Standardized Precipitation Index (SPI)

"The Standardized Precipitation Index (SPI) is a widely used index to characterize meteorological drought on a range of timescales. On short timescales, the SPI is closely related to soil moisture, while at longer timescales, the SPI can be related to groundwater and reservoir storage. The SPI can be compared across regions with markedly different climates. It quantifies observed precipitation as a standardized departure from a selected probability distribution function that models the raw precipitation data. [...]

SPI = (P - P*) /  σp

where P = precipitation

p* = mean precipitation

σp = standard deviation of precipitation" (Keyantash, John & National Center for Atmospheric Research Staff, 2018)

Sources

"The Climate Data Guide: Standardized Precipitation Index (SPI)". Keyantash, John & National Center for Atmospheric Research Staff (Eds). Last modified 07 Aug 2018.   https://climatedataguide.ucar.edu/climate-data/standardized-precipitati….
Accessed Mar 1, 2019.

Related Content

Article

Real-time drought monitoring from Climate Hazards group Infrared Precipitation with Stations (CHIRPS)

Different parts of world are experiencing extreme hydrological hazards such as droughts, flooding and other related events. Droughts are associated with absence of rainfall occurrence over an extended period. According to the United Nations (2022), the frequency and intensity of drought events in the last two decades has increased by 29%. These figures are expected to increase further in the coming years due to climate change (Gunathilake et al., 2020). 

Space-based Solution

Collaborating actors (stakeholders, professionals, young professionals or Indigenous voices)
Suggested solution

Why is the approach suggested

  • Not as time-consuming as a borehole drilling

  • Cheaper 

  • January is a very dry month in Kenya: time to build reserves 

  • In July, it should be ready for the harvesting

Requirements

  • Standardized Precipitation Index (SPI)
  • Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)
  • Normalized Difference Vegetation Index (NDVI)

Outline of the Solution

  1. Map available rainfall with CHIRPS with Kenya Space Agency data 
  2. Determine if enough water can be stored during the rainy season to last the dry season
  3. Combine with meteorological data (SPI values)
  4. NDVI to get stationality of the water cycle
Decadal Precipitation in Kenya
Figure 1: Decadal Precipitation in Kenya
Decadal rainfall data
Figure 2: Dekadal rainfall in mm from July 2020 to July 2023
NDVI in the Samburu County
Figure 3: NDVI in the Samburu County

 

Relevant publications
Related space-based solutions
Keywords (for the solution)
Climate Zone (addressed by the solution)
Dry
Habitat (addressed by the solution)
Region/Country (the solution was designed for, if any)
Relevant SDGs