This study developed a hierarchical Bayesian (HB) model for local and regional flood frequency analysis in the Dongting Lake Basin, in China. The annual maximum daily flows from 15 streamflow-gauged sites in the study area were analyzed with the HB model. The generalized extreme value (GEV) distribution was selected as the extreme flood distribution, and the GEV distribution location and scale parameters were spatially modeled through a regression approach with the drainage area as a covariate. The Markov Chain Monte Carlo (MCMC) method with Gibbs sampling was employed to calculate the posterior distribution in the HB model. The results showed that the proposed HB model provided satisfactory Bayesian credible intervals for flood quantiles, while the traditional delta method could not provide reliable uncertainty estimations for large flood quantiles, due to the fact that the lower confidence bounds tended to decrease as the return periods increased. Furthermore, the HB model for regional analysis allowed for a reduction in the value of some restrictive assumptions in the traditional index flood method, such as the homogeneity region assumption and the scale invariance assumption. The HB model can also provide an uncertainty band of flood quantile prediction at a poorly gauged or ungauged site, but the index flood method with L-moments does not demonstrate this uncertainty directly. Therefore, the HB model is an effective method of implementing the flexible local and regional frequency analysis scheme, and of quantifying the associated predictive uncertainty.

Based on the regional water resources carrying capacity (WRCC) evaluation principles and evaluation index system in the National Technical Outline of Water Resources Carrying Capacity Monitoring and Early Warning (hereafter referred to as the Technical Outline), this paper elaborates on the collection and sorting of the basic data of water resources conditions, water resources development and utilization status, social and economic development in basins, analysis and examination of integrity, consistency, normativeness, and rationality of the basic data, and the necessity of WRCC evaluation. This paper also describes the technique of evaluating the WRCC in prefecture-level cities and city-level administrative divisions in the District of the Taihu Lake Basin, which is composed of the Taihu Lake Basin and the Southeastern River Basin. The evaluation process combines the binary index evaluation method and reduction index evaluation method. The former, recommended by the Technical Outline, uses the total water use and the amount of exploited groundwater as evaluation indices, showing stronger operability, while the latter is developed by simplifying and optimizing the comprehensive index system with greater systematicness and completeness. The mutual validation and adjustment of the results of the above-mentioned two evaluation methods indicate that the WRCC of the District of the Taihu Lake Basin is overloaded in general because some prefecture-level cities and city-level administrative divisions in the Taihu Lake Basin and the Southeastern River Basin are in a severely overloaded state. In order to explain this conclusion, this paper analyzes the causes of WRCC overloading from the aspects of basin water environment, water resources development and utilization, water resources regulation and control ability, water resources utilization efficiency, and water resources management.

Increased urbanisation, economic growth, and long-term climate variability have made both the UK and China more susceptible to urban and river flooding, putting people and property at increased risk. This paper presents a review of the current flooding challenges that are affecting the UK and China and the actions that each country is undertaking to tackle these problems. Particular emphases in this paper are laid on (1) learning from previous flooding events in the UK and China, and (2) which management methodologies are commonly used to reduce flood risk. The paper concludes with a strategic research plan suggested by the authors, together with proposed ways to overcome identified knowledge gaps in flood management. Recommendations briefly comprise the engagement of all stakeholders to ensure a proactive approach to land use planning, early warning systems, and water-sensitive urban design or redesign through more effective policy, multi-level flood models, and data driven models of water quantity and quality.

In recent decades, a series of policies and practices for environmental flows (e-flows) have been implemented in China, with the sustainable development goal of balancing the utilization and protection of water resources among social, economic, and ecological needs. The aims of this study were to determine the main challenges and issues in e-flows implementation at different scales by analyzing policies and practices for e-flows in China, and to propose some recommendations for bridging the gaps between the science and implementation of e-flows. The gaps between the science and implementation of e-flows were found after review of literature, policies, and practices, and it was found that ecological flow was a more widely used term by the government, rather than e-flows, in implementation. The plans and effects of e-flows implementation are discussed in this paper and challenges of e-flows implementation are recognized: (1) limited water resources and uneven spatial and temporal distribution, (2) a weak scientific basis for e-flows implementation, (3) poor operability of e-flows science, and (4) ineffective supervision and guarantee measures. The recommendations are (1) to strengthen the scientific foundation of e-flows, (2) to improve effectiveness in application of e-flows science, and (3) to propose operable and effective supervision and guarantee measures. This paper elaborates the current understanding of e-flows science and provides practical recommendations for implementing e-flows and for improving the effectiveness in e-flows implementation. To bridge the gaps between science and implementation of e-flows and improve the operability of policies in future practices, more scientific research on practices is recommended through adaptive management.

Two kinetic models were established for conservative estimates of photodegradation rates of contaminants under sunlight irradiation, in particular for wastewater stabilisation ponds and clarifiers in conventional wastewater treatment plants. These two models were designated for (1) contaminants with high photolytic rates or high photolytic quantum yields, whose photodegradation is unlikely to be enhanced by aquatic photosensitisers; and (2) contaminants withstanding direct photolysis in sunlit waters but subjected to indirect photolysis. The effortlessly intelligible prediction procedure involves sampling and analysis of real water samples, simulated solar experiments in the laboratory, and transfer of the laboratory results to realist water treatment using the prediction models. Although similar models have been widely used for laboratory studies, this paper provides a preliminary example of translating laboratory results to the photochemical fate of contaminants in real waters.

Nano mesocellular foam silica (MCFs) was synthesized through the hydrothermal method in this study. Powder X-ray diffraction and scanning electron microscopy were used to characterize the MCFs sample. The sample presented spherical particles and regular morphology. The results of transmission electron microscopy showed that synthesized MCFs has a three-dimensional honeycomb pore structure, which aids in the adsorption of nickel ion (Ni2+). The results of low-temperature nitrogen gas adsorption-desorption showed that the pore diameter of the synthesized MCFs was 19.6 nm. The impacts of pH, temperature, amount of adsorbent, initial concentration of Ni2+, and contact time on the adsorption effect of Ni2+ by MCFs were studied. Under the optimized adsorption conditions, the adsorption rate reached 96.10% and the adsorption capacity was 7.69 mg/g. It has been determined through the study of kinetics and adsorption isotherms that the adsorption of Ni2+ by MCFs follows the pattern of the pseudo-second-order kinetic model, simultaneously belonging to the Freundlich adsorption type. The thermodynamic results of adsorption showed that, when the temperature is between 25 and 45 ℃, the adsorption is a spontaneous exothermic reaction.

This paper introduces an optimization method (SCE-SR) that combines shuffled complex evolution (SCE) and stochastic ranking (SR) to solve constrained reservoir scheduling problems, ranking individuals with both objectives and constrains considered. A specialized strategy is used in the evolution process to ensure that the optimal results are feasible individuals. This method is suitable for handling multiple conflicting constraints, and is easy to implement, requiring little parameter tuning. The search properties of the method are ensured through the combination of deterministic and probabilistic approaches. The proposed SCE-SR was tested against hydropower scheduling problems of a single reservoir and a multi-reservoir system, and its performance is compared with that of two classical methods (the dynamic programming and genetic algorithm). The results show that the SCE-SR method is an effective and efficient method for optimizing hydropower generation and locating feasible regions quickly, with sufficient global convergence properties and robustness. The operation schedules obtained satisfy the basic scheduling requirements of reservoirs.

The velocity structures of flow through vertically double-layered vegetation (VDLV) as well as single-layered rigid vegetation (SLV) were investigated computationally with a three-dimensional (3D) Reynolds stress turbulence model, using the computational fluid dynamics (CFD) code FLUENT. The detailed velocity distribution was explored with a varying initial Froude number (Fr), with consideration of the steady subcritical flow conditions of an inland tsunami. In VDLV flows, the numerical model successfully captured the inflection point in the profiles of mean streamwise velocities in the mixing-layer region around the top of short submerged vegetation. An upward and downward movement of flow occurred at the positions located just behind the tall and short vegetation, respectively. Overall, higher streamwise velocities were observed in the upper vegetation layer due to high porosity, with  = 98% (sparse vegetation, where  is the porosity), as compared to those in the lower vegetation layer, which had comparatively low porosity, with = 91% (dense vegetation). A rising trend of velocities was found as the flow passed through the vegetation region, followed by a clear sawtooth distribution, as compared to the regions just upstream and downstream of vegetation, where the flow was almost uniform. In VDLV flows, a rising trend in the flow resistance was observed with the increase in the initial Froude number, i.e., Fr = 0.67, 0.70, and 0.73. However, the flow resistance in the case of SLV was relatively very low. The numerical results also show the flow structures within the vicinity of short and tall vegetation, which are difficult to attain through experimental measurements.

    Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems. The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete. The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences. To this end, this paper introduces a new grey relational model to analyze heterogeneous data. In this study, a set of security risk factors for small reservoirs was first constructed based on theoretical analysis, and heterogeneous data of these factors were recorded as sequences. The sequences were regarded as random variables, and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors. Then, a new grey relational analysis model for heterogeneous data was constructed, and a comprehensive security risk factor identification method was developed. A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.