A MCMC-based maximum entropy copula method for bivariate drought risk analysis of the Amu Darya River Basin

Yang, Xiaojing; Li, Y.P.; Liu, Y.R.; Gao, Pan

doi:10.1016/j.jhydrol.2020.125502

Cited by 30 publications

(18 citation statements)

References 27 publications

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“…SPI was proposed by Mckee [8], its calculation is based on a multi-year monthly precipitation data series. The information of SPI response on different time scales is also different [29]. In this study, the SPIProgram downloaded from the website http://drought.unl.edu/Monitoring Tools/Downloadable SPIProgram.aspx (accessed on 15 January 2021) is only used to calculate the value of SPI on 1, 3 and 12 month time scales (SPI1, SPI3 and SPI12), the drought situation in the study area was analyzed by SPI3.…”

Section: Meteorological Drought Index Spi and Drought Characteristicsmentioning

confidence: 99%

“…Commonly used Copula functions are generally divided into five types, including Archimedean Copula, Metaelliptical Copula, Plackette Copula, mixed Copula, and empirical Copula. Since Archimedean Copula and Metaelliptical Copula functions are easy to construct and can capture dependent structures with several characteristics, they have become very attractive functions in bivariate hydrological frequency analysis [29,39]. In this paper, three commonly used Archimedean Copula (Clayton, Frank and Gumbol-Hougaard) and two commonly used Metaelliptical Copula (Gaussian and t Student Copula) were selected, and the inference function for margin (IFM) method [40] was used to estimate the parameters of copula functions, that is, first calculate the parameter values of the marginal distribution through the MLE method, and then use the obtained marginal distribution parameters to obtain the unknown parameters in the copula functions.…”

Section: Copula Functionmentioning

confidence: 99%

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Bivariate Frequency of Meteorological Drought in the Upper Minjiang River Based on Copula Function

Qin

Chen

2021

Water

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Based on the Standardized Precipitation Index (SPI) and copula function, this study analyzed the meteorological drought in the upper Minjiang River basin. The Tyson polygon method is used to divide the research area into four regions based on four meteorological stations. The monthly precipitation data of four meteorological stations from 1966 to 2016 were used for the calculation of SPI. The change trend of SPI1, SPI3 and SPI12 showed the historical dry-wet evolution phenomenon of short-term humidification and long-term aridification in the study area. The major drought events in each region are counted based on SPI3. The results show that the drought lasted the longest in Maoxian region, the occurrence of minor drought events was more frequent than the other regions. Nine distribution functions are used to fit the marginal distribution of drought duration (D), severity (S) and peak (P) estimated based on SPI3, the best marginal distribution is obtained by chi-square test. Five copula functions are used to create a bivariate joint probability distribution, the best copula function is selected through AIC, the univariate and bivariate return periods were calculated. The results of this paper will help the study area to assess the drought risk.

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Section: Meteorological Drought Index Spi and Drought Characteristicsmentioning

confidence: 99%

Section: Copula Functionmentioning

confidence: 99%

Bivariate Frequency of Meteorological Drought in the Upper Minjiang River Based on Copula Function

Qin

Chen

2021

Water

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“…where p(θ) and p(θ, Y) signify prior and posterior distribution of parameters, respectively. p(Y ,θ) denotes likelihood function, and p(Y) is coned evidence (Yang et al, 2020). Then, according to the parameter distribution, the estimated parameter in the 95% confidence interval was selected as the calculation input of the copula function.…”

Section: Bayesian Copula and Multivariate Riskmentioning

confidence: 99%

“…SPI and SPEI are developed based on the discrepancy between precipitation and water balance, which are widely applied to meteorological drought (Hamal et al, 2020). HDI is developed by meteorological indicators and runoff, which represents a drought that river runoff is below the normal level; and HDI is usually applied to hydrological drought (Yang et al, 2020). On a large regional scale, areas with scarce precipitation and intense evapotranspiration are usually characterized by meteorological drought.…”

Section: Introductionmentioning

confidence: 99%

“…Jin et al (2019) proposed a Bayesian parameter identification approach for applying to advanced soil models, and its robustness and effectiveness were verified based on multiple independent calculations. Yang et al (2020) combined maximum entropy principle, Bayesian copula into a general framework, which provided an efficient and accurate method for fitting optimal marginal distribution. Overall, using Bayesian inference to improve copula can minimize the uncertainty in parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

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Spatial-Temporal Change Analysis for Multivariate Drought Risk Based on Bayesian Copula: Application to the Balkhash Lake Basin

Yang

2021

Preprint

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In this study, a spatial-temporal Bayesian copula (SBC) method is developed through integrating spatial-temporal analysis and Bayesian copula into a general framework. SBC method can help model dependence structures of variable pairs and handle the uncertainty caused by parameter in copulas, and SBC can reveal the spatial and temporal changes of drought events. SBC is applied to the Balkhash Lake Basin (in Central Asia) to analyze spatial-temporal characteristic and drought risk in 1901-2020. Several findings can be summarized: (1) Balkhash Lake Basin suffered 53 drought events in 1901-2020, and five typical severe drought events occurred in 1916-1920, 1943-1945, 1973-1977, 1995-1998 and 2007-2009; (2) the most severe drought event lasted for 40 months (1973.10-1977.1), affecting 335,800 km2 of the study basin; (3) drought usually develops from east to west, and Ili River delta and alluvial plain has the highest frequency of drought (47.2%), following by plateau desert (28.3%) and arid grassland in north Balkhash Lake (24.5%); (4) drought shows significant seasonality in the study basin, which usually begins in spring and summer (64.2%) and ends in summer and autumn (66.0%); and drought risk of middle and lower reaches of Ili River is highest in spring and summer; (5) in Balkhash Lake Basin, multivariate characteristics (duration, severity and affected area) significantly affect drought risk; (6) the range of drought risk is [1.9%, 18.1%], [3.7%, 33.1%], [8.7%, 46.0%], [16.0%, 55.1%] and [27.6%, 59.8%] when guarantee rate is 0.99, 0.98, 0.95, 0.90 and 0.80, respectively.

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Occurrence of drought events at the land–atmosphere interface in Central Asia assessed via advanced microwave scanning radiometer data

Yang

Zhang

Xia

et al. 2022

Intl Journal of Climatology

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The timely and effective early warning of sudden drought events is of great importance for reducing disaster losses and drought risk. Satellite microwave remote sensing with the advanced microwave scanning radiometer (AMSR) has enabled the global monitoring of multiple components of water, such as soil moisture (SM), vapour pressure deficit (VPD), and open water fraction (OWF), on a daily scale since 2002. In this study, the applicability of the AMSR surface wetness index (ASWI) and drought features in Central Asia were studied from 2002 to 2017 based on AMSR data. This study concluded that (1) the spatial and temporal differences in the various water components and coefficient of variation of the Central Asia land-atmosphere interface were large, which led to large spatial and temporal differences under both dry and wet conditions in Central Asia; (2) although the ASWI was not significantly correlated with other drought indices (i.e., gravity recovery and climate experiment (GRACE) combined climatological deviation index (CCDI) and GRACE drought severity index (DSI)) over Central Asia, the maximum Pearson correlation coefficient (CC) between the ASWI and Palmer drought severity index (PDSI) in the Irtysh River Basin was 0.824 (p < 0.05), corresponding to a moving average window of 11 months. The CCs of ASWI and Standardized Precipitation Evapotranspiration Index on a 6-month scale (SPEI-6) in Central Asia were generally more than 0.8 (p < 0.05), indicating that the ASWI was effective for retrieving drought conditions in Central Asia. In addition, these four drought indices (ASWI, PDSI, GRACE-DSI, and GRACE-CCDI) revealed that drought occurred continuously after 2003 in Central Asia; (3) the spatial and temporal distribution of dry and wet conditions in Central Asia is closely related to the teleconnection indices (i.e., MEI, SOI, and PDO). Thus, AMSR data are critical for understanding drought events and their evolution in regions influenced by climate change.

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A MCMC-based maximum entropy copula method for bivariate drought risk analysis of the Amu Darya River Basin

Cited by 30 publications

References 27 publications

Bivariate Frequency of Meteorological Drought in the Upper Minjiang River Based on Copula Function

Bivariate Frequency of Meteorological Drought in the Upper Minjiang River Based on Copula Function

Spatial-Temporal Change Analysis for Multivariate Drought Risk Based on Bayesian Copula: Application to the Balkhash Lake Basin

Occurrence of drought events at the land–atmosphere interface in Central Asia assessed via advanced microwave scanning radiometer data

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