Drought
Drought data in Python: US Drought Monitor county statistics, the Standardized Precipitation Index from rain gauges, drought events, flash droughts, streamflow and the people affected.
Drought is measured two ways in the United States. The US Drought Monitor publishes a weekly map that its authors draw from dozens of indicators, and a county table behind it: 4,218,913 county-weeks from 2000 to 2025, each giving the share of the county in categories D0 to D4. The Standardized Precipitation Index (SPI) is a calculation anyone can run on a station's rainfall, expressing how unusual the last few months have been. They agree in direction and differ in timing, and each answers questions the other cannot.
The guides here read the Drought Monitor tables, compute SPI from GHCN-Daily records, turn an index into drought events, find flash droughts, relate rainfall deficits to river flow and count the people living in drought. Three pages deal with the traps: percentages that add up to more than 100, SPI in climates with rainless months, and map dates that point a week the wrong way.
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- How to Make an Animated Drought Map in Python
- How to Analyse US Drought Monitor County Statistics in Python
- How to Calculate the Standardized Precipitation Index (SPI) in Python
- How to Identify Drought Events, Their Duration and Severity, from an Index
- How to Detect Flash Droughts in Drought Monitor Data
- How to Relate Drought to River Flow: Precipitation Deficits and Low Flows
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