Drought Analysis Using Copulas

Chen, Lu; Singh, Vijay P.; Mishra, Ashok Kumar; Guo, Jing

doi:10.1007/978-981-13-0574-0_5

Cited by 30 publications

(25 citation statements)

References 31 publications

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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%

Joint Modeling of Precipitation and Temperature Using Copula Theory for Current and Future Prediction under Climate Change Scenarios in Arid Lands (Case Study, Kerman Province, Iran)

Mesbahzadeh

Miglietta

Mirakbari

et al. 2019

Advances in Meteorology

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Precipitation and temperature are very important climatic parameters as their changes may affect life conditions. Therefore, predicting temporal trends of precipitation and temperature is very useful for societal and urban planning. In this research, in order to study the future trends in precipitation and temperature, we have applied scenarios of the fifth assessment report of IPCC. The results suggest that both parameters will be increasing in the studied area (Iran) in future. Since there is interdependence between these two climatic parameters, the independent analysis of the two fields will generate errors in the interpretation of model simulations. Therefore, in this study, copula theory was used for joint modeling of precipitation and temperature under climate change scenarios. By the joint distribution, we can find the structure of interdependence of precipitation and temperature in current and future under climate change conditions, which can assist in the risk assessment of extreme hydrological and meteorological events. Based on the results of goodness of fit test, the Frank copula function was selected for modeling of recorded and constructed data under RCP2.6 scenario and the Gaussian copula function was used for joint modeling of the constructed data under the RCP4.5 and RCP8.5 scenarios.

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Section: Introductionmentioning

confidence: 99%

Joint Modeling of Precipitation and Temperature Using Copula Theory for Current and Future Prediction under Climate Change Scenarios in Arid Lands (Case Study, Kerman Province, Iran)

Mesbahzadeh

Miglietta

Mirakbari

et al. 2019

Advances in Meteorology

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“…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%

Joint Modeling of Wet Condition Characteristics of Makassar City

Sanusi¹,

Sidjara²,

Sudarmin³

et al. 2020

Proceedings of the Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 20

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A multivariate analysis of rainfall variables is needed to understand relationships among characteristics of rainfall variables. The monthly rainfall amount of records from three stations during the period of 1985-2014 in Makassar is used. The Standardized Precipitation Index (SPI) is employed to classify data into the wet category. The study aims are to investigate the most appropriate joint distribution function of wet duration and wet severity based on bivariate Archimedean copulas. The copulas are Gumbel-Hougaard, Frank, Joe, and Clayton. Parameter of copulas is estimated by Kendall's tau correlation coefficient. The study result shows that the Frank copula was identified as the best copula in joint modeling between wet duration and wet severity at Meteorologi Maritim Paotere (MMP) and Biring Romang Panakkukang (BRP) stations, meanwhile the Gumbel-Hougaard copula at Balai Besar Meteorologi, Klimatologi, and Geofisika (BBMKG) station. This result could be useful information for determining return periods of wet condition characteristics.

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“…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%

Neural Gaussian Copula for Variational Autoencoder

Wang

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Variational language models seek to estimate the posterior of latent variables with an approximated variational posterior. The model often assumes the variational posterior to be factorized even when the true posterior is not. The learned variational posterior under this assumption does not capture the dependency relationships over latent variables. We argue that this would cause a typical training problem called posterior collapse observed in all other variational language models. We propose Gaussian Copula Variational Autoencoder (VAE) to avert this problem. Copula is widely used to model correlation and dependencies of high-dimensional random variables, and therefore it is helpful to maintain the dependency relationships that are lost in VAE. The empirical results show that by modeling the correlation of latent variables explicitly using a neural parametric copula, we can avert this training difficulty while getting competitive results among all other VAE approaches.

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Drought Analysis Using Copulas

Cited by 30 publications

References 31 publications

Joint Modeling of Precipitation and Temperature Using Copula Theory for Current and Future Prediction under Climate Change Scenarios in Arid Lands (Case Study, Kerman Province, Iran)

Joint Modeling of Precipitation and Temperature Using Copula Theory for Current and Future Prediction under Climate Change Scenarios in Arid Lands (Case Study, Kerman Province, Iran)

Joint Modeling of Wet Condition Characteristics of Makassar City

Neural Gaussian Copula for Variational Autoencoder

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