Application of River Network Hydrodynamic Model in Determining Water Distribution Scale of Haishu Plain

The water distribution network is an important part of the plain water environment improvement system. To make efficient use of the regional water diversion source, scientifically distribute the water diversion flow and improve the water environment carrying capacity of Haishu Plain, the river network hydrodynamic model is used in this paper to simulate the water intake location, reasonable water quantity and influence range of water transfer in Haishu Plain. The simulation results have high accuracy, which can provide a scientific basis for the scale, water transfer mechanism and project layout of water transfer construction in Haishu Plain and show a strong reference value for the study of water diversion and distribution scheme of coastal plain river network.

Water level updating model for flow calculation of river networks

Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up based on a three-step method at key nodes, and model correction values were collected from gauge stations. To improve the accuracy of water level and discharge forecasts for the entire network, the discrete coefficients of the Saint-Venant equations for river sections were regarded as the media carrying the correction values from observation locations to other cross-sections of the river network system. To examine the applicability, the updating model was applied to flow calculation of an ideal river network and the Chengtong section of the Yangtze River. Comparison of the forecast results with the observed data demonstrates that this updating model can improve the forecast accuracy in both ideal and real river networks.

An Implicit Coupled 1D/2D Model for Unsteady Subcritical Flow in Channel Networks and Embayment

In this study, 1D and 2D shallow-water models were coupled to simulate unsteady flow in channel networks and embayment. The 1D model solved the 1D shallow-water equations (St. Venant) using the Preissmann box method and targeted long narrow reaches of the river networks, while the 2D model targeted broad channels and embayment and solved the 2D shallow-water equations using a semi-implicit scheme applied to an unstructured grid of triangular cells. The 1D and 2D models were solved simultaneously by building a matrix for the free surface elevation at every 1D junction and 2D cell center. Velocities were then computed explicitly based on the results at the previous time step and the updated water level. The originality of the scheme arose from a novel coupling method. The results showed that the coupled 1D/2D model produced identical results as the full 2D model in classical to benchmark problems with considerable savings in computational effort. Application of the model to the Pearl River Estuary in southern China showed that complex patterns of tidal wave propagation could be efficiently modeled.

分支河流体系沉积学工作框架与流程

基于现有的研究成果和存在的问题,探讨了分支河流体系(DFS)研究中的关键科学问题、主要研究内容、研究方法和工作流程。研究结果表明:①DFS研究中最关键的3个科学问题是明确河网结构和河型演变规律、构建沉积标志和沉积模式、分析其形成和分布的控制因素。②DFS研究的主要内容包括建设形态沉积学数据库、现代沉积机理研究、分类研究、建立沉积模式、储层建模与储层预测等5个方面。③DFS研究中的关键技术包括基于遥感图像的形态数据采集、形成机理的水槽和模拟实验、河网重构、顶点位置预测与河道分汊点自动生成方法、储层建模知识库平台等。④DFS研究的基本工作流程是先建立形态沉积学数据库,搭建数据库软件平台,在此基础上选择具有代表性的DFS进行现代沉积解剖,然后综合现代沉积调查、露头解剖和模拟实验成果,形成分类体系,总结各类DFS的识别标志和沉积模式,分层次建立储层预测模型,形成沉积结构储层预测模型的建模软件平台,从而预测沉积体系中有利储层的分布。

Mapping Urban Networks through Inter-Firm Linkages: The Case of Listed Companies in Yangtze River Delta, China

Recently, literature on urban network research from the perspective of ?rm networks has been increasing. This research mainly used data from the headquarters and branches of all 2581 listed manufacturing companies in the Yangtze River Delta from 1990 to 2017, and studied the urban network through an interlocking network model that quantifies the links between enterprises. The results showed that the spatial distribution of listed manufacturing industries in the Yangtze River Delta was relatively concentrated, and cities such as Shanghai, Nanjing, and Hangzhou were hot spots for the spatial distribution of listed manufacturing industries. However, Fuyang, Suqian, Chizhou, Lishui and other network edge cities were less distributed in manufacturing. The urban network of the Yangtze River Delta has significant hierarchical characteristics. The urban network of the Yangtze River Delta presents a multi-center network development mode with Shanghai as the center and Nanjing, Hangzhou, and Hefei as the sub-centers. Moreover, we found that the development of inter-city connections in the Yangtze River Delta was driven by network mechanisms of priority attachment and path dependence. The radiating capacity and agglomeration capacity of cities in the Yangtze River Delta have a strong polarization characteristic. The core cities such as Shanghai, Nanjing, Hangzhou, and Hefei have much higher network radiation capabilities than network aggregation capabilities. However, other non-core cities and network edge cities have weak network radiation capabilities, and mainly accept network radiation from core cities. It enriches the research of urban networks based on real inter-?rm connections, and provides ideas for the wider regional study and the combination of econometric techniques and social network analysis.

A Comparison of ANN and HSPF Models for Runoff Simulation in Balkhichai River Watershed, Iran

In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The considered area is the Balkhichai River watershed in northwest of Iran. HSPF is a semi-distributed deterministic, continuous and physically-based model that can simulate the hydrologic cycle, associated water quality and quantity and process on pervious and impervious land surfaces and streams. Artificial neural network (ANN) is probably the most successful learning machine technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach the understanding of the nature of the phenomena. Statistical approach depending on cross-, auto- and partial-autocorrelation of the observed data is used as a good alternative to the trial and error method in identifying model inputs. The performances of ANN and HSPF models in calibration and validation stages are compared with the observed runoff values in order to identify the best fit forecasting model based upon a number of selected performance criteria. Results of runoff simulation indicated that the simulated runoff by ANN was generally closer to the observed values than those predicted by HSPF.

Joint optimization scheduling for water conservancy projects incomplex river networks

In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.

平原河网水动力优化调度与水环境改善的响应关系研究

平原河网河道流动性较差是引起水环境污染严重的主要原因,引调水是改善平原河网地区水环境的重要措施之一。本文以佛山市三山围为例,基于实测资料构建了河网水动力水质耦合模型,采用15天的连续水动力水质监测数据对模型进行了验证,并将NSE和RMSE模型评价指标用于模型评价。同时结合地势、潮汐规律和景观水位等设计了4种闸控模式和7种景观控制水位,共28种模拟工况,模拟分析了不同条件下的水动力水质改善与内外江潮位变化、引排水流量及其空间分布、闸控方式和景观水位等综合响应机理。结果表明:构建的模型合理可靠。研究区域受引排水路径的影响,河道流动性差异较为显著。综合考虑河道流量分布、水流路径、分汊河道分流作用和外江污染物浓度等对河道水质的作用效果并有效结合潮汐河网的动态水环境容量和污染源排放的时空分布对水环境的改善作用将非常显著。与低景观控制水位相比,高景观控制水位的内江动态水环境容量相对较大,污染物浓度较低。景观控制水位从0.2 m上升至0.8 m时,不同引调水路径的引水流量上升28.00%~64.70%,断面氨氮浓度削减了0.85~5.50 mg/L,削减比例达到28.89%~67.23%。本研究为平原潮汐河网水环境优化调度研究提供了新的思路,为相关部门对平原河网的水环境改善提供了重要的参考。

数据驱动与物理机制耦合的菜子湖流域水位预报模型研究

菜子湖作为引江济淮工程的关键调蓄湖泊之一,湖泊水位的变化同时受到自然降水和工程调蓄的影响。为了准确模拟及预报菜子湖水位,构建了菜子湖流域四水源新安江模型和菜子湖水位预报的神经网络(LSTM)模型。在此基础上,采用外部耦合的方法,构建了四水源新安江-LSTM耦合模型,进一步将物理机制模型模拟的入湖流量作为补充因子,驱动神经网络(LSTM)模型模拟菜子湖水位,从而实现两种不同模型在湖泊水位预报中的耦合应用。结果得出:直接模拟水位的洪水误差小于0.1 m,耦合模拟水位的洪水误差小于0.02 m,后者相较前者,水位误差精度提升了0.08 m。直接模拟水位验证期的洪水误差在0.02 m之内,纳什系数R^(2)分别为0.89、0.75及0.88,均方根误差RMSE分别为0.034、0.027及0.015;耦合模拟水位验证期的洪水误差在0.015 m之内,纳什系数R^(2)分别为0.91、0.82及0.88,均方根误差RMSE分别为0.019、0.021及0.008。研究结果表明,与单驱动因子得出的结果相比,双驱动因子得出的结果更有效地提高了水位的模拟精度。同时在考虑对应降雨的洪水过程中,数据驱动和物理机制相结合的方法与直接预测水位误差相对比,有效地提高了场次洪水水位预报的精度,得到更精确的模拟结果。为引江济淮工程的调水提供了重要的参考依据,也为相似调水工程的洪水水位预报提供一定的参考。

The Research and Application of BP Neural Networks in River-basin Water and Sediment

Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall conditions serving as affecting factors. The model has satisfactory performance of learning and generalization and can be also used to assess the influence of human activities on water and sediment yield in a river basin. The model is applied to compute the runoff and sediment transmission at Xingshan, Bixi and Shunlixia stations. Comparison between the results from the model and the observed data shows that the model is basically reasonable and reliable.

考虑水动力条件及水系连通的平原河网圩区畅流活水方案

针对平原河网地区水动力条件不足等问题,以嘉兴市新塍镇为例,探究不同畅流活水方案对河道水质的改善效果。构建MIKE11水动力学水质耦合模型,分析“引水/水系连通—水质”驱动响应规律,以NH3-N浓度为主要指标,分析不同活水方案的水质改善效果,并提出优化调控方案。结果表明,集中引水对区域水质改善影响较小,分散设置多个引水点可明显提高水质;直接对水质较差河道进行补水,污染物消减率在8%~30%之间,水质改善效果较明显;合理的水系连通工程可进一步增加畅流活水方案水质改善效果。

“双碳”目标下黄河流域绿色技术创新效率评价及影响因素

基于“双碳”目标视角,采用超效率动态网络SBM模型测算黄河流域82市(盟)2011—2021年绿色技术创新的综合效率、科技研发效率及成果转化效率,并利用面板Tobit回归模型检验外部环境因素对三种效率的影响。结果表明:将CO_(2)排放量纳入指标体系后,中下游地区绿色技术创新综合效率得以提升,上游地区效率反而下降;黄河流域绿色技术创新综合效率呈波动上升趋势,科技研发效率起引擎作用;黄河流域绿色技术创新综合效率区域差异明显,成果转化效率差异是综合效率差异的主要来源;经济发展水平、产业结构高级化、人力资本禀赋、对外开放水平、政府支持力度和环境规制强度对三种效率的影响具有区域异质性。建议黄河流域提高绿色技术创新能力,完善创新成果转化平台,推动能源和产业结构升级。

河网地区洪水预报模型开发及在长江下游地区的应用

平原水网地区的洪水预报一直是水文学科的难点问题,尤其受人类活动影响剧烈。为了更好地模拟流域水文循环过程,解决工程实际应用中的挑战,提出了一种新颖的分布式架构水文模型。该模型具有分布式特征,能够根据流域特征和需求灵活选择最合适的水文特征单元进行组合,从而支持对不同水文特征单元精确化、理论化的研究。为了实现实时预报功能,还研发了数据库耦合技术、大型河网实时校正技术及水利工程调度技术。在此基础上,应用分布式架构思想建立了太湖流域模型,对其进行了概化处理,并选取2016年实况数据对模型参数进行了率定。通过对太湖水位1 d、2 d、3 d预见期的实时预报,计算成果基本能够反映平原水网地区太湖流域的水流运动实际状况。综合而言,该分布式架构水文模型方法不仅在科学性上得到验证,而且在实现技术上具有可行性,成功地解决了从理念到现实的应用实践问题,为平原水网地区洪水预报提供了新的思路和方法。

黄河流域城市网络关联格局及影响因素——基于数字经济企业投资视角

基于2010年、2015年和2020年数字经济上市企业投资数据,采用社会网络分析法,分析了基于数字经济企业投资的黄河流域城市网络时空演变特征,并运用QAP模型揭示其影响因素。研究发现:第一,西安、青岛、济南一直是主要投资城市,数字制造业是数字经济企业投资的主要方向。第二,黄河流域城市网络结构显示,整体网络通达度高且稳定,城市间数字经济联系日益紧密;个体网络呈邻近扩散和跳跃扩散模式,高行政级别指向性导致“马太效应”显著;局部网络呈地理空间邻近特征,子群分布由中西部过渡到东部地区,突破“孤岛式”发展,形成“带式”格局。第三,交通密度、创新能力、地理距离、行政关系是驱动黄河流域数字产业发展和布局的主要因素,但存在行业异质性特征。传统区位因素对数字制造业影响较大,容易形成“区位锁定”和“行业锁定”,而数字服务业则倾向于布局在高行政级别聚集地。

分支河流体系河网形态演变规律——以格尔木河流扇为例

为了探索分支河流体系河网分布规律,为创建储层建模训练图像提供知识库信息。利用Google Earth、Global Mapper、91卫图助手等现代地理信息软件,系统测量了现代格尔木河流扇表面河道分叉特征,应用Horton定律和分形方法建立了河流扇的河网形态模型。结果表明:①格尔木河流扇从顶点到入湖段共识别出26级河道,882个节点和2162个河道段,泉线上、下的河道表现出不同的河网形态;②泉线上部共发育64个节点,每级河道数目增长是上一级河道的R_(B)=1.8900倍,每级河道平均长度是下一级河道的R_(L)=1.0095倍,即河网形态特征参数R_(B)=1.8900和R_(L)=1.0095;泉线下部共发育818个节点,每级河道数目增长是上一级河道的R_(B)=1.0279倍,每级河道平均长度是下一级河道的R_(L)=0.9899倍,即河网形态特征参数是R_(B)=1.0279和R_(L)=0.9899;③河网形态特征参数受坡度影响变化较大,坡度较大则河网形态特征参数较大,坡度较小则河网形态特征参数较小。河网形态特征参数定量表征促进了分支河流体系沉积学发展,为开展陆相盆地储层建模和砂体预测提供了新的知识库信息.

城市河网工程群多目标联合调度优化模型

城市河网工程群联合调度优化是一项复杂的系统工程,涉及多种闸、泵、堰等水利工程,是多时段、多对象、多约束、多目标非线性优化问题。基于此,提出了面向河网水环境改善目标的水工程群多目标联合调度优化模型,综合运用了“水量水质模型—优化调度模型—优化求解算法—方案优选方法”耦合方式,构建面向河网水环境改善的工程群多目标联合调度优化模型,并采用EMMOPSO算法的优化模型求解、基于AHP-TOPSIS的联合调度方案优选,以实现对河网水工程群联合调度的优化与决策支持,选择了长三角地区某城市河网开展了优化模型关键技术的数值试验与案例分析。结果表明:相较于水工程常规调度方式,本优化模型可以减少河网生态引水量23.9%~28.5%、提升河网水体水质达标度11.8%~90.4%,优化模型能够支撑平原城市河网地区取得显著的防洪除涝、水资源调配、水环境改善等多方面的综合效益。

水量调度对平原河网能量影响分析

平原河网水动力条件相对较差,水质问题较为突出,为改善平原河网地区水环境状况,选取江苏省无锡市滨湖河网2018年洪季和枯季19个断面水文水质监测数据,基于MIKE21滨湖河网水动力-水质模型及Python能量计算模型,探究能量耗散规律以及能量、水质间的关系,并确定最佳调水流量区间。结果表明:滨湖河网水质浓度与河流能量显著负相关;一定调水流量的增加使得河流总能量随之上升,能量到达阈值后,随调水流量的上升,河流总能量呈降低趋势;在能量目标层面上优选引调水方案,能有效协调引调水过程中存在的异质性因素,确定逐日最佳调水流量区间及河网总能量最佳区间,提供流量优化调控方案,保障平原河网水动力与水环境条件。

基于静态—动态变量解耦的城市积淹水深快速分析方法

随着全球气候变化和我国城镇化快速发展,极端暴雨天气防范应对已成为城市防汛工作的重点之一。极端降雨条件下的实时滚动洪涝风险分析可以为预报预警提供参考,增强决策的科学性,是防范洪涝灾害的有效方法。尽管传统基于水文水动力专业模型的仿真可以在精细网格上计算出洪水演进及退水的全过程,但精细网格数值计算往往需要消耗大量的算力资源,滚动洪涝预测预报的时效性往往达不到要求。通过建立城市洪涝淹没深度与降雨量之间的函数映射关系,提出了基于静态—动态变量解耦的动态水深高效插值方法,结合分布式数据库和并行计算技术,可以在极短的时间内提供与水文水动力专业模型模拟相对一致的结果。本研究以期为城市洪涝实时风险分析提供有效支撑。

概念性水文模型与智能模型在中小河流洪水模拟中的比较研究

相较于大江大河的流域水文预报研究,中小流域的研究相对匮乏。以沅江河溪水文站以上流域为例,研究LSTM(Long Short-Term Memory)模型和新安江模型在场次洪水中的模拟效果。通过对比,新安江模型的整体模拟精度较高,洪量、洪峰、峰现时间的平均相对误差分别为9.39%、9.55%、1.6h,确定性系数为0.73,综合合格率为100%,达到甲级精度标准;LSTM模型的模拟精度较低,洪量、洪峰、峰现时间的平均相对误差分别为11.76%、12.33%、2.3 h,确定性系数为0.60,综合合格率为75%,达到乙级精度标准。结果表明,新安江模型和LSTM模型是中小河流洪水预报的有效方法,均可用于河溪流域的正式预报,且对于河溪流域,新安江模型的模拟精度比LSTM模型更高。