from pyhspf.preprocessing import Preprocessor
# 8-digit hydrologic unit code of interest; the lists here of states, years,
# and RPUs are just used to point to location of the data files below
HUC8 = '02040101'
state = 'Delaware'
start = datetime.datetime(1980, 1, 1)
end = datetime.datetime(2011, 1, 1)
drainmax = 400
aggregation = 'cdlaggregation.csv'
landuse = 'lucs.csv'
if __name__ == '__main__':
processor = Preprocessor()
processor.set_network(source)
processor.set_output(destination)
processor.set_parameters(HUC8 = HUC8,
start = start,
end = end,
state = state,
cdlaggregate = aggregation,
landuse = landuse)
processor.preprocess(drainmax = drainmax, parallel = False)
# this took about 40 minutes to run on my 3 year old laptop not counting the
# time to download the raw data from the NHDPlus and CDL
# set up the directory locations
processor.set_network(network)
processor.set_output(destination)
# set the simulation-specific parameters
processor.set_parameters(HUC8=HUC8,
start=start,
end=end,
cdlaggregate=aggregation,
landuse=landuse)
# preprocess the HUC8
processor.preprocess(drainmax=drainmax)
# build the HSPFModel and turn on the flags for the air temperature
# (ATEMP for PERLNDs and IMPLNDs), snow (SNOW for PERLNDs and IMPLNDs),
# and hydrology (PWATER for PERLNDs, IWATER for IMPLNDs, HYDR for RCHRESs)
# for the resulting model (the flags for all modules default to "False",
# so no simulation options will be used unless they are activated)
processor.build_hspfmodel(landuseyear=landuseyear,
atemp=True,
snow=True,
hydrology=True)
# the routine in the previous step will build and pickle an HSPFModel
# to the destination/hspf directory named "<landuseyear>baseline" (in this
# case 2001) stored in the "hspfmodel" attribute of the preprocessor
source = 'Z:'
destination = 'C:/HSPF_data'
from pyhspf.preprocessing import Preprocessor
# 8-digit hydrologic unit code of interest; the lists here of states, years,
# and RPUs are just used to point to location of the data files below
HUC8 = '02040101'
start = datetime.datetime(1980, 1, 1)
end = datetime.datetime(2011, 1, 1)
drainmax = 400
aggregation = 'cdlaggregation.csv'
landuse = 'lucs.csv'
if __name__ == '__main__':
processor = Preprocessor()
processor.set_network(source)
processor.set_output(destination)
processor.set_parameters(HUC8=HUC8,
start=start,
end=end,
cdlaggregate=aggregation,
landuse=landuse)
processor.preprocess(drainmax=drainmax, parallel=False)
# this took about 40 minutes to run on my 3 year old laptop not counting the
# time to download the raw data from the NHDPlus and CDL
processor.set_network(network)
processor.set_output(destination)
# set the simulation-specific parameters
processor.set_parameters(HUC8 = HUC8,
start = start,
end = end,
state = state,
cdlaggregate = aggregation,
landuse = landuse)
# preprocess the HUC8
processor.preprocess(drainmax = drainmax)
# If the script runs succesfully, the following file structure will be created:
#
# <network>
# /NHDPlus
# /NHDPlusMS
# /NHDPlus07
# /EROMExtension
# HUC07 Erosion Runoff Model (dbf)
# /NEDSnapshot
# HUC07 Elevation Rasters (tif)
# /NHDPlusAttributes
# HUC07 Flowline attributes (dbf)
# /NHDPlusCatchment
# HUC07 Catchment shapefile
# if the watershed is in more than one state, this will probably not work
# (this is a feature that should be added in the future).
# start and end dates (2001 to 2010)
start = datetime.datetime(2001, 1, 1)
end = datetime.datetime(2011, 1, 1)
# comma separated value file linking land use codes from the cropland data
# layer to RGB colors and HSPF land segments
landcodes = 'aggregate.csv'
# because parallel processing is (optionally) used, the process method has
# to be called at runtime as shown below
if __name__ == '__main__':
# make an instance of the preprocessor
processor = Preprocessor(network, destination, landcodes = landcodes)
# preprocess the HUC8
processor.preprocess(HUC8, state, start, end)
# so using the preprocessor in other watersheds *should* be as simple as
# supplying the state and 8-digit HUC; if you try and get errors please
# report them!
