def test_lookup_bmp_infiltration(self):
"""
Do some spot-checks on the data from Table B.
"""
self.assertEqual(lookup_bmp_infiltration('d', 'green_roof'), 1.6)
self.assertEqual(lookup_bmp_infiltration('c', 'porous_paving'), 1.73)
self.assertEqual(lookup_bmp_infiltration('b', 'rain_garden'), 0.6)
self.assertEqual(lookup_bmp_infiltration('a', 'infiltration_trench'), 2.4) # noqa
def simulate_cell_day(precip, evaptrans, cell, cell_count):
"""
Simulate a bunch of cells of the same type during a one-day event.
`precip` is the amount of precipitation in inches.
`evaptrans` is evapotranspiration.
`cell` is a string which contains a soil type and land use
separated by a colon.
`cell_count` is the number of cells to simulate.
The return value is a dictionary of runoff, evapotranspiration, and
infiltration as volumes of water.
"""
soil_type, land_use, bmp = cell.lower().split(':')
# If there is no precipitation, then there is no runoff or
# infiltration. There is evapotranspiration, however (is
# understood that over a period of time, this can lead to the sum
# of the three values exceeding the total precipitation).
if precip == 0.0:
return {
'runoff-vol': 0.0,
'et-vol': cell_count * evaptrans,
'inf-vol': 0.0
}
# Deal with the Best Management Practices (BMPs). For most BMPs,
# the infiltration is read from the table and the runoff is what
# is left over after infiltration and evapotranspiration. Rain
# gardens are treated differently.
if bmp and is_bmp(bmp) and bmp != 'rain_garden':
inf = lookup_bmp_infiltration(soil_type, bmp) # infiltration
runoff = precip - (evaptrans + inf) # runoff
return {
'runoff-vol': cell_count * runoff,
'et-vol': cell_count * evaptrans,
'inf-vol': cell_count * inf
}
elif bmp and bmp == 'rain_garden':
# Here, return a mixture of 20% ideal rain garden and 80% high
# intensity residential.
inf = lookup_bmp_infiltration(soil_type, bmp)
runoff = precip - (evaptrans + inf)
hi_res_cell = soil_type + ':hi_residential:'
hi_res = simulate_cell_day(precip, evaptrans, hi_res_cell, 1)
hir_run = hi_res['runoff-vol']
hir_et = hi_res['et-vol']
hir_inf = hi_res['inf-vol']
return {
'runoff-vol': cell_count * (0.2 * runoff + 0.8 * hir_run),
'et-vol': cell_count * (0.2 * evaptrans + 0.8 * hir_et),
'inf-vol': cell_count * (0.2 * inf + 0.8 * hir_inf)
}
# At this point, if the `bmp` string has non-zero length, it is
# equal to either 'no_till' or 'cluster_housing'.
land_use = bmp or land_use
# When the land use is a built-type and the level of precipitation
# is two inches or less, use the Pitt Small Storm Hydrology Model.
# When the land use is a built-type but the level of precipitation
# is higher, the runoff is the larger of that predicted by the
# Pitt model and NRCS model. Otherwise, return the NRCS amount.
if is_built_type(land_use) and precip <= 2.0:
runoff = runoff_pitt(precip, land_use)
elif is_built_type(land_use):
pitt_runoff = runoff_pitt(2.0, land_use)
nrcs_runoff = runoff_nrcs(precip, evaptrans, soil_type, land_use)
runoff = max(pitt_runoff, nrcs_runoff)
else:
runoff = runoff_nrcs(precip, evaptrans, soil_type, land_use)
inf = precip - (evaptrans + runoff)
return {
'runoff-vol': cell_count * runoff,
'et-vol': cell_count * evaptrans,
'inf-vol': cell_count * max(inf, 0.0),
}
