def run(
self,
model,
targets=['reach_outvolume'],
verbose=False,
):
"""
Creates a copy of the base model, adjusts a parameter value, runs
the simulation, calculates and returns the perturbation.
"""
# build the input files and run
model.build_wdminfile()
if self.submodel is None:
model.build_uci(targets,
self.start,
self.end,
atemp=self.atemp,
snow=self.snow,
hydrology=self.hydrology)
else:
model.build_uci(self.comid,
self.start,
self.end,
atemp=self.atemp,
snow=self.snow,
hydrology=self.hydrology)
model.run(verbose=verbose)
# get the regression information using the postprocessor
dates = self.start + datetime.timedelta(days=self.warmup), self.end
# use WDMUtil to get the simulated values
wdm = WDMUtil()
f = '{}_out.wdm'.format(model.filename)
wdm.open(f, 'r')
dsns = wdm.get_datasets(f)
staids = [wdm.get_attribute(f, n, 'STAID') for n in dsns]
data = wdm.get_data(f,
dsns[staids.index(self.comid)],
start=dates[0],
end=dates[1])
wdm.close(f)
if model.units == 'Metric': conv = 10**6
else: conv = 43560
# the submodel is daily, full model is hourly
if self.submodel is None:
sflows = [
sum(data[i:i + 24]) * conv / 86400
for i in range(0,
len(data) - 23, 24)
]
else:
sflows = [d * conv / 86400 for d in data]
stimes = [
self.start + i * datetime.timedelta(days=1)
for i in range(self.warmup, (self.end - self.start).days)
]
otimes = self.otimes
oflows = self.oflows
# remove points with missing data from both simulated and oberved flows
sflows = [
sflows[stimes.index(t)] for t, f in zip(otimes, oflows)
if t in stimes and f is not None
]
oflows = [
oflows[otimes.index(t)] for t, f in zip(otimes, oflows)
if f is not None
]
# return the appropriate performance metric
if self.optimization == 'Nash-Sutcliffe Product':
# daily log flows
log_o = [numpy.log(f) for f in oflows]
log_s = [numpy.log(f) for f in sflows]
logdNS = (1 - sum(
(numpy.array(log_s) - numpy.array(log_o))**2) / sum(
(numpy.array(log_o) - numpy.mean(log_o))**2))
# daily NS
dNS = (1 - sum(
(numpy.array(sflows) - numpy.array(oflows))**2) / sum(
(numpy.array(oflows) - numpy.mean(oflows))**2))
return dNS * logdNS
if self.optimization == 'Nash-Sutcliffe Efficiency':
# daily NS
dNS = (1 - sum(
(numpy.array(sflows) - numpy.array(oflows))**2) / sum(
(numpy.array(oflows) - numpy.mean(oflows))**2))
return dNS
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 ext_targets_block(self,
comid,
year,
tcode = 4,
tsstep = 1,
verbose = False,
):
"""
Adds the EXT TARGETS block to a UCI file and creates the output WDM
file.
tcode is the time code: 2 = minutes, 3 = hours, 4 = days
tsstep is the time step in tcode units
e.g., tcode = 3, tsstep = 4 is a 4-hour time step
this method enables a single external target with aggregation that
isn't possible using the HSPFModel in the core.
"""
lines = ['EXT TARGETS',
'<-Volume-> <-Grp> <-Member-><--Mult-->Tran <-Volume->' +
' <Member> Tsys Aggr Amd ***',
'<Name> x <Name> x x<-factor->strg <Name>' +
' x <Name>qf tem strg strg***']
wdm = WDMUtil(verbose = verbose, messagepath = self.messagepath)
wdm.open(self.wdmoutfile, 'w')
# dataset numbers are assigned by reach in order (subject to revision)
# keep track of dsns in a dictionary
n = 1
# since this class is just for calibration of a single gage, only need
# to keep up with reach outflow volume
otype = 'RCHRES'
group = 'HYDR'
var = 'ROVOL'
tstype = 'VOL'
tsform = 1
idcons = 'ROVOL'
func = 'SUM '
# this overwrites all the rchreses with just the comid for the gage
reaches = [r for r in self.rchreses if r.subbasin == comid]
new = self.add_ext_targets(reaches, wdm, year, n, otype,
group, var, tsform, tstype, idcons, func,
tcode, tsstep)
lines = lines + new
n += len(new)
# close the wdmeditor
wdm.close(self.wdmoutfile)
wdm.close_message()
# finish up
lines = lines + ['END EXT TARGETS', '']
return lines
def run(self, model, targets=["reach_outvolume"], verbose=False):
"""
Creates a copy of the base model, adjusts a parameter value, runs
the simulation, calculates and returns the perturbation.
"""
# build the input files and run
model.build_wdminfile()
if self.submodel is None:
model.build_uci(targets, self.start, self.end, atemp=self.atemp, snow=self.snow, hydrology=self.hydrology)
else:
model.build_uci(
self.comid, self.start, self.end, atemp=self.atemp, snow=self.snow, hydrology=self.hydrology
)
model.run(verbose=verbose)
# get the regression information using the postprocessor
dates = self.start + datetime.timedelta(days=self.warmup), self.end
# use WDMUtil to get the simulated values
wdm = WDMUtil()
f = "{}_out.wdm".format(model.filename)
wdm.open(f, "r")
dsns = wdm.get_datasets(f)
staids = [wdm.get_attribute(f, n, "STAID") for n in dsns]
data = wdm.get_data(f, dsns[staids.index(self.comid)], start=dates[0], end=dates[1])
wdm.close(f)
if model.units == "Metric":
conv = 10 ** 6
else:
conv = 43560
# the submodel is daily, full model is hourly
if self.submodel is None:
sflows = [sum(data[i : i + 24]) * conv / 86400 for i in range(0, len(data) - 23, 24)]
else:
sflows = [d * conv / 86400 for d in data]
stimes = [self.start + i * datetime.timedelta(days=1) for i in range(self.warmup, (self.end - self.start).days)]
otimes = self.otimes
oflows = self.oflows
# remove points with missing data from both simulated and oberved flows
sflows = [sflows[stimes.index(t)] for t, f in zip(otimes, oflows) if t in stimes and f is not None]
oflows = [oflows[otimes.index(t)] for t, f in zip(otimes, oflows) if f is not None]
# return the appropriate performance metric
if self.optimization == "Nash-Sutcliffe Product":
# daily log flows
log_o = [numpy.log(f) for f in oflows]
log_s = [numpy.log(f) for f in sflows]
logdNS = 1 - sum((numpy.array(log_s) - numpy.array(log_o)) ** 2) / sum(
(numpy.array(log_o) - numpy.mean(log_o)) ** 2
)
# daily NS
dNS = 1 - sum((numpy.array(sflows) - numpy.array(oflows)) ** 2) / sum(
(numpy.array(oflows) - numpy.mean(oflows)) ** 2
)
return dNS * logdNS
if self.optimization == "Nash-Sutcliffe Efficiency":
# daily NS
dNS = 1 - sum((numpy.array(sflows) - numpy.array(oflows)) ** 2) / sum(
(numpy.array(oflows) - numpy.mean(oflows)) ** 2
)
return dNS
def ext_targets_block(
self,
comid,
year,
tcode=4,
tsstep=1,
verbose=False,
):
"""
Adds the EXT TARGETS block to a UCI file and creates the output WDM
file.
tcode is the time code: 2 = minutes, 3 = hours, 4 = days
tsstep is the time step in tcode units
e.g., tcode = 3, tsstep = 4 is a 4-hour time step
this method enables a single external target with aggregation that
isn't possible using the HSPFModel in the core.
"""
lines = [
'EXT TARGETS',
'<-Volume-> <-Grp> <-Member-><--Mult-->Tran <-Volume->' +
' <Member> Tsys Aggr Amd ***',
'<Name> x <Name> x x<-factor->strg <Name>' +
' x <Name>qf tem strg strg***'
]
wdm = WDMUtil(verbose=verbose, messagepath=self.messagepath)
wdm.open(self.wdmoutfile, 'w')
# dataset numbers are assigned by reach in order (subject to revision)
# keep track of dsns in a dictionary
n = 1
# since this class is just for calibration of a single gage, only need
# to keep up with reach outflow volume
otype = 'RCHRES'
group = 'HYDR'
var = 'ROVOL'
tstype = 'VOL'
tsform = 1
idcons = 'ROVOL'
func = 'SUM '
# this overwrites all the rchreses with just the comid for the gage
reaches = [r for r in self.rchreses if r.subbasin == comid]
new = self.add_ext_targets(reaches, wdm, year, n, otype, group, var,
tsform, tstype, idcons, func, tcode, tsstep)
lines = lines + new
n += len(new)
# close the wdmeditor
wdm.close(self.wdmoutfile)
wdm.close_message()
# finish up
lines = lines + ['END EXT TARGETS', '']
return lines
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 run(
self,
model,
targets=['reach_outvolume'],
verbose=False,
):
"""
Creates a copy of the base model, adjusts a parameter value, runs
the simulation, calculates and returns the perturbation.
"""
# build the input files and run
model.build_wdminfile()
if self.submodel is None:
model.build_uci(targets,
self.start,
self.end,
atemp=self.atemp,
snow=self.snow,
hydrology=self.hydrology)
else:
model.build_uci(self.comid,
self.start,
self.end,
atemp=self.atemp,
snow=self.snow,
hydrology=self.hydrology)
model.run(verbose=verbose)
# regression period
dates = self.start + datetime.timedelta(days=self.warmup), self.end
# use WDMUtil to get the simulated values
# Mengfei 20180501
# WDMUtil function is changed!
messagepath = 'C:/BASINS41/bin/Plugins/BASINS/hspfmsg.wdm'
print('messagepath is changed to:', messagepath)
wdm = WDMUtil(verbose=True, messagepath=messagepath)
#wdm = WDMUtil()
#
f = '{}_out.wdm'.format(model.filename)
wdm.open(f, 'r')
dsns = wdm.get_datasets(f)
staids = [wdm.get_attribute(f, n, 'STAID') for n in dsns]
# Mengfei 20180501
print('self.comid:',self.comid, 'staids.index(self.comid:',staids.index(self.comid)\
, 'dsn:', dsns[staids.index(self.comid)])
#
data = wdm.get_data(f,
dsns[staids.index(self.comid)],
start=dates[0],
end=dates[1])
wdm.close(f)
if model.units == 'Metric': conv = 10**6
else: conv = 43560
# the submodel is daily, full model is hourly
if self.submodel is None:
sflows = [
sum(data[i:i + 24]) * conv / 86400
for i in range(0,
len(data) - 23, 24)
]
else:
sflows = [d * conv / 86400 for d in data]
stimes = [
self.start + i * datetime.timedelta(days=1)
for i in range(self.warmup, (self.end - self.start).days)
]
otimes = self.otimes
oflows = self.oflows
# remove points with missing data from both simulated and oberved flows
sflows = [
sflows[stimes.index(t)] for t, f in zip(otimes, oflows)
if t in stimes and f is not None
]
oflows = [
oflows[otimes.index(t)] for t, f in zip(otimes, oflows)
if f is not None
]
# return the appropriate performance metric
if self.optimization == 'Nash-Sutcliffe Product':
# daily log flows
log_o = [numpy.log(f) for f in oflows]
log_s = [numpy.log(f) for f in sflows]
logdNS = (1 - sum(
(numpy.array(log_s) - numpy.array(log_o))**2) / sum(
(numpy.array(log_o) - numpy.mean(log_o))**2))
# daily NS
dNS = (1 - sum(
(numpy.array(sflows) - numpy.array(oflows))**2) / sum(
(numpy.array(oflows) - numpy.mean(oflows))**2))
return dNS * logdNS
elif self.optimization == 'Nash-Sutcliffe Efficiency':
# daily NS
dNS = (1 - sum(
(numpy.array(sflows) - numpy.array(oflows))**2) / sum(
(numpy.array(oflows) - numpy.mean(oflows))**2))
return dNS
# Mengfei Mu 20180529
elif self.optimization == 'Log Nash-Sutcliffe Efficiency':
# daily Log NS
for n, i in enumerate(oflows):
if oflows[n] == 0 or sflows[n] == 0:
try:
oflows[n] = (oflows[n - 1] + oflows[n + 1]) / 2.0
sflows[n] = (sflows[n - 1] + sflows[n + 1]) / 2.0
except:
oflows[n] = oflows[n - 1]
sflows[n] = sflows[n - 1]
log_sflows = [log10(f) for f in sflows]
log_oflows = [log10(f) for f in oflows]
dLogNS = (1 - sum(
(np.array(log_sflows) - np.array(log_oflows))**2) / sum(
(np.array(log_oflows) - np.mean(log_oflows))**2))
return dLogNS
#
else:
print('error: optimization parameter ' +
'{} not recognized'.format(self.optimization))
raise
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('')
