Modeling the spatial structure of annual temperature in Iran

Balyani, Saeid; Khosravi, Younes; Ghadami, Fardin; Naghavi, Maryam; Bayat, Ali

doi:10.1007/s40808-017-0319-7

Cited by 12 publications

(9 citation statements)

References 14 publications

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“…e study of the pattern of temperature and precipitation changes is crucial due to climate change [25][26][27]. Knowing the temporal variation of temperature and its relationship with other climate variables is very important for climate planning [28]. e precipitation and temperature have very high temporal variations [29,30].…”

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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“…P , T , and PW are important climate parameters, and the study of changes in their spatiotemporal pattern is crucial due to climate change (Cong & Brady, 2012; Pandey et al., 2018) and the knowledge about their interrelationship is vital for planning natural ecosystems. Knowing the variation of T and its relationship with other climate variables is very important for climate planning (Balyani et al., 2017). The P , T , and PW have very high temporal variations (Balyani et al., 2017), and the dependence of P on T causes its high variation over the time (Cong & Brady, 2012; Pandey et al., 2018).…”

Section: Resultsmentioning

confidence: 99%

“…Knowing the variation of T and its relationship with other climate variables is very important for climate planning (Balyani et al., 2017). The P , T , and PW have very high temporal variations (Balyani et al., 2017), and the dependence of P on T causes its high variation over the time (Cong & Brady, 2012; Pandey et al., 2018). The dependence between these climatic parameters makes their analysis problematic especially when they are treated as independent of each other (Cong & Brady, 2012; Pandey et al., 2018).…”

Section: Resultsmentioning

confidence: 99%

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Modeling Joint Relationship and Design Scenarios Between Precipitation, Surface Temperature, and Atmospheric Precipitable Water Over Mainland China

Ayantobo

Wei

Wang

2021

Earth and Space Science

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• We present a framework for modeling the joint relationship between precipitation, temperature, and precipitable water. • Birnbaum Saunders distribution was discovered for fitting precipitation in regions a and f. • We estimated copula parameters using Markov Chain Monte Carlo simulation in a Bayesian framework. • Bivariate probabilities and return periods in distinctive regions suggest tremendous variations. • Marginal distribution and copula choice could significantly impact probabilities and design return periods.

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“…Nevertheless, standard data at time intervals of interest required for most sophisticated studies and simulations are not readily available and even when available they are very expensive, thereby constituting a restraint on research at micro-scale levels. Thus, diurnal changes in these variables need to be calculated from available daily average values using meteorological models (Ephrath et al 1996;Balyani et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Modeling of diurnal pattern of air temperature in a tropical environment: Ile-Ife and Ibadan, Nigeria

Akinnubi¹,

Adeniyi

2017

Model. Earth Syst. Environ.

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Modeling the spatial structure of annual temperature in Iran

Cited by 12 publications

References 14 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)

Modeling Joint Relationship and Design Scenarios Between Precipitation, Surface Temperature, and Atmospheric Precipitable Water Over Mainland China

Modeling of diurnal pattern of air temperature in a tropical environment: Ile-Ife and Ibadan, Nigeria

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