# 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))
# here are some examples of things that can be done with the postprocessor. # many of these require certain external targets to be specified when building # the model (e.g. runoff, groundwater, snow) # make a plot of daily or monthly flows, precipitation, and evapotranspiration p.plot_hydrograph(tstep = 'daily') # 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
# here are some examples of things that can be done with the postprocessor. # many of these require certain external targets to be specified when building # the model (e.g. runoff, groundwater, snow) # make a plot of daily or monthly flows, precipitation, and evapotranspiration p.plot_hydrograph(tstep='daily') # 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
"runoff",
"groundwater",
"snow_state",
"snowpack",
"snowfall",
]
# 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))
