A NEW METHOD FOR ESTIMATING FUTURE RESERVOIR STORAGE CAPACITIES1

ABSTRACT: A new method has been developed for estimating future reservoir storage capacities, allowing for sediment deposition and compaction. Reservoir sedimentation surveys for 117 reservoirs, conducted by the Illinois State Water Survey over the past 60 years, were used to determine regional constants K to represent the severity of sediment deposition in the reservoirs. More than half of the 82 water supply reservoirs investigated had records of reservoir sedimentation surveys, and their K values were calculated by using data from those sediment surveys. The average K values of the remaining non-surveyed water supply reservoirs were estimated from the regional distribution of the K values. Other important factors considered in the estimation of future reservoir storage capacities are the trap efficiency of the reservoirs and the variation of density of sediment deposits due to compaction. The model can also be used for analyzing the economics of alternative sites and of design features that can be incorporated in dams for reducing reservoir sedimentation.     

THE EFFECTS OF RISK AND RELIABILITY ON OPTIMAL RESERVOIR DESIGN1

A chance-constrained linear programming model, which utilizes multiple linear decision rules and is useful for river basin planning, is used to evaluate the effects of risk and reliability on optimal reservoir design. Streamflow forecasts or predictions can be explicitly included in the linear program. The risk associated with the predictions is included in the model through the use of cumulative distribution functions (CDF) of streamflows which are conditioned on the predictions. A multiple-purpose reservoir on the Gunpowder River in Maryland is used to illustrate the effectiveness of the model. In order to provide the decision makers with complete and useful information, trade-off curves relating minimum reservoir capacity (a surrogate for dam costs), water supply and flood control targets, and the reliability of achieving the targets are developed. The trade-off curves may enhance the decision maker's ability to select the best dam capacity, considering technological and financial constraints as well as the trade-offs between targets, risks, and costs.     

SEASONAL ARIMA INFLOW MODELS FOR RESERVOIR SIZING1

ABSTRACT: The reliable sizing of reservoirs is a very important task of hydraulic engineering. Although many reservoirs throughout the world have been designed using Rippl's mass curves with historical inflow volumes at the dam site, this technique is now considered outdated. In this paper, synthetic series of monthly inflows are used as an alternative to historical inflow records. These synthetic series are generated from stochastic SARIMA (Seasonal Autoregressive Integrated Moving Average) models. The analyzed data refer to the planned Almopeos Reservoir on the Almopeos River in Northern Greece with 19‐year monthly inflow series. The analysis of this study demonstrates the ability of SARIMA models, in conjunction with the adequate transformation, to forecast monthly inflows of one or more months ahead and generate synthetic series of monthly inflows that preserve the key statistics of the historical monthly inflows and their persistence Hurst coefficient K. The forecasted monthly inflows would be of help in evaluating the optimal real time reservoir operation policies and the generated synthetic series of monthly inflows can be used to provide a probabilistic framework for reservoir design and to cope with the situation where the design horizon of interest exceeds the length of the historical inflow record.     

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real‐time decisions given that flood conditions change rapidly. This study's objective is to build real‐time data‐driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics‐based model that estimates errors in the flow predictions from a physics‐based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short‐term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics‐based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser‐based Web application, enabling ease of use by both technical and nontechnical personnel.     

BENEFITS OF TRANSFERRING STREAMFLOW PRIORITY FROM AGRICULTURAL TO NON-AGRICULTURAL USE1

ABSTRACT: In Virginia, as in many states, priority to streamflow is held by riparian landowners who are predominantly agricultural users. The streamfiow may also have a high potential value to non-agricultural users who do not have riparian rights. The potential benefits of transferring streamfiow priority rights from agricultural to non-agricultural use were evaluated using simulation for an eastern Virginia watershed. Lowering irrigators' priority to streamflow reduced crop yields and irrigated returns in some years because of inadequate water supplies. However, the transfer of priorities increased the likelihood that the urban reservoir would be able to withdraw water from the stream without interruption. As a result, priority trades reduced the size of reservoir needed to meet a given water requirement by municipal users. The resulting savings in reservoir construction and maintenance costs more than offset the losses to irrigators. Net savings could be achieved even if the reservoir were required to release water periodically to maintain a minimum level of instream flow. The conclusion is that the state should encourage trading of access to streamflow in order to increase the use efficiency of streamfiows. Alternative means by which the state can facilitate water exchanges are discussed.     

RESERVOIR SEDIMENTATION1

ABSTRACT: Reservoirs, as well as lakes and estuaries, are subject to sediment accumulation. The rate at which sedimentation occurs is accelerated or diminished by man's activities. Acceptable rates of sedimentation are considered on the basis of costs of sediment storage and removal and a review of reservoirs constructed with sediment removal capabilities is given. The rates of reservoir sedimentation in the United States are summarized based on reliable data obtained from all sizes of reservoirs. Problems confronting the engineer concerning reservoir construction and maintenance are discussed on the basis of these data.     

RESERVOIR TROPHIC STATE EVALUATION USING LANISAT TM IMAGES1

ABSTRACT: Reservoir water quality is traditionally monitored and evaluated based on field data. Collecting and analyzing field water quality data are costly and time consuming tasks, and whether a limited number of field data truly characterize the spatial variation of the trophic state within a vast water body is often disputed. In this study we utilize Landsat TM data to estimate the water quality and trophic state of the Te‐Chi reservoir in Central Taiwan. A modified multi‐parameter model of Carlson's trophic state index (TSI) was developed for the Te‐chi reservoir. Water quality parameters (concentration of chlorophyll‐a, total phosphorous measurement, and secchi disk depth) required by the model are found to have high correlations with combinations of TM bands. Therefore, TM data are used to map the trophic state of the reservoir. TM‐derived TSI maps of the reservoir reveal that, in summer, the trophic state in the reservoir generally improves from upstream to downstream and that zones of distinct trophic state exist. A trophic state index based on secchi disk depth may give erroneous values in the upstream section of the reservoir pool due to high sediment concentration in the reservoir inflow. We conclude that the Te‐Chi reservoir is eutrophic or worse in summer and meso‐eutrophic in winter. Implementation of best management practices to reduce nonpoint source pollution in the upstream watershed is highly recommended. This study demonstrates the capability of mapping the trophic state in impounded water bodies using the Landsat TM data.     

APPLICATION OF THE GENETIC ALGORITHM FOR OPTIMIZING OPERATION RULES OF THE LiYuTan RESERVOIR IN TAIWAN1

ABSTRACT: A procedure to apply genetic algorithm to optimize operation rules is proposed and applied to the LiYuTan Reservoir in Taiwan. The designed operation rules are operation zones with discount rates of water supply. The first step of the procedure is to predefine the shape of boundary curves of operation zones according to reservoir storage routing. Then, relatively fewer variables are used to describe the curves, and a last genetic algorithm (GA) is applied to optimize the curves. The procedure is applied to the newly built LiYuTan Reservoir for increasing domestic water demands. Shortage index is used to evaluate the performance of operation zones. A year is divided into 36 operational periods, with each month containing three operational periods. The shortage indexes calculated in operational periods are 9.81, 8.27, and 7.13, respectively, for the reservoir without operation rules, applying operation zones optimized by GA with encoding 36 storage levels for each curve, and adopting operation zones optimized by GA with encoding the curves with predefined shape. The average deficits for the three cases are 77.2, 43.6, and 33.3 (10⁴ m³/day), respectively. The results indicate that operation zones optimized by the proposed procedure have smaller shortage indexes and lower average deficits. In addition, the optimized operation zones have less variation and thus are more practical for operation. Conclusively, the proposed procedure utilizing GA to optimize operation zones with predefined shape can provide better and realistic outcomes through limited iterations.     

RESERVOIR RELEASES TO USES WITH DIFFERENT RELIABILITY REQUIREMENTS1

ABSTRACT: Releases from a reservoir may be allocated to a number of uses, each of which may require a given volume of water at a different reliability. The paper provides a method that can be used to estimate the volume of water associated with a given reliability for each use of water when the proportion of releases allocated to each use is known. These results can be used to evaluate the meeting of specified objectives under a published release policy derived by stationary stochastic dynamic programming. The results can also be used to solve water allocation problems when the probability distribution of available water is known (or can be estimated) and water has multiple uses, each of which has different volume and reliability requirements.     

Comparison of Machine Learning Models Performance on Simulating Reservoir Outflow: A Case Study of Two Reservoirs in Illinois,U.S.A.

Reservoir outflow is an important variable for understanding hydrological processes and water resource management. Natural streamflow variation, in addition to the streamflow regulation provided by dams and reservoirs, can make streamflow difficult to understand and predict. This makes them a challenge to accurately simulate hydrologic processes at a daily scale. In this study, three Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were examined and compared to model reservoir outflow. Past, current, and future hydrologic and meteorological data were used as model inputs, and the outflow of next day was used as prediction. Simulation results demonstrated that all three models can reasonably simulate reservoir outflow. For Carlyle Lake, the coefficient of determination and Nash–Sutcliffe efficiency were each close to one for the three models. The coefficient of determination, relative mean bias, and root mean square error indicated that the SVM performed better than the RF and ANN, but the SVM output displayed a larger relative mean bias than that from RF and ANN. For Lake Shelbyville, the ANN model performed better than RF and SVM when considering the coefficient of determination, Nash–Sutcliffe efficiency, relative mean bias, and root mean square error. The study results demonstrate that the three ML algorithms (RF, SVM, and ANN) are all promising tools for simulating reservoir outflow. Both the accuracy and efficacy of the three ML algorithms are considered to support practitioners in planning reservoir management.     

A Simple Framework for Incorporating Seasonal Streamflow Forecasts into Existing Water Resource Management Practices1

Gong, Gavin, Lucien Wang, Laura Condon, Alastair Shearman, and Upmanu Lall, 2010. A Simple Framework for Incorporating Seasonal Streamflow Forecasts Into Existing Water Resource Management Practices. Journal of the American Water Resources Association (JAWRA) 46(3):574-585. DOI: 10.1111/j.1752-1688.2010.00435.x Abstract: Climate-based streamflow forecasting, coupled with an adaptive reservoir operation policy, can potentially improve decisions by water suppliers and watershed stakeholders. However, water suppliers are often wary of straying too far from their current management practices, and prefer forecasts that can be incorporated into existing system modeling tools. This paper presents a simple framework for utilizing streamflow forecasts that works within an existing management structure. Climate predictors are used to develop seasonal inflow forecasts. These are used to specify operating rules that connect to the probability of future (end of season) reservoir states, rather than to the current storage, as is done now. By considering both current storage and anticipated inflow, the likelihood of meeting management goals can be improved. The upper Delaware River Basin in the northeastern United States is used to demonstrate the basic idea. Physically plausible climate-based forecasts of March-April reservoir inflow are developed. Existing simulation tools and rule curves for the system are used to convert the inflow forecasts to reservoir level forecasts. Operating policies are revised during the forecast period to release less water during forecasts of low reservoir level. Hindcast simulations demonstrate reductions of 1.6% in the number of drought emergency days, which is a key performance measure. Forecasts with different levels of skill are examined to explore their utility.     

A LAW AND ECONOMICS APPROACH TO RESOLVING RESERVOIR SEDIMENT MANAGEMENT CONFLICTS1

The loss of the world's reservoir capacity to sedimentation can be mitigated by altering dam operations to release sediment downstream. However, legal uncertainty regarding whether dam owners are liable for damages to surrounding landowners due to altered operations provides a significant disincentive for sustainable sediment management. Past work recommends that courts apply a “rule of reasonableness” in assessing liability: dam owners should be held liable for damages only if they act unreasonably in altering operations, and surrounding landowners should take reasonable measures to mitigate foreseeable damage. Based on past cases, judicial determinations of reasonable reservoir management and reasonable precautionary measures by landowners are generally highly speculative, controversial, and based on limited information. Courts can ease the future burden of making these difficult determinations with rulings that create economic incentives for parties to act reasonably. For example, courts might entitle landowners to be free from sediment related damages, and protect the entitlement with a liability rule. This gives dam owners an economic incentive to release sediment only if the benefits of doing so outweigh court ordered damages to landowners. Past judicial decisions are largely consistent with this legal regime.     

Scenario‐Based and Scenario‐Neutral Assessment of Climate Change Impacts on Operational Performance of a Multipurpose Reservoir

Scenario‐based and scenario‐neutral impacts assessment approaches provide complementary information about how climate change‐driven effects on streamflow may change the operational performance of multipurpose dams. Examining a case study of Cougar Dam in Oregon, United States, we simulated current reservoir operations under scenarios of plausible future hydrology. Streamflow projections from the CGCM3.1 general circulation model for the A1B emission scenario were used to generate stochastic reservoir inflows that were then further perturbed to simulate a potentially drier future. These were then used to drive a simple reservoir model. In the scenario‐based analysis, we found reservoir operations are vulnerable to climate change. Increases in fall and winter inflow could lead to more frequent flood storage, reducing flexibility to store incoming flood flows. Uncertainty in spring inflow volume complicates projection of future filling performance. The reservoir may fill more or less often, depending on whether springs are wetter or drier. In the summer, drawdown may occur earlier to meet conservation objectives. From the scenario‐neutral analysis, we identified thresholds of streamflow magnitude that can predict climate change impacts for a wide range of scenarios. Our results highlight projected operational challenges for Cougar Dam and provide an example of how scenario‐based and scenario‐neutral approaches may be applied concurrently to assess climate change impacts.     

STOCHASTIC DYNAMIC PROGRAMMING FOR OPTIMUM RESERVOIR OPERATION1

ABSTRACT. For a multipurpose single reservoir a deterministic optimal operating policy can be readily devised by the dynamic programming method. However, this method can only be applied to sets of deterministic stream flows as might be used repetitively in a Monte Carlo study or possibly in a historical study. This paper reports a study in which an optimal operating policy for a multipurpose reservoir was determined, where the optimal operating policy is stated in terms of the state of the reservoir indicated by the storage volume and the river flow in the preceding month and uses a stochastic dynamic programming approach. Such a policy could be implemented in real time operation on a monthly basis or it could be used in a design study. As contrasted with deterministic dynamic programming, this method avoids the artificiality of using a single set of stream flows. The data for this study are the conditional probabilities of the stream flow in successive months, the physical features of the reservoir in question, and the return functions and constraints under which the system operates.     

RESOLUTION OF IMPACTS OF RUNOFF EVENTS ON A WATER SUPPLY RESERVOIR WITH A ROBOTIC MONITORING NETWORK1

A robotic water quality monitoring network is used to resolve the coupled patterns of a natural tracer, specific conductance (SC), and metrics of light scattering and turbidity for Schoharie Creek and downstream Schoharie Reservoir, with particular emphasis on the impacts of runoff events. Strong relationships between these parameters and streamflow, and the propensity for this tributary to plunge in the reservoir in summer and fall based on its lower temperature, are reported. The entry of this stream, the primary tributary, into the reservoir as a turbid density current during runoff events is depicted as distinct and vertically coincident subsurface SC minima and peaks in measures of light scattering. The magnitudes of these signatures imparted to the reservoir's water column are demonstrated to be strongly dependent on the magnitude of the runoff event. The time course of the diminishment of these signatures and longitudinal differences in turbidity within the reservoir are described. The documented patterns of SC and metrics of light scattering provided by the robotic monitoring network offer a rare opportunity to support development and testing of a turbidity model with the necessary attributes of fine temporal and spatial resolution.     

COMPARATIVE STUDY OF DROUGHT PREDICTION TECHNIQUES FOR RESERVOIR OPERATION1

ABSTRACT: Predicting the likelihood of a drought markedly enhances the efficiency of reservoir operations. This study applies the kriging method and time series analysis to predict inflows to Shihmen Reservoir in northern Taiwan. A subsequent reservoir operation simulation is employed to determine the drought lead time (DLT), the time before the onset of a drought. A more efficient reservoir operational strategy can be established with the aid of DLT and the probability of successful drought prediction (P s). Simulation results of reservoir operation over a period of three decades demonstrate that, at one month DLT, the kriging approach achieves 0.86 of P s for moderate droughts and 0.94 of P s for severe droughts. The kriging approach generally outperformed the time series approach in terms of DLT, P s of drought prediction, and the number of correctly predicted drought events.     

The Aging of America's Reservoirs: In‐Reservoir and Downstream Physical Changes and Habitat Implications

Reservoirs are important for various purposes including flood control, water supply, power generation, and recreation. The aging of America's reservoirs and progressive loss of water storage capacity resulting from ongoing sedimentation, coupled with increasing societal needs, will cause the social, economic, environmental, and political importance of reservoirs to continually increase. The short‐ and medium‐term (<50 years) environmental consequences of reservoir construction and operation are well known and include an altered flow regime, lost connectivity (longitudinal, floodplain), an altered sediment regime, substrate compositional change, and downstream channel degradation. In general, reservoir‐related changes have had adverse consequences for the natural ecosystem. Longer term (>50 years) environmental changes as reservoirs enter “old” age are less understood. Additional research is needed to help guide the future management of aging reservoir systems and support the difficult decisions that will have to be made. Important research directions include assessment of climate change effects on aging and determination of ecosystem response to ongoing aging and various management actions that may be taken with the intent of minimizing or reversing the physical effects of aging.     

REAL CODED GENETIC ALGORITHM OPTIMIZATION OF LONG TERM RESERVOIR OPERATION1

ABSTRACT: An optimization and simulation model holds promise as an efficient and robust method for long term reservoir operation, an increasingly important facet of managing water resources. Recently, genetic algorithms have been demonstrated to be highly effective optimization methods. According to previous studies, a real coded genetic algorithm (RGA) has many advantages over a binary coded genetic algorithm. Accordingly, this work applies an RGA to obtain the 10‐day (the traditional period of reservoir operation in Taiwan) operating rule curves for the proposed reservoir system. The RGA is combined with an effective and flexible scheme for coding the reservoir rule curves and applied to an important reservoir in Taiwan, considering a water reservoir development scenario to the year 2021. Each rule curve is evaluated using a complex simulation model to determine a performance index for a given flow series. The process of generating and evaluating decision parameters is repeated until no further improvement in performance is obtained. Many experiments were performed to determine the suitable RGA components, including macro evolutionary (ME) selection and blend‐α crossover. Macro evolution (ME) can be applied to prevent the premature problem of the conventional selection scheme of genetic algorithm. The purpose of adjusting a of a crossover scheme is to determine the exploratory or exploitative degree of various subpopulations. The appropriate rule curve searched by an RGA can minimize the water deficit and maintain the high water level of the reservoir. The results also show that the most promising RGA for this problem consists of these revised operators significantly improves the performance of a system. It is also very efficient for optimizing other highly nonlinear systems.     

REAPPRAISAL OF WATER SUPPLY RESOURCES IN DELAWARE RIVER BASIN USING SYNTHETIC HYDROLOGY1

ABSTRACT The 60's drought (1961 1966) which hit the Northeastern United States, had its center over the Delaware River Basin and caused water supply shortages to New York City, Philadelphia, and many other towns and industries in the Basin. Until this event occurred, the existing water supply sources and those planned for the future had been considered adequate, as they were designed for the worst drought of record (usually the 1930-31 drought). In view of this “change in hydrology,” the Delaware River Basin Commission authorized a study (DRBC Resolution 67-4) to re-evaluate the adequacy of existing and planned water supply sources of the Delaware River Basin and its Service Area (New York City and northern New Jersey). Synthetic hydrology is a tool which can be used to overcome many of the limitations of the traditional approach. By analyzing generated streamflow traces in this study, it has been determined that there is a definite relationship between the accumulated rainfall deficiency during the drought and the return periods associated with various durations of runoff in the drought. This indicated that generated traces can be used to standardize the hydrology over an area where the intensity of drought varied. This represented an important facet in the study, because it provided a means to equalize the effects of this drought over the study area, and gave the Delaware River Basin Commission more information so that it could better plan and manage its water resources equitably, not only for the people within the Basin, but for the New York City and northern New Jersey areas as well. Synthetic hydrology was used to determine yield-probability relationships for 50-year periods, and storage-yield-frequency relationships for existing and planned water-supply reservoirs. It was also used to determine yield-probability relationships for reservoir systems within the Basin. In the study, it was determined that monthly streamflow traces and uniform draft rates could be used in yield analysis because of the magnitude of the reservoirs and because seasonal variations of draft rate are small in the study area. Although it was found that with the streamflow generating models (first order Markov) in common use today, it is not possible to definitely determine the actual frequency of a very severe historic drought, it is possible to place a drought in perspective by using synthetic hydrology. The study showed that it is a useful tool in determining water availability over a basin and is useful in studying water management problems such as interbasin transfers, and reservoir systems operations.