm_depth=data['mean_storm_depth_dry'],
random_seed=None)
precip_wet = PrecipitationDistribution(
mean_storm_duration=data['mean_storm_wet'],
mean_interstorm_duration=data['mean_interstorm_wet'],
mean_storm_depth=data['mean_storm_depth_wet'],
random_seed=None)
radiation = Radiation(grid_veg)
if pet_method == 'Cosine':
pet_tree = PotentialEvapotranspiration(
grid_veg,
method=data['PET_method'],
MeanTmaxF=data['MeanTmaxF_tree'],
delta_d=data['DeltaD'])
pet_shrub = PotentialEvapotranspiration(
grid_veg,
method=data['PET_method'],
MeanTmaxF=data['MeanTmaxF_shrub'],
delta_d=data['DeltaD'])
pet_grass = PotentialEvapotranspiration(
grid_veg,
method=data['PET_method'],
MeanTmaxF=data['MeanTmaxF_grass'],
delta_d=data['DeltaD'])
elif pet_method == 'PriestleyTaylor':
pet_met = PotentialEvapotranspiration(grid_veg,
method='PriestleyTaylor')
soil_moisture = SoilMoisture(grid_veg, **data) # Soil Moisture object
vegetation = Vegetation(grid_veg, **data) # Vegetation object
vegca = VegCA(grid, **data) # Cellular automaton object
# # Initializing inputs for Soil Moisture object
grid_veg.at_cell['vegetation__live_leaf_area_index'] = (
1.6 * np.ones(grid_veg.number_of_cells))
grid_veg.at_cell['soil_moisture__initial_saturation_fraction'] = (
0.59 * np.ones(grid_veg.number_of_cells))
# Initializing Soil Moisture
if pet_method == 'Cosine':
return (precip_dry, precip_wet, radiation, pet_tree, pet_shrub,
pet_grass, soil_moisture, vegetation, vegca)
elif pet_method == 'PriestleyTaylor':
return (precip_dry, precip_wet, radiation, pet_met, soil_moisture,
vegetation, vegca)
plt.show()
# Read an existing esri grid as watershed and map the elevation field
(watershed, z) = read_esri_ascii('DEM_10m.asc', name='topographic__elevation')
imshow_grid(watershed, 'topographic__elevation')
plt.show()
# Constructions of Landlab Radiation and Potential Evapotranspiration components and codes can be found in the respective links below.
# http://landlab.readthedocs.io/en/latest/landlab.components.radiation.html and
# http://landlab.readthedocs.io/en/latest/landlab.components.pet.html
# The radation components return relative radiation (ratio of radiation of a slopes surface to flat surface), used to scale PET calculated for flat surface by the PET component.
#
# Let's instantiate the radiation and PET components using only variables relevant to this exercise.
rad = Radiation(watershed, method='Grid', latitude=34.)
PET = PotentialEvapotranspiration(
watershed,
method='PriestleyTaylor',
albedo=0.2,
latitude=34.,
elevation_of_measurement=2,
)
# Set the current_time (0-1) in a year, (calculate as DOY/365).
# To see how the magnitude and spatial distribution of radiation and PET changes as a function of DOY we will use two different days, summer and winter solstice. In both cases we will use PET based on the Priestley and Taylor model that requires temperature as input. Temperatures are from cenral NM climatology. In the calculation of PET modeled solar radiation will be used. The Potential Evapotranspiration component of Landlab currently offers several other methods for PET calculation which can be found: http://landlab.readthedocs.io/en/latest/landlab.components.pet.html
#
# For summer solstice use current_time=0.466 (June 20) and Tmin=14, Tmax=32, Tavg=23 (degree C)
#
# For summer solstice use current_time=0.997 (December 21) and Tmin=-5, Tmax=11, Tavg=3 (degree C).
# As you do this exercise notice when the spatial variability of radiation and PET becomes more pronounced.
ObsMODIS_Years = mat_data['MODIS_Years'] # Observed MODIS years
ObsMODIS_LAI = mat_data['MODIS_LAI'] # Observed MODIS LAI
ObsT = mat_data['T'] # years for observed biomass
ObsBioMlow = mat_data['BioMlow'] # Observed live Biomass low
ObsDeadBlow = mat_data['DeadBlow'] # Observed dead Biomass low
ObsS = mat_data['ObsS']
BGMBiol = mat_data['BGMBiol']
BGMBiot = mat_data['BGMBiot']
PClim = mat_data['P']
TbClim = mat_data['Tb']
TrClim = mat_data['Tr']
PET_grass = mat_data['Ep'] # Penman Monteith data from file
# Create radiation, soil moisture and Vegetation objects
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, runon_switch=0, **data) # Soil Moisture object
VEG = Vegetation(grid, **data) # Vegetation object
vegca = VegCA(grid1, **data) # Cellular automaton object
##########
#nfs_mon1_end = np.where(np.array(strm_days) == dt.datetime(2008, 10, 01, 0, 0))[0][0]
# 04Apr18 - extending precip_mult_1 to datetime(2009, 5, 21, 0, 0)
nfs_mon1_end = np.where(np.array(strm_days) == dt.datetime(2009, 5, 21, 0, 0))[0][0]
precip[:nfs_mon1_end] *= data['precip_mult_1'] # introduced 30Mar18
precip[nfs_mon1_end:] *= data['precip_mult_2']
# Input veg composition
grid['cell']['vegetation__plant_functional_type'] = compose_veg_grid(
grid, percent_bare=data['percent_bare_initial'],
percent_grass=data['percent_grass_initial'],
percent_shrub=data['percent_shrub_initial'],
percent_tree=data['percent_tree_initial'])
#grid.at_cell['vegetation__plant_functional_type'][[1748, 1692, 1523]] = 2 # All Trees - 2 means Tree
# Create radiation, PET and soil moisture objects
## For modeled Radiation model
Rad = Radiation(grid)
pet_grass = PotentialEvapotranspiration(grid, method='PenmanMonteith',
albedo=data['albedo_grass'], rl=data['rl_grass'],
zveg=data['zveg_grass'], LAI=data['LAI_grass'],
zm=data['zm_grass'], zh=data['zh_grass'])
pet_shrub = PotentialEvapotranspiration(grid, method='PenmanMonteith',
albedo=data['albedo_shrub'], rl=data['rl_shrub'],
zveg=data['zveg_shrub'], LAI=data['LAI_shrub'],
zm=data['zm_shrub'], zh=data['zh_shrub'])
pet_tree = PotentialEvapotranspiration(grid, method='PenmanMonteith',
albedo=data['albedo_tree'], rl=data['rl_tree'],
zveg=data['zveg_tree'], LAI=data['LAI_tree'],
zm=data['zm_tree'], zh=data['zh_tree'])
if runon_switch:
(ordered_cells, grid2) = get_ordered_cells_for_soil_moisture(
def initialize(data, grid, grid1, grid2, elevation):
"""Initialize random plant type field.
Plant types are defined as the following:
* GRASS = 0
* SHRUB = 1
* TREE = 2
* BARE = 3
* SHRUBSEEDLING = 4
* TREESEEDLING = 5
"""
grid['cell']['vegetation__plant_functional_type'] = compose_veg_grid(
grid,
percent_bare=data['percent_bare_initial'],
percent_grass=data['percent_grass_initial'],
percent_shrub=data['percent_shrub_initial'],
percent_tree=data['percent_tree_initial'])
# Assign plant type for representative ecohydrologic simulations
grid1.at_cell['vegetation__plant_functional_type'] = np.arange(6)
grid1.at_node['topographic__elevation'] = np.full(grid1.number_of_nodes,
1700.)
grid.at_node['topographic__elevation'] = elevation
grid2.at_node['topographic__elevation'] = elevation
if data['runon_switch']:
(ordered_cells, grid2) = get_ordered_cells_for_soil_moisture(
grid2, outlet_id=4877) # hugo10mws: 1331 # 36704
grid.at_node['flow__receiver_node'] = (
grid2.at_node['flow__receiver_node'])
else:
ordered_cells = None
precip_dry = PrecipitationDistribution(
mean_storm_duration=data['mean_storm_dry'],
mean_interstorm_duration=data['mean_interstorm_dry'],
mean_storm_depth=data['mean_storm_depth_dry'],
random_seed=None)
precip_wet = PrecipitationDistribution(
mean_storm_duration=data['mean_storm_wet'],
mean_interstorm_duration=data['mean_interstorm_wet'],
mean_storm_depth=data['mean_storm_depth_wet'],
random_seed=None)
radiation = Radiation(grid)
rad_pet = Radiation(grid1)
pet_tree = PotentialEvapotranspiration(grid1,
method=data['PET_method'],
MeanTmaxF=data['MeanTmaxF_tree'],
delta_d=data['DeltaD'])
pet_shrub = PotentialEvapotranspiration(grid1,
method=data['PET_method'],
MeanTmaxF=data['MeanTmaxF_shrub'],
delta_d=data['DeltaD'])
pet_grass = PotentialEvapotranspiration(grid1,
method=data['PET_method'],
MeanTmaxF=data['MeanTmaxF_grass'],
delta_d=data['DeltaD'])
soil_moisture = SoilMoisture(grid, ordered_cells=ordered_cells,
**data) # Soil Moisture object
vegetation = Vegetation(grid, **data) # Vegetation object
vegca = VegCA(grid, **data) # Cellular automaton object
# # Initializing inputs for Soil Moisture object
grid['cell']['vegetation__live_leaf_area_index'] = (
1.6 * np.ones(grid.number_of_cells))
grid['cell']['soil_moisture__initial_saturation_fraction'] = (
0.59 * np.ones(grid.number_of_cells))
# Initializing Soil Moisture
return (precip_dry, precip_wet, radiation, rad_pet, pet_tree, pet_shrub,
pet_grass, soil_moisture, vegetation, vegca, ordered_cells)
