Abstract
Drought is one of the most drastic events, which has imposed irreparable damages on human societies and may occur in any climate regime. To define drought, given its properties of multidimensionality and randomity, one cannot rely on a single variable/index (e.g., precipitation, soil moisture, and runoff). Accordingly, implementing a novel approach, this study investigated drought events in two basins with different climatic regimes, using multivariate frequency analyses of drought duration, severity, and severity peak, based on developing a Two-variate Standardized Index (TSI). The index was developed based on the concept of copula, by applying rainfall-runoff data (1974–2019) and comparing them with two popular drought indices, the Standardized Precipitation Index (SPI) and Standardized Stream Flow Index (SSFI), in terms of derived drought characteristics. The results show that TSI determined more severe drought conditions with fewer return periods than SPI and SSFI in a specific drought event. This implies that the disadvantages of SPI and SSFI might not be found in TSI. The developed index can be employed by policymakers and planners to protect water resources from drought.
Highlights
According to the World Disaster Report, overall damages originated by drought were over79.3 billion dollars from 2008 to 2017, worldwide
They indicated that the new index, the Copula-based Drought Index (COPDI), has an attractive property that describes the drought development based on the state of both rainfall and potential evapotranspiration (PET), so that any temporal change in one variable does not influence the COPDI directly
Multivariate frequency analyses were performed based on the extracted drought characteristics of the developed index, Standardized Precipitation Index (SPI), and the Standardized
Summary
According to the World Disaster Report, overall damages originated by drought were over79.3 billion dollars from 2008 to 2017, worldwide. In terms of affecting areas and people, drought was in second place among natural disasters [1]. There are different types of drought, such as meteorological, agricultural, hydrological, and socioeconomic. A drought definition based on a single variable may not be sufficient and is not appropriate for reliable risk assessment and decision making [5,6]. There are uncertainties associated with random hydrological phenomena [7], such as floods and drought, which are multivariate functions. To model these phenomena, descriptions and assessments in the multivariate forms are necessary. Copulas are mathematical functions by which joint probability distributions can be extracted, based on the
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