Analysis of Hydrological Drought Severity, Duration and Magnitude Using Copula Functions (Case study: Bandar-Sedij and Kol-Mehran Watershed)

Document Type : Research Article


1 Ph.D candidate, Department of Natural Resources, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar-Abbas, Iran

2 Associate Professor, Department of Natural Resources and Head of EDA Research Center, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar-Abbas, Iran

3 Assistant Professor, Department of Mathematics and Statistics and EDA Research Center, Faculty of Science, University of Hormozgan, Bandar-Abbas, Iran


The drought characteristics are often highly correlated. But, the univariate drought analysis is not a proper approach since it doesn’t involve the dependence structure of drought characteristics. Therefore, the multivariate drought analysis is used since it considers the dependence structure of drought characteristics in the model. The aim of this study is to analyze multivariate hydrological drought in the Mehran and Sedij basins. For this reason, the empirical copula was used to compute the cumulative distribution function of the run-off and the joint deficit index. Then, the drought severity, duration and magnitude were extracted and several theoretical copulas belongs to the Archimedean and Eliptical families were fitted to obtain the trivariate distribution of drought variables. Results of the JDI and SPI-12 evaluation in the study area represented that the JDI is a proper index of monitoring hydrological drought and provides a more precise estimation than the SPI-12. Further, results of joint return period indicated that the joint trivariate return period is larger than the conditional trivariate return period. So that, the joint or conditional probability with high or low return periods is important in predicting drought events. Because the under-estimation or over-estimation of drought risk have serious impact on environmental resources, soil moisture and water quality. Generally, the multi-dimensional copulas are useful approaches in evaluating the complicated and non-linear relationship of variables and constructing a comprehensive index for evaluating drought condition.


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Volume 7, Issue 1
April 2020
Pages 237-249
  • Receive Date: 06 November 2019
  • Revise Date: 09 March 2020
  • Accept Date: 09 March 2020
  • First Publish Date: 20 March 2020
  • Publish Date: 20 March 2020