Historical Inflow Research Articles

Economic consequences of optimized water management for a prolonged, severe drought in California

If abrupt climate change has occurred in the past and may be more likely under human forcing, it is relevant to look at the adaptability of current infrastructure systems to severe conditions of the recent past. Geologic evidence suggests two extreme droughts in California during the last few thousand years, each 120–200 years long, with mean annual streamflows 40%–60% of the historical mean. This study synthesized a 72 year drought with half of mean historical inflows using random sampling of historical dry years. One synthetic hydrological record is used, and sensitivity to different interpretations of the paleorecord is not evaluated. Economic effects and potential adaptation of California's water supply system in 2020 to this drought is explored using a hydroeconomic optimization model. The model considers how California could respond to such an extreme drought using water trading and provides best‐case estimates of economic costs and effects on water operations and demands. Results illustrate the ability of extensive, intertied, and flexible water systems with heterogeneous water demands to respond to severe stress. The study follows a different approach to climate change impact studies, focusing on past climate changes from the paleorecord rather than downscaled general circulation model results to provide plausible hydrologic scenarios. Adaptations suggested for the sustained drought are similar for dry forms of climate warming in California and are expensive but not catastrophic for the overall economy but would impose severe burdens on the agricultural sector and environmental water uses.

Reservoir optimization model incorporating inflow forecasts with various lead times as hydrologic state variables

This paper presents two Stochastic Dynamic Programming models (SDP) to investigate the potential value of inflow forecasts with various lead times in hydropower generation. The proposed SDP frameworks generate hydropower operating policies for the Ertan hydropower station, China. The objective function maximizes the total hydropower generation with the firm capacity committed for the system. The two proposed SDP-derived operating policies are simulated using historical inflows, as well as inflow forecasts with various lead times. Four performance indicators are chosen to assist in selecting the best reservoir operating policy: mean annual hydropower production, Nash–Sutcliffe sufficiency score, reliability and vulnerability. Performances of the proposed SDP-derived policies are compared with those of other existing policies. The simulation results demonstrate that including inflow forecasts with various lead times is beneficial to the Ertan hydropower generation, and the chosen operating policy cannot only yield higher hydropower production, but also produces reasonable storage hydrographs effectively.

Predictive Control Approach for Long-Term Hydropower Scheduling Using Annual Inflow Forecasting Model

Abstract This paper proposes an annual inflow forecasting model in an open-loop feedback control operational policy for long-term hydropower scheduling. A deterministic optimization model precisely represents hydropower generation by taking into consideration water head as a nonlinear function of storage, discharge and spillage. The inflow is made available by a forecasting model based on a fuzzy inference system that captures the nonlinear correlation of consecutive inflows on an annual basis, with disaggregation of the results on a monthly basis. The performance of the proposed approach is evaluated by simulation for a multi-reservoir system, based on historical inflow records and compared to the same approach on monthly basis. The results show that the proposed approach leads to an operational performance closer to that of the perfect foresight solution, providing lower spillages and higher average hydropower efficiency and generation.

Neural Network Model for Nile River Inflow Forecasting Based on Correlation Analysis of Historical Inflow Data

Neural Network Model for Nile River Inflow Forecasting Based on Correlation Analysis of Historical Inflow Data

Seasonal reservoir inflow forecasting with low-frequency climatic indices: a comparison of data-driven methods

This paper investigates the potential of using data-driven methods, namely Bayesian neural networks (BNN), recurrent multi-layer perceptrons (RMLP), time-lagged feed-forward networks (TLFN), and conventional multi-layer perceptrons (MLP) to forecast seasonal reservoir inflows of the Churchill Falls watershed in northeastern Canada. A climate variability indicator (the El Niño-Southern Oscillation, ENSO) is used as additional information to historical inflow time series in order to predict seasonal reservoir inflows. The prediction results showed that the Bayesian neural network model was best able to capture the additional information provided by the ENSO series, and provided improved predictions in spring and summer seasons relative to the same model using only reservoir inflows. Similarly, time-lagged feed-forward networks and recurrent multi-layer perceptrons showed some improved forecast skill in spring when the ENSO index series are used but generally provided superior performance overall. The conventional multi-layer perceptron appears unable to capture relevant information from the ENSO series regardless of the season. However, when only historical flow series are used, all the selected data-driven methods provide very competitive forecast performances.

The Sensitivity of California Water Resources to Climate Change Scenarios1

Abstract: Using the latest available General Circulation Model (GCM) results we present an assessment of climate change impacts on California hydrology and water resources. The approach considers the output of two GCMs, the PCM and the HadCM3, run under two different greenhouse gas (GHG) emission scenarios: the high emission A1fi and the low emission B1. The GCM output was statistically downscaled and used in the Variable Infiltration Capacity (VIC) macroscale distributed hydrologic model to derive inflows to major reservoirs in the California Central Valley. Historical inflows used as inputs to the water resources model CalSim II were modified to represent the climate change perturbed conditions for water supply deliveries, reliability, reservoir storage and changes to variables of environmental concern. Our results show greater negative impacts to California hydrology and water resources than previous assessments of climate change impacts in the region. These impacts, which translate into smaller streamflows, lower reservoir storage and decreased water supply deliveries and reliability, will be especially pronounced later in the 21st Century and south of the San Francisco bay Delta. The importance of considering how climate change impacts vary for different temporal, spatial, and institutional conditions in addition to the average impacts is also demonstrated.

Optimizing Operational Policies of a Korean Multireservoir System Using Sampling Stochastic Dynamic Programming with Ensemble Streamflow Prediction

This study presents state-of-the-art optimization techniques for enhancing reservoir operations which use sampling stochastic dynamic programming (SSDP) with ensemble streamflow prediction (ESP). SSDP used with historical inflow scenarios (SSDP/Hist) derives an off-line optimal operating policy through a backward-moving solution procedure. In contrast, SSDP used with monthly forecasts of ESP (SSSDP/ESP) reoptimizes the off-line policy. These stochastic models are used to derive a monthly joint operating policy during the drawdown period of the Geum River multireservoir system in Korea. A cross-validation test of 1,900 simulation runs demonstrates that: (1) proposed stochastic models that explicitly include inflow uncertainty are superior to those that do not; (2) updating policy with ESP forecasts is appropriate in this reservoir system; (3) the lower dam of the Geum River multireservoir system should maintain elevation of 66.5m during the beginning of the drawdown period to avoid significant increase in the downstream water shortages; and (4) forecasting accuracy may result in considerable effects on joint reservoir operations.

Climate-Based Estimation of Hydrologic Inflow into Lake Okeechobee, Florida

This paper presents a comparative evaluation of methods for climate-based estimation of the net inflow rate into Lake Okeechobee, Fla. The estimated net inflow rate is used by the South Florida Water Management District (SFWMD) to support the management and operations of the Lake Okeechobee hydrologic system. The first method evaluated in this paper (Croley) uses rainfall outlooks provided by the National Oceanic and Atmospheric Administration’s Climate Prediction Center (CPC) to calculate a weighed average of historical inflow values for each month. The second method evaluated in this paper (SFWMD Empirical) uses a linear regression on statistics of historical data to predict the net inflow rate. These two methods were developed and have been used operationally by the SFWMD since 2000. Three new methods are presented and comparatively evaluated to gauge their ability in estimating net inflow rates. The first two of these methods are based on CPC issued forecasts in decile probability density format. The ...

Water Reservoir Control with Data Mining

This paper describes the development of a general operating policy for a water supply system using the methodology of data mining. To define an operating policy using this approach, both a single-reservoir and a multireservoir water system were modeled and optimized for a set of historical inflows. These optimization results defined the best possible performance for the systems with historical hindsight, and were used as input for the data mining process. The data mining algorithm then generated the set of control rules that gave the best historical operating policy. The data mining tool used in this work is based on the induction tree technique, C5.0, reported by Quinlan in 1993. However, the process of reservoir control rule extraction is not straightforward and requires several data preparation steps to enhance the performance of the data mining algorithm. To demonstrate the effectiveness of the rules developed through data mining, simulation runs of the system were performed. The results of these simulations were compared with simulation results using operating policies derived from linear regression. Another comparison between operating rules derived using different methodologies was performed for the multireservoir system where, in addition to data mining and regression-based rules, there were rules available from the U.K. Environment Agency (South West). The paper shows that “data-mined” rules come closest to the optimization results.

THE INFLUENCE OF HYDROLOGIC INFORMATION IN LONG-TERM HYDROTHERMAL SCHEDULING

This paper presents an evaluation of the benefits of streamflows forecasting in long-term hydroelectric scheduling problem. In the approach considered, at each stage of planning a forecast of the future inflows is made and an operational decision for the following stage is obtained by a deterministic optimization model, in a partial open-loop feedback control framework. The influence of the forecasting model in the performance of this control policy is analysed by simulation using historical inflows record. The effectiveness of the approach was measured using the mean and standard deviation values for hydro generation and operational costs during the planning period, taking into account the hydroelectric plants of the Southeast Brazilian System as a case study.

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.

Assessment of Folsom Lake response to historical and potential future climate scenarios: 2. Reservoir management

An integrated forecast–decision system for Folsom Lake (California) is developed and used to assess the sensitivity of reservoir performance to various forecast–management schemes under historical and future climate scenarios. The assessments are based on various combinations of inflow forecasting models, decision rules, and climate scenarios. The inflow forecasting options include operational forecasts, historical analog ensemble forecasts, hydrologic ensemble forecasts, GCM-conditioned hydrologic ensemble forecasts, and perfect forecasts. Reservoir management is based on either heuristic rule curves or a decision system which includes three coupled models pertinent to turbine load dispatching, short-range energy generation scheduling, and long/mid-range reservoir management. The climate scenarios are based on historical inflow realizations, potential inflow realizations generated by General Circulation Models assuming no CO 2 increase, and potential inflow realizations assuming 1% CO 2 annual increase. The study demonstrates that (1) reliable inflow forecasts and adaptive decision systems can substantially benefit reservoir performance and (2) dynamic operational procedures can be effective climate change coping strategies.

Bi-objective dynamic programming for trading off hydropower and irrigation

Reservoir management is examined from the bicriterion viewpoints of hydropower generation and irrigation. Specifically, a decision support system is developed which includes constructing the set of non-inferior solutions, using the constraint, penalty and weighting methods. The reservoir operation model, as well as the optimization and simulation software, were initially provided by the R & D Division of the French Electrical Utility (Electricité de France); the method is based on a Stochastic Dynamic Programming—SDP—model with daily time steps, using 50 annual values of daily historical inflows and marginal electricity production costs. The water requirement for irrigation is taken either as preset using a demand curve, or as a soil reservoir model based on SDP and running simultaneously with the reservoir model. The model yields trade-off curves of electrical production losses versus, respectively, irrigated surface area, crop yield, and agricultural benefits. A numerical application illustrates the methodology in the French case of the Bort reservoir, in the Dordogne valley. The model reveals that releasing water for irrigation could present economic advantages within certain constraints on maximum irrigated surface area and minimum water price. Consequently, the current economic justification of single purpose reservoir management for hydropower ought to be changed. Negotiation should be initiated for the purpose of selecting one of the non-dominated reservoir management policies, hereby minimizing the distance of the “most preferred” solution point from the ideal one or by using the Nash cooperative game solution. Multicriterion decision-making seems to be a promising operational management tool for hydrosystems.

Autoregressive Decision Rule in Aggregated Reservoir Operation

Lag-1 correlation for inflow between consecutive periods is considered into reservoir model in many publications. This consideration greatly improves the reservoir models to incorporate the uncertainty of inflow. However, no publication was found to incorporate a correlation relationship for releases between consecutive periods to improve the operating policy for reservoir operations. This paper presents a methodology of autoregressive decision rule for an aggregated reservoir operation as a surrogate of a multireservoir system of the Upper Colorado River Basin. The method incorporates a lag-1 correlation for the release between consecutive periods with the optimal operating policy solved by stochastic dynamic program. The decision rules with and without incorporation of the autoregressive correlation for the releases were then used in simulated operation of the reservoir with historical inflow records to evaluate their relative effectiveness. The results showed that the autoregressive decision rule yields more stable and higher reliability of annual water supply for the aggregated reservoir operations.

A COMPARISON OF TWO METHODS FOR MULTIOBJECTIVE OPTIMIZATION FOR RESERVOIR OPERATION1

ABSTRACT: Two major objectives in operating the multireservoir system of the Upper Colorado River basin are maximization of hydroelectric power production and maximization of the reliability of annual water supply. These two objectives conflict. Optimal operation of the reservoir system to achieve both is unattainable. This paper seeks the best compromise solution for an aggregated reservoir as a surrogate of the multireservoir system by using two methods: the constraint method and the method of combined stochastic and deterministic modeling. Both methods are used to derive the stationary optimal operating policy for the aggregated reservoir by using stochastic dynamic programming but with different objective functions and minimum monthly release constraints. The resulting operating policies are then used in simulated operation of the reservoir with historical inflow records to evaluate their relative effectiveness. The results show that the policy obtained from the combination method would yield more hydropower production and higher reliability of annual water supply than that from the constraint‐method policy.

Flow regulation and restoration of Florida's Kissimmee river

AbstractChannelization of the Kissimmee River in central Florida destroyed or degraded most of the fish and wildlife habitat once provided by the river and its floodplain wetlands. Between 1984 and 1989 a demonstration project was conducted to evaluate the feasibility of restoring the river's biological resources. Reintroduction of flow through remnant river channels improved river channel habitat diversity and led to favourable responses by fish and invertebrate communities. However, results indicated that more complete restoration of biological attributes will require the re‐establishment of historical inflow characteristics. Owing to the flood control regulation schedule of its headwater lakes, current river discharge regimes are pulse‐like, include extended periods of low or no flow, and have high and low flow periods which are out of phase compared with typical seasonal patterns that occurred before channelization. These flow characteristics will preclude effective river restoration by contributing to chronic low dissolved oxygen regimes and repetitive fish kills, interfering with fish reproduction and limiting floodplain inundation. Simulation modelling was used to develop a modified headwater lakes regulation schedule which re‐establishes season flow patterns, smooths discharge peaks and maintains base flows for a greater portion of the year. Implementation of the new schedule, combined with extensive canal backfilling, will lead to discharge and stage characteristics that meet established criteria for achieving ecosystem restoration goals.

Topographic position and water chemistry of fens in a Dutch river plain

Abstract. On the Vecht river plain (western Netherlands), small fens, remnants of a large mesotrophic wetland bordering a moraine, of 1 to 5 ha are found in a man‐made matrix of lakes and pastures. The regional position of the fens, local position of sampling sites, composition of the vegetation and local hydrological variables were measured.Polders in the river plain produce a complex hydrology obscuring the regional zonation between moraine and river. Water supply and species composition are determined more by a site's regional than local position. High‐productivity reedlands are abundant close to the river. Carex paniculata reedlands receive large amounts of river water, which gives their fen water a high K+ concentration. Low‐productivity C. diandra fens and litter fens have their optimum closer to the moraine. C. diandra fens are fed mainly by inflowing nutrient‐poor ground‐ or surface water; litter fens receive primarily rainwater.Nutrients in fen water and in peat are lowest in C. diandra and C. lasiocarpa fens, but do not differ significantly between the communities. In both, iron seems to be more important than calcium in reducing phosphate solubility. Iron richness in the C. diandra fens is caused by present inflows of ground‐ or surface water, while in C. lasiocarpa fens, which succeed the former, iron richness is the result of historical inflows.

Operation of Large Multireservoir Systems Using Optimal‐Control Theory

The utility of optimal‐control theory for the deterministic operation of very large multireservoir systems is demonstrated for a real situation, the complex multireservoir Mahaweli system in Sri Lanka. This system includes 19 nodes, or reservoirs, and 35 release links. The model developed for the Mahaweli is designed to minimize a hydroelectric energy shortage objective and satisfy prespecified irrigation demand constraints. Two alternative approaches are explored for optimal operation of the Mahaweli system. The first involves monthly application of the optimal‐control algorithm to find an optimal policy for the next year, based on current storage and forecasted or historical inflows and demands. The second alternative is an implicit stochastic approach, in which linear operating rules are derived using deterministic optimal control and historical data. Both of the alternatives give reasonable and comparable results. The implicit stochastic optimization alternative has a great advantage regarding computer time and storage requirements. This makes it usable on a small personal computer, providing the system operator with decisions in a few seconds, even with very large systems.

Bayesian stochastic optimization of reservoir operation using uncertain forecasts

Operation of reservoir systems using stochastic dynamic programming (SDP) and Bayesian decision theory (BDT) is investigated in this study. The proposed model, called Bayesian stochastic dynamic programming (BSDP), which includes inflow, storage, and forecast as state variables, describes streamflows with a discrete lag 1 Markov process, and uses BDT to incorporate new information by updating the prior probabilities to posterior probabilities, is used to generate optimal reservoir operating rules. This continuous updating can significantly reduce the effects of natural and forecast uncertainties in the model. In order to test the value of the BSDP model for generating optimal operating rules, real‐time reservoir operation simulation models are constructed using 95 years of monthly historical inflows of the Gunpowder River to Loch Raven reservoir in Maryland. The rules generated by the BSDP model are applied in an operation simulation model and their performance is compared with an alternative stochastic dynamic programming (ASDP) model and a classical stochastic dynamic programming (SDP) model. BSDP differs from the other two models in the selection of state variables and the way the transition probabilities are formed and updated.

Optimal Stochastic Operation of Salt River Project, Arizona

The application of extended linear quadratic Gaussian (ELQG) control, a nonlinear stochastic optimization method, to the Salt River Project reservoir system is described. The optimization problem was posed as the maximization of total hydropower avoided cost, subject to constraints that represented system mass balance and physical or operating limitations on releases. The six surface reservoirs, ground‐water storage, and planned cyclic ground‐water storage were modeled as six equivalent reservoirs. The ELQG control algorithm was applied to the historic reservoir inflow for the 51‐yr period 1931–1981 using a 12‐month sliding window. Operating policies for each of the 600 months in the test period were developed and compared with the present operating policy for two reservoir configurations: the existing configuration, and a reduced storage configuration that mimicked temporary system constraints during the construction period for the planned alteration of Roosevelt Dam. The ELQG control results showed that...