Assessment of parametric approaches to calculate the Evaporative Demand Drought Index
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...
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| Published in: | International journal of climatology Vol. 42; no. 2; pp. 834 - 849 |
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Chichester, UK
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01-02-2022
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Author | Reig, Fergus Vicente‐Serrano, Sergio M. Domínguez‐Castro, Fernando Noguera, Iván |
| Author_xml | – sequence: 1 givenname: Iván orcidid: 0000-0002-0696-9504 surname: Noguera fullname: Noguera, Iván email: i.noguera@ipe.csic.es organization: Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE–CSIC) – sequence: 2 givenname: Sergio M. orcidid: 0000-0003-2892-518X surname: Vicente‐Serrano fullname: Vicente‐Serrano, Sergio M. organization: Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE–CSIC) – sequence: 3 givenname: Fernando orcidid: 0000-0003-3085-7040 surname: Domínguez‐Castro fullname: Domínguez‐Castro, Fernando organization: ARAID Foundation – sequence: 4 givenname: Fergus orcidid: 0000-0002-9482-1170 surname: Reig fullname: Reig, Fergus organization: Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE–CSIC) |
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| SubjectTerms | 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 |
| Title | Assessment of parametric approaches to calculate the Evaporative Demand Drought Index |
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