Standardized Precipitation Index (SPI)

Standardized Precipitation Index (SPI) calculated from CHIRPS. Last update: May 2020

This app is part of Indicators of the Planet.  Please see https://livingatlas.arcgis.com/indicators

Droughts are natural occurring events in which dry conditions persist over time. Droughts are complex to characterize because they depend on water and energy balances at different temporal and spatial scales. The Standardized Precipitation Index (SPI) is used to analyze meteorological droughts. SPI estimates the deviation of precipitation from the long-term probability function at different temporal periods (e.g. 1, 3, 6, 9, or 12 months). SPI only uses monthly precipitation as an input, which can be helpful for characterizing meteorological droughts. Other variables should be included (e.g. temperature or evapotranspiration) in the characterization of other types of droughts (e.g. agricultural droughts).
This layer shows the SPI index at different temporal periods calculated using the SPEI library in R and precipitation data from CHIRPS data set.
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Additional Info

Field Value
Last Updated March 24, 2026, 21:07 (UTC)
Created March 24, 2026, 21:07 (UTC)
Domain / Topic
Domain or topic of the dataset being cataloged.
Title
Title for the Dataset.
Standardized Precipitation Index (SPI)
Description
A description of the dataset.

Standardized Precipitation Index (SPI) calculated from CHIRPS. Last update: May 2020

This app is part of Indicators of the Planet.  Please see https://livingatlas.arcgis.com/indicators

Droughts are natural occurring events in which dry conditions persist over time. Droughts are complex to characterize because they depend on water and energy balances at different temporal and spatial scales. The Standardized Precipitation Index (SPI) is used to analyze meteorological droughts. SPI estimates the deviation of precipitation from the long-term probability function at different temporal periods (e.g. 1, 3, 6, 9, or 12 months). SPI only uses monthly precipitation as an input, which can be helpful for characterizing meteorological droughts. Other variables should be included (e.g. temperature or evapotranspiration) in the characterization of other types of droughts (e.g. agricultural droughts).
This layer shows the SPI index at different temporal periods calculated using the SPEI library in R and precipitation data from CHIRPS data set.
Sources:
Tags / Keywords
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Format (CSV, XLS, TXT, PDF, etc)
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Dataset Size
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2.3
Metadata Identifier
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Published Date
Published date of the dataset.
2020-07-10
Time Period Data Span (start date)
Start date of the data in the dataset.
Time Period Data Span (end date)
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GeoSpatial Area Data Span
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Field Value
Access category
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public
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This work is licensed under the Esri Master License Agreement. View Summary | View Terms of Use
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Location
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https://climate.esri.ca/datasets/arcgis-content::standardized-precipitation-index-spi-1
Data Service
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Owner
Owner of the dataset.
EMeriam
Contact Point
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ArcGIS Living Atlas Team
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Publisher
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ArcGIS Living Atlas Team
Publisher Email
Email of the publisher.
Author
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EMeriam
Author Email
Email of the author.
Accessed At
Date the data and metadata was accessed.
2020-07-14
Field Value
Identifier
Unique identifier for the dataset.
7761ea3cbdc94d68a610d9765efba1aa
Language
Language(s) of the dataset
English
Link to dataset description
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https://climate.esri.ca/datasets/arcgis-content::standardized-precipitation-index-spi-1
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Contains Indigenous Data
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None
Source
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None
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Provenance Text
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ArcGIS Living Atlas Team
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Temporal resolution
Describes how granular the date/time data in the dataset is.
GeoSpatial resolution in meters
Describes how granular (in meters) geospatial data is in the dataset.
GeoSpatial resolution (in regions)
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Field Value
Indigenous Community Permission
Who holds the Indigenous Community Permission. Who to contact regarding access to a dataset that has data about Indigenous communities.
Community Permission
Community permission (who gave permission).
The Indigenous communities the dataset is about
Indigenous communities from which data is derived.
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Data quality
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Metric for data quality
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