Snow & cryosphere — Alpine winter snowpack¶

Track the build-up and ablation of a winter snowpack at a mountain site using ERA5-Land's snow variables. Two complementary fields:

snow-depth — geometric depth of the snowpack in metres. Convenient for visual / engineering work.
snow-depth-water-equivalent — water content of the snowpack in metres of water equivalent. The hydrologically meaningful quantity (snowmelt feeds runoff during spring).

Both are state variables (instantaneous), so monthly aggregation uses op="auto" → mean.

Domain context. Snow water equivalent (SWE) is the operational metric for spring melt forecasting, reservoir management, and avalanche risk. Mountain SWE typically peaks in March–April depending on elevation, then drops to zero by mid-summer.

Step 1 — pull a winter at monthly resolution¶

Box: 1° around the central Alps (46°–47°N, 10°–11°E) — Ortler / Engadine area. Range Oct 2021–Jun 2022 covers a full snow season.

In [1]:

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from pathlib import Path
from earthlens import EarthLens, AggregationConfig

OUT = Path("data/era5-land-alps-snow")
OUT.mkdir(parents=True, exist_ok=True)

earthlens = EarthLens(
    data_source="ecmwf",
    temporal_resolution="monthly",
    start="2021-10-01",
    end="2022-06-01",
    variables={
        "reanalysis-era5-land-monthly-means": [
            "snow-depth",
            "snow-depth-water-equivalent",
            "2m-temperature",
        ],
    },
    lat_lim=[46.0, 47.0],
    lon_lim=[10.0, 11.0],
    path=str(OUT),
)
earthlens.download(aggregate=AggregationConfig(freq="1MS", op="auto", cell_size=0.1))
from pathlib import Path
from earthlens import EarthLens, AggregationConfig

OUT = Path("data/era5-land-alps-snow")
OUT.mkdir(parents=True, exist_ok=True)

earthlens = EarthLens(
    data_source="ecmwf",
    temporal_resolution="monthly",
    start="2021-10-01",
    end="2022-06-01",
    variables={
        "reanalysis-era5-land-monthly-means": [
            "snow-depth",
            "snow-depth-water-equivalent",
            "2m-temperature",
        ],
    },
    lat_lim=[46.0, 47.0],
    lon_lim=[10.0, 11.0],
    path=str(OUT),
)
earthlens.download(aggregate=AggregationConfig(freq="1MS", op="auto", cell_size=0.1))

2026-05-10 01:43:08.826 | INFO     | earthlens.ecmwf.backend:download:536 - Download ECMWF reanalysis-era5-land-monthly-means/snow-depth data for period 2021-10-01 00:00:00 till 2022-06-01 00:00:00

2026-05-10 01:43:10.012 | INFO     | earthlens.ecmwf.backend:_api:724 - Requesting reanalysis-era5-land-monthly-means from CDS; this may take several minutes

2026-05-10 01:43:12,056 INFO Request ID is 498da82e-1b0b-4a29-a7ba-fa66c25bf64e

2026-05-10 01:43:12,131 INFO status has been updated to accepted

2026-05-10 01:43:25,760 INFO status has been updated to running

2026-05-10 01:43:33,417 INFO status has been updated to successful

2026-05-10 01:43:34 | INFO | pyramids.base.config | Logging is configured.

2026-05-10 01:43:34 | WARNING | pyramids.base.config.gdal | GDAL[1] Cannot find variable corresponding to coordinate time

2026-05-10 01:43:34.775 | INFO     | earthlens.ecmwf.backend:download:536 - Download ECMWF reanalysis-era5-land-monthly-means/snow-depth-water-equivalent data for period 2021-10-01 00:00:00 till 2022-06-01 00:00:00

2026-05-10 01:43:34.777 | INFO     | earthlens.ecmwf.backend:_api:724 - Requesting reanalysis-era5-land-monthly-means from CDS; this may take several minutes

2026-05-10 01:43:35,132 INFO Request ID is 22d14a78-db8f-4ece-84b2-8ad6f62a34b0

2026-05-10 01:43:35 | INFO | ecmwf.datastores.legacy_client | Request ID is 22d14a78-db8f-4ece-84b2-8ad6f62a34b0

2026-05-10 01:43:35,213 INFO status has been updated to accepted

2026-05-10 01:43:35 | INFO | ecmwf.datastores.legacy_client | status has been updated to accepted

2026-05-10 01:43:57,668 INFO status has been updated to running

2026-05-10 01:43:57 | INFO | ecmwf.datastores.legacy_client | status has been updated to running

2026-05-10 01:44:09,133 INFO status has been updated to successful

2026-05-10 01:44:09 | INFO | ecmwf.datastores.legacy_client | status has been updated to successful

2026-05-10 01:44:09 | INFO | multiurl.base | Downloading https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-cache-2/2026-05-09/8c97f9f672c4ca06d698fdc815ec8d22.zip

2026-05-10 01:44:09 | WARNING | pyramids.base.config.gdal | GDAL[1] Cannot find variable corresponding to coordinate time

2026-05-10 01:44:09.970 | INFO     | earthlens.ecmwf.backend:download:536 - Download ECMWF reanalysis-era5-land-monthly-means/2m-temperature data for period 2021-10-01 00:00:00 till 2022-06-01 00:00:00

2026-05-10 01:44:09.971 | INFO     | earthlens.ecmwf.backend:_api:724 - Requesting reanalysis-era5-land-monthly-means from CDS; this may take several minutes

2026-05-10 01:44:10,173 INFO Request ID is 4343faa5-b2d8-47bf-959a-68f572f45cd7

2026-05-10 01:44:10 | INFO | ecmwf.datastores.legacy_client | Request ID is 4343faa5-b2d8-47bf-959a-68f572f45cd7

2026-05-10 01:44:10,257 INFO status has been updated to accepted

2026-05-10 01:44:10 | INFO | ecmwf.datastores.legacy_client | status has been updated to accepted

2026-05-10 01:44:31,512 INFO status has been updated to running

2026-05-10 01:44:31 | INFO | ecmwf.datastores.legacy_client | status has been updated to running

2026-05-10 01:44:43,149 INFO status has been updated to successful

2026-05-10 01:44:43 | INFO | ecmwf.datastores.legacy_client | status has been updated to successful

2026-05-10 01:44:43 | INFO | multiurl.base | Downloading https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-cache-3/2026-05-09/9b0cb472ede8df0d0eb3dced7e84e543.zip

2026-05-10 01:44:44 | WARNING | pyramids.base.config.gdal | GDAL[1] Cannot find variable corresponding to coordinate time

2026-05-10 01:44:44.532 | INFO     | earthlens.ecmwf.backend:download:575 - ECMWF download summary: all 3 variables succeeded ([('reanalysis-era5-land-monthly-means', 'snow-depth'), ('reanalysis-era5-land-monthly-means', 'snow-depth-water-equivalent'), ('reanalysis-era5-land-monthly-means', '2m-temperature')])

Step 2 — assemble the seasonal time series¶

Extract the spatial mean over the box for each month.

In [2]:

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import numpy as np
import pandas as pd
from pyramids.dataset import Dataset

agg = OUT / "aggregated"

def stack(cds_variable: str) -> np.ndarray:
    paths = sorted(agg.glob(f"{cds_variable}_1MS_*.tif"))
    return np.stack([Dataset.read_file(str(p)).read_array() for p in paths])

sde  = np.nanmean(stack("snow_depth"),                    axis=(1, 2))   # m (geometric)
swe  = np.nanmean(stack("snow_depth_water_equivalent"),   axis=(1, 2))   # m of water eq
T2m  = np.nanmean(stack("2m_temperature"),                axis=(1, 2)) - 273.15  # °C

months = pd.date_range("2021-10-01", periods=len(sde), freq="MS")
df = pd.DataFrame(
    {"Snow depth [m]": sde.round(3),
     "SWE [m]":         swe.round(3),
     "Snow density":    (swe / np.where(sde > 0, sde, np.nan)).round(2),
     "T_2m [°C]":       T2m.round(1)},
    index=months,
)
df
import numpy as np
import pandas as pd
from pyramids.dataset import Dataset

agg = OUT / "aggregated"

def stack(cds_variable: str) -> np.ndarray:
    paths = sorted(agg.glob(f"{cds_variable}_1MS_*.tif"))
    return np.stack([Dataset.read_file(str(p)).read_array() for p in paths])

sde  = np.nanmean(stack("snow_depth"),                    axis=(1, 2))   # m (geometric)
swe  = np.nanmean(stack("snow_depth_water_equivalent"),   axis=(1, 2))   # m of water eq
T2m  = np.nanmean(stack("2m_temperature"),                axis=(1, 2)) - 273.15  # °C

months = pd.date_range("2021-10-01", periods=len(sde), freq="MS")
df = pd.DataFrame(
    {"Snow depth [m]": sde.round(3),
     "SWE [m]":         swe.round(3),
     "Snow density":    (swe / np.where(sde > 0, sde, np.nan)).round(2),
     "T_2m [°C]":       T2m.round(1)},
    index=months,
)
df

Out[2]:

	Snow depth [m]	SWE [m]	Snow density	T_2m [°C]
2021-10-01	2.319	0.571	0.25	-11.5
2021-11-01	2.402	0.681	0.28	-5.2
2021-12-01	2.250	0.708	0.31	-4.5
2022-01-01	2.136	0.709	0.33	-2.2
2022-02-01	1.912	0.643	0.34	2.4
2022-03-01	1.188	0.388	0.33	10.8
2022-04-01	0.936	0.279	0.30	3.1
2022-05-01	1.295	0.342	0.26	-4.7
2022-06-01	1.634	0.408	0.25	-8.3
2022-07-01	1.659	0.445	0.27	-8.5
2022-08-01	1.903	0.502	0.26	-6.9
2022-09-01	1.755	0.521	0.30	-3.0
2022-10-01	1.671	0.524	0.31	0.6
2022-11-01	1.222	0.394	0.32	7.2
2022-12-01	0.923	0.277	0.30	11.9
2023-01-01	0.929	0.278	0.30	6.3
2023-02-01	1.076	0.300	0.28	-2.1
2023-03-01	1.481	0.372	0.25	-7.6

Step 3 — plot snowpack evolution¶

SWE rises through fall as cold-season precipitation accumulates, peaks in late winter / early spring, and drops to zero through the melt season. The 0 °C 2 m temperature line is a useful reference for when the snowpack is energetically melting vs accumulating.

In [3]:

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import matplotlib.pyplot as plt

fig, ax1 = plt.subplots(figsize=(9, 5))
ax1.fill_between(months, 0, sde, alpha=0.4, color="tab:blue", label="Snow depth [m]")
ax1.plot(months, swe, marker="o", color="tab:cyan", label="SWE [m]")
ax1.set_ylabel("Snow [m]", color="tab:blue")

ax2 = ax1.twinx()
ax2.plot(months, T2m, marker="^", color="tab:red", label="T_2m [°C]")
ax2.axhline(0, color="gray", lw=0.5, ls="--")
ax2.set_ylabel("T_2m [°C]", color="tab:red")

ax1.set_title("Central Alps snowpack evolution — Oct 2021 to Jun 2022")
fig.legend(loc="upper right", bbox_to_anchor=(0.98, 0.95))
ax1.grid(alpha=0.3)
plt.tight_layout()
plt.show()
import matplotlib.pyplot as plt

fig, ax1 = plt.subplots(figsize=(9, 5))
ax1.fill_between(months, 0, sde, alpha=0.4, color="tab:blue", label="Snow depth [m]")
ax1.plot(months, swe, marker="o", color="tab:cyan", label="SWE [m]")
ax1.set_ylabel("Snow [m]", color="tab:blue")

ax2 = ax1.twinx()
ax2.plot(months, T2m, marker="^", color="tab:red", label="T_2m [°C]")
ax2.axhline(0, color="gray", lw=0.5, ls="--")
ax2.set_ylabel("T_2m [°C]", color="tab:red")

ax1.set_title("Central Alps snowpack evolution — Oct 2021 to Jun 2022")
fig.legend(loc="upper right", bbox_to_anchor=(0.98, 0.95))
ax1.grid(alpha=0.3)
plt.tight_layout()
plt.show()

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Notes¶

Snow depth vs SWE. Geometric depth (sde) and water equivalent (sd) differ by snow density (~0.1–0.5). New snow is fluffy (~0.1); compacted late-winter snowpack is denser (~0.3–0.5). The ratio swe / sde recovers the bulk density.
ERA5-Land vs single-levels. ERA5-Land at 0.1° captures Alpine topography much better than the 0.25° single-levels product. The bbox-mean still smooths over high peaks vs valleys; for site-level work, slice to a specific pixel rather than averaging.
Snowfall vs snowpack. ERA5 also reports snowfall (a flux — monthly accumulation). For the seasonal accumulation story, sum monthly snowfall; for the snowpack at a moment, use SWE.
Glacier-relevant variables. Combine SWE with ERA5 2m-temperature and surface-net-solar-radiation for a temperature-index melt model (degree-day approach).