The Standardized Precipitation Evapotranspiration Index (SPEI)

Dataset date

Citation Information:

  • Title: The Standardized Precipitation Evapotranspiration Index (SPEI)
  • Edition: Version 2.5
  • Publication Date: 2017
  • Data Form: raster
  • Publisher: Spanish National Research Council
  • Online Host: http://digital.csic.es/handle/10261/153475
  • DANTE Citekey: Vicente-Serrano2009

Dataset Contact Information:

Sergio M. Vicente-Serrano, Instituto Pirenaico de Ecología, Zaragoza, Spain 
Santiago Beguería, Estación Experimental de Aula Dei, Zaragoza, Spain 
Juan I. López-Moreno, Instituto Pirenaico de Ecología, Zaragoza, Spain 

Dataset Strengths:

  • Standardized anomolies allow for temporal and spatial climatic comparisons.
  • Historic data to 1901.
  • R package available to generate a user dataset.

Dataset Weaknesses:

  • Coarse resoltuion.
  • Data quality subject to model input quality and goodness of distribution fit.

Abstract:

Drought is a major cause of agricultural, economic and environmental damage. Drought effects are apparent after a long period with a shortage of precipitation, making it very difficult to determine their onset, extent and end. Thus, it is hard to objectively quantify the characteristics of drought episodes in terms of their intensity, magnitude, duration and spatial extent. Much effort has been devoted to developing techniques for drought analysis and monitoring. Among these, the definition of quantitative indices is the most widespread approach, but subjectivity in the definition of drought has made it very difficult to establish a unique and universal drought index. Most studies related to drought analysis and monitoring systems have been conducted using either i) the Palmer Drought Severity Index (PDSI), based on a soil water balance equation, or ii) the Standardised Precipitation Index (SPI), based on a precipitation probabilistic approach.

Use Constraints:

Open Databse License (ODbL) v1.0

Discussion:

The standardized precipitation evapotranspiration index (SPEI) is a recent entry into the vast landscape of drought indices. Introduced in 2010 by Vicente-Serrano, SPEI sought to improve upon established drought indices such as the Palmer drought severity index (PDSI) and the standardized precipitation index (SPI) by introducing a multi-scalar model utilizing reference evapotranspiration (Vicente-Serrano, Beguería, and López-Moreno 2009). This framework permits a wide array of applications and comparisons across wide spatial and temporal extents. For more detailed comparisons of SPEI with additional drought indices or using alternative model parameterizations see Tirivarombo, Osupile, and Eliasson (2018), Potopová et al. (2015), Beguería et al. (2014), and Hayes et al. (1999).

Although anomoly based drought indices provide a platform to analyze drought severity across large temporal ranges and a variety of biomes, they are not without their drawbacks. Foremost, drought indices are dependent on the quality of data used for their inputs. At the minimum, this includes precipitation data, however, SPEI also requires temperature data to calculate the evaporative demand. When employed across large spatial extents precipitation and temperature data may be irregurly distributed, recorded with varying levels of precision and accuracy, or be subject to additional underlying biases. SPEI is also sensitive to the method for calculating evapotranspiration. For a more thorough discussion of SPEI outputs as a function of actual or reference evapotranspiration see Beguería et al. (2014). Lastly, drought indices require long base periods of reference data in order to accurately assess the severity of the drought and accurately fit the theoretical distribution of anomolies. Ideally, models should be calculated using at least 50 years of data.

Despite its relative immaturity in comparison to long established indices like SPI (1993) and PDSI (1965), SPEI has been widely adopted in the academic and practitioner communities. SPEI has recently been featured in examinations of the impacts of drought on cereal crops in China (Chen et al. 2016), agricultural drought risks in the Czech Republic (Potopová et al. 2015), grassland and livestock management (Starks et al. 2019), and characterizing future drought under increasing climactic stress in India (Bisht et al. 2018). SPEI routinely performs well in hindcast comparisons with other popular drought indices, however, the development team has responded to user feedback and peer reviewed critiques by further adjusting distribution parameter fitting, performing a thorough examination of evapotranspiration methods, introducing an R package for custom user dataset creation (Beguería and Vicente-Serrano 2017), and introduced a real-time monitoring system (Beguería et al. 2014).

Screenshot or Representative Figure:

Figure 1: Standardized precipitation evapotranspiration index for 2015 with a 4 year integration period.

Figure 1: Standardized precipitation evapotranspiration index for 2015 with a 4 year integration period.

Reference

Beguería, Santiago, and Sergio M. Vicente-Serrano. 2017. “SPEI: Calculation of the Standardised Precipitation-Evapotranspiration Index.”

Beguería, Santiago, Sergio M. Vicente-Serrano, Fergus Reig, and Borja Latorre. 2014. “Standardized Precipitation Evapotranspiration Index (SPEI) Revisited: Parameter Fitting, Evapotranspiration Models, Tools, Datasets and Drought Monitoring.” International Journal of Climatology 34 (10): 3001–23. https://doi.org/10.1002/joc.3887.

Bisht, D. S., V. R. Sridhar, A. Mishra, C. Chatterjee, and N. S. Raghuwanshi. 2018. “Characterization of Future Drought Conditions in India Using Standardized Precipitation Evapotranspiration Index.” AGU Fall Meeting Abstracts 51 (December).

Chen, Taotao, Guimin Xia, Tiegang Liu, Wei Chen, and Daocai Chi. 2016. “Assessment of Drought Impact on Main Cereal Crops Using a Standardized Precipitation Evapotranspiration Index in Liaoning Province, China.” Sustainability 8 (10): 1069. https://doi.org/10.3390/su8101069.

Hayes, Michael J., Mark. D. Svoboda, Donald A. Wiihite, and Olga V. Vanyarkho. 1999. “Monitoring the 1996 Drought Using the Standardized Precipitation Index.” Bulletin of the American Meteorological Society 80 (3): 429–38. https://doi.org/10.1175/1520-0477(1999)080<0429:MTDUTS>2.0.CO;2.

Potopová, Vera, Petr Štěpánek, Martin Možný, Luboš Türkott, and Josef Soukup. 2015. “Performance of the Standardised Precipitation Evapotranspiration Index at Various Lags for Agricultural Drought Risk Assessment in the Czech Republic.” Agricultural and Forest Meteorology 202 (March): 26–38. https://doi.org/10.1016/j.agrformet.2014.11.022.

Starks, Patrick J., Jean L. Steiner, James P. S. Neel, Kenneth E. Turner, Brian K. Northup, Prasanna H. Gowda, and Michael A. Brown. 2019. “Assessment of the Standardized Precipitation and Evaporation Index (SPEI) as a Potential Management Tool for Grasslands.” Agronomy 9 (5): 235. https://doi.org/10.3390/agronomy9050235.

Tirivarombo, S., D. Osupile, and P. Eliasson. 2018. “Drought Monitoring and Analysis: Standardised Precipitation Evapotranspiration Index (SPEI) and Standardised Precipitation Index (SPI).” Physics and Chemistry of the Earth, Parts A/B/C 106 (August): 1–10. https://doi.org/10.1016/j.pce.2018.07.001.

Vicente-Serrano, Sergio M., Santiago Beguería, and Juan I. López-Moreno. 2009. “A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index.” Journal of Climate 23 (7): 1696–1718. https://doi.org/10.1175/2009JCLI2909.1.

Additional Metadata

Metadata Submission Contact:

Joshua BrinksISciences, L.L.C. 

Spatial Information:

Bounding Coordinates:

  • West Bounding Coordinate: -179.75
  • East Bounding Coordinate: 179.75
  • North Bounding Coordinate: 89.75
  • South Bounding Coordinate: -89.75

Spatial Reference Information:

  • Coordinate System: Longitude / Latitude
  • Resolution: 0.5
  • Units: decimal degrees
  • Geodetic Model: WGS84

Time Period Information:

  • Beginning Date: 1901
  • Ending Date: 2015
  • Resolution: monthly

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