Assessment of parametric approaches to calculate the Evaporative Demand Drought Index

• Published in: International journal of climatology Vol. 42; no. 2; pp. 834 - 849
• Main Authors: Noguera, Iván, Vicente‐Serrano, Sergio M., Domínguez‐Castro, Fernando, Reig, Fergus
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01-02-2022; Wiley Subscription Services, Inc
• Subjects: atmospheric evaporative demand; Climatic conditions; Demand; Distribution; Drought; Drought analysis; Drought index; EDDI; Evaporation; Log‐logistic distribution; Mathematical analysis; parametric approach; Parametric statistics; Probability theory; reference evapotranspiration; Robustness
• ISSN: 0899-8418; 1097-0088
• DOI: 10.1002/joc.7275

The Evaporative Demand Drought Index (EDDI), based on atmospheric evaporative demand, was proposed by Hobbins et al. (2016) to analyse and monitor drought. The EDDI uses a nonparametric approach in which empirically derived probabilities are converted to standardized values. This study evaluates the suitability of eight probability distributions to compute the EDDI at 1‐, 3‐ and 12‐month time scales, in order to provide more robust calculations. The results showed that the Log‐logistic distribution is the best option for generating standardized values over very different climate conditions. Likewise, we contrasted this new parametric methodology to compute EDDI with the original nonparametric formulation. Our findings demonstrate the advantages of adopting a robust parametric approach based on the Log‐logistic distribution for drought analysis, as opposed to the original nonparametric approach. The method proposed in this study enables effective implementation of EDDI in the characterization and monitoring of droughts.
This study evaluates the suitability of eight probability distributions to compute the Evaporative Demand Drought Index (EDDI) at 1‐, 3‐ and 12‐month time scales. The results showed that the Log‐logistic distribution is the best option for generating standardized values over very different climate conditions. For this reason, we recommended a robust parametric methodology to compute the EDDI based on the Log‐logistic distribution as opposed to the original nonparametric approach. The method proposed in this study enables effective implementation of EDDI in the analysis and monitoring of droughts.