Incorporating Groundwater-Surface Water Interaction into River Management Models

Accurate representation of groundwater-surface water interactions is critical to modeling low river flows in the semi-arid southwestern United States. Although a number of groundwater-surface water models exist, they are seldom integrated with river operation/management models. A link between the object-oriented river and reservoir operations model, RiverWare, and the groundwater model, MODFLOW, was developed to incorporate groundwater-surface water interaction processes, such as river seepage/gains, riparian evapotranspiration, and irrigation return flows, into a rule-based water allocations model. An explicit approach is used in which the two models run in tandem, exchanging data once in each computational time step. Because the MODFLOW grid is typically at a finer resolution than RiverWare objects, the linked model employs spatial interpolation and summation for compatible communication of exchanged variables. The performance of the linked model is illustrated through two applications in the Middle Rio Grande Basin in New Mexico where overappropriation impacts endangered species habitats. In one application, the linked model results are compared with historical data; the other illustrates use of the linked model for determining management strategies needed to attain an in-stream flow target. The flows predicted by the linked model at gauge locations are reasonably accurate except during a few very low flow periods when discrepancies may be attributable to stream gaging uncertainties or inaccurate documentation of diversions. The linked model accounted for complex diversions, releases, groundwater pumpage, irrigation return flows, and seepage between the groundwater system and canals/drains to achieve a schedule of releases that satisfied the in-stream target flow.     

A stochastic conflict resolution model for water quality management in reservoir–river systems

In this paper, optimal operating rules for water quality management in reservoir–river systems are developed using a methodology combining a water quality simulation model and a stochastic GA-based conflict resolution technique. As different decision-makers and stakeholders are involved in the water quality management in reservoir–river systems, a new stochastic form of the Nash bargaining theory is used to resolve the existing conflict of interests related to water supply to different demands, allocated water quality and waste load allocation in downstream river. The expected value of the Nash product is considered as the objective function of the model which can incorporate the inherent uncertainty of reservoir inflow. A water quality simulation model is also developed to simulate the thermal stratification cycle in the reservoir, the quality of releases from different outlets as well as the temporal and spatial variation of the pollutants in the downstream river. In this study, a Varying Chromosome Length Genetic Algorithm (VLGA), which has computational advantages comparing to other alternative models, is used. VLGA provides a good initial solution for Simple Genetic Algorithms and comparing to Stochastic Dynamic Programming (SDP) reduces the number of state transitions checked in each stage. The proposed model, which is called Stochastic Varying Chromosome Length Genetic Algorithm with water Quality constraints (SVLGAQ), is applied to the Ghomrud Reservoir–River system in the central part of Iran. The results show, the proposed model for reservoir operation and waste load allocation can reduce the salinity of the allocated water demands as well as the salinity build-up in the reservoir.     

Probabilistic reservoir operation using Bayesian stochastic model and support vector machine

Available water resources are often not sufficient or too polluted to satisfy the needs of all water users. Therefore, allocating water to meet water demands with better quality is a major challenge in reservoir operation. In this paper, a methodology to develop operating strategies for water release from a reservoir with acceptable quality and quantity is presented. The proposed model includes a genetic algorithm (GA)-based optimization model linked with a reservoir water quality simulation model. The objective function of the optimization model is based on the Nash bargaining theory to maximize the reliability of supplying the downstream demands with acceptable quality, maintaining a high reservoir storage level, and preventing quality degradation of the reservoir. In order to reduce the run time of the GA-based optimization model, the main optimization model is divided into a stochastic and a deterministic optimization model for reservoir operation considering water quality issues.The operating policies resulted from the reservoir operation model with the water quantity objective are used to determine the released water ranges (permissible lower and upper bounds of release policies) during the planning horizon. Then, certain values of release and the optimal releases from each reservoir outlet are determined utilizing the optimization model with water quality objectives. The support vector machine (SVM) model is used to generate the operating rules for the selective withdrawal from the reservoir for real-time operation. The results show that the SVM model can be effectively used in determining water release from the reservoir. Finally, the copula function was used to estimate the joint probability of supplying the water demand with desirable quality as an evaluation index of the system reliability. The proposed method was applied to the Satarkhan reservoir in the north-western part of Iran. The results of the proposed models are compared with the alternative models. The results show that the proposed models could be used as effective tools in reservoir operation.     

Two solution methods for dynamic game in reservoir operation

During the last decade, a number of models have been developed to consider the conflict in dynamic reservoir operation. Most of these models are discrete dynamic models which are developed based on game theory. In this study, a continuous model of dynamic game and its corresponding solutions are developed for reservoir operation. Two solution methods are used to solve the model of continuous dynamic game, namely the Ricatti equations and collocation methods. The Ricatti equations method is a closed form solution, requiring less computational efforts compared with discrete models. The collocation solution method applies Newton's method or a quasi-Newton method to find the problem solution. These approaches are able to generate operating policies for dynamic reservoir operation. The Zayandeh-Rud river basin in central Iran is used as a case study and the results are compared with alternative water allocation models. The results show that the proposed solution methods are quite capable of providing appropriate reservoir operating policies, while requiring rather short computational times due to continuous formulation of state and decision variables. Reliability indices are used to compare the overall performance of the proposed models. Based on the results from this study, the collocation method leads to improved values of the reliability indices for total reservoir system and utility satisfaction of water users, compared to the Ricatti equations method. This is attributed to the flexible structure of the collocation model. When compared to alternative water allocation models, lower values of reliability indices are achieved by the collocation method.     

Assessing the potential of reservoir outflow management to reduce sedimentation using continuous turbidity monitoring and reservoir modelling

In‐stream sensors are increasingly deployed as part of ambient water quality‐monitoring networks. Temporally dense data from these networks can be used to better understand the transport of constituents through streams, lakes or reservoirs. Data from existing, continuously recording in‐stream flow and water quality monitoring stations were coupled with the two‐dimensional hydrodynamic CE‐QUAL‐W2 model to assess the potential of altered reservoir outflow management to reduce sediment trapping in John Redmond Reservoir, located in east‐central Kansas. Monitoring stations upstream and downstream from the reservoir were used to estimate 5.6 million metric tons of sediment transported to John Redmond Reservoir from 2007 through 2010, 88% of which was trapped within the reservoir. The two‐dimensional model was used to estimate the residence time of 55 equal‐volume releases from the reservoir; sediment trapping for these releases varied from 48% to 97%. Smaller trapping efficiencies were observed when the reservoir was maintained near the normal operating capacity (relative to higher flood pool levels) and when average residence times were relatively short. An idealized, alternative outflow management scenario was constructed, which minimized reservoir elevations and the length of time water was in the reservoir, while continuing to meet downstream flood control end points identified in the reservoir water control manual. The alternative scenario is projected to reduce sediment trapping in the reservoir by approximately 3%, preventing approximately 45 000 metric tons of sediment from being deposited within the reservoir annually. This article presents an approach to quantify the potential of reservoir management using existing in‐stream data; actual management decisions need to consider the effects on other reservoir benefits, such as downstream flood control and aquatic life. Copyright © 2012 John Wiley & Sons, Ltd.     

Single multipurpose reservoir design: a modified optimal control problem by chance-constrained programming

This paper presents a chance-constrained programming model for optimal control of a multipurpose reservoir and its modification to a model for single reservoir design. An algorithm is developed for solving complex stochastic problems of multipurpose reservoir planning and design. The complexity of the problem is resolved by a two-step algorithm: (1) transformation of chance constraints on the state and control variables is performed at the first step; and (2) the choice of optimum control or optimal reservoir storage is carried out in the second step. The method of iterative convolution is chosen for the first step, while linear programming is selected for the second step. The algorithm allows the use of random inflows and random demands together with other deterministic demands. The reservoir design problem is presented as a modified optimal control problem. The procedure is illustrated with an example of a hypothetical reservoir design problem with three different types of downstream releases (hydropower production, municipal water supply, and irrigation).     

Adaptive neuro-fuzzy inference system for prediction of water level in reservoir

Accurate prediction of the water level in a reservoir is crucial to optimizing the management of water resources. A neuro-fuzzy hybrid approach was used to construct a water level forecasting system during flood periods. In particular, we used the adaptive network-based fuzzy inference system (ANFIS) to build a prediction model for reservoir management. To illustrate the applicability and capability of the ANFIS, the Shihmen reservoir, Taiwan, was used as a case study. A large number (132) of typhoon and heavy rainfall events with 8640 hourly data sets collected in past 31 years were used. To investigate whether this neuro-fuzzy model can be cleverer (accurate) if human knowledge, i.e. current reservoir operation outflow, is provided, we developed two ANFIS models: one with human decision as input, another without. The results demonstrate that the ANFIS can be applied successfully and provide high accuracy and reliability for reservoir water level forecasting in the next three hours. Furthermore, the model with human decision as input variable has consistently superior performance with regard to all used indexes than the model without this input.     

Optimization of operation rule curves and flushing schedule in a reservoir

Flushing sediment through a reservoir has been practiced successfully and found to be inexpensive in many cases. However, the great amount of water consumed in the flushing operation might affect the water supply. To satisfy the water demand and water consumed in the flushing operation, two models combining the reservoir simulation model and the sediment flushing model are established. In the reservoir simulation model, the genetic algorithm (GA) is used to optimize and determine the flushing operation rule curves. The sediment‐flushing model is developed to estimate the amount of the flushed sediment volume, and the simulated results update the elevation‐storage curve, which can be taken into account in the reservoir simulation model. The models are successfully applied to the Tapu reservoir, which has faced serious sedimentation problems. Based on 36 years historical sequential data, the results show that (i) the simulated flushing operation rule curves model has superior performance, in terms of lower shortage index (SI) and higher flushing efficiency (FE), than that by the original reservoir operation; (ii) the rational and riskless flushing schedule for the Tapu reservoir is suggested to be set within an interval of every 2 or 4 years in the months of May or June. Copyright © 2003 John Wiley & Sons, Ltd.     

A neural network based general reservoir operation scheme

Construction of dams and the resulting water impoundments are one of the most common engineering procedures implemented on river systems globally; yet simulating reservoir operation at the regional and global scales remains a challenge in human–earth system interactions studies. Developing a general reservoir operating scheme suitable for use in large-scale hydrological models can improve our understanding of the broad impacts of dams operation. Here we present a novel use of artificial neural networks to map the general input/output relationships in actual operating rules of real world dams. We developed a new general reservoir operation scheme (GROS) which may be added to daily hydrologic routing models for simulating the releases from dams, in regional and global-scale studies. We show the advantage of our model in distinguishing between dams with various storage capacities by demonstrating how it modifies the reservoir operation in respond to changes in capacity of dams. Embedding GROS in a water balance model, we analyze the hydrological impact of dam size as well as their distribution pattern within a drainage basin and conclude that for large-scale studies it is generally acceptable to aggregate the capacity of smaller dams and instead model a hypothetical larger dam with the same total storage capacity; however we suggest limiting the aggregation area to HUC 8 sub-basins (approximately equal to the area of a 60 km or a 30 arc minute grid cell) to avoid exaggerated results.     

Prediction of Water Temperature as Affected by a Pre‐Constructed Reservoir Project Based on MIKE11

Reservoir construction can lead to much more water stored in front of the dam and significantly increase heat storage capacity of the reservoir waters, thus resulting in different distribution pattern of water temperature in reservoir area compared to river. Especially for large reservoir, the obvious stratification of water temperature will appear in the reservoir with deeper water levels. Meanwhile, the low water temperature will be observed in the downstream river due to the operation of the reservoir. The vertical numerical simulation model for reservoir from MIKE 11 was used to predict the changes of water temperature of Wuxikou Reservoir to check the effects of the reservoir construction on water temperature. The water temperature prediction model was developed to simulate the water temperature of the reservoir and the discharged outflow water. The predicted results can contribute to assessing the feasibility of the pre‐constructed project based on the environmental influence of water temperature.     

Including daily constraints in a monthly reservoir operation model for low-flow

In many real-world situations, operation of water resources systems are subject to constraints which are formulated on a daily basis. Since mathematical models (simulation or optimization models) are often developed on a monthly basis to avoid dimensionality problems and to reduce necessary computer time, some degree of approximation is necessary. Two examples are presented in order to show how this approximation can be done:

• •- a reservoir is operated for hydroelectric power production and low-flow augmentation is provided on a daily basis as a function of inflows (Lech River System in Germany)
• •- a reservoir is operated optimally for daily low-flow augmentation at a control gage downstream. The model includes also mandatory releases as constraints (Wupper River System in Germany).

In the first example the daily requirements are part of the constraints in the system, while in the second example the daily constraint concerns directly the objective function. Further, in the first case water can be saved and in the second case more water is needed as compared to calculations on a monthly basis.These examples are presented to show two solutions using envelope curves but other possibilities (i.e., regression analysis, constraints, coefficients) could be considered.     

Including spatial distribution in a data‐driven rainfall‐runoff model to improve reservoir inflow forecasting in Taiwan

Multi‐step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3‐h warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context, makes the development of real‐time rainfall‐runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3 h. In this paper, we develop a novel semi‐distributed, data‐driven, rainfall‐runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network‐based Fuzzy Inference System solutions is created using various combinations of autoregressive, spatially lumped radar and point‐based rain gauge predictors. Different levels of spatially aggregated radar‐derived rainfall data are used to generate 4, 8 and 12 sub‐catchment input drivers. In general, the semi‐distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead times greater than 3 h. Performance is found to be optimal when spatial aggregation is restricted to four sub‐catchments, with up to 30% improvements in the performance over lumped and point‐based models being evident at 5‐h lead times. The potential benefits of applying semi‐distributed, data‐driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, are thus demonstrated. Copyright © 2012 John Wiley & Sons, Ltd.     

Development of stochastic dynamic Nash game model for reservoir operation. I. The symmetric stochastic model with perfect information

Increasing water demands, higher standards of living, depletion of resources of acceptable quality and excessive water pollution due to agricultural and industrial expansions have caused intense social and political predicaments, and conflicting issues among water consumers. The available techniques commonly used in reservoir optimization/operation do not consider interaction, behavior and preferences of water users, reservoir operator and their associated modeling procedures, within the stochastic modeling framework. In this paper, game theory is used to present the associated conflicts among different consumers due to limited water. Considering the game theory fundamentals, the Stochastic Dynamic Nash Game with perfect information (PSDNG) model is developed, which assumes that the decision maker has sufficient (perfect) information regarding the associated randomness of reservoir operation parameters. The simulated annealing approach (SA) is applied as a part of the proposed stochastic framework, which makes the PSDNG solution conceivable. As a case study, the proposed model is applied to the Zayandeh-Rud river basin in Iran with conflicting demands. The results are compared with alternative reservoir operation models, i.e., Bayesian stochastic dynamic programming (BSDP), sequential genetic algorithm (SGA) and classical dynamic programming regression (DPR). Results show that the proposed model has the ability to generate reservoir operating policies, considering interactions of water users, reservoir operator and their preferences.     

Optimized reservoir operation to balance human and riverine ecosystem needs: model development,and a case study for the Tanghe reservoir,Tang river basin,China

Reservoirs impose many negative impacts on riverine ecosystems. To balance human and ecosystem needs, we propose a reservoir operation method that combines reservoir operating rule curves with the regulated minimum water release policy to meet the environmental flow requirements of riverine ecosystems. Based on the relative positions of the reservoir and the water intakes, we consider three scenarios: water used for human needs (including industrial, domestic and agricultural) is directly withdrawn from (1) the reservoir; (2) both reservoirs and downstream river channels and (3) downstream river. The proposed method offers two advantages over traditional methods: First, it can be applied to finding the optimal reservoir operating rule curves with the consideration of environmental flow requirement, which is beneficial to the sustainable water uses. Second, it avoids a problem with traditional approaches, which prescribe the minimum environmental flow requirements as the regulated minimum environmental flow releases from reservoirs, implicitly giving lower priority to the riverine ecosystem. Our method instead determines the optimal regulated minimum releases of water to sustain environmental flows while more effectively balancing human and ecosystem needs. To demonstrate practical use of the model, we present a case study for operation of the Tanghe reservoir in China's Tang river basin for the three above‐mentioned scenarios. The results demonstrate that this approach will help the reservoir's managers satisfy both human and environmental requirements. Copyright © 2009 John Wiley & Sons, Ltd.     

Development of stochastic dynamic Nash game model for reservoir operation II. The value of players’ information availability and cooperative behaviors

Stochastic dynamic game models can be applied to derive optimal reservoir operation policies by considering interactions among water users and reservoir operator, their preferences, their levels of information availability and cooperative behaviors. The stochastic dynamic game model with perfect information (PSDNG) has been developed by [Ganji A, Khalili D, Karamouz M. Development of stochastic dynamic Nash game model for reservoir operation. I. The symmetric stochastic model with perfect information. Adv Water Resour, this issue]. This paper develops four additional versions of stochastic dynamic game model of water users interactions based on the cooperative behavior and hydrologic information availability of beneficiary sectors of reservoir systems. It is shown that the proposed models are quite capable of providing appropriate reservoir operating policies when compared with alternative operating models, as indicated by several reservoir performance characteristics. Among the proposed models, the selected model by considering cooperative behavior and additional hydrologic information (about the randomness nature of reservoir operation parameters), as exercised by reservoir operator, provides the highest attained level of performance and efficiency. Furthermore, the selected model is more realistic since it also considers actual behavior of water users and reservoir operator in the analysis.     

Flow-ecology modelling to inform reservoir releases for riparian restoration and management

Linked hydrologic, hydraulic, and ecological models can facilitate planning and implementing water releases from reservoirs to achieve ecological objectives along rivers. We applied a flow-ecology model, the Ecosystem Functions Model (HEC-EFM), to the Bill Williams River in southwestern USA to estimate areas suitable for recruitment of riparian tree seedlings in the context of managing flow releases from a large dam for riparian restoration. Ecological variables in the model included timing of seed dispersal, tolerable rates of flow recession, and tolerable duration of inundation following germination and early seedling establishment for native Fremont cottonwood and Goodding's willow, and non-native tamarisk. Hydrological variables included peak flow timing, rate of flow recession following the peak, and duration of inundation. A one-dimensional hydraulic model was applied to estimate stage-discharge relationships along ~58 river kilometres. We then used HEC-EFM to apply relationships between seedling ecology and streamflow to link hydrological dynamics with ecological response. We developed and validated HEC-EFM based on an examination of seedling recruitment following an experimental flow release from Alamo Dam in spring 2006. The model predicted the largest area of potential recruitment for cottonwood (280–481 ha), with smaller areas predicted for willow (174–188 ha) and tamarisk (59–60 ha). Correlations between observed and predicted patches with successful seedling recruitment for areas within 40 m of the main channel ranged from 0.66 to 0.94. Finally, we examined arrays of hydrographs to identify which are most conducive to seedling recruitment along the river, given different combinations of peak flow, recession rate, and water volume released. Similar application of this model could be useful for informing reservoir management in the context of riparian restoration along other rivers facing similar challenges.     

A Compartmental–Spatial System Dynamics Approach to Ground Water Modeling

High-resolution, spatially distributed ground water flow models can prove unsuitable for the rapid, interactive analysis that is increasingly demanded to support a participatory decision environment. To address this shortcoming, we extend the idea of multiple cell (Bear 1979) and compartmental (Campana and Simpson 1984) ground water models developed within the context of spatial system dynamics (Ahmad and Simonovic 2004) for rapid scenario analysis. We term this approach compartmental–spatial system dynamics (CSSD). The goal is to balance spatial aggregation necessary to achieve a real-time integrative and interactive decision environment while maintaining sufficient model complexity to yield a meaningful representation of the regional ground water system. As a test case, a 51-compartment CSSD model was built and calibrated from a 100,000+ cell MODFLOW (McDonald and Harbaugh 1988) model of the Albuquerque Basin in central New Mexico (McAda and Barroll 2002). Seventy-seven percent of historical drawdowns predicted by the MODFLOW model were within 1 m of the corresponding CSSD estimates, and in 80% of the historical model run years the CSSD model estimates of river leakage, reservoir leakage, ground water flow to agricultural drains, and riparian evapotranspiration were within 30% of the corresponding estimates from McAda and Barroll (2002), with improved model agreement during the scenario period. Comparisons of model results demonstrate both advantages and limitations of the CCSD model approach.     

岩石物理模版在储层定量解释中的应用

岩石物理模板采用测井解释的各类岩性矿物骨架点值,选取适合该地区的岩石物理模型,模拟在不同储层组合、不同孔隙及不同饱和度情况下,储层岩石物理参数变化引起的储层测井参数及地球物理响应特征的变化,定量地建立起储层参数同地球物理弹性参数间的解释关系模版.本文根据新场JS42气藏储层参数分析结果,尝试性地将岩石物理解释模板应用于储层定量解释中,对储层高产气区、含水区域进行定量解释,并预测了该气藏的气水界面,该预测结果与实钻井测试情况吻合,证实了该方法的科学性.     

Optimization and simulation of reservoir operation with sediment evacuation: a case study of the Tarbela Dam,Pakistan

Many reservoirs around the world are being operated based on rule curves developed without considering the evacuation of deposited sediment. Current reservoir simulation and optimization models fall short of incorporating the concept of sustainability because the reservoir storage losses due to sedimentation are not considered. This study develops a new model called Reservoir Optimization‐Simulation with Sediment Evacuation (ROSSE) model. The model utilizes genetic algorithm based optimization capabilities and embeds the sediment evacuation module into the simulation module. The sediment evacuation module is implemented using the Tsinghua university flushing equation. The ROSSE model is applied to optimize the rule curves of Tarbela Reservoir, the largest reservoir in Pakistan with chronic sedimentation problems. In the present study, rule curves are optimized for maximization of net economic benefits from water released. The water released can be used for irrigation, power production, sediment evacuation, and for flood control purposes. Relative weights are used to combine the benefits from these conflicting water uses. Nine sets of rule curves are compared, namely existing rule curves and proposed rule curves for eight scenarios developed for various policy options. These optimized rule curves show an increase of net individual economic benefits ranging from 9 to 248% over the existing rule curves. The shortage of irrigation supply during the simulation period is reduced by 38% and reservoir sustainability is enhanced by 28% through increased sediment evacuation. The study concludes that by modifying the operating policy and rule curves, it is possible to enhance the reservoir's sustainability and maximize the net economic benefits. The developed methodology and the model can be used for optimization of rule curves of other reservoirs with sedimentation problems. Copyright © 2008 John Wiley & Sons, Ltd.     

A stochastic model of surface contaminant releases to support assessment of site contamination at a former industrial site

An industrial site is usually contaminated by accidental (and occasionally intentional) releases of pollutants to the environment from various operations carried out on that site. Consequently, the pattern of contamination created during the life of the site depends in part, at least, on the pattern of operations. Thus, the assessment of the pattern of contamination over the site should be improved: if it is possible to identify the pattern of operations on the site, the duration of the different activities and the perceived likelihood of releases from the different operations. A stochastic model has been developed that can be used to simulate alternative realizations of contaminant releases (duration, extent and timing). The model employs release zones associated with particular activities or groups of activities on the site and the areas of each of the zones may be independent or overlapping. The period of activity in each zone is obtained from the site records, while the likelihood and extent of contamination in each zone is inferred from an analysis of the contamination data obtained by point sampling. The form of the model, the method of inference of the model parameter values from the site data and the application of the model to the study site are presented. The release model has been developed as part of a suite of stochastic models for site ground contamination analysis. The stochastic soil and transport models and the application of the integrated modelling system are described in separate papers.