/
CedarRiver.py
459 lines (358 loc) · 17.4 KB
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CedarRiver.py
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import os
import sys
from datetime import date
import shutil
from os.path import exists as _exists
from os.path import split as _split
from time import sleep
from copy import deepcopy
from wepppy.nodb.mods.locations.lt.selectors import *
from wepppy.all_your_base import isfloat
from wepppy.nodb import (
Ron, Topaz, Watershed, Landuse, Soils, Climate, Wepp, SoilsMode, ClimateMode, ClimateSpatialMode, LanduseMode
)
from wepppy.nodb.mods.locations import SeattleMod
from wepppy.wepp.soils.utils import modify_ksat
from os.path import join as _join
from wepppy.wepp.out import TotalWatSed
from wepppy.export import arc_export
from wepppy.climates.cligen import ClimateFile
from osgeo import gdal
gdal.UseExceptions()
from wepppy._scripts.utils import *
os.chdir('/geodata/weppcloud_runs/')
wd = None
def log_print(*msg):
now = datetime.now()
print('[{now}] {wd}: {msg}'.format(now=now, wd=wd, msg=', '.join(str(v) for v in msg)))
if __name__ == '__main__':
precip_transforms = {
'gridmet': {
'Cedar_River': 1,
'Tolt_NorthFork': 1,
'Taylor_Creek': 1
},
'daymet': {
'Cedar_River': 1,
'Tolt_NorthFork': 1,
'Taylor_Creek': 1
}
}
def _daymet_cli_adjust(cli_dir, cli_fn, watershed):
cli = ClimateFile(_join(cli_dir, cli_fn))
cli.discontinuous_temperature_adjustment(date(2005, 11, 2))
pp_scale = precip_transforms['daymet'][watershed]
cli.transform_precip(offset=0, scale=pp_scale)
cli.write(_join(cli_dir, 'adj_' + cli_fn))
return 'adj_' + cli_fn
def _gridmet_cli_adjust(cli_dir, cli_fn, watershed):
cli = ClimateFile(_join(cli_dir, cli_fn))
pp_scale = precip_transforms['gridmet'][watershed]
cli.transform_precip(offset=0, scale=pp_scale)
cli.write(_join(cli_dir, 'adj_' + cli_fn))
return 'adj_' + cli_fn
watersheds = [
dict(watershed='Cedar_River',
extent=[-121.77108764648439, 47.163108130899104, -121.29043579101564, 47.4889049944156],
map_center = [-121.49574279785158, 47.277365616965646],
map_zoom = 11,
outlet = [-121.62271296763015, 47.36246108025578],
landuse=None,
cs=100, erod=0.000001,
csa=10, mcl=100,
surf_runoff=0.003, lateral_flow=0.004, baseflow=0.005, sediment=1000.0,
gwstorage=100, bfcoeff=0.04, dscoeff=0.00, bfthreshold=1.001,
mid_season_crop_coeff=0.95, p_coeff=0.75, ksat=0.0999),
dict(watershed='Tolt_NorthFork',
extent=[-121.90086364746095, 47.56216409801383, -121.4202117919922, 47.88549944643875],
map_center=[-121.66053771972658, 47.72408264363561],
map_zoom=11,
outlet=[-121.78852559978455, 47.71242486609417],
landuse=None,
cs=100, erod=0.000001,
csa=10, mcl=100,
surf_runoff=0.003, lateral_flow=0.004, baseflow=0.005, sediment=1000.0,
gwstorage=100, bfcoeff=0.04, dscoeff=0.00, bfthreshold=1.001,
mid_season_crop_coeff=0.95, p_coeff=0.75, ksat=0.0999),
dict(watershed='Taylor_Creek',
extent=[-121.8981170654297, 47.26199018174824, -121.65779113769533, 47.424835479167825],
map_center=[-121.77795410156251, 47.34347562236255],
map_zoom=11,
outlet=[-121.84644704748708, 47.386266378562375],
landuse=None,
cs=100, erod=0.000001,
csa=10, mcl=100,
surf_runoff=0.003, lateral_flow=0.004, baseflow=0.005, sediment=1000.0,
gwstorage=100, bfcoeff=0.04, dscoeff=0.00, bfthreshold=1.001,
mid_season_crop_coeff=0.95, p_coeff=0.75, ksat=0.0999)
]
scenarios = [
dict(wd='CurCond.202009.cl532.chn_cs{cs}',
landuse=None,
cli_mode='PRISMadj', clean=True, build_soils=True, build_landuse=True, build_climates=True,
lc_lookup_fn='landSoilLookup.csv'),
dict(wd='CurCond.202009.cl532_gridmet.chn_cs{cs}',
landuse=None,
cli_mode='observed', clean=True, build_soils=True, build_landuse=True, build_climates=True,
lc_lookup_fn='landSoilLookup.csv'),
dict(wd='CurCond.202009.cl532_future.chn_cs{cs}',
landuse=None,
cli_mode='future', clean=True, build_soils=True, build_landuse=True, build_climates=True,
lc_lookup_fn='landSoilLookup.csv'),
dict(wd='SimFire_Eagle.202009.cl532.chn_cs{cs}',
landuse=None,
cfg='seattle-simfire-eagle-snow',
cli_mode='PRISMadj', clean=True, build_soils=True, build_landuse=True, build_climates=True,
lc_lookup_fn='landSoilLookup.csv'),
dict(wd='SimFire_Norse.202009.cl532.chn_cs{cs}',
landuse=None,
cfg='seattle-simfire-norse-snow',
cli_mode='PRISMadj', clean=True, build_soils=True, build_landuse=True, build_climates=True,
lc_lookup_fn='landSoilLookup.csv'),
dict(wd='PrescFireS.202009.chn_cs{cs}',
landuse=[(not_shrub_selector, 110), (shrub_selector, 122)],
cli_mode='PRISMadj', clean=True, build_soils=True, build_landuse=True, build_climates=True,
lc_lookup_fn='landSoilLookup.csv'),
dict(wd='LowSevS.202009.chn_cs{cs}',
landuse=[(not_shrub_selector, 106), (shrub_selector, 121)],
cli_mode='PRISMadj', clean=True, build_soils=True, build_landuse=True, build_climates=True,
lc_lookup_fn='landSoilLookup.csv'),
dict(wd='ModSevS.202009.chn_cs{cs}',
landuse=[(not_shrub_selector, 118), (shrub_selector, 120)],
cli_mode='PRISMadj', clean=True, build_soils=True, build_landuse=True, build_climates=True,
lc_lookup_fn='landSoilLookup.csv'),
dict(wd='HighSevS.202009.chn_cs{cs}',
landuse=[(not_shrub_selector, 105), (shrub_selector, 119)],
cli_mode='PRISMadj', clean=True, build_soils=True, build_landuse=True, build_climates=True,
lc_lookup_fn='landSoilLookup.csv'),
]
wc = sys.argv[-1]
if '.py' in wc:
wc = None
projects = []
for scenario in scenarios:
for watershed in watersheds:
projects.append(deepcopy(watershed))
projects[-1]['cfg'] = scenario.get('cfg', 'seattle-snow')
projects[-1]['landuse'] = scenario['landuse']
projects[-1]['cli_mode'] = scenario.get('cli_mode', 'observed')
projects[-1]['clean'] = scenario['clean']
projects[-1]['build_soils'] = scenario['build_soils']
projects[-1]['build_landuse'] = scenario['build_landuse']
projects[-1]['build_climates'] = scenario['build_climates']
projects[-1]['lc_lookup_fn'] = scenario['lc_lookup_fn']
projects[-1]['ksat'] = scenario['ksat']
projects[-1]['wd'] = 'seattle_k_{watershed}_{scenario}' \
.format(watershed=watershed['watershed'], scenario=scenario['wd']) \
.format(cs=watershed['cs'])
for proj in projects:
config = proj['cfg']
watershed_name = proj['watershed']
wd = proj['wd']
ksat = proj['ksat']
log_print(wd)
if wc is not None:
if not wc in wd:
continue
extent = proj['extent']
map_center = proj['map_center']
map_zoom = proj['map_zoom']
outlet = proj['outlet']
default_landuse = proj['landuse']
cli_mode = proj['cli_mode']
csa = proj['csa']
mcl = proj['mcl']
cs = proj['cs']
erod = proj['erod']
lc_lookup_fn = proj['lc_lookup_fn']
clean = proj['clean']
build_soils = proj['build_soils']
build_landuse = proj['build_landuse']
build_climates = proj['build_climates']
if clean:
if _exists(wd):
shutil.rmtree(wd)
os.mkdir(wd)
ron = Ron(wd, config + '.cfg')
ron.name = wd
ron.set_map(extent, map_center, zoom=map_zoom)
ron.fetch_dem()
log_print('building channels')
topaz = Topaz.getInstance(wd)
topaz.build_channels(csa=csa, mcl=mcl)
topaz.set_outlet(*outlet)
sleep(0.5)
log_print('building subcatchments')
topaz.build_subcatchments()
log_print('abstracting watershed')
watershed = Watershed.getInstance(wd)
watershed.abstract_watershed()
translator = watershed.translator_factory()
topaz_ids = [top.split('_')[1] for top in translator.iter_sub_ids()]
else:
ron = Ron.getInstance(wd)
topaz = Topaz.getInstance(wd)
watershed = Watershed.getInstance(wd)
landuse = Landuse.getInstance(wd)
if build_landuse:
landuse.mode = LanduseMode.Gridded
landuse.build()
landuse = Landuse.getInstance(wd)
log_print('setting default landuses')
if default_landuse is not None:
log_print('setting default landuse')
tops = []
for selector, dom in default_landuse:
_topaz_ids = selector(landuse, None)
bare_tops = bare_or_sodgrass_or_bunchgrass_selector(landuse, None)
_topaz_ids = [top for top in _topaz_ids if top not in bare_tops]
landuse.modify(_topaz_ids, dom)
tops.extend(_topaz_ids)
soils = Soils.getInstance(wd)
if build_soils:
log_print('building soils')
soils.mode = SoilsMode.Gridded
soils.build()
#soils.build_statsgo()
ksat_mod = 'f'
_domsoil_d = soils.domsoil_d
_soils = soils.soils
for topaz_id, ss in watershed._subs_summary.items():
lng, lat = ss.centroid.lnglat
dom = _domsoil_d[str(topaz_id)]
_soil = deepcopy(_soils[dom])
_dom = '{dom}-{ksat_mod}_{bedrock_name}' \
.format(dom=dom, ksat_mod=ksat_mod, bedrock_name=name)
if _dom not in _soils:
_soil_fn = '{dom}.sol'.format(dom=_dom)
log_print(_soil_fn, dst_soil_fn, ksat)
src_soil_fn = _join(_soil.soils_dir, _soil.fname)
dst_soil_fn = _join(_soil.soils_dir, _soil_fn)
log_print(src_soil_fn, dst_soil_fn, ksat, _dom)
modify_ksat(src_soil_fn, dst_soil_fn, ksat)
_soil.fname = _soil_fn
_soils[_dom] = _soil
_domsoil_d[str(topaz_id)] = _dom
soils.lock()
soils.domsoil_d = _domsoil_d
soils.soils = _soils
soils.dump_and_unlock()
soils = Soils.getInstance(wd)
if _exists(_join(wd, 'lt.nodb')):
location = SeattleMod.getInstance(wd)
location.modify_soils(default_wepp_type='Volcanic', lc_lookup_fn=lc_lookup_fn)
climate = Climate.getInstance(wd)
if build_climates:
log_print('building climate')
if cli_mode == 'observed':
log_print('building observed')
if 'linveh' in wd:
climate.climate_mode = ClimateMode.ObservedDb
climate.climate_spatialmode = ClimateSpatialMode.Multiple
climate.input_years = 21
climate.lock()
lng, lat = watershed.centroid
cli_path = lvdm.closest_cli(lng, lat)
_dir, cli_fn = _split(cli_path)
shutil.copyfile(cli_path, _join(climate.cli_dir, cli_fn))
climate.cli_fn = cli_fn
par_path = lvdm.par_path
_dir, par_fn = _split(par_path)
shutil.copyfile(par_path, _join(climate.cli_dir, par_fn))
climate.par_fn = par_fn
sub_par_fns = {}
sub_cli_fns = {}
for topaz_id, ss in watershed._subs_summary.items():
log_print(topaz_id)
lng, lat = ss.centroid.lnglat
cli_path = lvdm.closest_cli(lng, lat)
_dir, cli_fn = _split(cli_path)
run_cli_path = _join(climate.cli_dir, cli_fn)
if not _exists(run_cli_path):
shutil.copyfile(cli_path, run_cli_path)
sub_cli_fns[topaz_id] = cli_fn
sub_par_fns[topaz_id] = par_fn
climate.sub_par_fns = sub_par_fns
climate.sub_cli_fns = sub_cli_fns
climate.dump_and_unlock()
elif 'daymet' in wd:
stations = climate.find_closest_stations()
climate.climatestation = stations[0]['id']
climate.climate_mode = ClimateMode.Observed
climate.climate_spatialmode = ClimateSpatialMode.Multiple
climate.set_observed_pars(start_year=1990, end_year=2017)
climate.build(verbose=1)
climate.lock()
cli_dir = climate.cli_dir
adj_cli_fn = _daymet_cli_adjust(cli_dir, climate.cli_fn, watershed_name)
climate.cli_fn = adj_cli_fn
for topaz_id in climate.sub_cli_fns:
adj_cli_fn = _daymet_cli_adjust(cli_dir, climate.sub_cli_fns[topaz_id], watershed_name)
climate.sub_cli_fns[topaz_id] = adj_cli_fn
climate.dump_and_unlock()
elif 'gridmet' in wd:
log_print('building gridmet')
stations = climate.find_closest_stations()
climate.climatestation = stations[0]['id']
climate.climate_mode = ClimateMode.GridMetPRISM
climate.climate_spatialmode = ClimateSpatialMode.Multiple
climate.set_observed_pars(start_year=1980, end_year=2019)
climate.build(verbose=1)
climate.lock()
cli_dir = climate.cli_dir
adj_cli_fn = _gridmet_cli_adjust(cli_dir, climate.cli_fn, watershed_name)
climate.cli_fn = adj_cli_fn
for topaz_id in climate.sub_cli_fns:
adj_cli_fn = _gridmet_cli_adjust(cli_dir, climate.sub_cli_fns[topaz_id], watershed_name)
climate.sub_cli_fns[topaz_id] = adj_cli_fn
climate.dump_and_unlock()
elif cli_mode == 'future':
log_print('building gridmet')
stations = climate.find_closest_stations()
climate.climatestation = stations[0]['id']
climate.climate_mode = ClimateMode.Future
climate.climate_spatialmode = ClimateSpatialMode.Multiple
climate.set_future_pars(start_year=2006, end_year=2099)
climate.build(verbose=1)
climate.lock()
cli_dir = climate.cli_dir
adj_cli_fn = _gridmet_cli_adjust(cli_dir, climate.cli_fn, watershed_name)
climate.cli_fn = adj_cli_fn
for topaz_id in climate.sub_cli_fns:
adj_cli_fn = _gridmet_cli_adjust(cli_dir, climate.sub_cli_fns[topaz_id], watershed_name)
climate.sub_cli_fns[topaz_id] = adj_cli_fn
climate.dump_and_unlock()
elif cli_mode == 'PRISMadj':
stations = climate.find_closest_stations()
climate.climatestation = stations[0]['id']
log_print('climate_station:', climate.climatestation)
climate.climate_mode = ClimateMode.PRISM
climate.climate_spatialmode = ClimateSpatialMode.Multiple
climate.input_years = 100
climate.build(verbose=1)
elif cli_mode == 'vanilla':
stations = climate.find_closest_stations()
climate.climatestation = stations[0]['id']
log_print('climate_station:', climate.climatestation)
climate.climate_mode = ClimateMode.Vanilla
climate.climate_spatialmode = ClimateSpatialMode.Single
climate.input_years = 100
climate.build(verbose=1)
log_print('running wepp')
wepp = Wepp.getInstance(wd)
wepp.parse_inputs(proj)
wepp.prep_hillslopes()
log_print('running hillslopes')
wepp.run_hillslopes()
wepp = Wepp.getInstance(wd)
wepp.prep_watershed(erodibility=erod, critical_shear=cs)
wepp._prep_pmet(mid_season_crop_coeff=proj['mid_season_crop_coeff'], p_coeff=proj['p_coeff'])
wepp.run_watershed()
loss_report = wepp.report_loss()
log_print('running wepppost')
fn = _join(ron.export_dir, 'totalwatsed.csv')
totwatsed = TotalWatSed(_join(ron.output_dir, 'totalwatsed.txt'),
wepp.baseflow_opts, wepp.phosphorus_opts)
totwatsed.export(fn)
assert _exists(fn)
arc_export(wd)