Soil Moisture Anomalies - ERA5_QM

Full metadata OpenEO repository

FAIR Overall Score

58%

Abstract

The Soil Moisture Anomaly is a drought indicator used to detect and monitor agricultural drought conditions, defined by a prolonged period of deficit in the availability of soil moisture to plants. The soil moisture anomalies provided as part of the Alpine Drought Observatory was derived from ERA5 Volumetric Soil Water Layers at different depths with Layer 1 (0-7cm), Layer 2 (7-28cm), Layer 3 (28–100cm), and Layer 4 (100-289cm). The input ERA5 soil moisture dataset was downscaled using a quantile mapping approach. Daily anomalies were calculated using a climatological mean and standard deviation of soil moisture from a smoothed time-series (running mean on a 10-day window) for a reference period of 1981–2020. The Soil Moisture Anomalies values range from -5 to +5, with negative values indicating drier than conditions while positive values indicate wetter than normal conditions, and -1 to +1 values indicates near-normal conditions. Datasets contains modified Copernicus Climate Change Service Information [1980–current year]; contains modified Copernicus Atmosphere Monitoring Service Information [1980-current year]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

Keywords

collection, Soil Moisture, Alpine Drought Observatory, Soil Moisture Anomalies, ERA5, Collection, ADO project, ADO, cct, N/A, Land use, Land cover

Citation

Greifeneder, F., & Balogun, R. (2022). Soil Moisture Anomalies - ERA5_QM (Version v2) [Data set]. Institute for Earth Observation. https://doi.org/10.48784/ea665ca2-0ceb-11ed-86c5-02000a08f4e5

Supplemental information

Additional information can be added here

Legal constraints

CC BY 4.0

Contact for metadata

Eurac Research - Institute for Earth Observation
bartolomeo.ventura@eurac.edu
Viale Druso, 1 / Drususallee 1, Eurac Research, Bolzano, Autonomous Province of Bolzano, 39100, Italy

FAIR Overall Score: 58%

Principle Score Earned Level
Findable 43% 3 of 7 initial
Accessible 57% 4 of 7 advanced
Interoperable 67% 4 of 6 moderate
Reusable 67% 4 of 6 moderate

Assessed 2026-07-22 (metrics 0.8)

Evaluated by F-UJI web service: Anusuriya Devaraju, & Robert Huber. (2020). F-UJI - An Automated FAIR Data Assessment Tool. Zenodo. https://doi.org/10.5281/zenodo.6361400

Snippet code

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install.packages("openeo")
library(openeo)

# login ----
host = "https://openeo.eurac.edu"
con = connect(host = host)
login()

# check login ---
con$isConnected()
con$isLoggedIn()
describe_account()

# load collection - save result ----
p = processes()
data = p$load_collection(id = "ADO_SM_anomalies_ERA5", 
                                             spatial_extent = list(west = 3.997852,
                                                                                 east = 17.526151,
                                                                                 south = 42.91044,
                                                                                 north = 50.404898),
                                             temporal_extent = list("1979-12-31T12:00:00Z", "2024-05-27T12:00:00Z"))
result = p$save_result(data = data, format="netCDF")

# download results ----
# either directly (suitable for smaller requests)
compute_result(result,
                             format = "netCDF",
                             output_file = "ADO_SM_anomalies_ERA5.nc", 
                             con = con)

# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
                                   title = "ADO_SM_anomalies_ERA5",
                                   description = "ADO_SM_anomalies_ERA5",
                                   format = "netCDF")
start_job(job = job_id)
result_list = list_results(job = job_id)
download_results(job = job_id, folder = ".")
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#pip install openeo
import openeo

# login ----
euracHost        = "https://openeo.eurac.edu"
conn = openeo.connect(euracHost).authenticate_oidc(client_id="openEO_PKCE")

# load collection - save result ----
data = conn.load_collection("ADO_SM_anomalies_ERA5",spatial_extent={'west':3.997852,'east':17.526151,'south':42.91044,'north':50.404898},temporal_extent=["1979-12-31T12:00:00Z", "2024-05-27T12:00:00Z"])

result = data.save_result(format="NetCDF")

# download results ----
# either directly (suitable for smaller requests, closes the connection after 2 minutes)
result.download("ADO_SM_anomalies_ERA5.nc",format="netCDF")

# or start a batch job (suitable for larger requests, e.g. when .download() timeouts)

job = result.create_job(title = "ADO_SM_anomalies_ERA5",description = "ADO_SM_anomalies_ERA5",out_format = "netCDF")
jobId = job.job_id
job.start_job()

jobResults = job.get_results()
jobResults.download_files('.')

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