Nonstationarity of univariate flood series has been widely studied, while nonstationarity of some multivariate flood series, such as discharge, water stage, and suspended sediment concentrations, has been studied rarely. This paper presents a procedure for using the time-varying copula model to describe the nonstationary dependence structures of two correlated flood variables from the same flood event. In this study, we focus on multivariate flood event consisting of peak discharge (Q), peak water stage (Z) and suspended sediment load (S) during the period of 1964–2013 observed at the Waizhou station in the Ganjiang River, China. The time-varying copula model is employed to analyze bivariate distributions of two flood pairs of (Z-Q) and (Z-S). The main channel elevation (MCE) and the forest coverage rate (FCR) of the basin are introduced as the candidate explanatory variables for modelling the nonstationarities of both marginal distributions and dependence structure of copula. It is found that the marginal distributions for both Z and S are nonstationary, whereas the marginal distribution for Q is stationary. In particular, the mean of Z is related to MCE, and the mean and variance of S are related to FCR. Then, time-varying Frank copula with MCE as the covariate has the best performance in fitting the dependence structures of both Z-Q and Z-S. It is indicated that the dependence relationships are strengthen over time associated with the riverbed down-cutting. Finally, the joint and conditional probabilities of both Z-Q and Z-S obtained from the best fitted bivariate copula indicate that there are obvious nonstationarity of their bivariate distributions. This work is helpful to understand how human activities affect the bivariate flood distribution, and therefore provides supporting information for hydraulic structure designs under the changing environments.

This study reports on the impact of rainfall patterns and land surface changes on flood dynamics in the Meijiang River Basin, located in the upper reaches of the Ganjiang River. We formulated a range of rainfall patterns and spatial distribution scenarios and employed the MIKE SHE model to evaluate variations in flood volume, flood peak, and the timing of flood peaks. We found that under comparable areal rainfall conditions, flood volumes fluctuated by up to 6.22% among the different rainfall patterns, whereas flood peaks exhibited differences of up to 36.23%. When the rainfall center moved from upstream to downstream, both flood volume and flood peak initially increased before decreasing, with maximum values of 4.2 billion m³ and 4900 m³/s, respectively. We selected three basin scales (i.e., 10,000, 1000, and 100 km²) for comparative analysis. In the period between 1985 and 2020, the changes in land surface conditions resulted in decreases in the flood peaks of the three basins by 7.61, 11.53, and 15.79%, respectively; a reduction in the flood volumes of the three basins by 6.58, 9.60, and 10.48%, respectively; and delayed peak times by 3, 2, and 2 h, respectively. The results of this study show the significant influence exerted by rainfall patterns, the location of the rainfall centers, and the impact of changes in land surface conditions on flood processes. In particular, when the area of the basin was reduced, the influence of the underlying surface was more obvious. These results also show that flood prediction needs to consider the complex interaction of multiple factors.

Poyang Lake is the largest freshwater lake in China. As an important tributary of Poyang Lake, Xinjiang River has an important influence on the water ecology and water resources of the Poyang Lake basin. Based on the hydrological simulation of the SWAT (Soil and Water Assessment Tool) model, the spatiotemporal distribution and evaluation of the blue and green water during the period (1982–2016) in the basin were explored by the Mann–Kendall test, precipitation anomaly percentage, and scenario simulation. It is found that the SWAT model presents a satisfactory performance in runoff simulation of the basin. The multi-year average blue water in the Xinjiang River basin is 1138 mm, and the green water is 829 mm, with a green water coefficient of 0.42. The amount of blue water in wet years is about 1.5 times that in normal years and 2.4 times that in dry years. Compared with the green water, the blue water of the basin is more sensitive to the variations in precipitation. In spatial distribution, the blue and green water in the middle of the basin is obviously more than those in other parts of the basin. During the study period, the blue water in the basin shows a slight decreasing trend, and the green water shows a significant decreasing trend. It is also found that climatic factors have a greater influence on the trend of blue and green water than land use, and the decrease in precipitation is the dominant cause for the trend of blue and green water.

Due to underlying surface changes (USCs), the changes in the Taojiang River Basin’s flood generation conditions could impact the flooding process in the basin. However, most studies have typically focused on either land-use changes (LUCs) or soil and water conservation measures (SWCMs) to assess the impact of the USCs on floods, which may not provide a more comprehensive understanding of the response of floods to the USCs. To investigate how the USCs have altered the floods in the Taojiang River Basin, located upstream of Poyang Lake, China, the HEC-HMS model, which incorporates the influence of the USCs into the parameter calibration, is established in this study to investigate the flood processes on an hourly scale. The flood peak and the maximum 72 h flood volume are selected as two indexes and are applied to analyze the changes in floods caused by the USCs. The 1981–2020 period is divided into three sub-periods (i.e., 1981–1992, 1993–2007, and 2008–2020) based on the conditions of the USCs. It is found that the two indexes have exhibited decreasing trends, mainly due to the USCs during 1981–2020. Benchmarked against the baseline period of 1981–1992, the two indexes decreased by 3.06% (the flood peak) and 4.00% (the maximum 72 h flood volume) during 1993–2007 and by 5.92% and 7.58% during 2008–2020. Moreover, the impacts of the LUCs and SWCMs are separated through parameter adjustments in the model, revealing that the SWCMs played a dominant role in the USCs in the Taojiang River Basin. The quantification and assessment of the impact of the USCs on floods of different magnitudes revealed that the influence decreases with increasing flood magnitude. The results of this study improve our understanding of how USCs affect the flooding process and therefore provide support for flood control management under changing environments.

Plain lakes play a crucial role in the hydrological cycle of a watershed, but their interactions with adjacent rivers and downstream water bodies can create complex river–lake relationships, often leading to frequent flooding disasters. Taking Poyang Lake as an example, this paper delves into its interaction with the Yangtze River, revealing the spatiotemporal patterns of flood propagation within the lake and its impact on surrounding flood control measures. The aim is to provide insights for flood management in similar environments worldwide. This study employs a comprehensive approach combining hydrological statistical analysis and two-dimensional hydrodynamic modeling, based on extensive hydrological, topographic, and socio-economic data. The results indicate that the annual maximum outflow from Poyang Lake is primarily controlled by floods within the watershed, while the highest annual lake water level is predominantly influenced by floods from the Yangtze River. The peak discharge typically reaches the lake outlet within 48 h, with the peak water level taking slightly longer at 54 h. However, water storage in the lake can shorten the time that it takes for the peak discharge to arrive. When converging with floods from the Yangtze River, the peak water level may be delayed by up to 10 days, due to the top-supporting interaction. Furthermore, floods from the “Five Rivers” propagate differently within the lake, affecting various lake regions to differing degrees. Notably, floods from the Fu River cause the most significant rise in the lake’s water level under the same flow rate. The top-supporting effect from the Yangtze River also significantly impacts the water surface slope of Poyang Lake. When the Yangtze River flood discharge significantly exceeds that of the “Five Rivers” (i.e., when the top-supporting intensity value, f, exceeds four), the lake surface becomes as flat as a reservoir. During major floods in the watershed, the water level difference in the lake can increase dramatically, potentially creating a “dynamic storage capacity” of up to 840 million cubic meters.

Plain lakes play a crucial role in the hydrological cycle of a watershed, but their interactions with adjacent rivers and downstream water bodies can create complex river-lake relationships, often leading to frequent flooding disasters. Taking Poyang Lake as an example, this paper delves into its interaction with the Yangtze River, revealing the spatiotemporal patterns of flood propagation within the lake and its impact on surrounding flood control measures. The aim is to provide insights for flood management in similar environments worldwide. This study employs a comprehensive approach combining hydrological statistical analysis and two-dimensional hydrodynamic modeling, based on extensive hydrological, topographic, and socio-economic data. The results indicate that the annual maximum outflow from Poyang Lake is primarily controlled by floods within the watershed, while the highest annual lake water level is predominantly influenced by floods from the Yangtze River. The peak discharge typically reaches the lake outlet within 48 hours, with the peak water level taking slightly longer at 54 hours. However, water storage in the lake can shorten the time for the peak discharge to arrive. When converging with floods from the Yangtze River, the peak water level may be delayed by up to 10 days due to the top-supporting interaction. Furthermore, floods from the "Five Rivers" propagate differently within the lake, affecting various lake regions to differing degrees. Notably, floods from the Fu River cause the most significant rise in lake water level under the same flow rate. The top-supporting effect from the Yangtze River also significantly impacts the water surface slope of Poyang Lake. When the Yangtze River flood discharge far exceeds that of the "Five Rivers" (i.e., when the top-supporting intensity f value exceeds 4), the lake surface becomes as flat as a reservoir. During major floods in the watershed, the water level difference in the lake can increase dramatically, potentially creating a "dynamic storage capacity" of up to 840 million cubic meters.