Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-3

FAIR Overall Score

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

SPEI, STANDARDISED PRECIPITATION-EVAPOTRANSPIRATION INDEX,SURFACE WATER BALANCE ANOMALIES, ERA5, ADO PROJECT, ADO

Legal constraints

CC-BY-4.0

FAIR Overall Score: 77%

Principle	Score	Earned	Level
Findable	57%	4 of 7	moderate
Accessible	100%	7 of 7	advanced
Interoperable	67%	4 of 6	moderate
Reusable	83%	5 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

##### ----Explore and download STAC data with Python ----- #####

from pystac_client import Client

## Read the example catalog
URL = 'https://stac.eurac.edu/'
catalog = Client.open(URL)

## List the Collections in the given Catalog
stac_collections = list(catalog.get_collections())
print(f"Number of collections: {len(stac_collections)}")

## Print collection IDs
print("Collections IDs:")
for collection in stac_collections:
    print(f"- {collection.id}")
print("-------------------------")

## Retrieve a specific collection
collection = catalog.get_collection("ID_OfTheCollection")

## Search for items in the collection
collection_items = list(catalog.search(collections=['ID_OfTheCollection'], max_items=10).items())
print(collection_items)

## Retrieve a list of the first 10 items belonging to a specific collection
item = collection.get_item("NameOfTheItem")
#print(list(item.assets.items())[0:10])

##Print the band name and the href of a specific item retrieved from the previous list
print(item.assets["ID_OfTheCollection"].title)
print(item.assets["ID_OfTheCollection"].href)

## Print Item’s assets through the assets attribute, which is a dictionary
for asset_key in item.assets:
    asset = item.assets[asset_key]
    print("{}: {} ({})".format(asset_key, asset.href, asset.media_type))

## ------------- DONWLOAD COG files from a specific collection ------- ####
from pystac_client import Client
import requests

## Read the example catalog
URL = 'https://stac.eurac.edu/'
catalog = Client.open(URL)

## List the Collections in the given Catalog
stac_collections = list(catalog.get_collections())
# print(f"Number of collections: {len(stac_collections)}")

## Print collection IDs
print("Collections IDs:")
for collection in stac_collections:
	print(f"- {collection.id}")
print("-------------------------")

## Retrieve a specific collection
collection = catalog.get_collection("ID_OfTheCollection")

## Search for items in the collection
collection_items = list(catalog.search(collections=['ID_OfTheCollection'], max_items=10).items())
print(collection_items)

url_basepath = "https://eurac-eo.s3-eu-west-1.amazonaws.com/"
collection_url = url_basepath + "ID_OfTheCollection" + "/"
print(collection_url)
for item_id in collection_items:
	# item = item.assets[asset_key]
	print(item_id.id)
	cog_file = str(item_id.id + '.tif')
	url = collection_url + cog_file
	response = requests.get(url)
	with open(cog_file, "wb") as f:
		f.write(response.content)