def main():
import application_properties
application_properties.set_current_directory()
plot_utils.apply_plot_params(font_size=12, width_cm=25, height_cm=25)
# Create folder for output images
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
with_lakes_c_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
with_lakes_label = "CRCM5-L"
no_lakes_c_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-r-cc-canesm2-1980-2010.hdf5"
no_lakes_label = "CRCM5-NL"
start_year_c = 1980
end_year_c = 2010
varname = "STFL"
nyears_to_future = 75
with_lakes_config_c = RunConfig(data_path=with_lakes_c_path,
start_year=start_year_c,
end_year=end_year_c,
label=with_lakes_label)
with_lakes_config_f = with_lakes_config_c.get_shifted_config(
nyears_to_future)
no_lakes_config_c = RunConfig(data_path=no_lakes_c_path,
start_year=start_year_c,
end_year=end_year_c,
label=no_lakes_label)
no_lakes_config_f = no_lakes_config_c.get_shifted_config(nyears_to_future)
bmp_info = analysis.get_basemap_info_from_hdf(file_path=with_lakes_c_path)
# Calculate daily climatologic fields
with_lakes_c_daily = analysis.get_daily_climatology_for_rconf(
with_lakes_config_c, var_name=varname, level=0)
with_lakes_f_daily = analysis.get_daily_climatology_for_rconf(
with_lakes_config_f, var_name=varname, level=0)
no_lakes_c_daily = analysis.get_daily_climatology_for_rconf(
no_lakes_config_c, var_name=varname, level=0)
no_lakes_f_daily = analysis.get_daily_climatology_for_rconf(
no_lakes_config_f, var_name=varname, level=0)
configs = DataConfig(bmp_info, no_lakes_config_c, no_lakes_config_f,
with_lakes_config_c, with_lakes_config_f)
args = (no_lakes_c_daily, no_lakes_f_daily, with_lakes_c_daily,
with_lakes_f_daily)
data = Data(*[arg[1] for arg in args])
plot_comparison_std(configs, data, varname=varname)
def main():
import application_properties
application_properties.set_current_directory()
plot_utils.apply_plot_params(font_size=12, width_cm=25, height_cm=25)
# Create folder for output images
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
with_lakes_c_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
with_lakes_label = "CRCM5-L"
no_lakes_c_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-r-cc-canesm2-1980-2010.hdf5"
no_lakes_label = "CRCM5-NL"
start_year_c = 1980
end_year_c = 2010
varname = "STFL"
nyears_to_future = 75
with_lakes_config_c = RunConfig(data_path=with_lakes_c_path, start_year=start_year_c, end_year=end_year_c,
label=with_lakes_label)
with_lakes_config_f = with_lakes_config_c.get_shifted_config(nyears_to_future)
no_lakes_config_c = RunConfig(data_path=no_lakes_c_path, start_year=start_year_c, end_year=end_year_c,
label=no_lakes_label)
no_lakes_config_f = no_lakes_config_c.get_shifted_config(nyears_to_future)
bmp_info = analysis.get_basemap_info_from_hdf(file_path=with_lakes_c_path)
# Calculate daily climatologic fields
with_lakes_c_daily = analysis.get_daily_climatology_for_rconf(with_lakes_config_c, var_name=varname, level=0)
with_lakes_f_daily = analysis.get_daily_climatology_for_rconf(with_lakes_config_f, var_name=varname, level=0)
no_lakes_c_daily = analysis.get_daily_climatology_for_rconf(no_lakes_config_c, var_name=varname, level=0)
no_lakes_f_daily = analysis.get_daily_climatology_for_rconf(no_lakes_config_f, var_name=varname, level=0)
configs = DataConfig(bmp_info, no_lakes_config_c, no_lakes_config_f, with_lakes_config_c, with_lakes_config_f)
args = (no_lakes_c_daily, no_lakes_f_daily, with_lakes_c_daily, with_lakes_f_daily)
data = Data(*[arg[1] for arg in args])
plot_comparison_std(configs, data, varname=varname)
def main():
import application_properties
application_properties.set_current_directory()
# Create folder for output images
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
rea_driven_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5"
rea_driven_label = "CRCM5-L-ERAI"
# gcm_driven_path_c = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-r-cc-canesm2-1980-2010.hdf5"
gcm_driven_path_c = "/home/huziy/skynet3_rech1/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
gcm_driven_label_c = "CRCM5-L"
gcm_driven_path_f = "/home/huziy/skynet3_rech1/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-2070-2100.hdf5"
start_year_c = 1980
end_year_c = 2010
varname = "STFL"
future_shift_years = 90
params = dict(
data_path=rea_driven_path, start_year=start_year_c, end_year=end_year_c, label=rea_driven_label)
geo_data_file = "/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
rea_driven_config = RunConfig(**params)
params.update(dict(data_path=gcm_driven_path_c, label=gcm_driven_label_c))
gcm_driven_config_c = RunConfig(**params)
gcm_driven_config_f = gcm_driven_config_c.get_shifted_config(shift_years=future_shift_years, data_path=gcm_driven_path_f)
r_obj = RPN(geo_data_file)
facc = r_obj.get_first_record_for_name("FAA")
fldr = r_obj.get_first_record_for_name("FLDR")
lkfr = r_obj.get_first_record_for_name("ML")
# get basemap information
bmp_info = analysis.get_basemap_info_from_hdf(file_path=rea_driven_path)
# get basin mask
_, bname_to_mask = plot_basin_outlets(bmp_info=bmp_info, directions=fldr, accumulation_areas=facc, lake_fraction_field=lkfr)
all_basins_mask = np.zeros(fldr.shape)
for bname, the_mask in bname_to_mask.items():
all_basins_mask += the_mask
rs_gcm_c = get_return_levels_and_unc_using_bootstrap(gcm_driven_config_c, varname=varname)
rs_gcm_f = get_return_levels_and_unc_using_bootstrap(gcm_driven_config_f, varname=varname)
plot_utils.apply_plot_params(font_size=10, width_cm=20, height_cm=18)
# Plot return level changes
fig = plt.figure()
nplots = 0
for the_type, rp_to_rl in rs_gcm_c.return_lev_dict.items():
nplots += len(rp_to_rl)
ncols = 2
nrows = nplots // ncols + int(nplots % ncols != 0)
gs = GridSpec(nrows, ncols + 1, width_ratios=[1.0, ] * ncols + [0.05, ])
xx, yy = bmp_info.get_proj_xy()
cmap = cm.get_cmap("bwr", 20)
limits = {
"high": (-50, 50),
"low": (-150, 150)
}
for row, (the_type, rp_to_rl) in enumerate(sorted(rs_gcm_c.return_lev_dict.items(), key=lambda itm: itm[0])):
for col, rp in enumerate(sorted(rp_to_rl)):
ax = fig.add_subplot(gs[row, col])
rl = rp_to_rl[rp]
# Ignore 0 return levels in the current climate for percentage calculations
# rl = np.ma.masked_where(rl <= 0, rl)
rl_future = rs_gcm_f.return_lev_dict[the_type][rp]
# Calculate climate change signal
diff = (rl_future - rl) / (np.abs(rl + rl_future) * 0.5) * 100
diff[(rl_future <= 0) & (rl <= 0)] = 0
# diff = rl
diff = maskoceans(bmp_info.lons, bmp_info.lats, diff)
print(diff.min(), diff.max())
std_c = rs_gcm_c.std_dict[the_type][rp]
std_f = rs_gcm_f.std_dict[the_type][rp]
significance = (np.ma.abs(diff) >= 1.96 * (std_c + std_f)) & (~diff.mask)
significance = significance.astype(int)
vmin, vmax = limits[the_type]
diff = np.ma.masked_where(all_basins_mask < 0.5, diff)
im = bmp_info.basemap.pcolormesh(xx, yy, diff, vmin=vmin, vmax=vmax, cmap=cmap)
significance = np.ma.masked_where(all_basins_mask < 0.5, significance)
cs = bmp_info.basemap.contourf(xx, yy, significance,
levels=[0, 0.5, 1],
hatches=["////", None, None],
colors="none")
if row == nrows - 1 and col == ncols - 1:
# create a legend for the contour set
artists, labels = cs.legend_elements()
ax.legend([artists[0], ], ["not significant", ], handleheight=0.5,
bbox_to_anchor=(1, -0.1), loc="upper right", borderaxespad=0.)
ax.set_title("T = {}-year".format(rp))
if col == 0:
ax.set_ylabel("{} flow".format(the_type))
bmp_info.basemap.drawcoastlines(ax=ax)
bmp_info.basemap.drawmapboundary(fill_color="0.75")
bmp_info.basemap.readshapefile(".".join(quebec_info.BASIN_BOUNDARIES_DERIVED_10km.split(".")[:-1]).replace("utm18", "latlon"),
"basin",
linewidth=1.2, ax=ax)
if col == ncols - 1:
cax = fig.add_subplot(gs[row, -1])
plt.colorbar(im, cax=cax, extend="both")
cax.set_title("%")
# Print basin average changes
print("--- start ----------------{rp}-year {ext_type} flow ----------------".format(rp=rp, ext_type=the_type))
bname_to_diff = [(bname, diff[the_mask > 0.5].mean()) for bname, the_mask in bname_to_mask.items()]
bname_to_diff = list(sorted(bname_to_diff, key=lambda item: item[1]))
for bname, the_diff in bname_to_diff:
print("{bname}: cc = {cc:.2f}%".format(bname=bname, cc=the_diff))
print("--- end ----------------{rp}-year {ext_type} flow ----------------".format(rp=rp, ext_type=the_type))
img_file = img_folder.joinpath("rl_cc_{}.png".format(gcm_driven_config_c.label))
with img_file.open("wb") as f:
fig.savefig(f, bbox_inches="tight")
plt.close(fig)
def main():
start_year_c = 1980
end_year_c = 2010
img_folder = "cc_paper"
current_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-intfl-cc-canesm2-1980-2010.hdf5"
base_label = "CRCM5-LI"
# Need to read land fraction
geo_file_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
r_obj = RPN(geo_file_path)
mg_field = r_obj.get_first_record_for_name("MG")
lons, lats = r_obj.get_longitudes_and_latitudes_for_the_last_read_rec()
r_obj.close()
future_shift_years = 90
params = dict(data_path=current_path,
start_year=start_year_c,
end_year=end_year_c,
label=base_label)
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
varname = "INTF"
level = 0
daily_dates, intf_c = analysis.get_daily_climatology(
path_to_hdf_file=base_config_c.data_path,
var_name=varname,
level=level,
start_year=base_config_c.start_year,
end_year=base_config_c.end_year)
_, intf_f = analysis.get_daily_climatology(
path_to_hdf_file=base_config_f.data_path,
var_name=varname,
level=level,
start_year=base_config_f.start_year,
end_year=base_config_f.end_year)
mg_fields = np.asarray([mg_field for d in daily_dates])
mg_crit = 0.0001
the_mask = mg_fields <= mg_crit
# Convert to mm/day as well
intf_c = _avg_along_lon(intf_c, the_mask) * 24 * 3600
intf_f = _avg_along_lon(intf_f, the_mask) * 24 * 3600
lats_agg = lats.mean(axis=0)
num_dates = date2num(daily_dates)
lats_agg_2d, num_dates_2d = np.meshgrid(lats_agg, num_dates)
# Do the plotting
fig = plt.figure()
gs = GridSpec(2, 3, width_ratios=[1, 1, 0.05])
norm = SymLogNorm(5e-5)
all_axes = []
# Current
ax = fig.add_subplot(gs[0, 0])
cs = ax.contourf(num_dates_2d, lats_agg_2d, intf_c[:], 60, norm=norm)
ax.set_title("Current ({}-{})".format(base_config_c.start_year,
base_config_c.end_year))
all_axes.append(ax)
# Future
ax = fig.add_subplot(gs[0, 1])
ax.set_title("Future ({}-{})".format(base_config_f.start_year,
base_config_f.end_year))
cs = ax.contourf(num_dates_2d,
lats_agg_2d,
intf_f[:],
levels=cs.levels,
norm=norm)
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[0, 2])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-1, 2))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel("mm/day")
cax.yaxis.get_offset_text().set_position((-2, 10))
# CC
diff_cmap = cm.get_cmap("RdBu_r", 20)
diff = (intf_f - intf_c) / (0.5 * (intf_c + intf_f)) * 100
diff[(intf_f == 0) & (intf_c == 0)] = 0
print(np.min(diff), np.max(diff))
print(np.any(diff.mask))
print(np.any(intf_c.mask))
print(np.any(intf_f.mask))
delta = 200
vmin = -delta
vmax = delta
locator = MaxNLocator(nbins=20, symmetric=True)
clevs = locator.tick_values(vmin=vmin, vmax=vmax)
ax = fig.add_subplot(gs[1, 1])
cs = ax.contourf(num_dates_2d,
lats_agg_2d,
diff,
cmap=diff_cmap,
levels=clevs,
extend="both")
ax.set_title("Future - Current")
# ax.set_aspect("auto")
all_axes.append(ax)
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[1, -1]))
cb.ax.set_xlabel(r"%")
for i, the_ax in enumerate(all_axes):
the_ax.xaxis.set_minor_formatter(
FuncFormatter(lambda d, pos: num2date(d).strftime("%b")[0]))
the_ax.xaxis.set_major_formatter(FuncFormatter(lambda d, pos: ""))
the_ax.xaxis.set_minor_locator(MonthLocator(bymonthday=15))
the_ax.xaxis.set_major_locator(MonthLocator())
the_ax.grid()
if i != 1:
the_ax.set_ylabel(r"Latitude ${\rm \left(^\circ N \right)}$")
# identify approximately the melting period and lower latitudes
march1 = date2num(datetime(daily_dates[0].year, 3, 1))
june1 = date2num(datetime(daily_dates[0].year, 6, 1))
sel_mask = (num_dates_2d >= march1) & (num_dates_2d < june1) & (lats_agg_2d
<= 50)
print("Mean interflow decrease in the southern regions: {}%".format(
diff[sel_mask].mean()))
# identify the regions of max interflow rates in current and future climates
lat_min = 55
lat_max = 57.5
may1 = date2num(datetime(daily_dates[0].year, 5, 1))
july1 = date2num(datetime(daily_dates[0].year, 7, 1))
mean_max_current = intf_c[(lats_agg_2d >= lat_min)
& (lats_agg_2d <= lat_max) &
(num_dates_2d <= july1) &
(num_dates_2d >= june1)].mean()
mean_max_future = intf_f[(lats_agg_2d >= lat_min)
& (lats_agg_2d <= lat_max) &
(num_dates_2d <= june1) &
(num_dates_2d >= may1)].mean()
print("Mean change in the maximum interflow rate: {} %".format(
(mean_max_future - mean_max_current) * 100 / mean_max_current))
img_file = Path(img_folder).joinpath("INTF_rate_longit_avg.png")
fig.tight_layout()
from crcm5.analyse_hdf import common_plot_params
fig.savefig(str(img_file),
bbox_inches="tight",
transparent=True,
dpi=common_plot_params.FIG_SAVE_DPI)
def main():
start_year_c = 1980
end_year_c = 2010
img_folder = "cc_paper"
current_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
base_label = "CRCM5-L"
# Need to read land fraction
geo_file_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
r_obj = RPN(geo_file_path)
mg_field = r_obj.get_first_record_for_name("MG")
lons, lats = r_obj.get_longitudes_and_latitudes_for_the_last_read_rec()
r_obj.close()
future_shift_years = 75
params = dict(data_path=current_path,
start_year=start_year_c,
end_year=end_year_c,
label=base_label)
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
data_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-intfl-cc-canesm2-1980-2010.hdf5"
params.update(dict(data_path=data_path, label="CRCM5-LI"))
modif_config_c = RunConfig(**params)
modif_config_f = modif_config_c.get_shifted_config(future_shift_years)
varnames = ["I1+I2", "I0", "PR", "TRAF", "AV", "I0_max", "I0_min"]
levels = [0, 0, 0, 0, 0, 0, 0]
var_labels = ["SM", "ST", "PR", "TRAF", "LHF", "STmin", "STmax"]
# width of the first soil layer in mm
multipliers = [100, 1, 1000 * 24 * 3600, 24 * 3600, 1, 1, 1]
offsets = [
0,
] * len(varnames)
units = ["mm", "K", "mm/day", "mm/day", r"${\rm W/m^2}$", "K", "K"]
SimData = collections.namedtuple("SimData",
"base_c base_f modif_c modif_f")
mg_fields = None
fig = plt.figure()
gs = GridSpec(len(varnames), 5, width_ratios=2 * [
1,
] + [0.05, 1, 0.05])
lats_agg = lats.mean(axis=0)
diff_cmap = cm.get_cmap("RdBu_r", 10)
the_zip = zip(varnames, levels, var_labels, multipliers, offsets, units)
row = 0
for vname, level, var_label, multiplier, offset, unit_label in the_zip:
daily_dates, base_data_c = analysis.get_daily_climatology(
path_to_hdf_file=base_config_c.data_path,
var_name=vname,
level=level,
start_year=base_config_c.start_year,
end_year=base_config_c.end_year)
_, base_data_f = analysis.get_daily_climatology(
path_to_hdf_file=base_config_f.data_path,
var_name=vname,
level=level,
start_year=base_config_f.start_year,
end_year=base_config_f.end_year)
_, modif_data_c = analysis.get_daily_climatology(
path_to_hdf_file=modif_config_c.data_path,
var_name=vname,
level=level,
start_year=modif_config_c.start_year,
end_year=modif_config_c.end_year)
_, modif_data_f = analysis.get_daily_climatology(
path_to_hdf_file=modif_config_f.data_path,
var_name=vname,
level=level,
start_year=modif_config_f.start_year,
end_year=modif_config_f.end_year)
if mg_fields is None:
mg_fields = np.asarray([mg_field for d in daily_dates])
num_dates = date2num(daily_dates)
# create 2d dates and latitudes for the contour plots
lats_agg_2d, num_dates_2d = np.meshgrid(lats_agg, num_dates)
sim_data = SimData(_avg_along_lon(base_data_c, mg_fields),
_avg_along_lon(base_data_f, mg_fields),
_avg_along_lon(modif_data_c, mg_fields),
_avg_along_lon(modif_data_f, mg_fields))
# Unit conversion
sim_data = SimData(*[multiplier * si + offset for si in sim_data])
# Plot the row for the variable
all_axes = []
# Calculate nice color levels
delta = np.percentile(
np.abs([
sim_data.modif_c - sim_data.base_c,
sim_data.modif_f - sim_data.base_f
]), 99)
vmin = -delta
vmax = delta
locator = MaxNLocator(nbins=10, symmetric=True)
clevs = locator.tick_values(vmin=vmin, vmax=vmax)
# Current
ax = fig.add_subplot(gs[row, 0])
cs = ax.contourf(num_dates_2d,
lats_agg_2d,
sim_data.modif_c - sim_data.base_c,
extend="both",
levels=clevs,
cmap=diff_cmap)
if row == 0:
ax.set_title("Current ({}-{})".format(base_config_c.start_year,
base_config_c.end_year))
all_axes.append(ax)
# Future
ax = fig.add_subplot(gs[row, 1])
if row == 0:
ax.set_title("Future ({}-{})".format(base_config_f.start_year,
base_config_f.end_year))
cs = ax.contourf(num_dates_2d,
lats_agg_2d,
sim_data.modif_f - sim_data.base_f,
levels=cs.levels,
extend="both",
cmap=diff_cmap)
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, 2])
plt.colorbar(cs, cax=cax)
cax.set_title("{} ({})\n".format(var_label, unit_label))
diff = (sim_data.modif_f - sim_data.base_f) - (sim_data.modif_c -
sim_data.base_c)
delta = np.percentile(np.abs(diff), 99)
vmin = -delta
vmax = delta
locator = MaxNLocator(nbins=10, symmetric=True)
clevs = locator.tick_values(vmin=vmin, vmax=vmax)
ax = fig.add_subplot(gs[row, 3])
cs = ax.contourf(num_dates_2d,
lats_agg_2d,
diff,
cmap=diff_cmap,
levels=clevs,
extend="both")
all_axes.append(ax)
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[row, 4]))
if row == 0:
ax.set_title("Future - Current")
cb.ax.set_title("{}\n".format(unit_label))
for i, the_ax in enumerate(all_axes):
the_ax.xaxis.set_major_formatter(DateFormatter("%b"))
the_ax.xaxis.set_major_locator(MonthLocator(interval=2))
the_ax.grid()
if i == 0:
the_ax.set_ylabel("Latitude")
row += 1
fig.tight_layout()
img_path = Path(img_folder).joinpath(
"{}_long_avg_intf_impact_{}-{}_vs_{}-{}.png".format(
"_".join(varnames), base_config_f.start_year,
base_config_f.end_year, base_config_c.start_year,
base_config_c.end_year))
fig.savefig(str(img_path), bbox_inches="tight")
def main():
base_current_path = \
"/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-r-cc-canesm2-1980-2010.hdf5"
base_label = "CanESM2-CRCM5-NL"
modif_current_path = \
"/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
modif_label = "CanESM2-CRCM5-L"
start_year_c = 1980
end_year_c = 2010
varname = "STFL"
future_shift_years = 90
params = dict(data_path=base_current_path,
start_year=start_year_c,
end_year=end_year_c,
label=base_label)
geo_data_file = "/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
base_configs = [base_config_c, base_config_f]
params.update(dict(data_path=modif_current_path, label=modif_label))
modif_config_c = RunConfig(**params)
modif_config_f = modif_config_c.get_shifted_config(future_shift_years)
modif_configs = [modif_config_c, modif_config_f]
r_obj = RPN(geo_data_file)
facc = r_obj.get_first_record_for_name("FAA")
fldr = r_obj.get_first_record_for_name("FLDR")
lake_fraction = r_obj.get_first_record_for_name_and_level("ML")
lake_fraction = np.ma.masked_where((fldr <= 0) | (fldr > 128),
lake_fraction)
bmp_info = analysis.get_basemap_info_from_hdf(file_path=base_current_path)
# plots basin boundaries and names on the domain plot
basin_name_to_out_indices_map, basin_name_to_basin_mask = plot_basin_outlets(
bmp_info=bmp_info,
accumulation_areas=facc,
directions=fldr,
lake_fraction_field=lake_fraction)
# basin_name_to_out_indices_map, basin_name_to_basin_mask = get_basin_to_outlet_indices_map(bmp_info=bmp_info,
# accumulation_areas=facc,
# directions=fldr,
# lake_fraction_field=lake_fraction)
data_to_plot = read_cc_and_cc_diff(
base_configs,
modif_configs,
name_to_indices=basin_name_to_out_indices_map,
varname=varname)
basin_name_to_basin_mask = basin_name_to_basin_mask if varname not in [
"STFA", "STFL"
] else None
img_path = get_image_path(base_config_c, base_config_f, modif_config_c,
modif_config_f, varname)
# select lake rich basins
sel_basins = ["ARN", "PYR", "LGR", "RDO", "SAG", "WAS"]
basin_name_to_out_indices_map = {
k: v
for k, v in basin_name_to_out_indices_map.items() if k in sel_basins
}
calculate_and_plot_climate_change_hydrographs(
data_to_plot,
name_to_out_indices=basin_name_to_out_indices_map,
months=list(range(1, 13)),
varname=varname,
img_path=img_path)
def main():
season_to_months = get_default_season_to_months_dict()
# var_names = ["TT", "HU", "PR", "AV", "STFL"]
# var_names = ["TRAF", "STFL", "TRAF+TDRA"]
# var_names = ["TT", "PR", "STFL"]
# var_names = ["TT", "PR", "I5", "STFL", "AV", "AH"]
var_names = ["TT", ]
levels = [0, ] * len(var_names)
multipliers = {
"PR": 1.0,
"TRAF+TDRA": 24 * 60 * 60
}
name_to_units = {
"TRAF": "mm/day", "I1": "mm", "PR": "mm/day", "TRAF+TDRA": "mm/day",
"I5": "mm", "AV": r"${\rm W/m^2}$"
}
base_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-r-cc-canesm2-1980-2010.hdf5"
base_label = "CanESM2-CRCM5-NL"
modif_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
modif_label = "CanESM2-CRCM5-L"
plot_cc_only_for = modif_label
# plot_cc_only_for = None
start_year_c = 1980
end_year_c = 2010
future_shift_years = 90
params = dict(
data_path=base_current_path, start_year=start_year_c, end_year=end_year_c, label=base_label
)
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
params.update(
dict(data_path=modif_current_path, label=modif_label)
)
modif_config_c = RunConfig(**params)
modif_config_f = modif_config_c.get_shifted_config(future_shift_years)
config_dict = OrderedDict([
("Current", OrderedDict([(base_label, base_config_c), (modif_label, modif_config_c)])),
("Future", OrderedDict([(base_label, base_config_f), (modif_label, modif_config_f)]))
])
# Changes global plot properties mainly figure size and font size
plot_utils.apply_plot_params(font_size=12, width_cm=25, height_cm=10)
# Plot the differences
fig = plt.figure()
gs = GridSpec(len(var_names), len(season_to_months) + 1, width_ratios=[1., ] * len(season_to_months) + [0.05, ])
config_dict.fig = fig
config_dict.gs = gs
config_dict.label_modif = modif_config_c.label
config_dict.label_base = base_config_c.label
config_dict.season_to_months = season_to_months
config_dict.multipliers = multipliers
lons, lats, bmp = analysis.get_basemap_from_hdf(base_current_path)
config_dict.lons = lons
config_dict.lats = lats
config_dict.basemap = bmp
config_dict.name_to_units = name_to_units
# Calculate and plot seasonal means
for vname, level, the_row in zip(var_names, levels, list(range(len(levels)))):
config_dict.the_row = the_row
_plot_row(vname=vname, level=level, config_dict=config_dict, plot_cc_only_for=plot_cc_only_for, mark_significance=False)
# Save the image to the file
if plot_cc_only_for is None:
img_path = get_image_path(base_config_c, base_config_f, modif_config_c, season_to_months=season_to_months)
else:
config_c = base_config_c if base_config_c.label == plot_cc_only_for else modif_config_c
config_f = base_config_f if base_config_f.label == plot_cc_only_for else modif_config_f
img_path = get_image_path(config_c, config_f, config_c, season_to_months=season_to_months)
fig.savefig(img_path, bbox_inches="tight", transparent=True, dpi=common_plot_params.FIG_SAVE_DPI)
print("saving the plot to: {}".format(img_path))
plt.close(fig)
def main_interflow():
# Changes global plot properties mainly figure size and font size
plot_utils.apply_plot_params(font_size=12, width_cm=20)
# season_to_months = get_default_season_to_months_dict()
season_to_months = OrderedDict([("January", [1, ]), ("February", [2, ]), ("March", [3, ]), ])
var_names = ["TT", "HU", "PR", "AV", "TRAF", "I1", "STFL"]
levels = [0, ] * len(var_names)
plot_utils.apply_plot_params(font_size=10, width_pt=None, width_cm=20 * len(season_to_months) / 4.0,
height_cm=20 * len(var_names) / 5.0)
multipliers = {
"PR": 1.,
"TRAF": 1.,
"I1": infovar.soil_layer_widths_26_to_60[0] * 1000.0,
"TRAF+TDRA": 24 * 60 * 60
}
name_to_units = {
"TRAF": "mm/day", "I1": "mm", "PR": "mm/day", "TRAF+TDRA": "mm/day"
}
base_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
base_label = "CanESM2-CRCM5-L"
modif_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-hcd-rl-intfl-cc-canesm2-1980-2010.hdf5"
modif_label = "CanESM2-CRCM5-LI"
start_year_c = 1980
end_year_c = 2010
future_shift_years = 90
params = dict(
data_path=base_current_path, start_year=start_year_c, end_year=end_year_c, label=base_label
)
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
params.update(
dict(data_path=modif_current_path, label=modif_label)
)
modif_config_c = RunConfig(**params)
modif_config_f = modif_config_c.get_shifted_config(future_shift_years)
config_dict = OrderedDict([
("Current", OrderedDict([(base_label, base_config_c), (modif_label, modif_config_c)])),
("Future", OrderedDict([(base_label, base_config_f), (modif_label, modif_config_f)]))
])
# Plot the differences
fig = plt.figure()
gs = GridSpec(len(var_names), len(season_to_months) + 1, width_ratios=[1., ] * len(season_to_months) + [0.05, ])
config_dict.fig = fig
config_dict.gs = gs
config_dict.label_modif = modif_config_c.label
config_dict.label_base = base_config_c.label
config_dict.season_to_months = season_to_months
config_dict.multipliers = multipliers
lons, lats, bmp = analysis.get_basemap_from_hdf(base_current_path)
config_dict.lons = lons
config_dict.lats = lats
config_dict.basemap = bmp
config_dict.name_to_units = name_to_units
# Calculate and plot seasonal means
for vname, level, the_row in zip(var_names, levels, list(range(len(levels)))):
config_dict.the_row = the_row
_plot_row(vname=vname, level=level, config_dict=config_dict)
# Save the image to the file
img_path = get_image_path(base_config_c, base_config_f, modif_config_c, season_to_months=season_to_months)
fig.savefig(img_path, bbox_inches="tight")
def main():
"""
"""
season_to_months = get_default_season_to_months_dict()
# season_to_months = OrderedDict([("April", [4, ]), ("May", [5, ]), ("June", [6, ]), ("July", [7, ])])
var_names = ["TT", "HU", "PR", "AV", "AH", "STFL", "TRAF", "I5", "I0"]
# var_names = ["TT", "PR"]
levels = [
0,
] * len(var_names)
multipliers = {"PR": 1.0e3 * 24.0 * 3600., "TRAF": 24 * 3600.0}
base_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-r-cc-canesm2-1980-2010.hdf5"
base_label = "CanESM2-CRCM5-NL"
modif_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
modif_label = "CanESM2-CRCM5-L"
# base_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
# "quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
# base_label = "CRCM5-L"
#
# modif_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
# "quebec_0.1_crcm5-hcd-rl-intfl-cc-canesm2-1980-2010.hdf5"
# modif_label = "CRCM5-LI"
start_year_c = 1980
end_year_c = 2010
future_shift_years = 90
params = dict(data_path=base_current_path,
start_year=start_year_c,
end_year=end_year_c,
label=base_label)
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
params.update(dict(data_path=modif_current_path, label=modif_label))
modif_config_c = RunConfig(**params)
modif_config_f = modif_config_c.get_shifted_config(future_shift_years)
config_dict = OrderedDict([("Current",
OrderedDict([(base_label, base_config_c),
(modif_label, modif_config_c)])),
("Future",
OrderedDict([(base_label, base_config_f),
(modif_label, modif_config_f)]))])
# Plot the differences
config_dict.label_modif = modif_config_c.label
config_dict.label_base = base_config_c.label
config_dict.season_to_months = season_to_months
config_dict.multipliers = multipliers
lons, lats, bmp = analysis.get_basemap_from_hdf(base_current_path)
config_dict.lons = lons
config_dict.lats = lats
config_dict.basemap = bmp
# Calculate and plot seasonal means
for vname, level in zip(var_names, levels):
data = get_data(vname=vname, level=level, config_dict=config_dict)
_plot_var(vname=vname,
level=level,
config_dict=config_dict,
data_dict=data)
def main():
"""
"""
season_to_months = get_default_season_to_months_dict()
# season_to_months = OrderedDict([("April", [4, ]), ("May", [5, ]), ("June", [6, ]), ("July", [7, ])])
var_names = ["TT", "HU", "PR", "AV", "AH", "STFL", "TRAF", "I5", "I0"]
# var_names = ["TT", "PR"]
levels = [0, ] * len(var_names)
multipliers = {
"PR": 1.0e3 * 24.0 * 3600.,
"TRAF": 24 * 3600.0
}
base_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-r-cc-canesm2-1980-2010.hdf5"
base_label = "CanESM2-CRCM5-NL"
modif_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
modif_label = "CanESM2-CRCM5-L"
# base_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
# "quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
# base_label = "CRCM5-L"
#
# modif_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
# "quebec_0.1_crcm5-hcd-rl-intfl-cc-canesm2-1980-2010.hdf5"
# modif_label = "CRCM5-LI"
start_year_c = 1980
end_year_c = 2010
future_shift_years = 90
params = dict(
data_path=base_current_path, start_year=start_year_c, end_year=end_year_c, label=base_label
)
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
params.update(
dict(data_path=modif_current_path, label=modif_label)
)
modif_config_c = RunConfig(**params)
modif_config_f = modif_config_c.get_shifted_config(future_shift_years)
config_dict = OrderedDict([
("Current", OrderedDict([(base_label, base_config_c), (modif_label, modif_config_c)])),
("Future", OrderedDict([(base_label, base_config_f), (modif_label, modif_config_f)]))
])
# Plot the differences
config_dict.label_modif = modif_config_c.label
config_dict.label_base = base_config_c.label
config_dict.season_to_months = season_to_months
config_dict.multipliers = multipliers
lons, lats, bmp = analysis.get_basemap_from_hdf(base_current_path)
config_dict.lons = lons
config_dict.lats = lats
config_dict.basemap = bmp
# Calculate and plot seasonal means
for vname, level in zip(var_names, levels):
data = get_data(vname=vname, level=level, config_dict=config_dict)
_plot_var(vname=vname, level=level, config_dict=config_dict, data_dict=data)
def main():
lkfr_limit = 0.05
model_data_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
modif_label = "CanESM2-CRCM5-L"
start_year_c = 1980
end_year_c = 2010
future_shift_years = 90
params = dict(
start_year=start_year_c, end_year=end_year_c
)
params.update(
dict(data_path=model_data_current_path, label=modif_label)
)
model_config_c = RunConfig(**params)
model_config_f = model_config_c.get_shifted_config(future_shift_years)
bmp_info = analysis.get_basemap_info(r_config=model_config_c)
specific_cond_heat = 0.250100e7 # J/kg
water_density = 1000.0 # kg/m**3
season_to_months = OrderedDict([
("Summer", [6, 7, 8]),
])
lkfr = analysis.get_array_from_file(path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5", var_name=infovar.HDF_LAKE_FRACTION_NAME)
assert lkfr is not None, "Could not find lake fraction in the file"
# Current climate
traf_c = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_c, varname="TRAF", level=5, season_to_months=season_to_months)
pr_c = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_c, varname="PR", level=0, season_to_months=season_to_months)
lktemp_c = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_c, varname="L1", level=0, season_to_months=season_to_months)
airtemp_c = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_c, varname="TT", level=0, season_to_months=season_to_months)
lhc = OrderedDict([
(s, specific_cond_heat * (pr_c[s] * water_density - traf_c[s])) for s, traf in traf_c.items()
])
avc = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_c, varname="AV", level=0, season_to_months=season_to_months)
plt.figure()
lhc["Summer"] = np.ma.masked_where(lkfr < lkfr_limit, lhc["Summer"])
print("min: {}, max: {}".format(lhc["Summer"].min(), lhc["Summer"].max()))
cs = plt.contourf(lhc["Summer"].T)
plt.title("lhc")
plt.colorbar()
plt.figure()
cs = plt.contourf(avc["Summer"].T, levels=cs.levels, norm=cs.norm, cmap=cs.cmap)
plt.title("avc")
plt.colorbar()
# Future climate
traf_f = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_f, varname="TRAF", level=5, season_to_months=season_to_months)
pr_f = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_f, varname="PR", level=0,
season_to_months=season_to_months)
lktemp_f = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_f, varname="L1", level=0,
season_to_months=season_to_months)
airtemp_f = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_f, varname="TT", level=0,
season_to_months=season_to_months)
lhf = OrderedDict([
(s, specific_cond_heat * (pr_f[s] * water_density - traf_f[s])) for s, traf in traf_f.items()
])
plt.figure()
plt.pcolormesh(traf_c["Summer"].T)
plt.title("TRAF over lakes current")
plt.colorbar()
avf = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_f, varname="AV", level=0,
season_to_months=season_to_months)
plt.figure()
cs = plt.contourf(avf["Summer"].T)
plt.title("avf")
plt.colorbar()
plt.figure()
cs = plt.contourf(avf["Summer"].T - avc["Summer"].T, levels=np.arange(-40, 45, 5))
plt.title("d(av)")
plt.colorbar()
plt.figure()
plt.contourf(lhf["Summer"].T - lhc["Summer"].T, levels=cs.levels, cmap=cs.cmap, norm=cs.norm)
plt.title("d(lh)")
plt.colorbar()
# plotting
plot_utils.apply_plot_params(width_cm=15, height_cm=15, font_size=10)
gs = GridSpec(2, 2)
# tair_c_ts = analysis.get_area_mean_timeseries(model_config_c.data_path, var_name="TT", level_index=0,
# start_year=model_config_c.start_year, end_year=model_config_c.end_year,
# the_mask=lkfr >= lkfr_limit)
#
# tair_f_ts = analysis.get_area_mean_timeseries(model_config_f.data_path, var_name="TT", level_index=0,
# start_year=model_config_f.start_year, end_year=model_config_f.end_year,
# the_mask=lkfr >= lkfr_limit)
#
#
# tlake_c_ts = analysis.get_area_mean_timeseries(model_config_c.data_path, var_name="TT", level_index=0,
# start_year=model_config_c.start_year, end_year=model_config_c.end_year,
# the_mask=lkfr >= lkfr_limit)
#
# tlake_f_ts = analysis.get_area_mean_timeseries(model_config_f.data_path, var_name="TT", level_index=0,
# start_year=model_config_f.start_year, end_year=model_config_f.end_year,
# the_mask=lkfr >= lkfr_limit)
for season in season_to_months:
fig = plt.figure()
lktemp_c[season] -= 273.15
dT_c = np.ma.masked_where(lkfr < lkfr_limit, lktemp_c[season] - airtemp_c[season])
lktemp_f[season] -= 273.15
dT_f = np.ma.masked_where(lkfr < lkfr_limit, lktemp_f[season] - airtemp_f[season])
d = np.round(max(np.ma.abs(dT_c).max(), np.ma.abs(dT_f).max()))
vmin = -d
vmax = d
clevs = np.arange(-12, 13, 1)
ncolors = len(clevs) - 1
bn = BoundaryNorm(clevs, ncolors=ncolors)
cmap = cm.get_cmap("seismic", ncolors)
ax_list = []
fig.suptitle(season)
xx, yy = bmp_info.get_proj_xy()
# Current gradient
ax = fig.add_subplot(gs[0, 0])
ax.set_title(r"current: $T_{\rm lake} - T_{\rm atm}$")
cs = bmp_info.basemap.pcolormesh(xx, yy, dT_c, ax=ax, norm=bn, cmap=cmap)
bmp_info.basemap.colorbar(cs, ax=ax, extend="both")
ax_list.append(ax)
# Future Gradient
ax = fig.add_subplot(gs[0, 1])
ax.set_title(r"future: $T_{\rm lake} - T_{\rm atm}$")
cs = bmp_info.basemap.pcolormesh(xx, yy, dT_f, ax=ax, norm=cs.norm, cmap=cs.cmap, vmin=vmin, vmax=vmax)
bmp_info.basemap.colorbar(cs, ax=ax, extend="both")
ax_list.append(ax)
# Change in the gradient
ax = fig.add_subplot(gs[1, 0])
ax.set_title(r"$\Delta T_{\rm future} - \Delta T_{\rm current}$")
ddT = dT_f - dT_c
d = np.round(np.ma.abs(ddT).max())
clevs = np.arange(-3, 3.1, 0.1)
ncolors = len(clevs) - 1
bn = BoundaryNorm(clevs, ncolors=ncolors)
cmap = cm.get_cmap("seismic", ncolors)
cs = bmp_info.basemap.pcolormesh(xx, yy, ddT, norm=bn, cmap=cmap)
bmp_info.basemap.colorbar(cs, ax=ax, extend="both")
ax_list.append(ax)
# Change in the latent heat flux
# ax = fig.add_subplot(gs[1, 1])
# ax.set_title(r"$LE_{\rm future} - LE_{\rm current}$")
# dlh = np.ma.masked_where(lkfr < lkfr_limit, lhf[season] - lhc[season])
#
# d = np.round(np.ma.abs(dlh).max() // 10) * 10
# clevs = np.arange(0, 105, 5)
# bn = BoundaryNorm(clevs, ncolors=ncolors)
# cmap = cm.get_cmap("jet", ncolors)
#
# cs = bmp_info.basemap.pcolormesh(xx, yy, dlh, norm=bn, cmap=cmap)
# bmp_info.basemap.colorbar(cs, ax=ax, extend="max") # Change in the latent heat flux
# ax_list.append(ax)
for the_ax in ax_list:
bmp_info.basemap.drawcoastlines(linewidth=0.3, ax=the_ax)
fig.tight_layout()
fig.savefig(os.path.join(img_folder, "lake_atm_gradients_and_fluxes_{}-{}_{}-{}.png".format(model_config_f.start_year, model_config_f.end_year, start_year_c, end_year_c)),
dpi=800,
bbox_inches="tight")
def main():
# import seaborn as sns
# sns.set_context("paper", font_scale=2)
# sns.set_style("whitegrid")
level_widths_mm = MM_PER_METER * infovar.soil_layer_widths_26_to_60
avg_axis = "lon"
start_year_c = 1980
end_year_c = 2010
img_folder = Path("cc_paper/lake_props")
if not img_folder.exists():
img_folder.mkdir(parents=True)
# Configuration without interflow, to be compared with the one with intf.
base_config = RunConfig(
start_year=start_year_c,
end_year=end_year_c,
data_path=
"/home/huziy/skynet3_rech1/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
label="CRCM5-L")
current_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
# Need to read land fraction
geo_file_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
r_obj = RPN(geo_file_path)
mg_field = r_obj.get_first_record_for_name("MG")
depth_to_bedrock_mm = r_obj.get_first_record_for_name("8L") * MM_PER_METER
lake_fraction = r_obj.get_first_record_for_name("ML")
# recompute layer widths to account for the depth to bedrock
layer_widths_3d = np.ones(depth_to_bedrock_mm.shape +
level_widths_mm.shape)
layer_widths_3d *= level_widths_mm[np.newaxis, np.newaxis, :]
layer_bottoms_3d = layer_widths_3d.cumsum(axis=2)
corrections = depth_to_bedrock_mm[:, :, np.newaxis] - layer_bottoms_3d
layer_widths_3d[corrections < 0] += corrections[corrections < 0]
layer_widths_3d[layer_widths_3d < 0] = 0
lons, lats = r_obj.get_longitudes_and_latitudes_for_the_last_read_rec()
r_obj.close()
# Current and future configurations
current_config = RunConfig(start_year=start_year_c,
end_year=end_year_c,
data_path=current_path,
label="CRCM5-L")
n_shift_years = 90
future_config = current_config.get_shifted_config(n_shift_years)
print(future_config)
varname = "L1"
level = 0
daily_dates, lake_temp_c = analysis.get_daily_climatology_for_rconf(
current_config, var_name=varname, level=level)
_, lake_temp_f = analysis.get_daily_climatology_for_rconf(future_config,
var_name=varname,
level=level)
# average along a dim
lake_temp_c = _avg_along(lake_temp_c,
axis=avg_axis,
lake_fraction=lake_fraction)
lake_temp_f = _avg_along(lake_temp_f,
axis=avg_axis,
lake_fraction=lake_fraction)
zagg = None
ztitle = ""
if avg_axis == "lon":
zagg = lats.mean(axis=0)
ztitle = "Latitude"
elif avg_axis == "lat":
zagg = lons.mean(axis=1)
ztitle = "Longitude"
num_dates = date2num(daily_dates)
z_agg_2d, num_dates_2d = np.meshgrid(zagg, num_dates)
# Do the plotting
plot_utils.apply_plot_params(font_size=14,
width_pt=None,
width_cm=20,
height_cm=30)
fig = plt.figure()
gs = GridSpec(4, 2, width_ratios=[1, 0.02])
all_axes = []
# ----------------------------------Lake temperature----------------------------------
row = 0
ax = fig.add_subplot(gs[row, 0])
cmap = cm.get_cmap("bwr", 15)
to_plot = lake_temp_f - lake_temp_c[:, :]
vmax = max(to_plot.max(), 0)
vmin = min(to_plot.min(), 0)
vmax = max(abs(vmin), abs(vmax))
# norm = MidpointNormalize(vmin=vmin, vmax=vmax)
clevs = MaxNLocator(cmap.N + 1).tick_values(-vmax, vmax)
cs = ax.contourf(num_dates_2d,
z_agg_2d,
to_plot,
cmap=cmap,
levels=clevs,
extend="both")
ax.set_title("Lake surface water temperature")
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 3))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel(r"${\rm ^\circ C}$")
cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Lake ice thickness----------------------------------
level = 0
varname = "LD"
_, lake_ice_th_c = analysis.get_daily_climatology_for_rconf(
current_config, var_name=varname, level=level)
_, lake_ice_th_f = analysis.get_daily_climatology_for_rconf(
future_config, var_name=varname, level=level)
lake_ice_th = _avg_along(lake_ice_th_f - lake_ice_th_c,
axis=avg_axis,
lake_fraction=lake_fraction)
# get the points for which the average should be calculated
winter_points = np.asarray(
[i for i, d in enumerate(daily_dates) if d.month in [1, 2, 12]])
row += 1
ax = fig.add_subplot(gs[row, 0])
vmax = max(lake_ice_th.max(), 0)
vmin = min(lake_ice_th.min(), 0)
vmax = max(abs(vmin), abs(vmax))
# norm = MidpointNormalize(vmin=vmin, vmax=vmax)
clevs = MaxNLocator(cmap.N + 1).tick_values(-vmax, vmax)
cs = ax.contourf(num_dates_2d,
z_agg_2d,
lake_ice_th,
cmap=cmap,
levels=clevs,
extend="both")
ax.set_title("Lake ice thickness")
print("Mean change in ice thickness: {} m".format(
lake_ice_th.mean(axis=1)[winter_points].mean()))
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 2))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel("m")
cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Lake ice fraction----------------------------------
level = 0
varname = "LC"
_, lake_ice_fraction_c = analysis.get_daily_climatology_for_rconf(
current_config, var_name=varname, level=level)
_, lake_ice_fraction_f = analysis.get_daily_climatology_for_rconf(
future_config, var_name=varname, level=level)
lake_ice_fraction = _avg_along(lake_ice_fraction_f - lake_ice_fraction_c,
axis=avg_axis,
lake_fraction=lake_fraction)
row += 1
ax = fig.add_subplot(gs[row, 0])
vmax = max(lake_ice_fraction.max(), 0)
vmin = min(lake_ice_fraction.min(), 0)
vmax = max(abs(vmin), abs(vmax))
# norm = MidpointNormalize(vmin=vmin, vmax=vmax)
clevs = MaxNLocator(cmap.N + 1).tick_values(-vmax, vmax)
cs = ax.contourf(num_dates_2d,
z_agg_2d,
lake_ice_fraction,
cmap=cmap,
levels=clevs,
extend="both")
ax.set_title("Lake ice fraction")
print("Mean change in ice fraction: {}".format(
lake_ice_fraction.mean(axis=1)[winter_points].mean()))
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 2))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel("")
cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Lake depth----------------------------------
level = 0
varname = "CLDP"
_, lake_depth_c = analysis.get_daily_climatology_for_rconf(
current_config, var_name=varname, level=level)
_, lake_depth_f = analysis.get_daily_climatology_for_rconf(
future_config, var_name=varname, level=level)
lake_depth_cc = _avg_along(lake_depth_f - lake_depth_c,
axis=avg_axis,
lake_fraction=lake_fraction)
row += 1
ax = fig.add_subplot(gs[row, 0])
clevs = [0.01, 0.1, 0.5, 0.75, 1, 2, 3]
clevs = [-c for c in reversed(clevs)] + clevs
cmap = cm.get_cmap("bwr", len(clevs) - 1)
norm = BoundaryNorm(boundaries=clevs, ncolors=len(clevs) - 1)
cs = ax.contourf(num_dates_2d,
z_agg_2d,
lake_depth_cc,
cmap=cmap,
levels=clevs,
norm=norm,
extend="both")
ax.set_title("Lake level")
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 2))
plt.colorbar(cs, cax=cax, format=sfmt, ticks=clevs)
cax.set_xlabel("m")
cax.yaxis.get_offset_text().set_position((-2, 10))
for i, the_ax in enumerate(all_axes):
the_ax.xaxis.set_major_formatter(
FuncFormatter(lambda d, pos: num2date(d).strftime("%b")[0]))
the_ax.xaxis.set_major_locator(MonthLocator(bymonthday=15))
the_ax.xaxis.set_minor_locator(MonthLocator(bymonthday=1))
the_ax.grid(which="minor")
the_ax.set_ylabel(ztitle)
img_file = Path(img_folder).joinpath(
"cc_{}_Lake_props_current_{}_avg_{}-{}_vs_{fsy}-{fey}.png".format(
base_config.label,
avg_axis,
start_year_c,
end_year_c,
fsy=future_config.start_year,
fey=future_config.end_year))
fig.tight_layout()
fig.savefig(str(img_file), bbox_inches="tight")
plt.show()
def main():
# import seaborn as sns
# sns.set_context("paper", font_scale=2)
# sns.set_style("whitegrid")
level_widths_mm = MM_PER_METER * infovar.soil_layer_widths_26_to_60
avg_axis = "lon"
start_year_c = 1980
end_year_c = 2010
img_folder = Path("cc_paper/lake_props")
if not img_folder.exists():
img_folder.mkdir(parents=True)
# Configuration without interflow, to be compared with the one with intf.
base_config = RunConfig(start_year=start_year_c, end_year=end_year_c,
data_path="/home/huziy/skynet3_rech1/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
label="CRCM5-L")
current_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
# Need to read land fraction
geo_file_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
r_obj = RPN(geo_file_path)
mg_field = r_obj.get_first_record_for_name("MG")
depth_to_bedrock_mm = r_obj.get_first_record_for_name("8L") * MM_PER_METER
lake_fraction = r_obj.get_first_record_for_name("ML")
# recompute layer widths to account for the depth to bedrock
layer_widths_3d = np.ones(depth_to_bedrock_mm.shape + level_widths_mm.shape)
layer_widths_3d *= level_widths_mm[np.newaxis, np.newaxis, :]
layer_bottoms_3d = layer_widths_3d.cumsum(axis=2)
corrections = depth_to_bedrock_mm[:, :, np.newaxis] - layer_bottoms_3d
layer_widths_3d[corrections < 0] += corrections[corrections < 0]
layer_widths_3d[layer_widths_3d < 0] = 0
lons, lats = r_obj.get_longitudes_and_latitudes_for_the_last_read_rec()
r_obj.close()
# Current and future configurations
current_config = RunConfig(start_year=start_year_c, end_year=end_year_c,
data_path=current_path,
label="CRCM5-L")
n_shift_years = 90
future_config = current_config.get_shifted_config(n_shift_years)
print(future_config)
varname = "L1"
level = 0
daily_dates, lake_temp_c = analysis.get_daily_climatology_for_rconf(current_config,
var_name=varname, level=level)
_, lake_temp_f = analysis.get_daily_climatology_for_rconf(future_config,
var_name=varname, level=level)
# average along a dim
lake_temp_c = _avg_along(lake_temp_c, axis=avg_axis, lake_fraction=lake_fraction)
lake_temp_f = _avg_along(lake_temp_f, axis=avg_axis, lake_fraction=lake_fraction)
zagg = None
ztitle = ""
if avg_axis == "lon":
zagg = lats.mean(axis=0)
ztitle = "Latitude"
elif avg_axis == "lat":
zagg = lons.mean(axis=1)
ztitle = "Longitude"
num_dates = date2num(daily_dates)
z_agg_2d, num_dates_2d = np.meshgrid(zagg, num_dates)
# Do the plotting
plot_utils.apply_plot_params(font_size=14, width_pt=None, width_cm=20, height_cm=30)
fig = plt.figure()
gs = GridSpec(4, 2, width_ratios=[1, 0.02])
all_axes = []
# ----------------------------------Lake temperature----------------------------------
row = 0
ax = fig.add_subplot(gs[row, 0])
cmap = cm.get_cmap("bwr", 15)
to_plot = lake_temp_f - lake_temp_c[:, :]
vmax = max(to_plot.max(), 0)
vmin = min(to_plot.min(), 0)
vmax = max(abs(vmin), abs(vmax))
# norm = MidpointNormalize(vmin=vmin, vmax=vmax)
clevs = MaxNLocator(cmap.N + 1).tick_values(-vmax, vmax)
cs = ax.contourf(num_dates_2d, z_agg_2d, to_plot, cmap=cmap, levels=clevs, extend="both")
ax.set_title("Lake surface water temperature")
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 3))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel(r"${\rm ^\circ C}$")
cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Lake ice thickness----------------------------------
level = 0
varname = "LD"
_, lake_ice_th_c = analysis.get_daily_climatology_for_rconf(current_config,
var_name=varname, level=level)
_, lake_ice_th_f = analysis.get_daily_climatology_for_rconf(future_config,
var_name=varname, level=level)
lake_ice_th = _avg_along(lake_ice_th_f - lake_ice_th_c, axis=avg_axis, lake_fraction=lake_fraction)
# get the points for which the average should be calculated
winter_points = np.asarray([i for i, d in enumerate(daily_dates) if d.month in [1, 2, 12]])
row += 1
ax = fig.add_subplot(gs[row, 0])
vmax = max(lake_ice_th.max(), 0)
vmin = min(lake_ice_th.min(), 0)
vmax = max(abs(vmin), abs(vmax))
# norm = MidpointNormalize(vmin=vmin, vmax=vmax)
clevs = MaxNLocator(cmap.N + 1).tick_values(-vmax, vmax)
cs = ax.contourf(num_dates_2d, z_agg_2d, lake_ice_th, cmap=cmap, levels=clevs, extend="both")
ax.set_title("Lake ice thickness")
print("Mean change in ice thickness: {} m".format(lake_ice_th.mean(axis=1)[winter_points].mean()))
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 2))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel("m")
cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Lake ice fraction----------------------------------
level = 0
varname = "LC"
_, lake_ice_fraction_c = analysis.get_daily_climatology_for_rconf(current_config,
var_name=varname, level=level)
_, lake_ice_fraction_f = analysis.get_daily_climatology_for_rconf(future_config,
var_name=varname, level=level)
lake_ice_fraction = _avg_along(lake_ice_fraction_f - lake_ice_fraction_c, axis=avg_axis,
lake_fraction=lake_fraction)
row += 1
ax = fig.add_subplot(gs[row, 0])
vmax = max(lake_ice_fraction.max(), 0)
vmin = min(lake_ice_fraction.min(), 0)
vmax = max(abs(vmin), abs(vmax))
# norm = MidpointNormalize(vmin=vmin, vmax=vmax)
clevs = MaxNLocator(cmap.N + 1).tick_values(-vmax, vmax)
cs = ax.contourf(num_dates_2d, z_agg_2d, lake_ice_fraction, cmap=cmap, levels=clevs, extend="both")
ax.set_title("Lake ice fraction")
print("Mean change in ice fraction: {}".format(lake_ice_fraction.mean(axis=1)[winter_points].mean()))
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 2))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel("")
cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Lake depth----------------------------------
level = 0
varname = "CLDP"
_, lake_depth_c = analysis.get_daily_climatology_for_rconf(current_config,
var_name=varname, level=level)
_, lake_depth_f = analysis.get_daily_climatology_for_rconf(future_config,
var_name=varname, level=level)
lake_depth_cc = _avg_along(lake_depth_f - lake_depth_c, axis=avg_axis,
lake_fraction=lake_fraction)
row += 1
ax = fig.add_subplot(gs[row, 0])
clevs = [0.01, 0.1, 0.5, 0.75, 1, 2, 3]
clevs = [-c for c in reversed(clevs)] + clevs
cmap = cm.get_cmap("bwr", len(clevs) - 1)
norm = BoundaryNorm(boundaries=clevs, ncolors=len(clevs) - 1)
cs = ax.contourf(num_dates_2d, z_agg_2d, lake_depth_cc, cmap=cmap, levels=clevs, norm=norm, extend="both")
ax.set_title("Lake level")
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 2))
plt.colorbar(cs, cax=cax, format=sfmt, ticks=clevs)
cax.set_xlabel("m")
cax.yaxis.get_offset_text().set_position((-2, 10))
for i, the_ax in enumerate(all_axes):
the_ax.xaxis.set_major_formatter(FuncFormatter(lambda d, pos: num2date(d).strftime("%b")[0]))
the_ax.xaxis.set_major_locator(MonthLocator(bymonthday=15))
the_ax.xaxis.set_minor_locator(MonthLocator(bymonthday=1))
the_ax.grid(which="minor")
the_ax.set_ylabel(ztitle)
img_file = Path(img_folder).joinpath("cc_{}_Lake_props_current_{}_avg_{}-{}_vs_{fsy}-{fey}.png".format(
base_config.label, avg_axis, start_year_c, end_year_c,
fsy=future_config.start_year, fey=future_config.end_year))
fig.tight_layout()
fig.savefig(str(img_file), bbox_inches="tight")
plt.show()
def main():
start_year_c = 1980
end_year_c = 2010
img_folder = "cc_paper"
current_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-intfl-cc-canesm2-1980-2010.hdf5"
base_label = "CRCM5-LI"
# Need to read land fraction
geo_file_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
r_obj = RPN(geo_file_path)
mg_field = r_obj.get_first_record_for_name("MG")
lons, lats = r_obj.get_longitudes_and_latitudes_for_the_last_read_rec()
r_obj.close()
future_shift_years = 90
params = dict(
data_path=current_path,
start_year=start_year_c, end_year=end_year_c,
label=base_label
)
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
varname = "INTF"
level = 0
daily_dates, intf_c = analysis.get_daily_climatology(path_to_hdf_file=base_config_c.data_path,
var_name=varname, level=level,
start_year=base_config_c.start_year,
end_year=base_config_c.end_year)
_, intf_f = analysis.get_daily_climatology(path_to_hdf_file=base_config_f.data_path,
var_name=varname, level=level,
start_year=base_config_f.start_year,
end_year=base_config_f.end_year)
mg_fields = np.asarray([mg_field for d in daily_dates])
mg_crit = 0.0001
the_mask = mg_fields <= mg_crit
# Convert to mm/day as well
intf_c = _avg_along_lon(intf_c, the_mask) * 24 * 3600
intf_f = _avg_along_lon(intf_f, the_mask) * 24 * 3600
lats_agg = lats.mean(axis=0)
num_dates = date2num(daily_dates)
lats_agg_2d, num_dates_2d = np.meshgrid(lats_agg, num_dates)
# Do the plotting
fig = plt.figure()
gs = GridSpec(2, 3, width_ratios=[1, 1, 0.05])
norm = SymLogNorm(5e-5)
all_axes = []
# Current
ax = fig.add_subplot(gs[0, 0])
cs = ax.contourf(num_dates_2d, lats_agg_2d, intf_c[:], 60, norm=norm)
ax.set_title("Current ({}-{})".format(
base_config_c.start_year, base_config_c.end_year))
all_axes.append(ax)
# Future
ax = fig.add_subplot(gs[0, 1])
ax.set_title("Future ({}-{})".format(
base_config_f.start_year, base_config_f.end_year))
cs = ax.contourf(num_dates_2d, lats_agg_2d, intf_f[:], levels=cs.levels, norm=norm)
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[0, 2])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-1, 2))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel("mm/day")
cax.yaxis.get_offset_text().set_position((-2, 10))
# CC
diff_cmap = cm.get_cmap("RdBu_r", 20)
diff = (intf_f - intf_c) / (0.5 * (intf_c + intf_f)) * 100
diff[(intf_f == 0) & (intf_c == 0)] = 0
print(np.min(diff), np.max(diff))
print(np.any(diff.mask))
print(np.any(intf_c.mask))
print(np.any(intf_f.mask))
delta = 200
vmin = -delta
vmax = delta
locator = MaxNLocator(nbins=20, symmetric=True)
clevs = locator.tick_values(vmin=vmin, vmax=vmax)
ax = fig.add_subplot(gs[1, 1])
cs = ax.contourf(num_dates_2d, lats_agg_2d, diff, cmap=diff_cmap,
levels=clevs, extend="both")
ax.set_title("Future - Current")
# ax.set_aspect("auto")
all_axes.append(ax)
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[1, -1]))
cb.ax.set_xlabel(r"%")
for i, the_ax in enumerate(all_axes):
the_ax.xaxis.set_minor_formatter(FuncFormatter(lambda d, pos: num2date(d).strftime("%b")[0]))
the_ax.xaxis.set_major_formatter(FuncFormatter(lambda d, pos: ""))
the_ax.xaxis.set_minor_locator(MonthLocator(bymonthday=15))
the_ax.xaxis.set_major_locator(MonthLocator())
the_ax.grid()
if i != 1:
the_ax.set_ylabel(r"Latitude ${\rm \left(^\circ N \right)}$")
# identify approximately the melting period and lower latitudes
march1 = date2num(datetime(daily_dates[0].year, 3, 1))
june1 = date2num(datetime(daily_dates[0].year, 6, 1))
sel_mask = (num_dates_2d >= march1) & (num_dates_2d < june1) & (lats_agg_2d <= 50)
print("Mean interflow decrease in the southern regions: {}%".format(diff[sel_mask].mean()))
# identify the regions of max interflow rates in current and future climates
lat_min = 55
lat_max = 57.5
may1 = date2num(datetime(daily_dates[0].year, 5, 1))
july1 = date2num(datetime(daily_dates[0].year, 7, 1))
mean_max_current = intf_c[(lats_agg_2d >= lat_min) & (lats_agg_2d <= lat_max) & (num_dates_2d <= july1) & (num_dates_2d >= june1)].mean()
mean_max_future = intf_f[(lats_agg_2d >= lat_min) & (lats_agg_2d <= lat_max) & (num_dates_2d <= june1) & (num_dates_2d >= may1)].mean()
print("Mean change in the maximum interflow rate: {} %".format((mean_max_future - mean_max_current) * 100 / mean_max_current))
img_file = Path(img_folder).joinpath("INTF_rate_longit_avg.png")
fig.tight_layout()
from crcm5.analyse_hdf import common_plot_params
fig.savefig(str(img_file), bbox_inches="tight", transparent=True, dpi=common_plot_params.FIG_SAVE_DPI)
