Modelling of interdependence between rainfall and temperature using copula

Pandey, Pankaj; Das, Lakhyajit; Jhajharia, Deepak; Pandey, Vikas

doi:10.1007/s40808-018-0454-9

Cited by 36 publications

(22 citation statements)

References 28 publications

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“…Precipitation and temperature are two climatic parameters, and the knowledge about their future trend is essential for planning natural ecosystems. 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].…”

Section: Introductionmentioning

confidence: 99%

“…e precipitation and temperature have very high temporal variations [29,30]. e dependence of precipitation to temperature causes its high variation over the time [27]. e dependence between the two parameters of temperature and precipitation [27] makes their analysis problematic when they are treated as independent of each other, since it creates errors in results of calculations.…”

Section: Introductionmentioning

confidence: 99%

“…Pandey et al [27] determined the dependence between temperature parameters (Minimum, Maximum, Mean) and monthly precipitation using 5 copula functions including Gumbel, Frank, T, Clayton, and Normal, of which Normal distribution showed the best fit, so it was used for joint modeling of temperature and precipitation variables. Other studies on the two parameters of precipitation and temperature were also carried out by Favre et al [32], Serinaldi [33], Schölzel and Friederichs [34], and Laux et al [35].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…One of the most important tasks in fitting the copula functions for joint modeling is the creation of trend‐free stationary time series (Benth & Saltyte‐Benth, 2005; Cong & Brady, 2012; Pandey et al, 2018). For this purpose, an autocorrelation test was performed using the autocorrelation function (ACF) for the extreme precipitation and maximum temperature series.…”

Section: Extreme Maximum Temperature Indicesmentioning

confidence: 99%

“…Although interest in climate change studies has increased, there have been fewer studies investigating climate extreme events. Most studies have examined the variation of monthly, annual, and seasonal mean values of climate parameters (Almazroui, Nazrul Islam, Saeed, Alkhalaf, & Dambul, 2017; Alves et al, 2016; Gagnon, Singh, Rousselle, & Roy, 2005; Ludwing et al, 2019; Marengo et al, 2009; Palatella, Miglietta, Paradisi, & Lionello, 2010; Pandey, Das, Jhajharia, & Pandey, 2018; Pervez & Henebry, 2014; Silva & Mendes, 2015). To facilitate research on climate extremes and their trends, the Expert Team on Climate Change Detection and Indices (ETCCDI) of WMO and the World Climate Research Program (WCRP) have identified a set of 27 indicators describing the extreme character of climate events.…”

Section: Introductionmentioning

confidence: 99%

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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Probabilistic assessment of concurrent tornado and storm‐related flash flood events

Ngui

Najafi

Souza

et al. 2023

Intl Journal of Climatology

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The concurrent occurrence of extreme events can significantly impact societies and infrastructure systems, especially when the instructions provided to the exposed communities demand conflicting responses. In this study, a probabilistic assessment of concurrent tornado and storm-related flash flood (TORFF) events is performed across southern Canada. We quantify the interdependencies between tornadoes and flash floods (extreme precipitation as proxy) using ground-based and reanalysis datasets. Windspeed values corresponding to tornado events, categorized based on the recorded Fujita rating, are derived through a resampling approach. The TORFF events are clustered and the corresponding bivariate probability distributions of the resampled windspeed and associated precipitation are characterized based on Copula framework. The individual and joint return periods of concurrent tornadoes and flash floods are then assessed under the AND (when both variables exceed predefined thresholds), OR (when either one of the two variables exceeds predefined thresholds), and conditional hazard scenarios across Canada. Results show positive strong dependencies between resampled windspeed and associated precipitation in Saskatchewan, and weaker dependencies followed by Alberta, Manitoba, Ontario, and Quebec. The Saskatchewan region shows the highest risk underestimation considering the independence scenario compared to the other regions. Higher precipitation is also expected during extreme windspeed, as observed in the conditional assessment of precipitation given windspeed. This study provides insights for more robust recurrence interval estimation for tornadoes and flash floods to aid in emergency planning of evacuation decision-making process.

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Modelling of interdependence between rainfall and temperature using copula

Cited by 36 publications

References 28 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)

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

Probabilistic assessment of concurrent tornado and storm‐related flash flood events

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