A copula-based joint meteorological–hydrological drought index in a humid region (Kasilian basin, North Iran)

Cheraghalizadeh, Majid; Ghameshlou, Arezoo N.; Bazrafshan, Javad; Bazrafshan, Ommolbanin

doi:10.1007/s12517-018-3671-7

Cited by 13 publications

(7 citation statements)

References 35 publications

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“…Alternatively, SPI‐12 exhibits a biased estimation, especially for the historic drought year 2000. These results have been verified by Cheraghalizadeh et al (); Bazrafshan et al (); Mirabbasi et al () and Kao and Govindarajo ().…”

Section: Resultssupporting

confidence: 64%

“…The copula function was proposed by Sklar (1959) to derive the joint probability functions of correlated data and later applied by De Michele and Salvadori (2003) in Hydrological sciences. In addition, the copula was used in drought analysis by Wong, Lambert, Leonard, and Metcalfe (2010); Hao and Singh (2015); Kao and Govindaraju (2010); Mirabbasi et al (2013); Bazrafshan, Nadi, and Ghorbani (2015); Farahmand and AghaKouchak (2015); Cheraghalizadeh, Ghameshlou, Bazrafshan, and Bazrafshan (2018). Kao and Govindaraju (2010) applied the copula function in the analysis of precipitation and monthly streamflow.…”

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confidence: 99%

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A copula‐based index for drought analysis in arid and semi‐arid regions of Iran

Bazrafshan

Zamani

Shekari

2019

Natural Resource Modeling

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The copula functions are frequently used by researchers for modeling dependence structure among the correlated attributes in many areas. The copulas are widely used for the analysis of drought frequency, drought characteristics, drought coincidence risk, uncertainty, and drought forecasting. In this research, we have compared two indices of drought assessment, including SPI‐12 and copula‐based joint deficit index (JDI). In this regard, the drought characteristics, including the severity, duration, and drought frequency have been studied in 25 synoptic stations of Iran during the 1968–2014. The results showed that, unlike JDI, the SPI‐12 is not able to estimate the drought peak during the critical and extreme condition. Although JDI has identified a severe and extreme drought during the pervasive drought, the SPI‐12 estimated a normal condition. The results show that JDI accurately estimates the drought frequency, but the SPI‐12 provided an unexpected estimation is some stations. In addition, the MannKendal trend test for drought characteristics represents that JDI accurately estimates the expected trend (an increasing trend) whereas the SPI‐12 exhibits no significant trend in most stations. Finally, JDI provides a comprehensive assessment of drought for decision‐makers and natural managers.

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Section: Resultssupporting

confidence: 64%

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confidence: 99%

A copula‐based index for drought analysis in arid and semi‐arid regions of Iran

Bazrafshan

Zamani

Shekari

2019

Natural Resource Modeling

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“…Concurrent or compound extremes (CEs), which can be defined as the simultaneous or sequential occurrence of more than two extremes at a single or multiple locations, may cause greater havoc than a single extreme of a higher magnitude (Kopp et al, 2017;Hao et al, 2018c). Although the primary idea of the joint probability of multiple extremes emerged decades ago, the explicit research on CEs has surged in the last few years (2018)(2019)(2020)(2021). Especially on the verge of potential climate change, along with the plausible changes in meteorological variables such as temperature, precipitation, evapotranspiration, and wind speed, this research area has drawn more attention (Naveendrakumar et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Drought- and heatwave-associated compound extremes: A review of hotspots, variables, parameters, drivers, impacts, and analysis frameworks

Afroz

Chen²,

Anandhi³

2023

Front. Earth Sci.

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Droughts and heatwaves are rising concerns with regard to the frequent formation of the compound or concurrent extremes (CEs), which can cause greater havoc than an individual event of a higher magnitude. Recently, they have been frequently detected to form CEs together or with other events (e.g., floods, aridity, and humidity events) concurrently or with spatiotemporal lags. Therefore, this systematic review assesses these CEs by reviewing the following aspects: CE hotspots, events, and variable combinations that form CEs; frequently analyzed CE parameters (e.g., frequency and severity); large-scale modes of climate variability (CV) as drivers alongside the approaches to relate them to CEs; and CE impacts (e.g., yield loss and fire risk) alongside the impact integration approaches from 166 screened publications. Additionally, three varied analysis frameworks of CEs are summarized to highlight the different analysis components of drought- and heatwave-associated CEs, which is the novelty of this study. The analysis frameworks vary with regard to the three major assessment objectives: only CE parameters (event–event), driver association (event–driver), and impacts (event–impact). According to this review, the most frequently reported hotspots of these CEs in global studies are southern Africa, Australia, South America, and Southeast Asia. In regional studies, several vital hotspots (e.g., Iberian Peninsula, Balkans, and Mediterranean Basin) have been reported, some of which have not been mentioned in global studies because they usually report hotspots as broader regions. In addition, different event combinations (e.g., drought and heatwave; and heatwave and stagnation) are analyzed by varying the combination of variables, namely, temperature, precipitation, and their derived indices. Thus, this study presents three major analysis frameworks and components of drought- and heatwave-associated CE analysis for prospective researchers.

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“…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). However, few studies have been carried out on the joint frequency analysis of climate parameters and their extreme values under climate change conditions.…”

Section: Introductionmentioning

confidence: 99%

“…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). However, few studies have been carried out on the joint frequency analysis of climate parameters and their extreme values under climate change conditions.…”

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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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A copula-based joint meteorological–hydrological drought index in a humid region (Kasilian basin, North Iran)

Cited by 13 publications

References 35 publications

A copula‐based index for drought analysis in arid and semi‐arid regions of Iran

A copula‐based index for drought analysis in arid and semi‐arid regions of Iran

Drought- and heatwave-associated compound extremes: A review of hotspots, variables, parameters, drivers, impacts, and analysis frameworks

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

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