def get_NSE(values):
its = values['LZSN'], values['INFILT']
print(('running simulation LZSN: {:.1f}, INFILT: {:.4f}\n'.format(*its)))
# open the baseline HSPF model
with open(model, 'rb') as f:
hspfmodel = pickle.load(f)
# change the filename to prevent two simultaneous runs with the same name
its = working, values['LZSN'], values['INFILT']
hspfmodel.filename = '{}/LZSN{:.1f}INFILT{:.4f}'.format(*its)
# change the pan coefficient
hspfmodel.evap_multiplier = 0.76
# change the parameter values in all the land segments
for p in hspfmodel.perlnds:
p.LZSN = values['LZSN']
p.INFILT = values['INFILT']
# build the input WDM file
hspfmodel.build_wdminfile()
# build the UCI file (only need to save the reach_outvolume, ROVOL)
hspfmodel.build_uci(['reach_outvolume'], start, end, hydrology=True)
# run the simulation
hspfmodel.run(verbose=True)
# make an instance of the postprocessor to get the efficiency
p = Postprocessor(hspfmodel, (start, end), comid=gagecomid)
# the regressions between the simulated and observed flows can be
# computed using the "regression" function that returns:
#
# daily r2, log daily r2, daily NSE, log daily NSE
# montly r2, log monthly r2, monthly NSE, log monthly NSE
dr2, dlr2, dNS, dlNS, mr2, mlr2, mNS, mlNS = p.get_regression(gagecomid)
# close the Postprocessor
p.close()
return dNS
def get_postprocessor(self, hspfmodel, dates, snowdata=None, verbose=True):
"""Postprocesses the data."""
if verbose: print('postprocessing simulation results\n')
gagedata = self.directory + '/%s/NWIS/%s' % (self.HUC8, self.gageid)
postprocessor = Postprocessor(hspfmodel,
self.process_dates,
comid=self.gagecomid,
upcomids=self.upcomids)
return postprocessor
]
# 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,
# the Postprocessor class can be used to analyze results. the following lines # show how to use it to do some analysis and make some cool graphs. from pyhspf import Postprocessor # the dates of the processing period can be changed, and the postprocessor # can be used to analyze part of the watershed rather than the whole model. # for example, if a gage is located at a subbasin other than the last outlet. # these are optional, the last outlet is assumed to be the gage otherwise and # the run dates are used as the processing dates by default. process_dates = run_dates # postprocessing dates gagecomid = '30' # the subbasin identifier for the gage p = Postprocessor(hspfmodel, process_dates, comid=gagecomid) # 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
def get_NSE(values):
its = values['LZSN'], values['INFILT']
print('running simulation LZSN: {:.0f}, INFILT: {:.2f}\n'.format(*its))
# open the baseline HSPF model
with open(filename, 'rb') as f:
hspfmodel = pickle.load(f)
# change the filename to prevent two simultaneous runs with the same name
i = len(hspfmodel.filename) - 1
while hspfmodel.filename[i] != '/':
i -= 1
path = hspfmodel.filename[:i]
its = path, values['LZSN'], values['INFILT']
hspfmodel.filename = '{}/LZSN{:.0f}INFILT{:.2f}'.format(*its)
# change the parameter values in all the land segments
for p in hspfmodel.perlnds:
p.LZSN = values['LZSN']
p.INFILT = values['INFILT']
# build the input WDM file
hspfmodel.build_wdminfile()
# build the UCI file (only need to save the reach_outvolume, ROVOL)
hspfmodel.build_uci(['reach_outvolume'],
start,
end,
atemp=atemp,
snow=snow,
hydrology=hydrology)
# run the simulation
hspfmodel.run(verbose=True)
# calibration period
dates = start + datetime.timedelta(days=warmup), end
# find the common identifier for the NWIS gage
d = {v: k for k, v in hspfmodel.subbasin_timeseries['flowgage'].items()}
comid = d[gageid]
# make an instance of the postprocessor to get the efficiency
p = Postprocessor(hspfmodel, dates, comid=comid)
# the regressions between the simulated and observed flows can be
# computed using the "regression" function that returns:
#
# daily r2, log daily r2, daily NSE, log daily NSE
# montly r2, log monthly r2, monthly NSE, log monthly NSE
dr2, dlr2, dNS, dlNS, mr2, mlr2, mNS, mlNS = p.get_regression(comid)
# close the Postprocessor
p.close()
return dNS
# 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!
# targets needed are the reach outflow volume and groundwater flow targets = ['groundwater', 'reach_outvolume'] # build the input files hspfmodel.build_wdminfile() hspfmodel.build_uci(targets, start, end, hydrology = True) # and run it hspfmodel.run(verbose = True) # open the postprocessor to get the calibration info p = Postprocessor(hspfmodel, (start, end), comid = gagecomid) # 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() # close the open files p.close() # now let's change the value of some parameters, re-run the model, and see # the effect on the calibration statistics. we will change the default
simplified.build_uci(targets,
start,
end,
atemp=True,
snow=True,
hydrology=True)
# run it
simplified.run(verbose=True)
# use the Postprocessor to analyze and save the results to the folder
dates = start + datetime.timedelta(days=warmup), end
postprocessor = Postprocessor(simplified, dates, comid=comid)
postprocessor.get_hspexp_parameters(verbose=False)
postprocessor.plot_hydrograph(tstep='monthly',
show=False,
output='{}/hydrography'.format(output))
postprocessor.plot_calibration(output='{}/statistics'.format(output),
show=False)
postprocessor.plot_runoff(tstep='daily',
show=False,
output='{}/runoff'.format(output))
report = '{}/output_report.csv'.format(output)
postprocessor.calibration_report(output=report)
postprocessor.plot_dayofyear(output='{}/dayofyear'.format(output), show=False)
postprocessor.plot_storms(season='all',
show=False,
with open(p, 'rb') as f:
hspfmodel = pickle.load(f)
# find the part of the watershed contributing to the gage
d = {v: k for k, v in list(hspfmodel.subbasin_timeseries['flowgage'].items())}
comid = d[NWISgage]
# make an instance of the postprocessor for the thiry-year
start = datetime.datetime(1981, 1, 1)
end = datetime.datetime(2011, 1, 1)
ds = start, end
postprocessor = Postprocessor(hspfmodel, ds)
# get the flows from the 30-year model
ttimes, tflow = postprocessor.get_sim_flow(comid, dates=ds)
# close the processor
postprocessor.close()
# iterate through each two-year and make the plot
for y in range(1981, 2010, 2):
print(('reading year', y, 'data'))
volMod = wdm.get_data(wdmFile, 11)
wdm.close(wdmFile)
datDFOut = pd.DataFrame.from_items(zip(datNms, datOut))
datDFOut.to_csv('basin_1_output.csv', index=False)
qMod = [q * 10**4 * 35.314666721 / (60 * 60) for q in volMod]
# Read flow data
flwData = pd.read_csv(
os.path.abspath(os.path.curdir) + '\\siletz_HSPF_flw.csv')
qGge = flwData['Q_slz'].values.tolist()
# post-process
modelVolume = pd.DataFrame({'date': dttm, 'volm': volMod}, index=index)
modelVolume.to_csv('test.csv', index=False)
procDts = start, end
ggID = 11
p = Postprocessor(hspfmodel, procDts)
p.plot_hydrograph(tstep='daily')
plt.plot(dttm, qMod, label='Model')
plt.plot(dttm, qGge, label='Gaged')
plt.xlabel("Date-Time")
plt.ylabel("Flow (cfs)")
plt.yscale('log')
plt.legend()
plt.show()
