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• The indicator system includes nature, economy, society and infrastructure aspects. • The entropy method and space–time permutation scanning method are combined. • The indicator system considers local needs, standard, and key common indicators. • The aggregation characteristics are analyzed from the space–time dimension. Urban floods have become increasingly frequent in recent years, highlighting the need for resilience evaluation and the identification of strategies to improve urban flood resilience. In this study, a system of resilience evaluation indicators is proposed to quantitatively evaluate the level of urban flood resilience. The entropy method used to calculate the resilience index and the space–time permutation scan method used to analyze the space–time aggregation characteristics of urban resilience levels are combined to establish a methodology for evaluating urban flood resilience. An evaluation indicator system for urban flood resilience is proposed, encompassing aspects of natural, economy, society, and infrastructure. This system attuned to the local needs of urban flood resilience evaluation, tests the applicability of the Guide for Safety Resilient City Evaluation (GB/T 40947–2021) in this study. Moreover, it incorporates key common indicators extracted from prior studies on urban flood resilience evaluation. Zhejiang Province, a flood-prone area in China, is selected as the study area, and the proposed evaluation indicator system is used to evaluate the urban flood resilience levels in Zhejiang and their space–time trends from 2011 to 2020. The results show that resilience levels have improved in the natural, economic, and infrastructure aspects, while the resilience levels of social aspect remain high. From 2011 to 2014, two distinct aggregation patterns emerged, delineated as inland-type and coastal-type aggregation areas. An analysis of both patterns was conducted, considering an array of influencing indicators, such as green coverage rate in built-up areas, population age structure index, gross domestic product, density of resident population in built-up areas, and per capita disposable income. The period from 2014 to 2020, however, did not reveal any significant aggregation characteristics. This study provides a reference for establishing an urban flood resilience evaluation indicator system and an in-depth understanding of urban flood resilience, and targeted recommendations to bolster urban flood resilience in the study area. [ABSTRACT FROM AUTHOR]

Hydrological risk analysis is essential and provides useful information for dam safety management and decision-making. This study presents the application of bivariate flood frequency analysis to risk analysis of dam overtopping for Geheyan Reservoir in China. The dependence between the flood peak and volume is modelled with the copula function. A Monte Carlo procedure is conducted to generate 100,000 random flood peak-volume pairs, which are subsequently transformed to corresponding design flood hydrographs (DFHs) by amplifying the selected annual maximum flood hydrographs (AMFHs). These synthetic DFHs are routed through the reservoir to obtain the frequency curve of maximum water level and assess the risk of dam overtopping. Sensitive analysis is performed to investigate the influence of different AMFH shapes and correlation coefficients of flood peak and volume on estimated overtopping risks. The results show that synthetic DFH with AMFH shape characterized by a delayed time to peak results in higher risk, and therefore highlight the importance of including a range of possible AMFH shapes in the dam risk analysis. It is also demonstrated that the overtopping risk is increased as the correlation coefficient of flood peak and volume increases and underestimated in the independence case (i.e. traditional univariate approach), while overestimated in the full dependence case. The bivariate statistical approach based on copulas can effectively capture the actual dependence between flood peak and volume, which should be preferred in the dam risk analysis practice. [ABSTRACT FROM AUTHOR]

Urban easily waterlogged districts need more systematic monitoring as the key disaster-forming and managing spatial scale, hard to identify the early-warning precipitation (EP). We proposed new algorithms to extract the urban easily waterlogged hilly and plain districts' polygons in Jiangxi Province, China. The districts' disaster risk level prediction model was built through eleven intelligent algorithms' optimization based on the field survey data. The multi-period EP values were identified using the scenario simulation by the model and further verified compared to the government-issued ones. It indicated that 532 easily waterlogged districts possessed sufficient sample representativeness, represented by the significant coefficient of variation ≥ 0.66 for slope, waterlogging disaster area, and depth. The fine Gaussian support vector machine and fine K-Nearest Neighbor algorithms performed higher prediction accuracies of more than 86% and 83% in the model construction and verification districts, respectively. The 1-, 3-, and 6-h severe waterlogging disaster's EP medians were averaged to be 16.5–20.8, 31.5–35.8, and 35–39.3 mm differed among the optimum algorithms respectively, smaller than the government-issued ones. In comparison, the 1-, 3-, and 6-h moderate waterlogging disaster's EP medians were averaged to be 10.5–16.1, 25.5–31.1, and 29–34.6 mm respectively, larger than the issued ones. [ABSTRACT FROM AUTHOR]

Quantifying the relationship between reservoir flood capacity and peak flow reduction can identify reservoirs with good flood control effect and be important for reasonably allocating flood control capacity for larger peak flow reduction. In this study, an optimization-clustering-fitting framework consisting of the Dynamic Programming-Progressive Optimality Algorithm, K-means method, and multiple linear regression, is proposed and applied to a multireservoir system in the middle and lower reaches of the Ganjiang River, China. The results reveal the law of diminishing marginal benefit in reservoir flood control capacity, which means that as reservoir capacity increases, the peak flow reduction increase is larger in low water levels than that in high ones. In this multireservoir flood control system, the Wan'an reservoir has greater flood control potential and can reduce more peak flow of the downstream flood control points than the Xiajiang reservoir when the same capacity of both reservoirs is used for flood control. [ABSTRACT FROM AUTHOR]

The Ganjiang River is the largest tributary of Poyang Lake in China, and its hydrological regime variation greatly affects the utilization of regional water resources and the ecological environment of the lake. In this study, a novel trend analysis method, the Moving Average over Shifting Horizon (MASH), was applied to investigate the inter- and intra-annual trends of flow and water level from 1976 to 2016 at the Xiajiang and the Waizhou hydrological stations in the Ganjiang River. The Significant Change Rate Method (SCRM) was proposed to determine the MASH averaging parameters. The trend analysis results show a statistically significant decrease in water level series throughout the year and the relationship of flow and water level have changed greatly at the Waizhou station. The sediment load reduction, large-scale sand mining and water level decrease of Poyang Lake are identified as the main causes for the water level decrease. [ABSTRACT FROM AUTHOR]

A one-dimensional numerical model was developed to simulate the hydrodynamic processes in a large and complex river network located in the middle and lower Yangtze River basin, China. The main stem of the river is connected to two large lakes, Dongting and Poyang, through many branches. A mass conservation method was used to determine the lateral inflows from local basins along the river. The model was verified against measured data at a large number of hydrological stations over a 2-year period. The water levels and discharges predicted by the model were found to generally agree well with the measured data. The model was then used to reproduce the flow process during a large flood event that occurred in 1998. It was found that the two large lakes could store a large volume of water and thus played an important role in attenuating the flood impact. [ABSTRACT FROM AUTHOR]

• A multi-objective cluster-decomposition framework optimizes flood control operation. • Pareto-front solutions improve flood control operation of cascade reservoirs. • The proposed framework simultaneously copes with three flood control objectives. • The proposed framework offers compromised decisions to boost flood control synergies. Multi-objective flood control operation of cascade reservoirs is a vital issue in river basin management. However, traditional multi-objective approaches commonly provide one operation scheme only and fail to offer decision-makers with more Pareto-front options. This study explores a multi-objective cluster-decomposition framework for optimizing the flood control operation rules of cascade reservoirs in a river basin. The proposed framework involves a multi-objective optimization module, a cluster-decomposition module, and an evaluation and sorting module. The multi-objective cluster-decomposition framework simultaneously deals with three objectives: to minimize the flood peaks of flood control points (O1); to minimize the reservoir capacity used for flood control (O2); and to minimize the flood diversion volume of the flood detention area (O3). The complex flood control system composed of two cascade reservoirs, four navigation-power junctions, one flood detection area, and three flood control points in the Ganjiang River basin of China constitutes the case study. The results demonstrate that the proposed framework can significantly improve the comprehensive benefits of the cascade reservoirs, where the maximum reduction in objectives O1–O3 is 2071 m3/s (the improvement rate is 2.64 %), 219 million m3 (the improvement rate is 44.60 %), and 167 million m3 (the improvement rate is 78.13 %), respectively. Furthermore, in contrast to the traditional multi-attribute evaluation method, the proposed framework can effectively identify compromised decisions through a cluster-decomposition module, which provides beneficial trade-off guidance in making a sound decision upon Pareto-front options. [ABSTRACT FROM AUTHOR]

• A multi-objective cluster-decomposition framework optimizes flood control operation. • Pareto-front solutions improve flood control operation of cascade reservoirs. • The proposed framework simultaneously copes with three flood control objectives. • The proposed framework offers compromised decisions to boost flood control synergies. Multi-objective flood control operation of cascade reservoirs is a vital issue in river basin management. However, traditional multi-objective approaches commonly provide one operation scheme only and fail to offer decision-makers with more Pareto-front options. This study explores a multi-objective cluster-decomposition framework for optimizing the flood control operation rules of cascade reservoirs in a river basin. The proposed framework involves a multi-objective optimization module, a cluster-decomposition module, and an evaluation and sorting module. The multi-objective cluster-decomposition framework simultaneously deals with three objectives: to minimize the flood peaks of flood control points (O1); to minimize the reservoir capacity used for flood control (O2); and to minimize the flood diversion volume of the flood detention area (O3). The complex flood control system composed of two cascade reservoirs, four navigation-power junctions, one flood detection area, and three flood control points in the Ganjiang River basin of China constitutes the case study. The results demonstrate that the proposed framework can significantly improve the comprehensive benefits of the cascade reservoirs, where the maximum reduction in objectives O1–O3 is 2071 m3/s (the improvement rate is 2.64 %), 219 million m3 (the improvement rate is 44.60 %), and 167 million m3 (the improvement rate is 78.13 %), respectively. Furthermore, in contrast to the traditional multi-attribute evaluation method, the proposed framework can effectively identify compromised decisions through a cluster-decomposition module, which provides beneficial trade-off guidance in making a sound decision upon Pareto-front options. [ABSTRACT FROM AUTHOR]