沿海缺水灌区水资源优化调配耦合模型

本文研究了一种基于含水层海水入侵模拟模型(子模型)和作物优化配水模型(子模型)的沿海缺水灌区水资源优化调配耦合模型，用数值方法求解了含水层海水入侵模拟子模型，并首次将基于分解协调的人工鱼群算法应用于作物优化配水的计算，通过地下水抽水量实现了子模型的耦合，利用Visual Basic6.0软件编制了耦合模型的计算程序。将模型应用于威海市节水灌溉示范区，得出其平水年(50％)、一般干旱年(75％)两种水文年型的水资源联合调配方案。结果表明基于分解协调的人工鱼群算法收敛性好，提高了计算速率，较好的解决了作物优化配水大系统中常见的变量维数高、约束方程多等问题；示范区平水年增加有效供水量997.05m3／hm2，一般干旱年增加有效供水量863.1m3／hm2，能高效利用水资源并能有效控制含水层海水入侵，经济效益和生态效益明显。     

Research on Runoff Sub-model of Non-point Source Pollution Model

Abstract

In light of the current ability to treat point source pollution, nonpoint source (NPS) pollution has become the primary cause of water pollution. In order to manage and control NPS pollution, we must conduct research on NPS pollution. An effective means for such an endeavor is to construct a mathematical model. However, in the present continuous time and distributed parameter NPS models, such as SWAT, parameter requirements are so numerous that their application is very difficult. In order to make such a model convenient for application, research was first conducted before constructing a new continuous time and distributed parameter NPS pollution model based on hydrodynamics. In this paper, as one of its sub-models, the runoff sub-model is introduced. This sub-model is composed of the SCS model and the water routing model that was constructed by the authors of this paper. This water routing model is based on Saint-Venant equations. Through Laplace transform and inverse Laplace transform mathematical modeling, the outflow hydrograph that is an S-curve was obtained. Then, the authors built the relation between S-curve and water- collecting area coefficient, from which the water routing model is derived. In order to calibrate and validate the new model, the authors applied it in the Guishuihe watershed with satisfactory results. The results show that it has value in application, especially in the area where data are scarce.     

基于区域经济层次的城市需水预测模型研究

针对现有需水量预测方法存在局限性等问题,结合经济社会发展状况和水资源的特点,建立了基于研究区域经济层次的交互式城市需水预测模型。该模型能够较好地体现出社会经济、生态、环境和水资源各个系统之间的复杂关系。模型分为经济总量预测、经济结构预测、用水定额预测和需水量预测四个子模块。将模型在乌鲁木齐市水资源综合规划中应用,效果很好。模型具有实用性强、适应性强等特点,适宜推广。     

Random number generator or sewer water quality model?

Integrated urban drainage modelling and environmental impact assessment require sewer emission models to be linked with submodels for treatment infrastructure and receiving rivers. The uncertainty in current water quality modelling is, however, huge, and environmental impact assessment looses more and more credibility. Based on an integrated modelling case for a combined sewer-WWTP-river system, it is shown in the paper that the integrated model does not produce more accurate results in comparison with the random simulation of emission concentrations from a frequency distribution. This should, however, not pose a serious problem as in most applications of impact assessment, model results are not needed in real time but in statistical terms. Further investigation makes clear that detail/sophistication in water quality modelling is not so important, but that more focus has to be given to long-term simulations, the use of parsimonious models and model validation based on concentration frequencies.     

A Numerical Simulation Model for Conjunctive Water Use in Basin Irrigated Canal Command Areas

Basin irrigation is a common practice for growing water intensive crops like paddy. Irrigation water, when supplied through a network of canal, is often found to be inadequate to meet the crop water requirement uniformly throughout the irrigated command area. The most deprived are the cultivators of the lower end of the command, who resort to supplementing the crop water requirement by extractions from the ground. This practice is noticeable in irrigation system without a proper canal water distribution schedule and often result in water logging in the upper command regions contrasted with excessively depleted groundwater table in the lower commands. The present contribution attempts to model the conjunctive water use of such a canal irrigated command using physically based numerical sub-models for simulating surface flow, groundwater flow and the interlinking process of moisture movement through the unsaturated zone for a given quantum of supplied water and crop water demand. Individual models are validated to demonstrate their applicability in an integrated framework. Various plausible conjunctive water use scenarios are tested on a hypothetical command area practising basin irrigation to identify the best possible water distribution strategy under given constraints.     

Incorporating concepts from physical theory into stochastic modelling of urban runoff pollution

On evaluating the present or future state of integrated urban water systems, sewer drainage models, with rainfall as primary input, are often used to calculate the expected return periods of given detrimental acute pollution events and the uncertainty thereof. The model studied in the present paper incorporates notions of physical theory in a stochastic model of water level and particulate chemical oxygen demand (COD) at the overflow point of a Dutch combined sewer system. A stochastic model based on physical mechanisms has been formulated in continuous time. The extended Kaiman filter has been used in conjunction with a maximum likelihood criteria and a non-linear state space formulation to decompose the error term into system noise terms and measurement errors. The bias generally obtained in deterministic modelling, by invariably and often inappropriately assuming all error to result from measurement inaccuracies, is thus avoided. Continuous time stochastic modelling incorporating physical, chemical and biological theory presents a possible modelling alternative. These preliminary results suggest that further work is needed in order to fully appreciate the method's potential and limitations in the field of urban runoff pollution modelling.     

Coastal Urban Flood Simulation Using FEM,GIS and Remote Sensing

In this paper, a rainfall runoff model for coastal urban watershed considering the effects of tidal variations using Finite Element Method (FEM) is presented. Overland flow is modeled using the mass balance equation considering the impervious character of the urban watershed. Storm water flow through the channel is modeled using the diffusion wave form of the Saint Venant’s equations and considering the tidal variations. Galerkin’s FEM is used in the approximation of the governing equations. One dimensional linear line elements are used in the channel discretization. Further the mass balance based overland flow model and diffusion wave based channel flow model have been integrated for prediction of flood. Slope values for the overland flow are determined using the Geographical Information System (GIS) from the Digital Elevation Model (DEM) of the area. The landuse is determined using the remote sensing data. Remote sensing data is analyzed using the ERDAS Imagine and ArcGIS and the Manning’s roughness is calculated for each subdivision of subcatchment. The developed models have been verified with the models available in literature and are found to be satisfactory. Further, the integrated model has been applied to the runoff simulation of a coastal urban watershed in Navi Mumbai, in Maharashtra state of India to analyze the flooding in monsoon season along with the tidal influences. The model could satisfactorily predict the runoff due to monsoon rains coupled with the tidal variations. The developed model will be useful in the urban coastal flood analysis due to heavy rainfall and tidal effects.     

Basin Scale Water Resources Systems Modeling Under Cascading Uncertainties

Global change in climate and consequent large impacts on regional hydrologic systems have, in recent years, motivated significant research efforts in water resources modeling under climate change. In an integrated future hydrologic scenario, it is likely that water availability and demands will change significantly due to modifications in hydro-climatic variables such as rainfall, reservoir inflows, temperature, net radiation, wind speed and humidity. An integrated regional water resources management model should capture the likely impacts of climate change on water demands and water availability along with uncertainties associated with climate change impacts and with management goals and objectives under non-stationary conditions. Uncertainties in an integrated regional water resources management model, accumulating from various stages of decision making include climate model and scenario uncertainty in the hydro-climatic impact assessment, uncertainty due to conflicting interests of the water users and uncertainty due to inherent variability of the reservoir inflows. This paper presents an integrated regional water resources management modeling approach considering uncertainties at various stages of decision making by an integration of a hydro-climatic variable projection model, a water demand quantification model, a water quantity management model and a water quality control model. Modeling tools of canonical correlation analysis, stochastic dynamic programming and fuzzy optimization are used in an integrated framework, in the approach presented here. The proposed modeling approach is demonstrated with the case study of the Bhadra Reservoir system in Karnataka, India.     

多源城市暴雨预报数据融合研究进展

全球气候变化和快速城市化改变了城市水循环过程,加剧了城市暴雨洪涝问题。为有效预报洪水,减轻洪涝灾害,借助水文水力模型对城市水循环的各个环节进行模拟。而单一暴雨预报数据的不确定性是使用水文水力模型模拟预报洪水面临的重大挑战。从城市雨洪模型输入不确定性的角度出发,回顾了暴雨预报技术的发展历程,总结了各种预报方法的特点、适用性和局限性,指出基于单一暴雨预报数据驱动模型预报洪水的不足,提出多源暴雨预报数据融合的基本框架。未来应强化降雨观测和定量降水预报能力,发展多源信息耦合技术,实现高精度、长预见期的洪水预报,为城市防洪减灾提供依据。     

基于灰色遗传BP神经网络的校园区间需水预测研究

水资源预测是城市安全用水的基础保障,而校园用水预测是城市用水规划和管理的组成部分。针对校园用水受很多因素影响产生的不确定性,提出了基于灰色遗传BP神经的校园用水预测模型。模型对校园用水的数据进行灰色关联分析,并加入遗传算法去优化BP神经网络,经过残差计算,输出区间的预测值。运用该模型可以充分提取小样本信息,解决神经网络无法自动寻优的问题。通过Matlab对校园的用水区间数据进行仿真,得出的结果显示,预测的数据和实际数据基本吻合,其仿真精度可以达到90. 32%,验证了该方法的可行性,此预测方法有一定的借鉴意义。     

GM组合模型用于城市生活用水量预测

以天津市为例,采用GM组合模型预测城市生活用水量,力求提高预测的精度。首先,通过对往年城市用水特点的分析,运用多元逐步回归的方法和等维灰数递补动态模型对天津市城市生活用水量进行预测,预测的平均误差分别为7.59%和11.55%;然后,采用上述两种模型的GM组合模型对天津市城市生活用水量进行预测,预测的平均误差降低为5.06%。实践证明,GM组合模型适用于城市生活用水量的预测,精度令人满意。     

地表水和地下水相互作用及集成模型研究

本文在总结国内外地表水和地下水相互作用研究的热点问题的基础上，归纳了定量计算的基本方法，评述了国内外主要的集成模型并对其进行了分类；认为集成模型应该解决时空尺度整合、参数的不确定性等关键问题，指出综合水循环各组成部分的复杂系统模型是模型发展的必然趋势。     

Quantifying the Uncertainty Interaction Between the Model Input and Structure on Hydrological Processes

Input error is one of the main sources of uncertainty in hydrological models. It mainly comes from the uncertainty of precipitation data, which is caused by inaccurate measurement at the point scale and imperfect representation at the regional scale. The structural error of the hydrological model is dependent on the input, and the uncertainty interaction between the model input and structural will increase the cumulative error of the hydrological process. Therefore, the objective of this study is to investigate the impacts of the uncertainties of rain gauge station input levels and hydrological models on flows with different magnitudes by setting nine input levels of rain gauge stations using three hydrological models (i.e., HyMod, XAJ and HBV). The variance decomposition method based on subsampling was used to dynamically quantify the contribution rates of rain gauge station input levels, hydrological models, and their interaction to the runoff simulation uncertainty. The results show that different rain gauge station input levels and hydrological models dynamically affected the hydrological simulation due to an uneven spatiotemporal distribution of precipitation. Moreover, the simulation accuracy was poor at low flow but better at high flow. Increasing the number of rainfall stations input under a certain threshold could significantly improve the hydrological simulation accuracy. In addition, the contributions of the uncertainties of the rain gauge station input levels and its interaction with the hydrological model to runoff were significantly enhanced in the flood season, but the contribution of the hydrological model uncertainty was still dominant. The results of this study can provide a decision-making basis and scientific guidance for the management and planning of water resources within basins under the influence of a changing environment.

     

流域水资源水量水质集成管理模型研究进展

将流域水资源水量水质集成管理模型分为优化模型、模拟模型、优化和模拟相结合模型以及决策支持系统四大类,按此分类总结了目前国内外研究的相关进展,并针对模型中出现的水量水质耦合项,分析了直接求解法、大系统分解协调技术、逐步求解法和情景优选法等水量水质变量的解耦原理和技术,并展望了流域水资源水量水质集成管理模型在水资源优化配置、生态环境需水配置和水资源冲突协调领域中的应用前景。     

Total Urban Water Cycle Models in Semiarid Environments��Quantitative Scenario Analysis at the Area of San Luis Potosi, Mexico

Systems view thinking and holistic urban water cycle concepts are increasingly called upon for integrated analysis of urban water systems to mitigate water stress in large urban agglomerations. However, integrated analysis is frequently not applied due to the inherent complexity, limitations in data availability and especially the lack of guidelines and suitable software tools. The paper presents the application of the total urban water balance model UVQ to the City of San Luis Potosi (1.2 Mio inhabitants) under the arid conditions of Northern Mexico. UVQ is a lumped parameter model which describes water and contaminant flows from source to sink in urban areas and includes all water types such as rainwater, imported water, surface runoff, wastewater and groundwater. The results were especially useful for spatially explicit groundwater recharge calculation in urban areas. A range of urban water scenarios, including different supply strategies and the effect of externalities such as demand change, were simulated and compared to a calibrated baseline scenario. The analysis demonstrated that shallow urban groundwater resources can substantially mitigate problems of water scarcity and overexploitation of deep aquifers if appropriate water quality protection or fit-for-use paradigms are put into place. The modelling exercise delivers relevant information for the decision making process and identifies the most relevant shortcomings in current monitoring systems. This represents a key step on the path to water sensitive and sustainable urban development, including the urban aquifers which have been neglected in the management policy of most cities of the Mexican arid zone.     

Modeling Multisource Multiuser Water Resources Allocation

Water shortage emerges and restricts the urban construction and the socio-economy development due to the rapid expansion of the cities throughout the world. Recently treated wastewater reuse, including rainwater collection and utilization, and seawater desalination, etc., has been put in practice in some cities. This paper presents the characteristics of urban multisource water and multiuser and a multi-objective optimization model of reasonable allocation on multisource water for multiuser under sufficiently considering the harmonious development among economy, society and environment. As a case study this model had been applied to the reasonable allocation of water supply and demand in Dalian City in 2010, 2015 and 2020, in which the maximal benefit of economy, society and environment was regarded as the multi-objectives and the step method was adopted to solve the model. The result indicates that the proportion of the reused water to the total water consumption is gradually increasing, but the proportion of the high quality water to the total water consumption is decreasing. In other words, as a secondary water resource, the reused water has replaced partial high quality water gradually. Consequently, the reasonable allocation of urban multisource water for multiuser is the available approach to alleviate urban water crisis and achieve the sustainable utilization of urban water resources.     

Operationalising uncertainty in data and models for integrated water resources management.

Key sources of uncertainty of importance for water resources management are (1) uncertainty in data; (2) uncertainty related to hydrological models (parameter values, model technique, model structure); and (3) uncertainty related to the context and the framing of the decision-making process. The European funded project 'Harmonised techniques and representative river basin data for assessment and use of uncertainty information in integrated water management (HarmoniRiB)' has resulted in a range of tools and methods to assess such uncertainties, focusing on items (1) and (2). The project also engaged in a number of discussions surrounding uncertainty and risk assessment in support of decision-making in water management. Based on the project's results and experiences, and on the subsequent discussions a number of conclusions can be drawn on the future needs for successful adoption of uncertainty analysis in decision support. These conclusions range from additional scientific research on specific uncertainties, dedicated guidelines for operational use to capacity building at all levels. The purpose of this paper is to elaborate on these conclusions and anchoring them in the broad objective of making uncertainty and risk assessment an essential and natural part in future decision-making processes.     

Identifiability analysis of the CSTR river water quality model.

Conceptual river water quality models are widely known to lack identifiability. The causes for that can be due to model structure errors, observational errors and less frequent samplings. Although significant efforts have been directed towards better identification of river water quality models, it is not clear whether a given model is structurally identifiable. Information is also limited regarding the contribution of different unidentifiability sources. Taking the widely applied CSTR river water quality model as an example, this paper presents a theoretical proof that the CSTR model is indeed structurally identifiable. Its uncertainty is thus dominantly from observational errors and less frequent samplings. Given the current monitoring accuracy and sampling frequency, the unidentifiability from sampling frequency is found to be more significant than that from observational errors. It is also noted that there is a crucial sampling frequency between 0.1 and 1 day, over which the simulated river system could be represented by different illusions and the model application could be far less reliable.