Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-6

Full metadata OpenEO repository

FAIR Overall Score

58%

Abstract

The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

Keywords

collection, SPEI, standardised precipitation-evapotranspiration index,surface water balance anomalies, ERA5, ADO project, ADO, cct, N/A, Land use, Land cover

Citation

Central Institution for Meteorology and Geodynamics, & Slovenian Environment Agency. (2022). Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-6 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/16c49734-534a-11ec-be9b-02000a08f41d

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

Copy to clipboard 
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$load_collection(id = "ADO_SPEI_6_ERA5_QM", 
                                             spatial_extent = list(west = 4.056369,
                                                                                 east = 17.360183,
                                                                                 south = 42.853812,
                                                                                 north = 50.310635),
                                             temporal_extent = list("1978-12-31T12:00:00Z", "2023-10-02T12:00:00Z"))
result = p$save_result(data = data, format="netCDF")

# download results ----
# either directly (suitable for smaller requests)
compute_result(result,
                             format = "netCDF",
                             output_file = "ADO_SPEI_6_ERA5_QM.nc", 
                             con = con)

# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
                                   title = "ADO_SPEI_6_ERA5_QM",
                                   description = "ADO_SPEI_6_ERA5_QM",
                                   format = "netCDF")
start_job(job = job_id)
result_list = list_results(job = job_id)
download_results(job = job_id, folder = ".")
Copy to clipboard 
#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_SPEI_6_ERA5_QM",spatial_extent={'west':4.056369,'east':17.360183,'south':42.853812,'north':50.310635},temporal_extent=["1978-12-31T12:00:00Z", "2023-10-02T12:00:00Z"])

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

# download results ----
# either directly (suitable for smaller requests, closes the connection after 2 minutes)
result.download("ADO_SPEI_6_ERA5_QM.nc",format="netCDF")

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

job = result.create_job(title = "ADO_SPEI_6_ERA5_QM",description = "ADO_SPEI_6_ERA5_QM",out_format = "netCDF")
jobId = job.job_id
job.start_job()

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

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