Response of Hydrodynamics and Water-quality Conditions to Climate Change in a Shallow Lake

Lake water resources operation and water quality management come up with higher challenges due to climate change. The frequency and intensity of extreme hydrological events are increasing under global warming, which may directly lead to more uncertainty and complexity for hydrodynamic and water-quality conditions in large shallow lake. However, studies about effects of climate change on lake hydrodynamic and water-quality conditions are not enough. Thus, a coupled model is es-tablished to investigate the potential responses of lake water level, flow field and pollutant migra-tion to the changing climatic factors. The results imply that water flow capacity and self-purification in the Hongze Lake can be improved by west, northwest, north, south and southeast winds indi-cating wind filed change has a great effect on the hydrodynamic and water-quality conditions in large shallow lake. It is further observed that both hydrodynamics and water quality are more sensitive to rainfall change than to temperature change; compared to the effect from temperature and rainfall, the effect from wind field appear to be more pronounced. Moreover, the results verify the feasibility of coupling basin hydrological model with lake hydrodynamic and water quality model. To the best of knowledge, the coupled model should not be used until independent calibra-tions and verifications for hydrodynamics and water quality modeling, the hydrological model and the coupled model.

     

Evaluating Particle Deposition in the Artificial Groundwater Recharge Process by Physical and CT Imaging Experiments

Particle clogging in the artificial groundwater recharge process is one of the main factors influencing the artificial groundwater recharge efficiency, and particle deposition is the microscopic mechanism of the occurrence and development of particle clogging. Particle deposition in porous media changes the pore structure. The computed tomography (CT) scanning technique is a nondestructive testing method and determines the spatial distribution of pores in porous media. This study combines physical and CT scanning experiments to identify the change process of the pore structure in the artificial groundwater recharge process and compares the pore changes during recharge experiments between two columns containing different media. Porous media changes are observed with the CT scanning technique. The fractal theory is applied in the analysis of CT scan images and physical experiment results. The results of this study indicate that particle deposition can be examined by using CT scan images to obtain pore-related parameters, the internal pore structure of porous media determined through CT scan images can be applied in numerical simulation, and a mathematical model for particle deposition calculation in porous media is established. Compared to the physical experiment measurements, the spatial particle deposition information acquired with the CT scanning technique exhibits a higher accuracy and contains much more relevant data. Not only does this research reveal more clearly the particle clogging mechanism which is based on particle deposition, but also characterize, simulate and predict more accurately the development tendency of particle clogging during artificial groundwater recharge.

     

Comparison of Representative Heuristic Algorithms for Multi-Objective Reservoir Optimal Operation

Heuristic algorithms (HAs) are widely used in multi-objective reservoir optimal operation (MOROO) due to the rapidity of the calculation and simplicity of their design. The literature usually focuses on one or two categories of HAs and simply reviews the state of the art. To provide an overall understanding and a specific comparison of HAs in MOROO, differential evolution (DE), particle swarm optimisation (PSO), and artificial physics optimisation (APO), which serve as typical examples of the three categories of HAs, are compared in terms of the development and applications using a designed experiment. Besides, the general model with constraints and fitness function, and the solution process using a hybrid feasible domain restoration method and penalty function method are also presented. Taking a designed experiment with multiple scenarios, the mean average of the optimal objective function values, the standard deviation of optimal objective function values, the mean average of the computational time, and population diversity are used for comparisons. Results of the comparisons show that (a) the problem of optimal multipurpose reservoir long-term operation is a mathematic programming problem with narrow feasible region and monotonic objective function; (b) it is easy to obtain the same optimal objective function value, but different optimal solutions using HAs; and (c) comparisons do not result in a clear winner, but DE can be more appropriate for MOROO.

     

Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.     

入海水道二期对洪泽湖地区洪水风险的影响研究

为探究淮河入海水道二期工程对洪泽湖的泄洪能力以及周边滞洪区的影响,在对研究区河网概化及地形处理的基础上,建立了研究区河网一维、洪泽湖湖区与周边滞洪区二维耦合的水动力数值模型,并采用1991,2003,2006,2007四年实测历史洪水资料对模型进行率定及验证。基于现状工况、规划工况以及不同洪水量级,设计了5个对比方案对研究区进行洪水演进数值模拟,对不同洪水量级进行横向对比,对不同工况进行纵向对比,并对不同方案进行淹没面积分析和洪水影响分析。结果表明,入海水道二期工程的启用可以有效降低洪泽湖水位,提高洪泽湖的防洪标准和周边滞洪区的启用标准,减少同等洪水量级下的受影响人口数和区域GDP,为洪泽湖的防洪安全建设以及周边蓄滞洪区的稳定发展创造了条件。     

浔江防洪堤对西江中下游洪水的归槽影响

基于河道洪水演算原理,建立符合河流水力特性及其变化规律的边滩滞洪水库调节法流量演算模型、校正因数法水位演算模型,利用实测洪水和防洪堤资料进行归槽或还原演算,综合分析防洪堤对洪水归槽的影响。保护区滞洪容积占次洪水总量9．50％的浔江防洪堤对西江中下游洪水过程的归槽影响大于对洪峰的归槽影响,而且洪水类型和干支流组合不同,影响的程度也不相同：对流量过程、洪峰流量影响的最大归槽流量、最大归槽流量比重分别为3680m^3／s、9．63％,2700m^3／s、5．90％,对水位过程、洪峰水位影响的最大变幅为0．60、0．22m。浔江防洪堤造成的洪水归槽并未显著改变西江中下游洪水特性,干支流洪水遭遇、区间降雨和雨洪重叠仍然是形成西江中下游大洪水的主要因素,西江中下游洪涝频繁是气候和流域环境变化综合影响的结果。     

金沙江下游梯级水库汛期分期及汛限水位合理性研究

汛期分期以及分期汛限水位的合理确定,能够较好地协调水库防洪效益与发电效益之间的矛盾,对充分发挥水库的防洪、发电能力,以及提高汛期洪水资源利用率具有重要意义。基于改进的模糊集分析方法并依据金沙江下游乌东德、白鹤滩、溪洛渡、向家坝4个梯级水库入库洪水对其汛期进行分期,通过隶属度与防洪库容的映射关系确定梯级水库分期汛限水位,提出利用跨期选样法对分期结果及汛限水位进行修正。研究结果表明:金沙江下游梯级水库前汛期为6月1日—7月14日,主汛期为7月15日—9月16日,后汛期为9月17日—10月24日,4个梯级水库合理汛限水位分别为962,800,575,374 m。通过不同典型洪水放大的洪水过程检验了分期汛限水位的合理性。研究得到的金沙江下游梯级分期结果及各库分期汛限水位,均能够在满足防洪安全的前提下提高水库发电等经济效益,在一定程度上提高了汛期洪水资源的利用率。     

重庆市农村欢用水安全现状及 水源地保护规划

根据重庆市农村水源地污染状况的调查和农村饮水水质现状,进行了农村集中式饮用水水源地保护工程规划和管理规划。规划工程措施包括农村饮用水水源地隔离防护工程、生态修复与保护工程、农村点源及面源污染综合治理工程。水源地管理规划包括饮用水水源地监控体系建设、农村集中式饮用水水源地安全信息管理系统建设、农村水源地突发性水污染事件应急机制建设。通过规划方案的试点实施和全面推广实施,以期改善水源地的生态环境,保障农村供水安全,促进水资源可持续利用,实现经济社会可持续发展。     

新安江流域取水许可总量控制指标体系研究

对新安江流域水资源及其开发利用和流域取水许可现状等进行综合分析,提出了包括约束性和预期性指标在内的新安江流域取水许可总量控制指标体系,并确定出2010年流域取水许可总量控制各项指标取值.旨在为新安江流域科学实施取水许可管理制度提供定量化的技术支撑.     

生态环境需水量对芹山水电站发电效益的影响

以福建省的芹山水电站为例,研究穆阳溪流域不同生态环境用水保证率下的生态环境需水量,估算不同生态环境用水量的发电效益损失,并对考虑不同比例生态环境用水量对水电站的发电量所产生的影响进行分析.结果表明:由于确定生态环境需水量的出发点和基本原理不同,用7Q10法、Tennant法、最小月径流法和NGPRP法计算的生态环境需水...