Standardised Snow Pack Index - ERA5_QM SSPI-30

FAIR Overall Score

Abstract

The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

Keywords

SSPI, SSPI_30D, STANDARDISED SNOW PACK INDEX, ERA5, SNOWGRID, 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)