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Snakefile
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"""Snakemake workflow for extreme heat and drought occurrence
Note that the isimip data server appears to refuse greedy downloading,
so we can limit the intensity using a custom "download_streams" resource.
See https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#resources
for more details.
For example, run with:
snakemake --cores 16 --resources download_streams=1 heat
snakemake --cores 16 --resources download_streams=1 data/lange2020_hwmid-humidex_hadgem2-es_ewembi_rcp60_nosoc_co2_leh_global_annual_2006_2099_2030_exposure.tif
"""
from pathlib import Path
import pandas
rule all:
input:
"lange2020_expected_occurrence.zip",
#
# Run annual occurrence/exposure over all models/scenarios
#
rule heat:
input:
expand(
"data/lange2020_hwmid-humidex_{GCM}_ewembi_{RCP}_nosoc_co2_leh_global_annual_2006_2099_{EPOCH}_{METRIC}.tif",
GCM=["gfdl-esm2m","hadgem2-es","ipsl-cm5a-lr", "miroc5"],
RCP=["rcp26","rcp60"],
EPOCH=["2030","2050","2080"],
METRIC=["occurrence", "exposure"]),
expand(
"data/lange2020_hwmid-humidex_{GCM}_ewembi_historical_nosoc_co2_leh_global_annual_1861_2005_{EPOCH}_{METRIC}.tif",
GCM=["gfdl-esm2m","hadgem2-es","ipsl-cm5a-lr", "miroc5"],
EPOCH=["baseline"],
METRIC=["occurrence", "exposure"])
rule drought:
input:
# Most models have all GCMs, 2005soc in future
expand(
"data/lange2020_{MODEL}_{GCM}_ewembi_{RCP}_2005soc_co2_led_global_annual_2006_2099_{EPOCH}_{METRIC}.tif",
MODEL=["clm45", "h08", "lpjml", "pcr-globwb", "watergap2"],
GCM=["gfdl-esm2m","hadgem2-es","ipsl-cm5a-lr", "miroc5"],
RCP=["rcp26","rcp60"],
EPOCH=["2030","2050","2080"],
METRIC=["occurrence", "exposure"]),
# "clm45", "mpi-hm" use "2005soc" for baseline
expand(
"data/lange2020_{MODEL}_{GCM}_ewembi_historical_2005soc_co2_led_global_annual_1861_2005_{EPOCH}_{METRIC}.tif",
MODEL=["clm45"],
GCM=["gfdl-esm2m","hadgem2-es","ipsl-cm5a-lr", "miroc5"],
EPOCH=["baseline"],
METRIC=["occurrence", "exposure"]),
# "h08", "lpjml", "pcr-globwb", "watergap2" use "histsoc" for baseline
expand(
"data/lange2020_{MODEL}_{GCM}_ewembi_historical_histsoc_co2_led_global_annual_1861_2005_{EPOCH}_{METRIC}.tif",
MODEL=["h08", "lpjml", "pcr-globwb", "watergap2"],
GCM=["gfdl-esm2m","hadgem2-es","ipsl-cm5a-lr", "miroc5"],
EPOCH=["baseline"],
METRIC=["occurrence", "exposure"]),
# MPI-HM has no HadGEM
expand(
"data/lange2020_{MODEL}_{GCM}_ewembi_{RCP}_2005soc_co2_led_global_annual_2006_2099_{EPOCH}_{METRIC}.tif",
MODEL=["mpi-hm"],
GCM=["gfdl-esm2m","ipsl-cm5a-lr", "miroc5"],
RCP=["rcp26","rcp60"],
EPOCH=["2030","2050","2080"],
METRIC=["occurrence", "exposure"]),
expand(
"data/lange2020_{MODEL}_{GCM}_ewembi_historical_histsoc_co2_led_global_annual_1861_2005_{EPOCH}_{METRIC}.tif",
MODEL=["mpi-hm"],
GCM=["gfdl-esm2m","ipsl-cm5a-lr", "miroc5"],
EPOCH=["baseline"],
METRIC=["occurrence", "exposure"]),
# Jules-W1 and Orchidee use "nosoc"
expand(
"data/lange2020_{MODEL}_{GCM}_ewembi_{RCP}_nosoc_co2_led_global_annual_2006_2099_{EPOCH}_{METRIC}.tif",
MODEL=["jules-w1", "orchidee"],
GCM=["gfdl-esm2m","hadgem2-es","ipsl-cm5a-lr", "miroc5"],
RCP=["rcp26","rcp60"],
EPOCH=["2030","2050","2080"],
METRIC=["occurrence", "exposure"]),
expand(
"data/lange2020_{MODEL}_{GCM}_ewembi_historical_nosoc_co2_led_global_annual_1861_2005_{EPOCH}_{METRIC}.tif",
MODEL=["jules-w1", "orchidee"],
GCM=["gfdl-esm2m","hadgem2-es","ipsl-cm5a-lr", "miroc5"],
EPOCH=["baseline"],
METRIC=["occurrence", "exposure"])
#
# Download ISIMIP extreme heat and drought timeseries/lat/lon data
#
def url_epoch(wildcards):
if wildcards.Y == "2006":
return "future"
else:
return "historical"
rule download:
output: "incoming_data/lange2020_{MODEL}_{GCM}_ewembi_{RCP}_{SOC}_{SEN}_{VAR}_global_annual_{Y}_{Z}.nc4"
resources: download_streams=1
params:
url_epoch=url_epoch
shell:
"""
sleep 2 && \
wget -w 2 -nc -P ./incoming_data \
https://files.isimip.org/ISIMIP2b/DerivedOutputData/Lange2020/{wildcards.MODEL}/{wildcards.GCM}/{params.url_epoch}/lange2020_{wildcards.MODEL}_{wildcards.GCM}_ewembi_{wildcards.RCP}_{wildcards.SOC}_{wildcards.SEN}_{wildcards.VAR}_global_annual_{wildcards.Y}_{wildcards.Z}.nc4
"""
#
# Download JRC GHSL population data
#
rule download_population_all:
input:
expand(
"incoming_data/GHS_POP_E{EPOCH}_GLOBE_R2023A_{RESOLUTION}_V1_0.tif",
EPOCH=["2020"], # Available in: 2030, 2025, 2020, 2015, 2010, 2005, 2000, 1995, 1990, 1985, 1980, 1975
RESOLUTION=["4326_30ss"], # Available in: 4326_3ss, 4326_30ss, 54009_100, 54009_1000
),
rule download_population:
output: "incoming_data/GHS_POP_E{EPOCH}_GLOBE_R2023A_{RESOLUTION}_V1_0.zip"
shell:
"""
wget -nc -P ./incoming_data \
https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/GHSL/GHS_POP_GLOBE_R2023A/GHS_POP_E{wildcards.EPOCH}_GLOBE_R2023A_{wildcards.RESOLUTION}/V1-0/GHS_POP_E{wildcards.EPOCH}_GLOBE_R2023A_{wildcards.RESOLUTION}_V1_0.zip
"""
rule extract_population:
input: "incoming_data/GHS_POP_E{EPOCH}_GLOBE_R2023A_{RESOLUTION}_V1_0.zip"
output: "incoming_data/GHS_POP_E{EPOCH}_GLOBE_R2023A_{RESOLUTION}_V1_0.tif"
shell:
"""
unzip {input} -d ./incoming_data
"""
#
# Find expected annual occurrence
#
EPOCH_BINS = {
"baseline": {
"bin_start": 1966,
"bin_end": 2005,
"file_year_start": 1861,
"file_year_end": 2005,
},
"2030": {
"bin_start": 2010,
"bin_end": 2049,
"file_year_start": 2006,
"file_year_end": 2099,
},
"2050": {
"bin_start": 2030,
"bin_end": 2069,
"file_year_start": 2006,
"file_year_end": 2099,
},
"2080": {
"bin_start": 2060,
"bin_end": 2099,
"file_year_start": 2006,
"file_year_end": 2099,
},
}
# e.g. heat
# data/lange2020_hwmid-humidex_gfdl-esm2m_ewembi_historical_nosoc_co2_leh_global_annual_1861_2005.nc4
# data/lange2020_hwmid-humidex_hadgem2-es_ewembi_rcp60_nosoc_co2_leh_global_annual_2006_2099_2030_occurrence.tif
rule average_nc_to_geotiff:
input: "incoming_data/lange2020_{MODEL}_{GCM}_ewembi_{RCP}_{SOC}_{SEN}_{VAR}_global_annual_{Y}_{Z}.nc4"
output: "data/lange2020_{MODEL}_{GCM}_ewembi_{RCP}_{SOC}_{SEN}_{VAR}_global_annual_{Y}_{Z}_{EPOCH}_occurrence.tif"
run:
import rioxarray
import xarray
ds = xarray.open_dataset(str(input), engine='netcdf4', decode_times=False)
ds['time'] = ds.time + 1661 # correct for time in years since 1661
epoch = EPOCH_BINS[wildcards.EPOCH]
time_range = range(epoch["bin_start"], epoch["bin_end"] + 1)
# find mean over range
epoch_mean = ds.sel(time=time_range).mean(dim="time")
# write out to TIFF
epoch_mean.rio.write_crs("epsg:4326", inplace=True)
epoch_mean[wildcards.VAR].rio.to_raster(str(output), compress='lzw')
rule resample:
input:
src_fname="incoming_data/GHS_POP_E2020_GLOBE_R2023A_4326_30ss_V1_0.tif",
output:
dst_fname="data/GHS_POP_E2020_GLOBE_R2023A_4326_0.5deg_V1_0.tif",
shell:
"""
gdalwarp \
-co "COMPRESS=LZW" \
-te -180 -90 180 90 \
-tr 0.5 0.5 \
-dstnodata 0 \
-ovr NONE \
-r sum \
{input.src_fname} \
{output.dst_fname}
"""
# e.g. heat
# data/lange2020_hwmid-humidex_hadgem2-es_ewembi_rcp60_nosoc_co2_leh_global_annual_2006_2099_2030_exposure.tif
rule population_exposure:
input:
population="data/GHS_POP_E2020_GLOBE_R2023A_4326_0.5deg_V1_0.tif",
occurrence="data/lange2020_{MODEL}_{GCM}_ewembi_{RCP}_{SOC}_{SEN}_{VAR}_global_annual_{Y}_{Z}_{EPOCH}_occurrence.tif",
output: "data/lange2020_{MODEL}_{GCM}_ewembi_{RCP}_{SOC}_{SEN}_{VAR}_global_annual_{Y}_{Z}_{EPOCH}_exposure.tif"
shell:
"""
gdal_calc.py \
--calc="A*B" \
--outfile={output} \
-A {input.population} \
-B {input.occurrence} \
--co="COMPRESS=LZW" \
--overwrite
"""
rule archive:
input:
rules.heat.input,
rules.drought.input,
output:
archive="lange2020_expected_occurrence.zip",
shell:
"""
zip -r {output.archive} data
"""