# build the model (file will all be called example02)
hspfmodel.build_from_watershed(watershed, 'example02', ifraction = ifraction,
tstep = tstep)
# now add the time series to the model
hspfmodel.add_timeseries('precipitation', 'hunting_prec', start, precip,
tstep = tstep)
hspfmodel.add_timeseries('evaporation', 'hunting_evap', start, evap,
tstep = tstep)
# and assign the time series to all the operations in the watershed
hspfmodel.assign_watershed_timeseries('precipitation', 'hunting_prec')
hspfmodel.assign_watershed_timeseries('evaporation', 'hunting_evap')
# this simulation used the hydrology modules (and no others); need to create the
# operations for the model and the default values for the hydrology parameters
hspfmodel.add_hydrology()
# build the input wdm file
hspfmodel.build_wdminfile()
# tell HSPF to keep track of the outflow volume from the reach, which
# has a Fortran name "ROVOL" and a PyHSPF name "reach_outvolume"
targets = ['reach_outvolume']
# specify the time series type
tstype = 'evaporation'
# give the dataset a unique identifier
identifier = 'example_evap'
# finally need the start date, a list of the data, and the time step (min)
hspfmodel.add_timeseries(tstype, identifier, start, evaporation, tstep=tstep)
# assign the time series for this model
hspfmodel.assign_watershed_timeseries(tstype, identifier)
# add some random rainfall
rainfall = [
random.randint(0, 20) if random.random() > 0.95 else 0.
for i in range(nsteps)
]
# assign the precipitation time series to the file
tstype = 'precipitation'
identifier = 'example_prec'
hspfmodel.add_timeseries(tstype, identifier, start, rainfall, tstep=tstep)
# give the dataset a unique identifier
identifier = 'example_evap'
# finally need the start date, a list of the data, and the time step (min)
hspfmodel.add_timeseries(tstype, identifier, start, evaporation, tstep = tstep)
# now tell HSPF how to use the time series for this model. the unique
# identifier for the time series and the unique subbasin numbers are used
# to make this connection. we will assign this time series to the whole
# watershed, although you can have a unique time series for each subbasin,
# landuse category, or each operation if you want.
hspfmodel.assign_watershed_timeseries(tstype, identifier)
# now add some random rainfall. here it is assumed there is a 5% chance of rain
# every 4-hour period and that the rainfall is an integer between 0 and 20.
import random
# make random numbers for each 4 hour timestep
# if the number is greater than 0.95 (5% chance), let's say it's raining and
# assign a value (this should give about a meter of rain per year)
rainfall = [random.randint(0,20) if random.random() > 0.95 else 0.
for i in range(nsteps)]
# assign the precipitation time series to the file
d = {
v: k
for k, v in list(hspfmodel.subbasin_timeseries['flowgage'].items())
}
comid = d[gageid]
s, tstep, data = hspfmodel.flowgages[gageid]
simplified.add_timeseries('flowgage', gageid, s, data, tstep=tstep)
simplified.assign_subbasin_timeseries('flowgage', comid, gageid)
# add and assign the snowfall and snowdepth from the old model
s, tstep, data = hspfmodel.snowdepths[HUC8]
simplified.add_timeseries('snowdepth', HUC8, s, data, tstep=tstep)
simplified.assign_watershed_timeseries('snowdepth', HUC8)
s, tstep, data = hspfmodel.snowfalls[HUC8]
simplified.add_timeseries('snowfall', HUC8, s, data, tstep=tstep)
simplified.assign_watershed_timeseries('snowfall', HUC8)
# add other time series to the model and assign them to the whole watershed
# note that the first year from the PyHSPF data is used for the warmup
# since the BASINS station data are incomplete
name = 'precipitation'
data = [p for p in pavg[:cutoff]] + [p * 25.4 for p in prec]
simplified.add_timeseries(name, 'simplified', start, data)
simplified.assign_watershed_timeseries(name, 'simplified')
name = 'evaporation'
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)
# and assign the watershed time series to all the operations
hspfmodel.assign_watershed_timeseries('precipitation', 'hunting_prec')
hspfmodel.assign_watershed_timeseries('evaporation', 'hunting_evap')
# assign the flowgage to the subbasin 30 (the outlet)
hspfmodel.assign_subbasin_timeseries('flowgage', '30', 'hunting_flow')
# this simulation used the hydrology modules (and no others); need to set the
# operations for the watershed and default values for the hydrology parameters
hspfmodel.add_hydrology()
# this example will stop here by pickling the hspfmodel for later--since the
# model will be run many times it just makes sense to save the work so far
# before moving on.
# 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]
simplified.add_timeseries('flowgage', gageid, s, data, tstep = tstep)
simplified.assign_subbasin_timeseries('flowgage', comid, gageid)
# add and assign the snowfall and snowdepth from the old model
s, tstep, data = hspfmodel.snowdepths[HUC8]
simplified.add_timeseries('snowdepth', HUC8, s, data, tstep = tstep)
simplified.assign_watershed_timeseries('snowdepth', HUC8)
s, tstep, data = hspfmodel.snowfalls[HUC8]
simplified.add_timeseries('snowfall', HUC8, s, data, tstep = tstep)
simplified.assign_watershed_timeseries('snowfall', HUC8)
# add other time series to the model and assign them to the whole watershed
# note that the first year from the PyHSPF data is used for the warmup
# since the BASINS station data are incomplete
name = 'precipitation'
data = [p for p in pavg[:cutoff]] + [p * 25.4 for p in prec]
simplified.add_timeseries(name, 'simplified', start, data)
simplified.assign_watershed_timeseries(name, 'simplified')
name = 'evaporation'
