# build the UCI and output WDM files
hspfmodel.build_uci(targets, start, end, atemp = True, snow = True,
hydrology = True)
# run it
hspfmodel.run(verbose = True)
# use the Postprocessor to analyze and save the results
dates = start + datetime.timedelta(days = warmup), end
postprocessor = Postprocessor(hspfmodel, dates, comid = comid)
postprocessor.get_hspexp_parameters(verbose = False)
postprocessor.plot_hydrograph(tstep = 'monthly', show = False,
output = '{}/hydrography'.format(calibration))
postprocessor.plot_calibration(output = '{}/statistics'.format(calibration),
show = False)
postprocessor.plot_runoff(tstep = 'daily', show = False,
output = '{}/runoff'.format(calibration))
output = '{}/calibration_report.csv'.format(calibration)
postprocessor.calibration_report(output = output)
postprocessor.plot_snow(output = '{}/snow'.format(calibration),
show = False)
postprocessor.plot_dayofyear(output = '{}/dayofyear'.format(calibration),
show = False)
postprocessor.plot_storms(season = 'all', show = False,
output = '{}/storms'.format(calibration))
# plot the runoff components, flows, and precipitation on linear and log scales p.plot_runoff(tstep='daily') # make a similar plot looking at the largest storm events for each year both # in summer and outside summer p.plot_storms(tstep='hourly') # make plots of calibration statistics including flow-duration curves and # parity plots for both daily and monthly flows p.plot_calibration(verbose=True) # get a mass balance of the components p.get_mass_balance() # calculate the HSP Expert parameters for the simulated and observed data p.get_hspexp_parameters() # calculate and show the errors in the calibration parameters. the product # of the daily log-flow and daily flow Nash-Sutcliffe model efficiency are # one possible optimization parameter for a calibration. the log-flow # captures relative errors (low-flow conditions) while the flow captures # absolute error (high-flow conditions). p.calculate_errors()
# plot the runoff components, flows, and precipitation on linear and log scales p.plot_runoff(tstep = 'daily') # make a similar plot looking at the largest storm events for each year both # in summer and outside summer p.plot_storms(tstep = 'hourly') # make plots of calibration statistics including flow-duration curves and # parity plots for both daily and monthly flows p.plot_calibration(verbose = True) # get a mass balance of the components p.get_mass_balance() # calculate the HSP Expert parameters for the simulated and observed data p.get_hspexp_parameters() # calculate and show the errors in the calibration parameters. the product # of the daily log-flow and daily flow Nash-Sutcliffe model efficiency are # one possible optimization parameter for a calibration. the log-flow # captures relative errors (low-flow conditions) while the flow captures # absolute error (high-flow conditions). p.calculate_errors()
# build the UCI and output WDM files
hspfmodel.build_uci(targets,
start,
end,
atemp=True,
snow=True,
hydrology=True)
# run it
hspfmodel.run(verbose=True)
# use the Postprocessor to analyze and save the results
postprocessor = Postprocessor(hspfmodel, (start, end),
comid=calibrator.comid)
postprocessor.get_hspexp_parameters()
postprocessor.plot_hydrograph(tstep='monthly',
output='{}/hydrography'.format(calibration))
postprocessor.plot_calibration(output='{}/statistics'.format(calibration))
postprocessor.plot_runoff(tstep='daily',
output='{}/runoff'.format(calibration))
# Using the preprocessor in other watersheds/gages *should* be as simple as
# supplying the parameters above (start and end date, state, 8-digit HUC,
# NWIS gage ID, land use year, maximum drainage area); if you try and
# get an error please report it!
'evaporation',
'runoff',
'groundwater',
]
# build the UCI and output WDM files
hspfmodel.build_uci(targets, start, end, atemp = True, snow = True,
hydrology = True)
# run it
hspfmodel.run(verbose = True)
# use the Postprocessor to analyze and save the results
postprocessor = Postprocessor(hspfmodel, (start, end),
comid = calibrator.comid)
postprocessor.get_hspexp_parameters()
postprocessor.plot_hydrograph(tstep = 'monthly',
output = '{}/hydrography'.format(calibration))
postprocessor.plot_calibration(output = '{}/statistics'.format(calibration))
postprocessor.plot_runoff(tstep = 'daily',
output = '{}/runoff'.format(calibration))
# Using the preprocessor in other watersheds/gages *should* be as simple as
# supplying the parameters above (start and end date, state, 8-digit HUC,
# NWIS gage ID, land use year, maximum drainage area); if you try and
# get an error please report it!
