Environmental Data Platform

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-6

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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].


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

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

CC BY 4.0

Eurac Research - Institute for Earth Observation
Viale Druso, 1 / Drususallee 1, Eurac Research, Bolzano, Autonomous Province of Bolzano, 39100, Italy

1978-12-31T12:00:00Z 2023-10-02T12:00:00Z

WGS-84 (3035:EPSG)



Imagery base maps earth cover

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

# check login ---

# 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)
                             format = "netCDF",
                             output_file = "ADO_SPEI_6_ERA5_QM.nc", 
                             con = eurac)

# 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 = ".")
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#pip install openeo
import openeo

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

# load collection - save result ----
data = eurac.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)

# 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

jobResults = job.get_results()

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