warnings.filterwarnings('ignore')
account_key = os.environ.get('BLOB_ACCOUNT_KEY')
gcms = [
('CanESM5-CanOE', 'r3i1p2f1'),
('MIROC-ES2L', 'r1i1p1f2'),
('ACCESS-CM2', 'r1i1p1f1'),
('ACCESS-ESM1-5', 'r10i1p1f1'),
('MRI-ESM2-0', 'r1i1p1f1'),
('MPI-ESM1-2-LR', 'r10i1p1f1'),
]
scenarios = ['ssp245', 'ssp370', 'ssp585']
website_mask = (
load.nlcd(store="az", year=2016).sel(band=[41, 42, 43, 90]).sum("band") > 0.5
).astype("float")
region_bboxes = {
'CONUS': {'x': slice(-3e6, 3e6), 'y': slice(4e6, 0)},
'PNW': {'x': slice(-2.5e6, -1e6), 'y': slice(3.5e6, 2.4e6)},
'Southwest': {'x': slice(-1.8e6, -0.9e6), 'y': slice(1.8e6, 0.9e6)},
'California': {'x': slice(-2.3e6, -1.8e6), 'y': slice(2.5e6, 1.2e6)},
'Southeast': {
'x': slice(0.6e6, 1.8e6),
'y': slice(1.6e6, 0.3e6),
},
}
def build_climate_cube(
print(f'[{dataset}] computing multi model mean')
for scenario in scenarios:
for target in targets:
keys = list(
filter(
lambda x: x is not None,
[
key if ((scenario in key) & (target in key)) else None
for key in pf.columns
],
))
pf[scenario + '_' + target] = pf[keys].mean(axis=1)
print(f'[{dataset}] regridding predictions')
nlcd = load.nlcd(store=store, year=2016, classes=[41, 42, 43, 90])
final_mask = nlcd.sum('band')
final_mask.attrs['crs'] = nlcd.attrs['crs']
if 'biomass' in dataset:
final_mask.values = final_mask.values * (final_mask.values > 0.5)
else:
final_mask.values = final_mask.values > 0.5
ds['historical'] = fit.interp(pf, final_mask, var='historical')
for scenario in tqdm(scenarios):
results = []
for target in targets:
key = scenario + '_' + target
gridded = fit.interp(pf, final_mask, var=key)
import xarray as xr
from scipy.stats import binom
from carbonplan_forest_risks import load
from carbonplan_forest_risks.utils import get_store
# flake8: noqa
warnings.filterwarnings('ignore')
impacts_to_process = ['fire']
account_key = os.environ.get('BLOB_ACCOUNT_KEY')
rolling = True
# specify the kind of mask you want to use
mask_for_website = True
website_mask = (load.nlcd(store="az", year=2016).sel(
band=[41, 42, 43, 90]).sum("band") > 0.5).astype("float")
gcms = [
("ACCESS-CM2", "r1i1p1f1"),
("ACCESS-ESM1-5", "r10i1p1f1"),
("MRI-ESM2-0", "r1i1p1f1"),
("MIROC-ES2L", "r1i1p1f2"),
("MPI-ESM1-2-LR", "r10i1p1f1"),
("CanESM5-CanOE", "r3i1p2f1"),
]
for impact in impacts_to_process:
ds = xr.open_zarr(
get_store(
'carbonplan-forests',
'risks/results/web/{}_cmip_high_res.zarr'.format(impact),
account_key=account_key,
model = fit.hurdle(x, y, log=False)
return score(x, y, model, da, method)
def append(df, results):
return df.append(results, ignore_index=True)
coarsen = 4
tlim = (1984, 2018)
variables = ["ppt", "tmean", "cwd"]
store = "az"
print('[stats] loading data')
mask = (load.nlcd(store=store, year=2001).sel(band=[41, 42, 43, 90]).sum('band') > 0.25).astype(
'float'
)
nftd = load.nftd(store=store, area_threshold=1500, coarsen=coarsen, mask=mask)
climate = load.terraclim(
store=store,
tlim=(tlim[0] - 1, tlim[1]),
coarsen=coarsen,
variables=variables,
mask=mask,
sampling="monthly",
)
mtbs = load.mtbs(store=store, coarsen=coarsen, tlim=tlim, mask=mask)
prepend = climate.sel(time=slice('1983', '1983'))
x, y = prepare.fire(
import os
import rioxarray
import xarray as xr
from scipy.stats import binom
from carbonplan_forest_risks import load
from carbonplan_forest_risks.utils import get_store
# flake8: noqa
account_key = os.environ.get('BLOB_ACCOUNT_KEY')
# this is only used to provide the x/y template for the insects/drought tifs
grid_template = (load.nlcd(store="az", year=2016).sel(
band=[41, 42, 43, 90]).sum("band") > 0.5).astype("float")
# # by passing mask as none we don't mask out any values
# # we'll pass a mask for when we do the webmap data prep
cmip_insect_url_template = "https://carbonplan.blob.core.windows.net/carbonplan-scratch/from-bill-05-03-2021/InsectProjections_Maps_5-5-21/InsectModelProjection_{}.{}.{}-{}.{}-v18climate_05-05-2021.tif"
da = load.impacts(cmip_insect_url_template, grid_template, mask=None) * 100
out_path = get_store('carbonplan-forests',
'risks/results/paper/insects_cmip_v5.zarr')
ds = xr.Dataset()
ds['probability'] = da.to_array(dim='vars').rename({'vars': 'gcm'})
ds.to_zarr(out_path, mode='w', consolidated=True)
cmip_drought_url_template = "https://carbonplan.blob.core.windows.net/carbonplan-scratch/from-bill-05-03-2021/DroughtProjections_Maps_5-5-21/DroughtModelProjection_{}.{}.{}-{}.{}-v18climate_05-05-2021.tif"
da = load.impacts(cmip_drought_url_template, grid_template, mask=None) * 100
out_path = get_store('carbonplan-forests',