[1.537, 4.238, 4.15, 17.05],
[1.757, 4.387, 5.2, 24.03],
[1.976, 4.497, 6.27, 32.02],
[2.196, 4.606, 7.37, 40.86],
[2.415, 4.716, 8.49, 50.51],
[2.635, 4.826, 9.63, 60.94],
[3.771, 63.882, 86.79, 311.2],
[4.907, 71.162, 163.46, 747.96],
[6.043, 78.442, 248.35, 1356.32],
[7.179, 83.771, 340.12, 2146.83],
[8.315, 91.541, 438.68, 3060.87],
]
# add the reach info to the subbasin
subbasin.add_reach(name, maxelev, minelev, slopelen, ftable = ftable)
# subbasin land use info (to create perlnds and implnds)
landuse_names = ['FOREST', 'ROWCRP', 'GRASS', 'Developed']
areas = [4428.9, 641.63, 776.87, 120.18] # acres
# fraction of developed land that is impervious (assume all impervious land)
ifraction = 1.
# add the landuse
subbasin.add_landuse(1988, landuse_names, areas)
# create a dictionary of the subbasins
def build_watershed(subbasinfile, flowfile, outletfile, damfile, gagefile,
landfile, aggregatefile, VAAfile, years, HUC8, output,
plots = True, overwrite = False, format = 'png'):
# create a dictionary to store subbasin data
subbasins = {}
# create a dictionary to keep track of subbasin inlets
inlets = {}
# read in the flow plane data into an instance of the FlowPlane class
sf = Reader(subbasinfile, shapeType = 5)
comid_index = sf.fields.index(['ComID', 'N', 9, 0]) - 1
len_index = sf.fields.index(['PlaneLenM', 'N', 8, 2]) - 1
slope_index = sf.fields.index(['PlaneSlope', 'N', 9, 6]) - 1
area_index = sf.fields.index(['AreaSqKm', 'N', 10, 2]) - 1
cx_index = sf.fields.index(['CenX', 'N', 12, 6]) - 1
cy_index = sf.fields.index(['CenY', 'N', 12, 6]) - 1
elev_index = sf.fields.index(['AvgElevM', 'N', 8, 2]) - 1
for record in sf.records():
comid = '{}'.format(record[comid_index])
length = record[len_index]
slope = record[slope_index]
tot_area = record[area_index]
centroid = [record[cx_index], record[cy_index]]
elevation = record[elev_index]
subbasin = Subbasin(comid)
subbasin.add_flowplane(length, slope, 0, centroid, elevation)
subbasins[comid] = subbasin
# read in the flowline data to an instance of the Reach class
sf = Reader(flowfile)
outcomid_index = sf.fields.index(['OutComID', 'N', 9, 0]) - 1
gnis_index = sf.fields.index(['GNIS_NAME', 'C', 65, 0]) - 1
reach_index = sf.fields.index(['REACHCODE', 'C', 8, 0]) - 1
incomid_index = sf.fields.index(['InletComID', 'N', 9, 0]) - 1
maxelev_index = sf.fields.index(['MaxElev', 'N', 9, 2]) - 1
minelev_index = sf.fields.index(['MinElev', 'N', 9, 2]) - 1
slopelen_index = sf.fields.index(['SlopeLenKM', 'N', 6, 2]) - 1
slope_index = sf.fields.index(['Slope', 'N', 8, 5]) - 1
inflow_index = sf.fields.index(['InFlowCFS', 'N', 8, 3]) - 1
outflow_index = sf.fields.index(['OutFlowCFS', 'N', 8, 3]) - 1
velocity_index = sf.fields.index(['VelFPS', 'N', 7, 4]) - 1
traveltime_index = sf.fields.index(['TravTimeHR', 'N', 8, 2]) - 1
for record in sf.records():
outcomid = '{}'.format(record[outcomid_index])
gnis = record[gnis_index]
reach = record[reach_index]
incomid = '{}'.format(record[incomid_index])
maxelev = record[maxelev_index] / 100
minelev = record[minelev_index] / 100
slopelen = record[slopelen_index]
slope = record[slope_index]
inflow = record[inflow_index]
outflow = record[outflow_index]
velocity = record[velocity_index]
traveltime = record[traveltime_index]
if isinstance(gnis, bytes): gnis = ''
subbasin = subbasins[outcomid]
flow = (inflow + outflow) / 2
subbasin.add_reach(gnis, maxelev, minelev, slopelen, flow = flow,
velocity = velocity, traveltime = traveltime)
inlets[outcomid] = incomid
# open up the outlet file and see if the subbasin has a gage or dam
sf = Reader(outletfile)
records = sf.records()
comid_index = sf.fields.index(['COMID', 'N', 9, 0]) - 1
nid_index = sf.fields.index(['NIDID', 'C', 7, 0]) - 1
nwis_index = sf.fields.index(['SITE_NO', 'C', 15, 0]) - 1
nids = {'{}'.format(r[comid_index]):r[nid_index] for r in records
if isinstance(r[nid_index], str)}
nwiss = {'{}'.format(r[comid_index]):r[nwis_index] for r in records
if r[nwis_index] is not None}
# open up the dam file and read in the information for the dams
sf = Reader(damfile)
records = sf.records()
name_index = sf.fields.index(['DAM_NAME', 'C', 65, 0]) - 1
nid_index = sf.fields.index(['NIDID', 'C', 7, 0]) - 1
long_index = sf.fields.index(['LONGITUDE', 'N', 19, 11]) - 1
lat_index = sf.fields.index(['LATITUDE', 'N', 19, 11]) - 1
river_index = sf.fields.index(['RIVER', 'C', 65, 0]) - 1
owner_index = sf.fields.index(['OWN_NAME', 'C', 65, 0]) - 1
type_index = sf.fields.index(['DAM_TYPE', 'C', 10, 0]) - 1
purp_index = sf.fields.index(['PURPOSES', 'C', 254, 0]) - 1
year_index = sf.fields.index(['YR_COMPL', 'C', 10, 0]) - 1
high_index = sf.fields.index(['NID_HEIGHT', 'N', 19, 11]) - 1
mstor_index = sf.fields.index(['MAX_STOR', 'N', 19, 11]) - 1
nstor_index = sf.fields.index(['NORMAL_STO', 'N', 19, 11]) - 1
area_index = sf.fields.index(['SURF_AREA', 'N', 19, 11]) - 1
# iterate through the subbasins and see if they have a dam
for comid, subbasin in subbasins.items():
if comid in nids:
# if the subbasin has a dam, find the data info in the file
nid = nids[comid]
r = records[[r[nid_index] for r in records].index(nid)]
subbasin.add_dam(nid,
r[name_index],
r[long_index],
r[lat_index],
r[river_index],
r[owner_index],
r[type_index],
r[purp_index],
r[year_index],
r[high_index],
r[mstor_index],
r[nstor_index],
r[area_index]
)
# open up the aggregate file to get the landuse group map
m, landtypes, groups = get_aggregate_map(aggregatefile)
# convert to a list of landuse names
names = [landtypes[group] for group in groups]
# read the land use data for each year into the subbasins
with open(landfile, 'rb') as f: landyears, landuse = pickle.load(f)
for comid in subbasins:
subbasin = subbasins[comid]
subbasin_data = landuse[comid]
for year, data in zip(landyears, zip(*subbasin_data)):
subbasin.add_landuse(year, names, data)
# create an instance of the watershed class
watershed = Watershed(HUC8, subbasins)
# open up the flowline VAA file to use to establish mass linkages
with open(VAAfile, 'rb') as f: flowlines = pickle.load(f)
# create a dictionary to connect the comids to hydroseqs
hydroseqs = {'{}'.format(flowlines[f].comid):
flowlines[f].hydroseq for f in flowlines}
# establish the mass linkages using a dictionary "updown" and a list of
# head water subbasins
updown = {}
for comid, subbasin in watershed.subbasins.items():
# get the flowline instance for the outlet comid
flowline = flowlines[hydroseqs[comid]]
# check if the subbasin is a watershed inlet or a headwater source
inlet = hydroseqs[inlets[comid]]
if flowlines[inlet].up in flowlines:
i = '{}'.format(flowlines[flowlines[inlet].up].comid)
subbasin.add_inlet(i)
elif flowlines[inlet].up != 0:
watershed.add_inlet(comid)
else:
watershed.add_headwater(comid)
# check if the subbasin is a watershed outlet, and if it is not, then
# find the downstream reach
if flowline.down in flowlines:
flowline = flowlines[flowline.down]
while '{}'.format(flowline.comid) not in subbasins:
flowline = flowlines[flowline.down]
updown[comid] = '{}'.format(flowline.comid)
else:
updown[comid] = 0
watershed.add_outlet('{}'.format(comid))
# open
watershed.add_mass_linkage(updown)
if output is None:
filename = os.getcwd() + '/watershed'
plotname = os.getcwd() + '/masslink.%s' % format
else:
filename = output + '/%s/watershed' % HUC8
plotname = output + '/%s/images/%smasslink.%s' % (HUC8, HUC8, format)
if not os.path.isfile(filename) or overwrite:
with open(filename, 'wb') as f: pickle.dump(watershed, f)
if not os.path.isfile(plotname) and plots or overwrite and plots:
plot_mass_flow(watershed, plotname)
name = 'dave stream' # something descriptive
maxelev = 110 # elevation at the top of the reach (m)
minelev = 100 # elevation at the bottom of the reach (m)
slopelen = 10 # the reach length (km)
# HSPF uses "FTABLES" to specify the stage-discharge relationship for a reach.
# PyHSPF can estimate the FTABLE using the average flow and velocity, or the
# FTABLE can be specified directly. here the FTABLE for this subbasin reach
# is generated from average flow and velocity.
flow = 10 # the inflow (cfs) must use these units
velocity = 1 # velocity (fps) again must use these units
# add the reach to the subbasin
subbasin.add_reach(name, maxelev, minelev, slopelen, flow = flow,
velocity = velocity)
# here is an alternative set of statements to supply the FTABLE directly.
# An FTABLE consists of 4 columns representing the relationships between
# depth, surface area, volume, and flow for a reach. HSPF does a linear
# interpolation between the depths in the first column to estimate the
# other parameters. Up to 18 rows can be used.
#ftable = [[0,0,0,0],
# [1,1,100,1],
# ]
#subbasin.add_reach(name, maxelev, minelev, slopelen, ftable = ftable)
# another piece of info needed for the subbasins is the land use (used to
# subdivide the subbasins into land segments, e.g. soils). so here this subbasin
centroid = [-90, 40] # long, lat
name = 'stream' # something descriptive
maxelev = 110 # elevation at the top of the reach (m)
minelev = 100 # elevation at the bottom of the reach (m)
slopelen = 10 # the reach length (km)
flow = 12 # the average flow must be in cfs
velocity = 1 # velocity must be in fps
landuse_names = ['Developed', 'Agriculture', 'Forest']
areas = [20, 40, 40]
# add the data for subbasin 100
subbasin.add_flowplane(length, planeslope, centroid, elev)
subbasin.add_reach(name,
maxelev,
minelev,
slopelen,
flow=flow,
velocity=velocity)
subbasin.add_landuse(2001, landuse_names, areas)
# add the subbasin to the dictionary of subbasins
subbasins[number] = subbasin
# make another subbasin
number = '101'
subbasin = Subbasin(number)
maxelev = 100
minelev = 90
flow = 12
# the ftable is supplied in the UCI file and replicated here
ftable = [[0.0, 0.0, 0.0, 0.0], [0.22, 0.294, 0.04, 0.11],
[0.439, 0.588, 0.14, 0.7], [0.659, 0.882, 0.32, 2.04],
[0.878, 1.176, 0.56, 4.4], [1.098, 1.398, 0.86, 8.16],
[1.318, 1.534, 1.22, 13.6], [1.537, 1.63, 1.6, 20.46],
[1.757, 1.688, 2.0, 28.84], [1.976, 1.73, 2.42, 38.42],
[2.196, 1.772, 2.84, 49.03], [2.415, 1.814, 3.26, 60.61],
[2.635, 1.856, 3.7, 73.13], [3.771, 24.57, 33.38, 373.44],
[4.907, 27.368, 62.86, 897.55], [6.043, 30.168, 95.52, 1627.58],
[7.179, 32.218, 130.82, 2576.2], [8.315, 35.206, 168.72, 3673.04]]
# add the reach info to the subbasin
subbasin.add_reach(name, maxelev, minelev, slopelen, ftable=ftable)
# subbasin land use info (to create perlnds and implnds)
landuse_names = ['Forest', 'Pasture/grass']
areas = [32, 6]
# fraction of developed land that is impervious
ifraction = 1.
# add the landuse
subbasin.add_landuse(1988, landuse_names, areas)
# add the subbasin to the dictionary
