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In the past century, drought events were likely to be frequent and severe in the arid areas under climate change impact. In this study, a Bayesian copula multivariate analysis (BCMA) method is developed for assessing the impact of spatial–temporal variation on drought risk, through coupling Bayesian copula with multivariate analysis. BCMA can reveal the dynamic characteristics of droughts and deal with the uncertainty caused by copula parameters when modeling the dependent structures of variable pairs (duration-severity-affected area). BCMA is applied to the Balkhash Lake basin in Central Asia for assessing multivariate drought risk during 1901–2020. Some major findings can be summarized: (1) in 1901–2020, the basin suffered 53 droughts; the most severe drought occurred from October 1973 to January 1977 (39 months), and 95% of the basin was affected (335,800 km2); (2) droughts usually develop in the direction of "east–west," and Ili River delta and alluvial plain are the most frequent areas (47.2%) in the basin; (3) droughts show significant seasonality and frequently occur in spring and summer (64.2%), and drought risks of the middle and lower reaches of Ili River are the highest in spring and summer; (4) multivariate characteristics significantly affect drought risk, and drought risk ranges from 1.9 to 18.1% when the guarantee rate is 0.99; (5) the possible causes of drought risk dynamics are meteorological factors (e.g., precipitation and evapotranspiration) and underlying surface factors (e.g., runoff and soil moisture). The findings suggest that droughts in the Balkhash Lake basin are affected by climatic factors, and BCMA can provide methodological support for the studies of drought in other arid regions. [ABSTRACT FROM AUTHOR]

[Display omitted] • A GBCM is developed for multivariate drought risk analysis. • It can reveal the impact of multivariate interaction on drought risk. • GBCM is applied to the Aral Sea Basin (ASB), Central Asia. • Droughts with high risk would expect to move from the south to the north in ASB. • In 2020–2050, drought risk would increase 1.6%∼4.1% under climate change. As one of the most pressing issues in the world, climate change has brought significant impacts on natural and human systems, where drought events are likely to be frequent and severe under climate change impact. In this study, a multi-GCMs Bayesian copula (MGBC) method is developed for assessing the impact of climate change on drought risk, through integrating multi-GCMs, Bayesian, and copula function into a general framework. MGBC can tackle uncertainties existing in copula parameters and global climate models (GCMs), as well as provide ensemble projections for drought risk. MGBC is applied to the Aral Sea basin for assessing multivariate drought risk (i.e., duration-severity, duration-peak, and severity-peak) during 1901–2017 and 2021–2050, where climate data are derived from multi-GCMs (CMCC-CM, GFDL-CM3, MRI-CGCM3) under RCP4.5 and RCP8.5. Some major findings can be summarized: (1) in the historical period (1901–2017), the basin suffered 119 drought events, where drought risks of middle and lower reaches are higher than that in the upstream; (2) in the future (2021–2050), the basin's drought risk level will increase (1.5%∼8.6%), particularly for middle and lower reaches due to climate change impact; (3) as the impacts of climate change intensify, drought risk would increase (i.e., RCP8.5 > RCP4.5), and drought events in association with high-risk level would move from the basin's south to north; (4) the main factors that bring the drought risk are meteorological condition (e.g., precipitation and evapotranspiration) and human activity (e.g., soil moisture and texture). The findings suggest that droughts in the Aral Sea basin are affected significantly by climatic factors and drought risk would intensify with climate change. [ABSTRACT FROM AUTHOR]

• A MCMC-MEC method is developed for bivariate drought risk. • It has advantages in reflecting uncertainty in bivariate drought risk assessment. • MCMC-MEC is applied to the Amu Darya River Basin, Central Asia. • The study basin experienced 42 drought events during 1935–2015. • In 2005–2015, drought risk is increased due to decreasing precipitation and runoff. In this study, a MCMC-based maximum entropy copula (abbreviated as MCMC-MEC) method is developed for assessing drought risk under uncertainty. MCMC-MEC has advantages in analyzing the bivariate drought risk (i.e. duration and severity) through runs-theory, improving the fitting processes of drought variables based on maximum entropy, and reflecting the uncertainties in bivariate drought risk assessment through MCMC. Then, MCMC-MEC is applied to the Amu Darya River Basin, a typical arid and semi-arid basin in Central Asia. Results disclose that, during 1935–2015, the study basin experienced about 42 drought events, average duration of each drought was 5 months (severity 4.38), and the most severe drought event lasted for 25.3 months (severity 27.9). Results also reveal that uncertainty in parameter of copula has effects on drought risk, and duration and severity would increase with confidence level of univariate drought probability. At confidence level of 0.9, the bivariate drought risk would be 4%-6% (duration ≥ 7.0 months and severity ≥ 5.8); at confidence level of 0.95, the drought risk would be 1%-2% (duration ≥ 10.0 months and severity ≥ 10.6). During 2005–2015, drought risk for the study basin was increased due to the decreasing precipitation and runoff, and their contribution rates to the drought risk were 55.8% and 44.2%, respectively. The findings are helpful for providing decision support of drought management and disaster prevention. [ABSTRACT FROM AUTHOR]

Temperature and precipitation are the two most critical climate variables and their extreme states have more severe impacts than average states on both human society and natural ecosystem. In this study, an integrated multivariate trend-frequency analysis (IMTFA) approach is developed for the risk assessment of climate extremes under the global warming. Through incorporating multiple time series analysis techniques (i.e., M-K test, Sen's slope estimator and Pettitt test) and copula function into a general framework, IMTFA is capable not only of analyzing the temporal trends and change points of extreme temperatures and precipitations, but also of quantifying their univariate and multivariate risks. IMTFA is applied to the Central Asia with considering a long-term (1881–2018) observation data. Our findings are: (i) significant wetting and warming trends were occurred in the Central Asia over past one hundred years, where 42.5%, 59.4% and 79.2% stations have change points for extreme precipitations, maximum and minimum temperatures, respectively; (ii) the occurrences of extreme climate events show obviously spatial heterogeneity, where the highest risks of meteorological drought, flood and frost events are occurred in the southwest, southeast and northeast regions, respectively; (iii) global warming significantly affects the intensities and frequencies of extreme precipitations and temperatures, and their univariate and multivariate risks are intensified in the most regions of Central Asia. The above findings can provide more valuable information for risk assessment and disaster adaptation of climate extremes in Central Asia. [Display omitted] • An integrated multivariate trend-frequency analysis (IMTFA) approach is developed. • IMTFA can assess spatial-temporal features of climate extremes under global warming. • IMTFA is applied to Central Asia with considering a long-term observation data. • Significant wetting and warming trends were occurred in Central Asia over 20th century. • Univariate and multivariate risks of climate extremes are intensified in Central Asia. [ABSTRACT FROM AUTHOR]