AgriMetSoft


SPEI Drought Index

Drought is one of the costliest natural disasters (He et al., 2011), and it is also the most complex and the least understood natural disaster that affects humans (Wilhite, 1996). The number of severe drought events and the drought duration are likely to increase (Li et al., 2011). In particular, severe drought can have devastating effects (Shen et al., 2007). Numerous drought indices have been progressed over time and are variously performed throughout the literature (Heim, 2002; Salehnia et al., 2020). One of the popular and interest of meteorological drought is the Standardized Precipitation Evapotranspiration Index (SPEI). The SPEI drought index was developed by Vicente-Serrano et al. (2012) with the intention of defining a drought index that would be sensitive to climate change.

Like the Palmer drought severity index (PDSI), the SPEI drought index considers the effect of reference evapotranspiration on drought severity, but the multi-scalar nature of the SPEI enables identification of different drought types and drought impacts on diverse systems (Vicente-Serrano et al., 2012, 2013). Thus, the SPEI drought index has the sensitivity of the PDSI in measurement of evapotranspiration demand (caused by fluctuations and trends in climatic variables other than precipitation), is simple to calculate, and is multi-scalar, like the standardized precipitation index (SPI).

Vicente-Serrano et al. (2012) noted that the main factor influencing drought is precipitation; although other factors such as air temperature, ET, wind speed, and soil water holding capacity can also influence drought. The SPEI drought index is based on a monthly climatic water balance (precipitation minus PET), which is adjusted using a three-parameter log-logistic distribution. The values are accumulated at different time scales, following the same approach used in the SPI, and converted to standard deviations with respect to average values.

SPEI is calculated in a similar fashion, but instead sums climatic water balance, defined as the difference between precipitation and PET (Vicente-Serrano et al., 2012). Once accumulated precipitation has been transformed to probabilities, these probabilities are converted to the standard normal distribution to create the final drought index values. SPI and SPEI values were limited to the range [-3, 3] to ensure reasonableness (Salehnia et al., 2020).

For following the details of how we can calculate the equations of the SPEI drought index, please review the paper of Salehnia et al. (2020). In this paper, all the steps are clearly presented. As you know, the SPEI drought index is a meteorological drought index that it consists of the precipitation data (mm) and the potential evapotranspiration calculation data (mm/day). We followed the FAO Penman-Monteith approach to calculate the potential evapotranspiration (ETO), and the full details of this equation have been published in the FAO Irrigation and Drainage Paper No.56 (Allen et al., 1998).

Agrimetsoft has developed a comprehensive tool for computing different drought indices in three classes of drought including Meteorological drought, Agricultural drought, and hydrological drought. The DMAP (Drought Monitor and Prediction) tool can calculate different indices such as SPEI drought index.

  1. Nasrin Salehnia, Narges Salehnia, Sohrab Kolsoumi, Ahmad Saradari Torshizi. 2020. Rainfed wheat (Triticum aestivum L.) yield prediction using economical, meteorological, and drought indicators through pooled panel data and statistical downscaling. Ecological Indicators, 11:105991, 10.1016/j.ecolind.2019.105991
  2. Vicente-Serrano SM, Zouber A, Lasanta T, Pueyo Y. 2012b. Dryness is accelerating degradation of vulnerable shrublands in semiarid Mediterranean environments. Ecol. Monogr. 82: 407-428.
  3. Vicente-Serrano SM, Azorin-Molina C, Sanchez-Lorenzo A, Moran Tejeda E, Lorenzo-Lacruz J, Revuelto J, L´ opez-Moreno JI, Espejo F. 2013b. Temporal evolution of surface humidity in Spain: recent trends and possible physical mechanisms. Clim. Dyn., DOI:10.1007/s00382-013-1885-7.
  4. He B, Lu A,Wu J, Zhao L, LiuM. 2011. Drought hazard assessment and spatial characteristics analysis in China. J. Geogr. Sci. 21: 235-249.
  5. Wilhite DA. 1996. A methodology for drought preparedness. Nat. Hazards 13: 229-252.
  6. Li X, Jiang F, Li L, Wang G. 2011. Spatial and temporal variability of precipitation concentration index, concentration degree and concentration period in Xinjiang, China. Int. J. Climatol. 31: 1679-1693, Doi:10.1002/joc.2181.
  7. Heim R, Brewer M. 2012. The global drought monitor portal: the foundation for a global drought information system. Earth Int. 16:1-28, DOI:10.1175/2012EI000446.1.
  8. Shen C, Wang W-C, Hao Z, Gong W. 2007. Exceptional drought events over eastern China during the last ive centuries. Clim. Change 85: 453-471.

PDSI - Palmer Drought Severity Index

Name: Hidden