Regional analysis and derivation of copula-based drought Severity-Area-Frequency curve in Lake Urmia basin, Iran

Amirataee, Babak; Montaseri, Majid; Rezaie, Hossein

doi:10.1016/j.jenvman.2017.10.027

Cited by 63 publications

(28 citation statements)

References 50 publications

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“…Iran has a predominantly arid and semi-arid climate, and therefore, precipitation and droughts are a vital concern in the country (Modarres, 2006). A number of studies have been conducted to assess trends in rainfall and droughts in Iran in recent years (Modarres and Sarhadi, 2009;Tabari et al, 2012;Kousari et al, 2014;Dashtpagerdi et al, 2015;Zarei and Eslamian, 2017;Amirataee et al, 2018). The literature revealed that the previous studies focused solely on trend analysis of the magnitude (intensity) of drought (e.g., SPI, SPEI and Palmer) using different methods (MK test and Sen's slope estimator).…”

Section: Introductionmentioning

confidence: 99%

Spatial assessment of meteorological drought features over different climate regions in Iran

Sharafati

Nabaei

Shahid

2019

Intl Journal of Climatology

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Iran has a predominantly arid and semi‐arid climate where the drought hazards and their variability are the crucial concerns for water resources management. This study assesses the characteristics and trends of meteorological droughts features in different climate regions of Iran. Monthly rainfall data are analysed using Standardized Precipitation Index (SPI) to reconstruct meteorological droughts for different time steps (1, 3, 6, 9 and 12). The drought events are decomposed into three features, namely Severity (S), Duration (D) and Peak (P) to assess their spatial variations in different climate regions. The Mann–Kendall (MK) test is used to detect the trends in drought features in different climate regions. The percentage area (PA) of increasing/decreasing trend in drought features is computed to assess the spatial variations of trends in each region. Besides, SPI is compared with Standardized Precipitation Evapotranspiration Index (SPEI) to understand the importance of evapotranspiration on meteorological droughts. The high correlations between SPI and SPEI indicate that the results obtained using SPI would not vary if a similar analysis is conducted using SPEI. The results obtained using SPI reveal that short‐duration droughts (6 months or less) are more frequent and severe in the northwest and the north along the Caspian plains while the longer‐duration droughts (more than 6 months) are more frequent and severe in the south, southeast and southwest. Overall, the northwest region of Iran is found most susceptible to droughts in terms of S, P and D. Moderate (40% < PA < 60%) or significant (PA > 60%) increase in the drought features are observed in all climate regions for shorter SPI (3 months or less) except northwest, while no drought feature is found to increase significantly for SPI‐12. Overall, the higher increase in drought S, P and D are found in the central arid region and at higher altitudes.

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Section: Introductionmentioning

confidence: 99%

Spatial assessment of meteorological drought features over different climate regions in Iran

Sharafati

Nabaei

Shahid

2019

Intl Journal of Climatology

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“…The empirical copula can be used to assess the fitting performance of the theoretical copula. Taking the root-mean-square error (RMSE) as an indicator [53], the fitting performance can be calculated as:…”

Section: Copula Typementioning

confidence: 99%

Assessing the Impacts of Univariate and Bivariate Flood Frequency Approaches to Flood Risk Accounting for Reservoir Operation

Zhou

Jin

et al. 2019

Water

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Flood frequency analysis plays a fundamental role in dam planning, reservoir operation, and risk assessment. However, conventional univariate flood frequency analysis carried out by flood peak inflow or volume does not account for the dependence between flood properties. In this paper, we proposed an integrated approach to estimate reservoir risk by combining the copula-based bivariate flood frequency (peak and volume) and reservoir routing. Through investigating the chain reaction of “flood frequency—reservoir operation-flood risk”, this paper demonstrated how to simulate flood hydrographs using different frequency definitions (copula “Or” and “And” scenario), and how these definitions affect flood risks. The approach was applied to the Meishan reservoir in central China. A set of flood hydrographs with 0.01 frequency under copula “Or” and “And” definitions were constructed, respectively. Upstream and downstream flood risks incorporating reservoir operation were calculated for each scenario. Comparisons between flood risks from univariate and bivariate flood frequency analysis showed that bivariate flood frequency analysis produced less diversity in the results, and thus the results are more reliable in risk assessment. More importantly, the peak-volume combinations in a bivariate approach can be adjusted according to certain prediction accuracy, providing a flexible estimation of real-time flood risk under different prediction accuracies and safety requirements.

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“…They found that the joint return period showed better fitting results than without considering the integrated factor when the actual dust storm return period is longer than 2 years. Amirataee et al (2018) used the Copula function to analyze the percentages of drought intensity and drought area in the Lake Ulmia basin in Iran, the results indicated that extreme drought and non-drought behaviors had affected the majority of study areas. The study included a probability analysis of different combinations of the variables, different design values of combined probability conditions, as well as the specific joint return period and over recurrence possible combinations of a variable period.…”

Section: Introductionmentioning

confidence: 99%

Multi-factor joint return period of rainstorms and its agricultural risk analysis in Liaoning Province, China

Feng

Zhang

et al. 2019

Geomatics, Natural Hazards and Risk

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Rainstorms are one of the frequent natural disasters which have significant impact on agricultural production and people's lives. Considering the comprehensive influence of various factors on the rainstorm events, this study extracts four elements of rainstorms in the Liaoning Province based on meteorological data collected between 1957 and 2015. Combined four elements of rainstorms by copula function, the cumulative probability of the rainstorm events and the probability of their return period can be analysed. The results show that the joint return period of multiple factors combination is less than the return period of a single factor. The degree of accuracy of the joint return period is higher, which is consistent with the actual situation. During periods of 5, 10, 20, and 50 years, the occurrence probability varies according to the idea 'the central region is higher, while the eastern and western regions are lower'. Special attention must be paid to the low values of the region in future rainstorm disaster prevention and mitigation work. Based on return periods, the agricultural risk grade can be divided into 5 levels. In ascending order, they are level I (Fuxin, Shenyang, Tieling), II (Dalian, Jinzhou, Panjin, Liaoyang), III (Huludao, Fushun), IV (Chaoyang, Yingkou, Anshan, Benxi), and V (Dandong).

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Regional analysis and derivation of copula-based drought Severity-Area-Frequency curve in Lake Urmia basin, Iran

Cited by 63 publications

References 50 publications

Spatial assessment of meteorological drought features over different climate regions in Iran

Spatial assessment of meteorological drought features over different climate regions in Iran

Assessing the Impacts of Univariate and Bivariate Flood Frequency Approaches to Flood Risk Accounting for Reservoir Operation

Multi-factor joint return period of rainstorms and its agricultural risk analysis in Liaoning Province, China

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