def main():
# make sure the metadata are set before starting the download
print('')
print(('preparing to delineate the subbasins for HUC {}\n'.format(HUC8)))
print(
('if you have already downloaded the NHDPlus, NWIS, and NID source ' +
'data make sure you set the directory paths, or all the data will ' +
'be downloaded again.\n'))
print('press "y" to continue or "n" to abort...\n')
s = 'n'
while s != 'y':
s = eval(input())
if s == 'n': exit()
# source data directory structure (ideally a data drive or server)
NHDPlus = '{}/NHDPlus'.format(source)
NWIS = '{}/NWIS'.format(source)
NID = '{}/NID'.format(source)
# download and extract the data using the PyHSPF data extractors (if needed)
# these steps can/will be skipped if they are not needed
nhdplusextractor = NHDPlusExtractor(HUC8[:2], NHDPlus)
nwisextractor = NWISExtractor(NWIS)
nidextractor = NIDExtractor(NID)
# extract or set the path to the source NHDPlus data
nhdplusextractor.download_data()
# extract the hydrography data for the HUC8 to the output directory
nhdplusextractor.extract_HUC8(HUC8, output)
# paths to the NHDPlus data files created above by the nhdplusextractor
bfile = '{}/boundary'.format(output) # watershed boundary
cfile = '{}/catchments'.format(output) # individual catchments
ffile = '{}/flowlines'.format(output) # individual flowlines
VAAs = '{}/flowlineVAAs'.format(output) # value-added attributes
efile = '{}/elevations'.format(output) # elevation geotiff
# extract the NWIS gages to a shapefile in the HUC8 directory
nwisextractor.extract_HUC8(HUC8, output)
# path to the gage shapefile created above
gfile = '{}/gagestations'.format(output, HUC8)
# extract the NID dams to a shapefile in the new HUC8 directory
dfile = '{}/dams'.format(output, HUC8)
nidextractor.extract_shapefile(bfile, dfile)
# use the locations of the gages and dams, the NHDPlus data files, and
# PyHSPF's HUC8Delineator to delineate subbasins subject to the criteria
# into a new file in the HUC8 output directory
delineator = HUC8Delineator(HUC8, VAAs, ffile, cfile, efile, gfile, dfile)
# delineate the watershed using the NHDPlus data and delineator
delineator.delineate(output, drainmax=drainmax, parallel=parallel)
# specify the units (English or Metric)
units = 'Metric'
# path to NWIS metadata (will be downloaded if it doesn't exist)
NWIS = 'NWIS'
# path to the flowline shapefile
flowfile = 'data/flowlines.shp'
# create an instance of the NWISExtractor
extractor = NWISExtractor(NWIS)
# download the metadata for all NWIS gages (will be skipped if it exists)
extractor.download_metadata()
# path to the gage metadata
gagefile = '{}/USGS_Streamgages-NHD_Locations.shp'.format(NWIS)
# download all the data for the gage
extractor.download_gagedata(gageid, start, end, output=gageid)
# open up the gage shapefile and use it to get information about the gage
NWIS = 'NWIS' # location for NWIS metadata files
directory = 'data/patuxent' # working directory for input/output
HUC8 = '02060006' # 8-digit HUC
# extracted files
gagepath = '{}/gagedata'.format(directory) # all HUC8 NWIS flow data path
# time series start and end
start = datetime.datetime(1980, 1, 1) # start date for timeseries
end = datetime.datetime(2010, 1, 1) # end date for timeseries
# create an instance of the NWIS extractor
nwisextractor = NWISExtractor(NWIS)
# extract the locations of the gage stations in the HUC8 (Patuxent watershed)
# into a new shapefile
nwisextractor.extract_HUC8(HUC8, directory)
# and download all the daily flow and water quality data across the period
# for the gages in the gage shapefile
if not os.path.isdir(gagepath):
nwisextractor.download_all(start, end, output = gagepath)
# alternatively, download the daily flow and water quality data for one gage
# given the USGS NWIS Site ID number (Hunting Creek)
directory = 'data' # working directory for input/output
HUC8 = '02060006' # 8-digit HUC
# extracted files
gagepath = '{}/gagedata'.format(directory) # all HUC8 NWIS flow data path
# time series start and end
start = datetime.datetime(1980, 1, 1) # start date for timeseries
end = datetime.datetime(2010, 1, 1) # end date for timeseries
# create an instance of the NWIS extractor and provide the location of the
# metadata file (that is downloaded if it doesn't exist)
nwisextractor = NWISExtractor(NWIS)
# extract the locations of the gage stations in the HUC8 (Patuxent watershed)
# into a new shapefile
nwisextractor.extract_HUC8(HUC8, directory)
# and download all the daily flow and water quality data across the period
# for the gages in the gage shapefile
if not os.path.isdir(gagepath):
nwisextractor.download_all(start, end, output = gagepath)
# alternatively, download the daily flow and water quality data for one gage
# given the USGS NWIS Site ID number (Hunting Creek)
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')
flowfile = 'data/flowlines.shp'
# name of the plot generated showing the results
plotname = 'ftable'
# make sure the flowline file exists
if not os.path.isfile(flowfile):
print('Please ensure that the file {} exists\n'.format(flowfile))
raise
# create an instance of the NWISExtractor to use to download the gage data
extractor = NWISExtractor(NWIS)
# download the metadata for all NWIS gages (will be skipped if it exists)
extractor.download_metadata()
# download all the data for the gage, including measured values of stage,
# discharge, channel width, and channel area and save it to "gageid" file
# (will be skipped if it already exists)
extractor.download_gagedata(gageid, start, end, output = gageid)
# need to know the reach length; so find the location of the gage, then find
# the flowline in the shapefile and use the record info to get the length
# first use the NWIS metadata file to get the latitude and longitude of the gage
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 extract():
"""Create an extract function to call from at runtime and to turn off
the extraction steps when they are done."""
# 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(gage, output=gagepath)
# download the daily flow and water quality data for the gage
nwisextractor.download_gagedata(gage, estart, eend, output=gagedata)
# open the NWIS flow data for the Hunting Creek gage station
with open(gagedata, 'rb') as f:
station = pickle.load(f)
# get the time series of daily flow values for the gage
flow = station.make_timeseries(estart, eend)
# add land use data from 1988 to the delineator
delineator.add_basin_landuse(1988, landuse)
# build the watershed
delineator.build_gage_watershed(gage, watershed, masslinkplot=masslink)