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Assessment of parametric approaches to calculate the Evaporative Demand Drought Index
Author(s) -
Noguera Iván,
VicenteSerrano Sergio M.,
DomínguezCastro Fernando,
Reig Fergus
Publication year - 2022
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.7275
Subject(s) - nonparametric statistics , parametric statistics , index (typography) , probability distribution , environmental science , econometrics , distribution (mathematics) , statistics , mathematics , climatology , computer science , geology , mathematical analysis , world wide web
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.