updown = {'100': '101'}
# add the info to the watershed and outlet
watershed.add_mass_linkage(updown)
watershed.add_outlet('101')
# names of the files used in the simulation (the HSPF input and output files
# are generated automatically); can also specify a directory to use elsewhere
filename = 'example06'
wdmoutfile = filename + '_out.wdm'
# create an instance of the HSPFModel class
hspfmodel = HSPFModel()
# and build the model from the watershed
hspfmodel.build_from_watershed(watershed, filename, tstep=tstep)
# add a special action, thawed ground on the agricultural land
# in the first subbasin on April 1 at 12 noon.
thawdate = datetime.datetime(2001, 4, 1, 12)
hspfmodel.add_special_action('thaw', '100', 'Agriculture', thawdate)
# add another special action, frozen ground on the agricultural land
# in the first subbasin on December 1 at midnight.
# create an instance of the watershed class to store the data to build the model watershed = Watershed(description, subbasins) # add the network and the outlet subbasin watershed.add_mass_linkage(updown) watershed.add_outlet(sname) # make the HSPFModel instance (the data for this example use the non-default # option of English instead of metric units) from pyhspf import HSPFModel hspfmodel = HSPFModel(units = 'English') # since the climate data are provided with hspexp in an export file called # "huntobs.exp." WDMUtil has a method to automatically import the data to a # WDM file. from pyhspf import WDMUtil wdm = WDMUtil() # path to hspexp2.4 data files (make sure the path is correct) # the data from the export file (*.exp) provided with hspexp need to be # imported into a wdm file; the WDMUtil class has a method for this huntday = 'huntday/huntobs.exp'
# use climate data from the PyHSPF base model for the warm up period since
# is incomplete (first find the cutoff index for the data)
cutoff = (bstart - start).days * 24
if not os.path.isfile(newmodel):
with open(basemodel, 'rb') as f:
hspfmodel = pickle.load(f)
# create a new model for the simplified climate data
print('building a new model with the simplified time series\n')
simplified = HSPFModel()
# build new model parameters from the base model; the build_from_existing
# method can be used to copy the perlnds, implnds, rchreses, special
# actions, and reach network from the old file but contains no time series
# or time series assignments
simplified.build_from_existing(hspfmodel, newmodel)
# find the comid of the gage and add the flow data to the new model
d = {
v: k
for k, v in list(hspfmodel.subbasin_timeseries['flowgage'].items())
}
comid = d[gageid]
# the UCI file generated by PyHSPF is named 'example01.uci' -- look at that
# file to see how the information in this script is translated to HSPF.
# the input and output WDM filenames are generated automatically, and are the
# model filename + '_in.wdm' for the input WDM file and '_out.wdm' for the
# output file (we'll need this later to retrieve results from the files)
wdmoutfile = filename + '_out.wdm'
# let's also generate an optional output file created by HSPF directly
outfile = filename + '.out'
# create an instance of the HSPFModel class
hspfmodel = HSPFModel()
# and build the model from the watershed
hspfmodel.build_from_watershed(watershed, filename, print_file = outfile,
tstep = tstep)
# to run a simulation it is necessary to assign precipitation, potential
# evapotranspiration, and any other time series to the subbasins.
# there are many different ways to estimate the potential evapotranspiration
# including correlation to observed pan evaporation, Penman-Monteith, etc.
# here the potential evapotranspiration is assumed to start at zero then
# increase to 12 mm in a day 7/01, then decreases to zero 1/01; thus max 4-hr
# potential evapotranspiration is 2 mm. the following statement will generate
# a time series with these assumptions.
updown = {'100':'101'}
# add the info to the watershed and outlet
watershed.add_mass_linkage(updown)
watershed.add_outlet('101')
# names of the files used in the simulation (the HSPF input and output files
# are generated automatically); can also specify a directory to use elsewhere
filename = 'example06'
wdmoutfile = filename + '_out.wdm'
# create an instance of the HSPFModel class
hspfmodel = HSPFModel()
# and build the model from the watershed
hspfmodel.build_from_watershed(watershed, filename, tstep = tstep)
# add a special action, thawed ground on the agricultural land
# in the first subbasin on April 1 at 12 noon.
thawdate = datetime.datetime(2001, 4, 1, 12)
hspfmodel.add_special_action('thaw', '100', 'Agriculture', thawdate)
# add another special action, frozen ground on the agricultural land
# in the first subbasin on December 1 at midnight.
# CREATE HSPF MODEL
watershedSiletz = Watershed("Siletz River", subbasins)
watershedSiletz.add_mass_linkage(flow_network)
for basin in range(0, len(basinRecords)):
if basinRecords[basin][6] == 0:
watershedSiletz.add_outlet(str(basin + 1)) # Assumes basin numbers
x = 1 # Don't need this but the loop wants to include 'hspfmodel...'
# Build the model
hspfmodel = HSPFModel(units='Metric')
filename = 'siletz_river'
outfile = filename + '.out'
wdmoutfile = filename + '_out.wdm'
hspfmodel.build_from_watershed(watershedSiletz,
'siletz_river',
ifraction=ifraction,
tstep=tstep,
print_file=outfile)
watershedSiletz.plot_mass_flow(output='siletz_basin_network')
# the evaporation data is daily so it needs to be disaggregated to hourly for
# an hourly simulation (see how easy this is with Python)
# the time series in the WDM file starts at 1 am so had to add one extra
# value to the beginning of the time series for consistency
evap = [0] + [e / 24 for e in evap for i in range(24)]
precip = [0] + [p for p in precip]
oflow = [0] + [o for o in oflow]
# list of times
times = [start + (end - start) / len(precip) * i for i in range(len(precip))]
# make the HSPFModel instance
hspfmodel = HSPFModel(units='English')
# build the model (file will all be called example03)
hspfmodel.build_from_watershed(watershed,
'example03',
ifraction=ifraction,
tstep=tstep)
# now add the time series to the model
hspfmodel.add_timeseries('precipitation',
'hunting_prec',
start,
precip,
tstep=60)
# create an instance of the watershed class to store the data to build the model watershed = Watershed(description, subbasins) # add the network and the outlet subbasin watershed.add_mass_linkage(updown) watershed.add_outlet(sname) # make the HSPFModel instance (the data for this example use the non-default # option of English instead of metric units) from pyhspf import HSPFModel hspfmodel = HSPFModel(units='English') # since the climate data are provided with hspexp in an export file called # "huntobs.exp." WDMUtil has a method to automatically import the data to a # WDM file. from pyhspf import WDMUtil wdm = WDMUtil() # path to hspexp2.4 data files (make sure the path is correct) # the data from the export file (*.exp) provided with hspexp need to be # imported into a wdm file; the WDMUtil class has a method for this huntday = 'huntday/huntobs.exp'
print('')
# use climate data from the PyHSPF base model for the warm up period since
# is incomplete (first find the cutoff index for the data)
cutoff = (bstart - start).days * 24
if not os.path.isfile(newmodel):
with open(basemodel, 'rb') as f: hspfmodel = pickle.load(f)
# create a new model for the simplified climate data
print('building a new model with the simplified time series\n')
simplified = HSPFModel()
# build new model parameters from the base model; the build_from_existing
# method can be used to copy the perlnds, implnds, rchreses, special
# actions, and reach network from the old file but contains no time series
# or time series assignments
simplified.build_from_existing(hspfmodel, newmodel)
# find the comid of the gage and add the flow data to the new model
d = {v:k for k, v in hspfmodel.subbasin_timeseries['flowgage'].items()}
comid = d[gageid]
s, tstep, data = hspfmodel.flowgages[gageid]
# the evaporation data is daily so it needs to be disaggregated to hourly for
# an hourly simulation (see how easy this is with Python)
# the time series in the WDM file starts at 1 am so had to add one extra
# value to the beginning of the time series for consistency
evap = [0] + [e / 24 for e in evap for i in range(24)]
precip = [0] + [p for p in precip]
oflow = [0] + [o for o in oflow]
# list of times
times = [start + (end-start) / len(precip) * i for i in range(len(precip))]
# make the HSPFModel instance
hspfmodel = HSPFModel(units = 'English')
# build the model (file will all be called example03)
hspfmodel.build_from_watershed(watershed, 'example03', ifraction = ifraction,
tstep = tstep)
# now add the time series to the model
hspfmodel.add_timeseries('precipitation', 'hunting_prec', start, precip,
tstep = 60)
hspfmodel.add_timeseries('evaporation', 'hunting_evap', start, evap,
tstep = 60)
hspfmodel.add_timeseries('flowgage', 'hunting_flow', start, oflow,
tstep = 60)
# the UCI file generated by PyHSPF is named 'example01.uci' -- look at that
# file to see how the information in this script is translated to HSPF.
# the input and output WDM filenames are generated automatically, and are the
# model filename + '_in.wdm' for the input WDM file and '_out.wdm' for the
# output file (we'll need this later to retrieve results from the files)
wdmoutfile = filename + '_out.wdm'
# let's also generate an optional output file created by HSPF directly
outfile = filename + '.out'
# create an instance of the HSPFModel class
hspfmodel = HSPFModel()
# and build the model from the watershed
hspfmodel.build_from_watershed(watershed,
filename,
print_file=outfile,
tstep=tstep)
# to run a simulation it is necessary to assign precipitation, potential
# evapotranspiration, and any other time series to the subbasins.
# there are many different ways to estimate the potential evapotranspiration
# including correlation to observed pan evaporation, Penman-Monteith, etc.
# here the potential evapotranspiration is assumed to start at zero then
# increase to 12 mm in a day 7/01, then decreases to zero 1/01; thus max 4-hr
# potential evapotranspiration is 2 mm. the following statement will generate
