Example #1
0
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)
Example #2
0
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')
Example #3
0
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('')