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, Tayyebeh; Miglietta, Mario Marcello; Mirakbari, Maryam; Sardoo, Farshad Soleimani; Abdolhoseini, M.

doi:10.1155/2019/6848049

Cited by 20 publications

(22 citation statements)

References 52 publications

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“…The parameter of the copulas was estimated by both parametric and nonparametric methods for extreme combinations and then the best fit copula was selected on the basis of the AIC, BIC, and OLS criteria. According to these criteria, the parametric method resulted in better estimation than the nonparametric method, consistent with previous work (Dodangeh et al, 2017;Mesbahzadeh et al, 2019;Mirakbari et al, 2010). Among the five fitted copula functions at the Daran station, the Gaussian copula was used to jointly model the combinations of (R10, SDII), (R10, PRCPTOT), (SDII, PRCPTOT), and the Frank, and Gumbel Copulas were used for the combinations of (R95p, SDII) and (R95, R10), respectively, in the historical period.…”

Section: Extreme Maximum Temperature Indicessupporting

confidence: 89%

“…Conventional methods of calculating these functions have limitations in selecting the type of marginal function, which can cause error in analysis. The copula is a family of multivariate functions that does not suffer from the limitations typical of multivariable distribution functions, as copulas can model the joint distribution of random variables with any marginal function (AghaKouchak, Bardossy, & Habib, 2010; Mesbahzadeh, Miglietta, Mirakbari, Soleimani Sardoo, & Abdolhoseini, 2019; Salvadori & De Michele, 2004; L. Zhang & Singh, 2007). The use of the copula functions in meteorological and hydrological analyses has facilitated multivariate modeling (De Michele & Salvadori, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…The use of the copula functions in meteorological and hydrological analyses has facilitated multivariate modeling (De Michele & Salvadori, 2003). Recently, copula functions have been used for multivariate analysis of annual peak floods, flood, and drought return periods, and similar climate‐related extremes (Bracken, Holman, Rajagopalan, & Moradkhani, 2018; Chen, Zhang, Xiao, Singh, & Zhang, 2016; J. Li, Zhang, Chen, & Singh, 2015; Mesbahzadeh et al, 2019; Mirakbari et al, 2010; Uttam, Goswami, Bhargav, Hazra, & Goyal, 2018; Q. Zhang, Li, Singh, & Xu, 2013). In Iran, many studies have analyzed multivariate modeling of extreme events such as floods and droughts using copulas (Amirataee, Montaseri, & Rezaie, 2018; Azam, Maeng, Kim, & Murtazaev, 2018; Cheraghalizadeh, Ghameshlou, Bazrafshan, & Bazrafshan, 2018; Dodangeh et al, 2017; Mirakbari et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, copula functions have been used for multivariate analysis of annual peak floods, flood, and drought return periods, and similar climate-related extremes (Bracken, Holman, Rajagopalan, & Moradkhani, 2018;Chen, Zhang, Xiao, Singh, & Zhang, 2016;J. Li, Zhang, Chen, & Singh, 2015;Mesbahzadeh et al, 2019;Mirakbari et al, 2010;Uttam, Goswami, Bhargav, Hazra, & Goyal, 2018;Q. Zhang, Li, Singh, & Xu, 2013).…”

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confidence: 99%

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Observed and projected trends of extreme precipitation and maximum temperature during 1992–2100 in Isfahan province, Iran using REMO model and copula theory

Mirakbari

Mesbahzadeh

Sardoo

et al. 2020

Natural Resource Modeling

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Meteorological extreme events have a major impact on water resources, economic development, and ecosystem health. In this study, maximum precipitation and maximum temperature indices were derived for Isfahan province, in central Iran, over the historical and future (2020-2100) periods. Precipitation and maximum temperature data from the REMO model under RCP4.5 scenario were used to investigate changes in extreme values over the future period. The results showed that extreme precipitation in the historical and future periods has respectively a decreasing and increasing trend. Based on the extreme indices, temperature in the study area has a significant increasing trend in the baseline and future period. Various combinations of extreme precipitation indicators were created for joint modeling by copula theory. Copula modeling for the three weather stations for which REMO had satisfactory performance in simulating extremes over the historic period showed that the average return period of extreme precipitation combinations will be reduced in the future period compared to the historical period at Daran and Shahreza, while the average return period of combinations will have both increasing and decreasing trends at Naeen. Recommendations for Resource Managers• Knowing information about the probability of occurrence of extreme precipitation with a certain value that exceeds a certain threshold will help planning for water resource systems under drought conditions and future increasing temperature.• The joint return period of extreme precipitation can help to know the return period of extreme events such as floods and droughts.• The findings of this study are important to assess the prediction of climate extreme. Also, these results can be useful to provide the appropriate strategies for water resources managers in drought conditions under future increasing temperature. K E Y W O R D S climate change, Copula, extreme precipitation and maximum temperature indices, REMO

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Section: Extreme Maximum Temperature Indicessupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Observed and projected trends of extreme precipitation and maximum temperature during 1992–2100 in Isfahan province, Iran using REMO model and copula theory

Mirakbari

Mesbahzadeh

Sardoo

et al. 2020

Natural Resource Modeling

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“…Moradkhani, and Qin 2017; Chen et al 2018;Li and Babovic 2019;Mesbahzadeh et al 2019) even though the usefulness of multivariate bias-correction methods for hydrological purposes has recently been put into question (Räty et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Copulas for hydroclimatic applications – A practical note on common misconceptions and pitfalls

Tootoonchi

Haerter

Räty

et al. 2020

Preprint

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Abstract. For most hydroclimatic applications, precipitation and temperature are of particular interest as they strongly affect the water cycle, can easily be measured and are often readily available from many meteorological stations worldwide. To account for precipitation and temperature variability, their co-dependence and their correlation, several multivariate analysis methods have been adopted in the hydroclimatic literature in recent years. In line with the steadily rising number of publications on this topic, the notion of copula-based probability distribution has also attracted tremendous interest to deal with the complexity of compound events in the multidimensional context. A copula is a function that connects a multivariate distribution to its one-dimensional margins, which allows for a joint distribution of random variables with great flexibility for the marginal distribution. However, there seems to be a lack of comprehensive understanding of the fundamental requirements of the copula concept such as the strength and significance of correlation between variables, autocorrelation effects and the choice of representative copula families, which potentially compromises the robustness of projections of future environmental processes and natural hazards. Therefore, by combining a systematic literature review with a specific hydroclimatic case study in Sweden, we illustrate a practical approach to copula-based modeling, which (1) provides end-users with an overview of necessary requirements, statistical assumptions and consequential limitations of copulas, (2) highlights possible pitfalls and misconceptions, and (3) offers a decision support framework for the application of copulas to support researchers and decision makers in addressing climatological hazards and sustainable development, thereby demystifying what is currently an area of great confusion.

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Bivariate bias correction of the regional climate model ensemble over the Adriatic region

Jurković

Güttler

Pasarić

2022

Intl Journal of Climatology

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The main goal of this analysis is to provide an assessment of the fine grid regional climate model (RCM) ensemble over the broader Adriatic region, which is the domain with orographically complex terrain and a developed coastline. The results of this study are a beneficial complement to earlier work on using modified bivariate bias correction methods. This analysis was based on an ensemble of 12 combinations of three RCMs and four global climate models (GCMs) from 1971 to 2004, divided into calibration and validation parts. A comparison of one univariate and three versions of bivariate bias correction methods of summer and winter monthly temperature and precipitation was performed. To examine the effect of various methods, we used different marginal distributions and distributions of dependence (copula). Bivariate bias correction was conducted with parametric and empirical marginal distributions. In the quantile mapping (univariate) and the parametric bivariate method, gamma (precipitation) and normal (temperature) probability distributions were used. We documented the bias and the influence of bias correction methods on precipitation and temperature, including their climate change signals in historical data. The considered methods maintained the spatial distribution of trends from the native ensemble. If the relationship between variables was not distinct and the correlation coefficient was low, bivariate bias correction methods tended to weaken the correlation coefficient from the model. Two experiments on how the bias correction methods influence statistical measures of the considered variables and their relationships showed that the bivariate method with empirical distributions (eeG) performed slightly better than other methods. Although the eeG method performed better than the other methods, none of the considered methods was able to fully preserve the correlation coefficient between variables detected from observations. Considering two copulas (Gaussian and Student's t), we conclude that the effect of different copula types on method performance is small.

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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)

Cited by 20 publications

References 52 publications

Observed and projected trends of extreme precipitation and maximum temperature during 1992–2100 in Isfahan province, Iran using REMO model and copula theory

Observed and projected trends of extreme precipitation and maximum temperature during 1992–2100 in Isfahan province, Iran using REMO model and copula theory

Copulas for hydroclimatic applications – A practical note on common misconceptions and pitfalls

Bivariate bias correction of the regional climate model ensemble over the Adriatic region

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