EFHM return periods — how flood depth grows with rarity¶

A return period expresses how rare a flood is: a 1-in-10-year event is common and shallow, a 1-in-500-year event is rare and deep. This notebook fetches three return periods for the same area and compares their depths — one windowed read per period.

In [ ]:

import tempfile
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
from pyramids.dataset import Dataset

from earthlens.core import EarthLens

out = Path(tempfile.mkdtemp(prefix="efhm-rp-"))
lat_lim, lon_lim = [51.7, 52.0], [4.6, 5.1]
periods = [10, 100, 500]
paths = EarthLens(
    data_source="jrc-flood",
    lat_lim=lat_lim,
    lon_lim=lon_lim,
    return_periods=periods,
    path=out,
).download()
[p.name for p in paths]

import tempfile from pathlib import Path import matplotlib.pyplot as plt import numpy as np from pyramids.dataset import Dataset from earthlens.core import EarthLens out = Path(tempfile.mkdtemp(prefix="efhm-rp-")) lat_lim, lon_lim = [51.7, 52.0], [4.6, 5.1] periods = [10, 100, 500] paths = EarthLens( data_source="jrc-flood", lat_lim=lat_lim, lon_lim=lon_lim, return_periods=periods, path=out, ).download() [p.name for p in paths]

Side-by-side depth maps¶

In [ ]:

def _depth(path):
    a = Dataset.read_file(str(path)).read_array().astype("float64")
    a[a <= -9999] = np.nan
    return a


depths = [_depth(p) for p in paths]
vmax = np.nanmax([np.nanmax(d) for d in depths])

fig, axes = plt.subplots(1, len(periods), figsize=(5 * len(periods), 4.5))
for ax, rp, d in zip(axes, periods, depths):
    im = ax.imshow(d, cmap="Blues", vmin=0, vmax=vmax)
    ax.set_title(f"RP{rp} (1-in-{rp}-year)")
    ax.set_xticks([])
    ax.set_yticks([])
    fig.colorbar(im, ax=ax, shrink=0.75, label="depth (m)")
plt.show()

def _depth(path): a = Dataset.read_file(str(path)).read_array().astype("float64") a[a <= -9999] = np.nan return a depths = [_depth(p) for p in paths] vmax = np.nanmax([np.nanmax(d) for d in depths]) fig, axes = plt.subplots(1, len(periods), figsize=(5 * len(periods), 4.5)) for ax, rp, d in zip(axes, periods, depths): im = ax.imshow(d, cmap="Blues", vmin=0, vmax=vmax) ax.set_title(f"RP{rp} (1-in-{rp}-year)") ax.set_xticks([]) ax.set_yticks([]) fig.colorbar(im, ax=ax, shrink=0.75, label="depth (m)") plt.show()

Flooded area and depth grow with the return period¶

In [ ]:

for rp, d in zip(periods, depths):
    flooded = np.isfinite(d).sum()
    print(
        f"RP{rp:>3}: {flooded:>7,} flooded cells | mean {np.nanmean(d):.2f} m | max {np.nanmax(d):.2f} m"
    )

for rp, d in zip(periods, depths): flooded = np.isfinite(d).sum() print( f"RP{rp:>3}: {flooded:>7,} flooded cells | mean {np.nanmean(d):.2f} m | max {np.nanmax(d):.2f} m" )

Takeaway¶

Rarer floods inundate more cells and reach greater depths. Selecting the right return period lets you match a hazard layer to a design standard (e.g. RP100 for many flood defences, RP500 for critical infrastructure).