2018
DOI: 10.1007/978-981-13-0574-0_5
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Drought Analysis Using Copulas

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Cited by 31 publications
(25 citation statements)
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“…e most important parameters in climate change studies are temperature and precipitation, which also play a crucial role in meteorological and hydrological phenomena such as droughts and floods. Furthermore, they are considered the most effective climate variables impacting agricultural productivity [16][17][18] so that the temperature affects the length of the growing season and the precipitation affects the yield [19,20]. Many studies have been carried out on the impacts of precipitation and temperature on agricultural products [21][22][23][24].…”
Section: Introductionmentioning
confidence: 99%
“…e most important parameters in climate change studies are temperature and precipitation, which also play a crucial role in meteorological and hydrological phenomena such as droughts and floods. Furthermore, they are considered the most effective climate variables impacting agricultural productivity [16][17][18] so that the temperature affects the length of the growing season and the precipitation affects the yield [19,20]. Many studies have been carried out on the impacts of precipitation and temperature on agricultural products [21][22][23][24].…”
Section: Introductionmentioning
confidence: 99%
“…The wet characteristics include wet duration and wet severity. The wet duration ( ) is defined as the number of consecutive months of wet condition, while wet severity ( ) is defined as a cumulative value of SPI within the wet duration [6].…”
Section: Definition Of the Wet Characteristicsmentioning
confidence: 99%
“…For drought frequency analysis, Shiau and Moderras [1] employed Clayton copula to derive the joint distribution function of drought duration and drought severity in Iran. Meanwhile, Chen et al [6] applied the Archimedean and metaelliptical copulas to model the joint distributions of drought duration, interval time, drought severity, and minimum SPI value.…”
Section: Introductionmentioning
confidence: 99%
“…Over the years, it is widely used to extract correlation within high-dimensional random variables, and achieves great success in many subjects such as risk management (Kole et al, 2007;McNeil et al, 2005), finance (Wang and Hua, 2014), civil engineering (Chen et al, 2012;Zhang and Singh, 2006), and visual description generation (Wang and Wen, 2015). In the past, copula is often estimated by Maximum Likelihood method (Choroś et al, 2010;Jaworski et al, 2010) via parametric or semi-parametric approaches (Tsukahara, 2005;Choroś et al, 2010).…”
Section: Related Workmentioning
confidence: 99%