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Published in Vitali Díaz Mercado, Spatio-Temporal Characterisation of Drought: Data Analytics, Modelling, Tracking, Impact and Prediction, 2022
Another drought indicator found in this group is the Standardized Precipitation Evaporation Index (SPEI). The process for calculating the SPEI (Vicente-Serrano et al., 2010) is similar to the one used to compute the SPI but considering precipitation (P) minus potential evaporation (E) instead of only P. Because the difference in P-E can be negative, gamma distribution is not recommended for calculating the SPEI. Log-normal, generalised logistic or generalised extreme value distribution is preferable in this case. Several studies have tested the SPEI's suitability for agricultural and hydrological drought monitoring; its inclusion of evapotranspiration, for example, gives the SPEI a higher correlation with agricultural and hydrological drought indices (Bachmair et al., 2015, 2016; Diaz-Mercado et al., 2016; Li et al., 2015; Naumann et al., 2014; Maskey and Trambauer, 2014; Vicente-Serrano et al., 2012).
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Published in Yared Ashenafi Bayissa, Developing an Impact-Based Combined Drought Index for Monitoring Crop Yield Anomalies in the Upper Blue Nile Basin, Ethiopia, 2018
Standardized Precipitation Evaporation Index (SPEI): The SPEI has an advantage over SPI because it incorporates the effect of potential evaporation in addition to rainfall (Abramowitz and Stegun, 1966; Trambauer et al. 2014; Vicente-Serrano et al. 2010). The calculation procedure of the SPEI is similar to that of the SPI, except that the SPEI accounts for the difference between precipitation and potential evaporation. Like the SPI, the SPEI is calculated at different time scales (e.g. 1-, 2-, and 3-month), and in this study, a log-logistic distribution is applied as it fits observations in the majority of rainfall stations (Bayissa et al. 2015). Negative SPEI values indicate dry conditions due to less precipitation and/or higher potential evaporation (dry conditions) compared to the historical mean.
Evapotranspiration, Evaporative Demand, and Drought
Published in Donald A. Wilhite, Roger S. Pulwarty, Drought and Water Crises, 2017
Mike Hobbins, Daniel McEvoy, Christopher Hain
McKee et al. (1993) were the first to recognize the value of examining drought at numerous timescales, implementing this concept in the now-popular Standardized Precipitation Index (SPI). Development of the SPI was a major breakthrough in the drought-monitoring community and allowed users to see that a drought could be occurring in the short term (e.g., 1–3 month Prcp deficits) even while long-term conditions were wet (e.g., a 24–48 month Prcp surplus). The primary limitation of SPI is that it only considers Prcp and ignores other atmospheric drivers of drought. To improve upon SPI, the Standardized Precipitation Evapotranspiration Index (SPEI) (Vicente-Serrano et al. 2010) was developed with the original goal of having a multiscalar drought index that could account for a warming climate. To accomplish this, SPEI uses a simple water balance (Prcp – E0) as the accumulating variable. The T-based Thornthwaite (1948)E0 approach was initially used but, as with PDSI, caution must be taken when using any T-based E0. Beguería et al. (2014) tested several different E0 approaches in computing global SPEI, and recommended the fully physical Penman–Monteith model if data are available.
Estimation of total water storage changes in India
Published in International Journal of Digital Earth, 2021
Arun Mondal, Venkataraman Lakshmi
After a standard normal distribution process, the SPEI can be assessed by When P ≤ 0.5, P = 1−F(x); When P > 0.5, P = 1−P, and the symbol of the SPEI is reversed. The constants are C0 = 2.515517, C1 = 0.802853, C2 = 0.010328, d1 = 1.432788, d2 = 0.189269, and d3 = 0.001308 (Vicente-Serrano, Beguería, and López-Moreno 2010; Wang et al. 2015). We used the SPEI time scales of 24, 48, and 60 months, which are referred to as SPEI-24, SPEI-48, and SPEI-60 in this paper. Droughts are classified as exceptional (SPI/SPEI less than−2.0), extreme (SPI/SPEI from −1.6 to −1.9), severe (SPI/SPEI between −1.3 and −1.5), and moderate (SPI/SPEI between −0.5 and −1.2).
Drought monitoring in Ceyhan Basin, Turkey
Published in Journal of Applied Water Engineering and Research, 2021
Palmer Drought Severity Index (PDSI) is the first used index quantifying drought impacts under various climates (Palmer 1965). However, it has some limitations, calibrations and spatial comparability issues (Dai 2013; Mo and Chelliah 2006; Vicente-Serrano et al. 2009). The Standardized Precipitation Index (SPI) is widely applied under different climate regions for describing and comparing drought events (McKee et al. 1993). However, several studies have noted that the rise of global temperature leads to increase in water demand and evapotranspiration (Heim, 2017; Koch and Vögele 2009; Kirshen et al. 2008; García-Ruiz et al. 2011). Hence, the Standardized Precipitation Evaporation Index (SPEI) is developed considering both precipitation and potential evapotranspiration (PET) (Vicente-Serrano et al. 2010). PET is calculated by applying the classic Thornthwaite (TH) method (Ahmadi and Fooladmand, 2008; Thornthwaite, 1948), which is vulnerable in arid and semiarid regions (Tabari et al., 2013). The Penman–Monteith (PM) method is another method that needs more parameters including wind speed, relative humidity and solar radiation (Trenberth et al., 2014).
Assessment of the ability of the standardized precipitation evapotranspiration index (SPEI) to model historical streamflow in watersheds of Western Canada
Published in Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 2021
Sunil Gurrapu, Kyle R. Hodder, David J. Sauchyn, Jeannine Marie St. Jacques
It was assumed that the climate of each watershed is captured by the data of the NRCAN grid nodes that lie within the watershed. The SPEI package (Beguería and Vicente-Serrano 2017) in the R statistical language (R Core Team 2018) and the NRCAN data were used to compute the SPEI series for each grid node at various time scales: 1, 3, 6, 9, 12 and 24 months (ie SPEI1, SPEI3, SPEI6, SPEI9, SPEI12 and SPEI24). The time scale determines the months of climate data used to compute the SPEI. For example, the 12-month SPEI for December (SPEI12, Dec) includes monthly precipitation and mean temperature from the preceding January through December (inclusive). The SPEI is computed based on the non-exceedance probability of precipitation (P) and potential evapotranspiration (PET) differences (P-PET). The computation involves fitting the P-PET series with a suitable probability distribution and the fitted series are then transformed into the standardized values that define the SPEI (Vicente-Serrano, Beguería, and López-Moreno 2010).