def __init__(self,
units = 'Metric',
):
HSPFModel.__init__(self, units = units)
def main():
# create an instance of the NWIS extractor
nwisextractor = NWISExtractor(NWIS)
# download and decompress the source metadata files
nwisextractor.download_metadata()
# extract all the gage stations and metadata into a shapefile for the HUC8
nwisextractor.extract_HUC8(HUC8, output)
# tell the extractor to use the metadata file above to find gage data
nwisextractor.set_metadata(gagefile)
# create an instance of the NHDPlus extractor
nhdplusextractor = NHDPlusExtractor(drainid, VPU, NHDPlus)
# download and decompress the source data for the Mid Atlantic Region
nhdplusextractor.download_data()
# extract the HUC8 data for the Patuxent watershed
nhdplusextractor.extract_HUC8(HUC8, output)
# create an instance of the NHDPlusDelineator to use to build the Watershed
delineator = NHDPlusDelineator(VAAfile, flowfile, catchfile, elevfile,
gagefile = gagefile)
# delineate the watershed (extract the flowlines, catchments and other data)
delineator.delineate_gage_watershed(gageid, output = gagepath)
# add land use data from 1988 to the delineator
delineator.add_basin_landuse(1988, landuse)
# build the watershed
delineator.build_gage_watershed(gageid, watershed, masslinkplot = masslink)
# make the working directory for HSPF simulation files
if not os.path.isdir(hspf): os.mkdir(hspf)
# import old data for Hunting Creek
wdm = WDMUtil()
# path to hspexp2.4 data files (modify as needed)
directory = os.path.abspath(os.path.dirname(__file__)) + '/data'
# the data from the export file (*.exp) provided with hspexp need to be
# imported into a wdm file. WDMUtil has a method for this.
hunthour = '{}/hunthour/huntobs.exp'.format(directory)
f = 'temp.wdm'
# import from exp to wdm
wdm.import_exp(hunthour, f)
# close the file and re-open the wdm for read access
wdm.close(f)
wdm.open(f, 'r')
# the dsns are known from the exp file so just use those this time
precip = wdm.get_data(f, 106)
evap = wdm.get_data(f, 111)
flow = wdm.get_data(f, 281)
s, e = wdm.get_dates(f, 106)
# add the time series to deal with HSPF looking backward stepping
precip = [0] + [p * 25.4 for p in precip]
evap = [e * 25.4 / 24 for e in evap for i in range(24)]
wdm.close(f)
# create an HSPF model instance
hunting = HSPFModel()
# open the watershed built above
with open(watershed, 'rb') as f: w = pickle.load(f)
# use the data to build an HSPFModel
hunting.build_from_watershed(w, model, ifraction = 1., verbose = True)
# turn on the hydrology modules to the HSPF model
hunting.add_hydrology()
# add precip timeseries with label BWI and provided start date to the model
hunting.add_timeseries('precipitation', 'BWI', s, precip)
# add evap timeseries with label Beltsville and provided start date
hunting.add_timeseries('evaporation', 'Beltsville', s, evap)
# add flow timeseries with label Hunting, start date, tstep (days)
hunting.add_timeseries('flowgage', 'Hunting', s, flow, tstep = 60)
# assign the evaporation and precipiation timeseries to the whole watershed
hunting.assign_watershed_timeseries('precipitation', 'BWI')
hunting.assign_watershed_timeseries('evaporation', 'Beltsville')
# find the subbasin indentfier for the watershed outlet
subbasin = [up for up, down in w.updown.items() if down == 0][0]
# assign the flowgage to the outlet subbasin
hunting.assign_subbasin_timeseries('flowgage', subbasin, 'Hunting')
# using pan evaporation data, so need a pan coefficient < 1
hunting.evap_multiplier = 0.75
calibrator = AutoCalibrator(hunting, start, end, hspf)
calibrator.autocalibrate(calibrated,
variables = variables,
optimization = optimization,
perturbations = perturbations,
parallel = parallel
)
for variable, value in zip(calibrator.variables, calibrator.values):
print('{:6s} {:5.3f}'.format(variable, value))
print('\nsaving the calibration results\n')
def __init__(
self,
units='Metric',
):
HSPFModel.__init__(self, units=units)
def preprocess():
# create an instance of the NWIS extractor
nwisextractor = NWISExtractor(NWIS)
# download and decompress the source metadata files
nwisextractor.download_metadata()
# extract all the gage stations and metadata into a shapefile for the HUC8
nwisextractor.extract_HUC8(HUC8, output)
# create an instance of the NHDPlus extractor
nhdplusextractor = NHDPlusExtractor(VPU, NHDPlus)
# download and decompress the source data for the Mid Atlantic Region
nhdplusextractor.download_data()
# extract the HUC8 data for the Patuxent watershed
nhdplusextractor.extract_HUC8(HUC8, output)
# create an instance of the NHDPlusDelineator to use to build the Watershed
delineator = NHDPlusDelineator(VAAfile, flowfile, catchfile, elevfile, gagefile=gagefile)
# delineate the watershed (extract the flowlines, catchments and other data)
delineator.delineate_gage_watershed(gageid, output=gagepath)
# add land use data from 1988 to the delineator
delineator.add_basin_landuse(1988, landuse)
# build the watershed
delineator.build_gage_watershed(gageid, watershed, masslinkplot=masslink)
# make the working directory for HSPF simulation files
if not os.path.isdir(hspf):
os.mkdir(hspf)
# import old data for Hunting Creek
wdm = WDMUtil()
# path to hspexp2.4 data files (modify as needed)
directory = os.path.abspath(os.path.dirname(__file__)) + "/data"
# the data from the export file (*.exp) provided with hspexp need to be
# imported into a wdm file. WDMUtil has a method for this.
hunthour = "calibrated/huntobs.exp".format(directory)
f = "temp.wdm"
# import from exp to wdm
wdm.import_exp(hunthour, f)
# close the file and re-open the wdm for read access
wdm.close(f)
wdm.open(f, "r")
# the dsns are known from the exp file so just use those this time
precip = wdm.get_data(f, 106)
evap = wdm.get_data(f, 111)
flow = wdm.get_data(f, 281)
s, e = wdm.get_dates(f, 106)
# add the time series to deal with HSPF looking backward stepping
precip = [0] + [p * 25.4 for p in precip]
evap = [e * 25.4 / 24 for e in evap for i in range(24)]
wdm.close(f)
# create an HSPF model instance
hunting = HSPFModel()
# open the watershed built above
with open(watershed, "rb") as f:
w = pickle.load(f)
# use the data to build an HSPFModel
hunting.build_from_watershed(w, model, ifraction=1.0, verbose=True)
# turn on the hydrology modules to the HSPF model
hunting.add_hydrology()
# add precip timeseries with label BWI and provided start date to the model
hunting.add_timeseries("precipitation", "BWI", s, precip)
# add evap timeseries with label Beltsville and provided start date
hunting.add_timeseries("evaporation", "Beltsville", s, evap)
# add flow timeseries with label Hunting, start date, tstep (days)
hunting.add_timeseries("flowgage", "Hunting", s, flow, tstep=60)
# assign the evaporation and precipiation timeseries to the whole watershed
hunting.assign_watershed_timeseries("precipitation", "BWI")
hunting.assign_watershed_timeseries("evaporation", "Beltsville")
# find the subbasin indentifier for the watershed outlet
subbasin = [up for up, down in w.updown.items() if down == 0][0]
# assign the flowgage to the outlet subbasin
hunting.assign_subbasin_timeseries("flowgage", subbasin, "Hunting")
# using pan evaporation data, so need a pan coefficient < 1
hunting.evap_multiplier = 0.75
with open(calibrated, "wb") as f:
pickle.dump(hunting, f)
def calibrate():
"""Builds and calibrates the model."""
# make the working directory for HSPF
if not os.path.isdir(hspf): os.mkdir(hspf)
# import old data for Hunting Creek
wdm = WDMUtil()
# path to hspexp2.4 data files (modify as needed)
directory = os.path.abspath(os.path.dirname(__file__)) + '/data'
# the data from the export file (*.exp) provided with hspexp need to be
# imported into a wdm file. WDMUtil has a method for this.
hunthour = '{}/hunthour/huntobs.exp'.format(directory)
f = 'temp.wdm'
# import from exp to wdm
wdm.import_exp(hunthour, f)
# close the file and re-open the wdm for read access
wdm.close(f)
wdm.open(f, 'r')
# the dsns are known from the exp file so just use those this time
precip = wdm.get_data(f, 106)
evap = wdm.get_data(f, 111)
flow = wdm.get_data(f, 281)
s, e = wdm.get_dates(f, 106)
# add the time series to deal with HSPF looking backward stepping
precip = [0] + [p * 25.4 for p in precip]
evap = [e * 25.4 / 24 for e in evap for i in range(24)]
wdm.close(f)
# create an HSPF model instance
hunting = HSPFModel()
# open the watershed built above
with open(watershed, 'rb') as f:
w = pickle.load(f)
# use the data to build an HSPFModel
hunting.build_from_watershed(w, model, verbose=True)
# turn on the hydrology modules to the HSPF model
hunting.add_hydrology()
# add precip timeseries with label BWI and provided start date to the model
hunting.add_timeseries('precipitation', 'BWI', s, precip)
# add evap timeseries with label Beltsville and provided start date
hunting.add_timeseries('evaporation', 'Beltsville', s, evap)
# add flow timeseries with label Hunting, start date, tstep (days)
#hunting.add_timeseries('flowgage', 'Hunting', start, flow, tstep = 1440)
hunting.add_timeseries('flowgage', 'Hunting', s, flow, tstep=60)
# assign the evaporation and precipiation timeseries to the whole watershed
hunting.assign_watershed_timeseries('precipitation', 'BWI')
hunting.assign_watershed_timeseries('evaporation', 'Beltsville')
# find the subbasin indentfier for the watershed outlet
subbasin = [up for up, down in w.updown.items() if down == 0][0]
# assign the flowgage to the outlet subbasin
hunting.assign_subbasin_timeseries('flowgage', subbasin, 'Hunting')
# using pan evaporation data, so need a pan coefficient < 1
hunting.evap_multiplier = 0.75
calibrator = AutoCalibrator(hunting, start, end, hspf)
calibrator.autocalibrate(calibrated,
variables=variables,
optimization=optimization,
perturbations=perturbations,
parallel=parallel)
for variable, value in zip(calibrator.variables, calibrator.values):
print('{:6s} {:5.3f}'.format(variable, value))
print('')
def copymodel(self, name):
"""Returns a copy of the HSPFModel."""
model = HSPFModel()
model.build_from_existing(self.hspfmodel, name)
for f in self.hspfmodel.flowgages:
start, tstep, data = self.hspfmodel.flowgages[f]
model.add_timeseries('flowgage', f, start, data, tstep = tstep)
for p in self.hspfmodel.precipitations:
start, tstep, data = self.hspfmodel.precipitations[p]
model.add_timeseries('precipitation', p, start, data, tstep = tstep)
for e in self.hspfmodel.evaporations:
start, tstep, data = self.hspfmodel.evaporations[e]
model.add_timeseries('evaporation', e, start, data, tstep = tstep)
for tstype, identifier in self.hspfmodel.watershed_timeseries.items():
model.assign_watershed_timeseries(tstype, identifier)
for tstype, d in self.hspfmodel.subbasin_timeseries.items():
for subbasin, identifier in d.items():
if subbasin in model.subbasins:
model.assign_subbasin_timeseries(tstype, subbasin,
identifier)
return model
