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• The contribution of TGD and climate change on the drought propagation in the YRB was assessed. • TGD's regulation had effectively raised the drought propagation threshold and prolonged the drought propagation time. • TGD's regulation had consequently weakened the drought propagation process and led to a decrease in HD characteristics. The propagation process from meteorological drought (MD) to hydrological drought (HD) is crucial for drought monitoring and mitigation. While it's widely recognized that the Three Gorges Dam (TGD) and climate change have substantially altered the hydrological conditions in the Yangtze River Basin (YRB), the effects of these changes on the drought propagation process have remained unclear. Using natural runoff, observational runoff and precipitation data during 1988–2018, and employing the Run theory, Copula function, and Bayesian network model, we quantified the impacts of the TGD's regulation and climate variability on drought propagation in the YRB. The results showed that the TGD's regulation raised the drought propagation threshold (DPTH) and prolonged the drought propagation time (DPT) in the upper, middle, and lower reaches of the Yangtze River (URYR, MRYR, and LRYR). It consequently weakened the drought propagation process and led to a decrease in HD characteristics (duration, severity and frequency) in the YRB by −20.57 % ∼ −0.96 %, except for the severity and frequency in the LRYR. However, the effects of climate variability on DPTH and DPT were relatively complex. It led to a decrease in HD characteristics by −27.46 % ∼ −10.84 % in the URYR and MRYR, excluding the frequency in the MRYR. Conversely, it resulted in an increase of 9.87 % ∼ 42.37 % in HD characteristics in the LRYR. This study effectively underscored the crucial role of the TGD in mitigating HD events in the YRB and contributed valuable insights into understanding how human activities and climate variability interacted in shaping drought propagation process. [ABSTRACT FROM AUTHOR]

• A new method for identifying large-area and long-duration drought (LLD) is proposed. • The joint distribution of drought area and duration ≥ 75 % can be used to identify LLD. • Drought duration has a greater impact on drought severity than drought area. • Along with changes in severity, LLD centroid moved 12 times over 33 months in NRB. The effects of droughts on food, economic, and social security have long been a worldwide significant issue. Recently droughts have showed a development trendency with a longer duration and a wider impact area due to the climate change and the human activities. In this study, the Gravity Recovery and Climate Experiment (GRACE) satellite data were used to develop the water storage deficit index, based on which the large-area and long-duration drought (LLD) were identified using the theory of runs and the copula functions. In the Nenjiang River basin, two drought events with a joint distribution frequency of greater than 75 % were identified as LLD events, with a drought area of 29.7 × 104 km2 and a drought duration of 27 and 33 months respectively. In terms of drought characteristic indicators, the average values of drought intensity, drought severity, and extreme intensity for LLD events were 1.04, 31.71, and 2.30, which is significantly higher than for other types of drought events. The LLD events take a longer time to develop to the peak intensity than other types of drought events, but their drought centroid migration is more widespread. This suggests that the propagation process of the LLD events is more complex and the spatial and temporal distribution of drought is more uneven. Thus, we recommended that appropriate actions and regulations be adopted for different regions in different periods to reallocate water resources according to the drought-related losses and management expenses. This study introduces an identification method of the LLD events, combined with the analyses of evolutionary charaterisitics in space and time. It is hoped that the outcomes of the study can help watershed managers to clearly and conveniently identify the LLD events based on the predicted drought duration and area. [ABSTRACT FROM AUTHOR]

[Display omitted] • A hydrological model coupled with reservoir module was developed for runoff projection. • Future runoff will increase in the mainstream and northern tributaries. • The duration and severity of hydrological droughts will decrease. • The probability of concurrent hydrological droughts will decrease. • Hydrological droughts occur in the downstream or mainstream with high probability when hydrological droughts occur in its upstream or tributaries. Hydrological droughts have caused greater damage due to climate change and rapid socio-economic development. Exploring future hydrological drought variations is imperative for drought defense and infrastructure construction. This study aims to investigate future changes in hydrological droughts across the Yangtze River Basin during 2025 ∼ 2100. The Distributed Time-Variant Gain Model coupled with the Standard Operation Policy module (DTVGM-SOP) was employed to project future runoff driven by the bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) data. Subsequently, future spatial–temporal changes in the characteristics of hydrological droughts were analyzed based on the Standardized Runoff Index (SRI). Lastly, future changes in the probability of concurrent hydrological droughts were evaluated using the bivariate copula functions. Results show that: (1) The DTVGM-SOP model can satisfactorily simulate monthly streamflow with a high Nash-Sutcliff Efficiency (NSE) of 0.75 ∼ 0.92. Future annual maximum discharge and annual average discharge in the mainstream and the northern tributaries are expected to increase by 3.34%∼58.19% and 3.11%∼32.66% respectively, while annual maximum discharge in the southern tributaries decreases by 0.54%∼20.09%. (2) The future annual and seasonal hydrological drought frequency is expected to decrease in most regions, while the frequency in spring increases in the Qinghai Tibet Plateau. The duration and severity of future hydrological droughts are expected to decrease by 9.52%∼78.69% and 5.13%∼81.36% in the whole basin except the source area and the Yalong River Basin. (3) Future concurrent hydrological droughts will be mitigated with decreasing frequency. The probability of concurrent hydrological droughts is expected to decrease by 46.15%∼96.47%, while future changes in the conditional probability of the mainstream are diverse in different concurrent scenarios with a range of −93.41%∼40.09%. The findings are helpful to improve the understanding of future hydrological drought variations in the Yangtze River Basin and provide essential information for damage reduction. [ABSTRACT FROM AUTHOR]

• A new DA-LSTM-RED model is developed to improve flood forecasting accuracy. • The new model combines a dual-attention mechanism and an encoder-decoder structure. • The copula-based HUP method has superior probabilistic forecast performance. • The input of forecasted precipitations effectively extends flood forecast horizons. In order to improve the flood forecasting accuracy and reflect the forecast uncertainty information in the Three Gorges Reservoir (TGR) interval-basin in China, this study integrates the feature and temporal dual-attention (DA) mechanism and recursive encoder-decoder (RED) structure into the long short-term memory (LSTM) neural network to develop a DA-LSTM-RED model. The feature attention acts on the input variables of the encoder, and the temporal attention mechanism acts on the hidden layer states extracted by the LSTM neural network during encoding process, prompting the proposed model to extract critical input information among different types and moments of input variables to improve the multi-step-ahead flood forecasting accuracy. Second, the copula-based Hydrological Uncertainty Processor (copula-HUP) is used to quantify the forecast uncertainty of the proposed model meanwhile creating multi-step-ahead flood probabilistic forecasts. Combining the long-term 6 h hydrologic data series of the Xiangjiaba-TGR interval-basin and the forecasted precipitation from the European Centre for Medium-Range Weather Forecasts (ECMWF), the effectiveness of the proposed model, the effect of forecast precipitation on multi-step-ahead flood forecasting, and the effect of different copula functions on the probabilistic forecast of copula-HUP are investigated, respectively. The results show that the DA-LSTM-RED model can effectively improve the forecasting accuracy for long forecast horizons (3-7d) compared to the LSTM-RED model, and the average absolute error metrics are reduced by 10%-17%. Meanwhile, the proposed model can identify input variables with a high correlation with the target output variables, which improves the interpretability of deep learning to a certain extent. The Student copula-HUP has the lowest RB and CRPS metrics than the Frank and Gaussian copula-HUP, which can better quantify the DA-LSTM-RED model's forecast uncertainty. Therefore, combining the proposed model with the Student copula-HUP can effectively reduce the forecasting uncertainty, enhance the forecasting reliability and accuracy for future horizons, and provide reliable risk information for the TGR flood control scheduling decision. [ABSTRACT FROM AUTHOR]

Stochastic dynamic programming (SDP) has been widely used to derive operating policies for reservoirs considering streamflow uncertainties. In SDP, there is a need to calculate the transition probability matrix more accurately and efficiently in order to improve the economic benefit of reservoir operation. In this study, we proposed a stochastic optimization model for hydropower generation reservoirs, in which 1) the transition probability matrix was calculated based on copula functions; and 2) the value function of the last period was calculated by stepwise iteration. Firstly, the marginal distribution of stochastic inflow in each period was built and the joint distributions of adjacent periods were obtained using the three members of the Archimedean copulas, based on which the conditional probability formula was derived. Then, the value in the last period was calculated by a simple recursive equation with the proposed stepwise iteration method and the value function was fitted with a linear regression model. These improvements were incorporated into the classic SDP and applied to the case study in Ertan reservoir, China. The results show that the transition probability matrix can be more easily and accurately obtained by the proposed copula function based method than conventional methods based on the observed or synthetic streamflow series, and the reservoir operation benefit can also be increased. [ABSTRACT FROM AUTHOR]

When employing streamflow forecasting in practical applications, such as reservoir operation, one important issue is to deal with the uncertainty involved in forecasting. Traditional studies dealing with the uncertainty in streamflow forecasting have been limited in describing the evolution of forecast uncertainty. This paper proposes a copula-based uncertainty evolution (CUE) model to describe the evolution of streamflow forecast uncertainty. The generated forecast uncertainty series fits the observed series well in terms of observed mean, standard deviation and skewness. Daily flow with forecast uncertainty are simulated and used to determine the effect of forecast uncertainty on real-time reservoir operation of the Three Gorges Reservoir (TGR), China. Results show that using the forecast inflow coupled with the pre-release module for reservoir operation of TGR in flood season cannot increase the flood risk. [ABSTRACT FROM AUTHOR]

Summary Multisite stochastic simulation of streamflow sequences is needed for water resources planning and management. In this study, a new copula-based method is proposed for generating long-term multisite monthly and daily streamflow data. A multivariate copula, which is established based on bivariate copulas and conditional probability distributions, is employed to describe temporal dependences (single site) and spatial dependences (between sites). Monthly or daily streamflows at multiple sites are then generated by sampling from the conditional copula. Three tributaries of Colorado River and the upper Yangtze River are selected to evaluate the proposed methodology. Results show that the generated data at both higher and lower time scales can capture the distribution properties of the single site and preserve the spatial correlation of streamflows at different locations. The main advantage of the method is that the trivairate copula can be established using three bivariate copulas and the model parameters can be easily estimated using the Kendall tau rank correlation coefficient, which makes it possible to generate daily streamflow data. The method provides a new tool for multisite stochastic simulation. [ABSTRACT FROM AUTHOR]

[Display omitted] • A vegetation suitability index is proposed using Clayton copula function. • A cellular automata module is developed for modeling oasis spatial dynamics. • The module is reasonably structured, practical, and reliable. • Ecological water diversion successfully recovered oasis in arid inland basins. • Predicting oasis dynamics provides implications for management. Assessing the impact of ecological water diversion on oasis restoration is critical for water resources and environmental management in arid inland basins. This study proposed a copula incorporated cellular automata (CA) module for modeling the oasis's spatial distribution driven by ecological water diversion. Given the effects of groundwater depth and community expansion on oasis evolution, a water availability index and a neighbor vegetation index were proposed. These two indices were joint using the Clayton copula function, producing a vegetation suitability index that quantified vegetation growth's habitat quality and provided the CA module's basis to identify cellular categories. The CA module was integrated into a previously developed conceptual lumped ecohydrological model (CLEM), resulting in an improved CA-CLEM that outputted groundwater depth, oasis area, oasis NDVI, and oasis distribution data. The CLEM provided the oasis area changes for the CA module, which specified the spatial distribution of these changes. The CA-CLEM had a simple structure, low computation cost, and good performance, indicated by the application in the Shiyang River basin in Northwest China. Results show that an ecological water diversion of 30 million m3, with an average groundwater depth of 2.95 m, an oasis area of 25 km2, and an oasis NDVI of 0.36, would successfully recover the terminal Qingtu Oasis in 2020. The oasis distribution and its dynamic evolution processes were irregular because of the spatial heterogeneity of vegetation habitat suitability. The present Qingtu Oasis was fragile, and its sustainability relied on ecological water diversion. The reduction in water diversion would cause water table decline, oasis area shrinkage, vegetation degradation, and oasis fragmentation. The CA-CLEM combined with scenario analysis provides practical and reliable implications for managers and policymakers to improve ecological water diversion strategies. [ABSTRACT FROM AUTHOR]

• Establishing a risk analysis model for multi-objective flood control operation of a complex reservoir system coupling multi-dimensional uncertainties. • Introducing the initial water level uncertainty to risk analysis for multi-reservoir operation. • Using copula theory to describe the correlation of the errors of reservoir initial water levels and the time-space correlation of flood forecast errors. Many uncertainties are involved in flood control operation of a multi-reservoir system and result in risks for the system. Risk analysis for reservoir flood control operation is essential for decision making. Traditional flood control risk analysis only takes flood forecast error as the main risk source, ignoring the influence of dynamic control of flood-limited water level on reservoir initial water level for flood regulation. In this study, the spatial correlation of reservoir initial water level errors and the spatiotemporal correlation of flood forecast errors are identified via copula function. A risk analysis model considering both upstream and downstream is established for multi-objective flood control operation of a complex reservoir system, coupling multi-dimensional uncertainties of reservoir initial water levels, flood forecast errors, reservoir capacity curve errors, reservoir discharge curve errors and river flood routing errors. The impact of multiple risk sources with spatiotemporal correlations on reservoir flood control operation is then evaluated and the competitive tradeoff between flood control risks in upstream and downstream analyzed. The model is applied to a mixed four-reservoir system in Pi River Basin in China. Results indicated that 1) there is a strong correlation between the reservoir initial water level errors in space and also between the flood forecast errors in space and time, which can be effectively described by copula function, and ignoring the correlations will underestimate the flood control risk; 2) the two objective values of upstream and downstream show a clear competitive relationship, and the solution that prefers one objective has low risk marginal benefit, thus choosing a compromise solution can balance risks; 3) coupling the initial water level uncertainty reduces the flood control risk caused by the flood forecast errors to a certain extent, but also lead to the increase of extreme risk loss. [ABSTRACT FROM AUTHOR]

• Proposed a robust multiobjective control operation and risk decision-making model. • Introduced Conditional Value-at-Risk to approximate extreme flood damage. • Revealed mechanism of robust flood control operation in different flood events. • Reduced maximum reservoir release by 7.33% in adverse scenarios. Forecast uncertainty in real-time flood forecasting is the primary source of risk regarding reservoir flood control operation, affecting both reliability and safety of the entire system. Informing reservoir operation for real-time flood control coping with forecast uncertainty encourages overall robust control of risk probability, vulnerability, and resilience of upstream and downstream protection areas. Current stochastic programming models or chance constrained programming models (CCP) focuses on reducing either risk loss or vulnerability, thereby failing to ensure systematic control. This study established a robust multiobjective operation and risk decision-making model to inform reservoir operations. The proposed model includes three modules. First, a copula function was applied to capture multidimensional temporal dependences of forecast uncertainties, following which stochastic inflow scenarios were generated. Second, a robust multiobjective optimization (RMOO) model was established with the objectives of minimizing the probability of risk events, Conditional Value-at-Risk of risk loss (vulnerability) and the maximum sequential duration for both upstream and downstream areas. Then, an adaptive reference multiobjective evolutionary algorithm was used to solve the model and obtain noninferior solutions. Finally, the TOPSIS multicriteria decision-making method was introduced to select the compromise solution. Methodologies were verified by application to Xianghongdian reservoir, China. The main findings were as follows. (1) The copula function effectively captured the multidimensional positive temporal dependences of forecast uncertainties. (2) RMOO provided more widely distributed noninferior solutions than CCP, and performed better in lowering vulnerability of the system. (3) RMOO reduced the maximum release by 135.6 m3/s (7.33%) by increasing prerelease before the occurrence of flood peak. The proposed methodologies provide insights for supporting robust control of real-time operations of a reservoir system through simultaneous control of flood risk probability, vulnerability, and resilience. [ABSTRACT FROM AUTHOR]

• Interactions of water resources in adjacent regions was examined. • Variations in water supply and demand were analyzed through autoregressive models. • Interaction relationship between multi-regional nexus water shortage was solved. • Water resources allocation strategies were obtained through the optimization model. • The models were applied in Beijing-Tianjin-Hebei urban agglomeration of China. Growing agricultural and economic activities have brought tremendous pressure to regional water resources management. Considering the interactions of water resources allocation among adjacent regions with connected water systems, it is necessary to pay attention to the unification of socio-economic development and environmental protection between the regions. This is of significance to many agglomeration areas that are composed of multiple large cities such as the Beijing-Tianjin-Hebei urban agglomeration (BTH) in China. In this research, an ensemble approach was proposed to tackle water resources allocation problems under the constraints of water resources to reflect interdependence of the composed three areas, i.e., Beijing, Tianjin and Hebei. The approach included ARIMA (Autoregressive Integrated Moving Average) method, ANN (Artificial Neural Network) modelling, copula functions, and multi-objective chance-constrained programming model. It can 1) reflect the variations characteristic of regional water supply and demands, 2) solve the interactions between multi-regional water resources, and 3) obtain desired water resources allocation strategies under multi- scenario and scale conditions. Then, the approach was applied in typical regions of China, i.e., Beijing-Tianjin-Hebei urban agglomeration. Considering 27 scenarios of the nexus water shortage risk, multi-objective joint opportunity constraint programming model aimed at maximizing economic benefits and ecological benefits, in combination with economic, agricultural, water resources and environmental constraints. As the optimal strategies indicated, the maximum GDP in BTH would remain at 8514 billion yuan. The minimum amount of water allocation and wastewater discharge would be 24–26 billion m3 and 2100–2101 million m3 respectively. [ABSTRACT FROM AUTHOR]

• Establish the IFPI based on high-dimensional Gaussian copula function. • Identify 12 large-scale flood events of varying intensity using IFPI thresholds. • The IFPI is better than FPI to monitor major floods in Yangtze River. By utilizing Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomaly (TWSA) and remote sensing precipitation data, Flood Potential Index (FPI) has been widely used in large-scale flood monitoring. However, divergent post-processing dynamics of different GRACE solutions result in substantial uncertainties in GRACE TWSA and thus affecting predictive skills of FPI. To overcome this, this study develops an Integrated Flood Potential Index (IFPI) by linking the FPI derived from six GRACE products. The Gaussian copula is employed to establish the joint distribution of FPI from six spherical harmonic (SH) products and mass concentration blocks solutions. One of the most flood-prone regions, Yangtze River basin in China, is selected as a case study. We have identified and characterized the floods with different intensities using IFPI, which is evaluated against standardized discharge observations as well as the Total Storage Deficit Index (TSDI), Water Storage Deficit Index (WSDI) and Combined Climatologic Deviation Index (CCDI). Results show that the area under curve (AUC) values of IFPI for different levels of floods are generally greater than FPIs and their ensemble mean, implying the better predictive skill for the large-scale flood events. During the three severest floods in 2010, 2015, and 2016, IFPI captures the flood variability exhibited by TSDI, WSDI, and CCDI, as well as hydrological observations. This proposed approach might provide reference for flood monitoring and from multi-mission satellite data. [ABSTRACT FROM AUTHOR]

• Proposed integrated flood risk identification, analysis, and diagnosis framework. • Characterized spatial-temporal dependent forecast uncertainties by Copula function. • Discovered the underestimation of risk when neglecting spatial-temporal dependences. • Constructed scenario-based risk diagnosis model to examine causes of risk. Uncertainties in flood forecasts that result from error propagation in meteorological–hydrological simulation processes present spatial and temporal dependences that are the principal sources of risk to real-time flood control in a multireservoir system. Risk analysis models that neglect such dependences potentially yield biased results regarding flood control operations. To enhance the accuracy of flood risk analysis and inform decision making regarding real-time flood control for a multireservoir system, this study proposed an integrated flood risk identification, analysis, and diagnosis model framework that incorporates the multidimensional dependences of forecast uncertainties. The framework includes an uncertainty identification model that uses the copula function to characterize the joint probability function of the mixed dependent uncertainties. It also incorporates a real-time flood risk analysis module that couples simulation and optimization to convert the complex multiobjective operation under uncertainty into a tractable model capable of flexibly evaluating individual and joint flood risks. The third component is a risk diagnosis module that calculates risk contributions using conditional probability, and identifies stable and vulnerable reservoirs within the system to alter operational decisions toward risk reduction. Methodologies were verified through application to a mixed four-reservoir system in the Pi River Basin, China. The principal findings were as follows. (1) Positive-dominated dependences of forecast uncertainties were identified because of error propagation through the meteorological and rainfall–runoff forecasts within the spatial and temporal continuity connection, and the copula function accurately described and quantified the multidimensional temporal and spatial dependences of the uncertainties. (2) In neglecting the spatial dependences of forecast uncertainties, the joint flood risk of the reservoir system was underestimated; in ignoring high-order temporal dependences, individual flood risk was underestimated owing to biased estimation in error variance. (3) Systematic reorganization of the operational strategies of diagnosed stable and vulnerable reservoirs could provide guidance for reduction and homogenization of flood risks. The proposed framework could be used to improve the accuracy of flood risk analysis and support reliable real-time flood control operation of a multireservoir system. [ABSTRACT FROM AUTHOR]

• The difference in the flood processes between lakes and reservoirs is illustrated. • A framework for establishing design floods for water-carrying lakes is proposed. • The method avoids the randomness of the traditional design flood methods for lakes. Lakes are typical plain reservoirs, with many similar functions to valley reservoirs; however, their flood processes differ significantly due to their specific topography. Flooding of the dam site, the entire reservoir inflow, is considered during flood control analysis of valley reservoirs. Whereas, the flood inundation risk of flood detention areas in the lake basin should be considered for lakes. Therefore, more attention should be paid to the flood processes of each sub-region. Here, based on the flood processes characteristics of large water-carrying lakes, a complete framework for establishing design floods for sizeable rivers-connected lakes is introduced. First, multiple Copula-based joint distribution functions are constructed based on the water system structure of the lake. Then, several confidence intervals are obtained using a methodology that identifies the boundary for multivariate combinations. Finally, through these confidence intervals are used in conjunction with each other, the design flood processes for water-carrying lake system can be determined. The proposed methodology was applied to the Hongze Lake, China. The results indicate that this method not only effectively avoids the randomness of the traditional method but also balances the characteristics of the flood process in each sub-region with the features of the entire flood process. The proposed method has a strong statistical theoretical foundation and expands the applicability of multi-variable flood frequency analysis techniques to water conservancy projects. [ABSTRACT FROM AUTHOR]

• Multivariable flood risk and its dynamics considering project reasonable service life were fully explored. • The risks of slight, moderate, severe, and extreme floods are 1.00, 0.96, 0.66, and 0.06, respectively. • The risk of severe and extreme (slight and moderate) floods shows a significantly increasing (decreasing) trend. • Teleconnection factors exert strong impacts on multivariable flood risk dynamics. Concern over the impacts of changing environments on extreme floods has made the multivariable flood risk assessment paid more attention by the whole world. Recent studies have taken the combination of flood peak, flood volume, and duration into account when assessing flood risk. Nevertheless, the dynamics of multivariable flood risk considering project reasonable service life remains unresolved. To this end, flood peak and flood volume were considered, and Copula function was used to describe their dependent structure, considering project effective service life based on the joint return period to calculate the risk rate for characterizing flood risk. Then, a sliding window of 25 years was adopted to explore its dynamics, and attribution analysis was subsequently conducted. The Pai-lung River, which belongs to the Yangtze River Basin in China, was selected as a case study. Results show that: (1) on basis of "and (∩)" joint return period, considering 50-year reasonable service life, the risks of slight, moderate, severe, and extreme floods are 1.00, 0.96, 0.66, and 0.06, respectively; (2) the flood risk dynamics vary with different levels, where the risks of slight and moderate floods tend to decrease whilst the risk of severe and extreme floods shows a significant increase; (3) the risk dynamics of severe and extreme floods are positively related to Sunspots, El Niño Southern Oscillation and Arctic Oscillation, while Pacific Decadal Oscillation has a weaker influence on flood risk dynamics. Generally, this study sheds new insights into multivariable flood risk assessment in a changing environment. [ABSTRACT FROM AUTHOR]

• Copulas-based bivariate probabilistic framework is adopted to characterize dry/wet combinations risk. • Four types of combination events at two scenarios are incorporated in risk assessment. • Dry/wet combinations risk is fully evaluated by static and dynamic methods. • The risk of dry/wet combinations with low frequency in the same scenario increases and vice versa. • Compared with the sunspot and AO, the ENSO has a dominant control on dry and wet combination dynamics. Previous studies on dry and wet combinations had focused on their evolution characteristics in a single season (i.e. spring or summer), while the dynamics and driving forces between the adjacent seasons remain unsolved, which is important for water resources planning and management in a changing environment. To this end, the dry and wet events in adjacent seasons in the Yellow River Basin (YRB) were evaluated based on the standardized precipitation index (SPI), and the joint return periods of various combinations of dry and wet conditions under two scenarios (moderate and severe) were calculated using a copula function. The Mann-Kendall test was used to explore the joint return period dynamics with a 31-year sliding window. The Copula-based likelihood-ratio method and cross-wavelet transform were adopted to explore the driving forces of dry and wet combination dynamics. The results indicate that (1) spring-summer and summer-autumn are prone to continuous dry (wet) events, while the probabilities of the combinations of dry and wet conditions occurring in autumn–winter and winter-spring are almost the same; (2) continuous wet events frequently occur above Longyang gorge and below Huayuankou, while the Inner flow area is prone to continuous dry events; (3) the risk of dry and wet combinations with high frequency decreases, whereas the risk of compound events with low frequency increases; (4) the dependency structure dynamics of the SPI series between adjacent seasons show the strongest correlation with the El Niño-Southern Oscillation (ENSO), followed by the Arctic Oscillation and sunspot activities, implying that the ENSO has a dominant control on the dry and wet combination dynamics in the YRB. This study brings new insights into dry and wet combination dynamics and driving forces in a changing environment and provides a scientific basis for the sustainable utilization of water resources in the YRB. [ABSTRACT FROM AUTHOR]

• Projected changes of bivariate flood quantiles are evaluated over 151 catchments across China. • The uncertainty envelope is quantified with a copula-based parametric bootstrapping uncertainty approach. • Outputs of multi-model ensembles after bias correction are used to drive four hydrological models. • Bivariate flood quantiles and estimation uncertainty of most catchments are increasing under climate warming. As atmospheric moisture capacity is highly sensitive to rising temperatures, precipitation extremes are widely projected to intensify with a warming climate and thus altering the flooding generation regime. Previous works seldomly focused on bivariate flood quantiles under climate change at a national scale, and fewer flooding projections quantified the estimation uncertainty sourced from sample size limitation. This study systematically investigates the changes in bivariate quantiles of flood peak and volume with incorporation of sampling uncertainty for 151 catchments over China, with climate trajectories projected by a set of multi-model ensemble under representative concentration pathway (RCP) 8.5. After correcting the systematical biases of eight CMIP5 GCM outputs, four state-of-the-art hydrological models are driven and validated for each catchment, and the best-simulation model is selected to project future streamflow scenarios. The copula function is employed to construct the joint distribution of flood peak and volume, and then the most likely realizations of bivariate quantiles are derived under different Joint Return Periods (JRPs), with the uncertainty envelope quantified with the area of 90% confidence ellipse by a copula-based parametric bootstrapping uncertainty (C-PBU) approach. Our results project an overall ascending trend of temperature and precipitation over China, and the bivariate flood quantiles and corresponding estimation uncertainty of most catchments in the future period (2056–2100) are much larger than the baseline (1961–2005), despite accompanied by substantial climate model uncertainty and spatial variability in magnitude. Many basins would be subjected to a dramatic increase of flood magnitude by over 50%, while only few basins are projected to experience a decreasing flood risk, suggesting an urgent need to increase societal resilience to a warming climate over China. [ABSTRACT FROM AUTHOR]

• A Novel Approach for Longitudinal Dispersion Coefficient Estimation based on Tri-variate Archimedean Copulas was proposed. • The matrix of Conditional probability for longitudinal dispersion coefficient was calculated. • 60–70% of conditional probability was calculated for 120 < K x HU * < 1400. Longitudinal dispersion coefficient (LDC) is a crucial component in the modeling of pollutants and sediment transport in rivers. Several researches carried out to find an equation for LDC based on geometrical and hydrodynamic parameters. Also, different approaches including, analytical, empirical, and soft computing were utilized for this purpose. While several researches were carried out in this field, the implementation of the estimated LDC is restricted due to the accuracy of estimation and the small number of datasets. In this research to overcome these problems, a novel approach based on copula functions is considered to propose a range of LDC instead of a single value. For this purpose, an extended dataset including 164 worldwide of geometrical and hydrodynamic parameters was utilized. These data were classified into six dimensionless classes. Also, tri-variate copula functions were used to the calculate conditional probability of LDC. Results indicated that Frank copula is the best fit in bi-variate and tri-variate modeling. Based on Frank copula, the conditional probability of different classes was calculated. Among all classes, the lowest conditional probability assigned to the K x HU * > 5000 and in the next rank to K x HU * < 30. 60–70% of conditional probability was calculated for 120 < K x HU * < 1400. Physical interpretation of the dispersion process justifies the results which strengthen the methodology. So, the results have the capability to use worldwide in pollutant and sediment transport modeling. [ABSTRACT FROM AUTHOR]