Aris P. Georgakakos

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 CO2 increase, and potential inflow realizations assuming 1% CO2 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.

Impacts of climate variability on the operational forecast and management of the Upper Des Moines River Basin

Data from the regulated 14,000 km2 upper Des Moines River basin and a coupled forecast-control model are used to study the sensitivity of flow forecasts and reservoir management to climatic variability over scales ranging from daily to interdecadal. Robust coupled forecast-control methodologies are employed to minimize reservoir system sensitivity to climate variability and change. Large-scale hydrologic-hydraulic prediction models, models for forecast uncertainty, and models for reservoir control are the building blocks of the methodology. The case study concerns the 833.8 × 106 m3 Saylorville reservoir on the upper Des Moines River. The reservoir is operated by the U.S. Corps of Engineers for flood control, low-flow augmentation, and water supply. The total record of 64 years of daily data is divided into three periods, each with distinct characteristics of atmospheric forcing. For each climatic period the coupled forecast-control methodology is simulated with a maximum forecast lead time of 4 months and daily resolution. For comparison, the results of operation using current reservoir control practices were obtained for the historical periods of study. Large differences are found to exist between the probabilistic long-term predictions of the forecast component when using warm or cool and wet or dry initial conditions in the spring and late summer. Using ensemble input corresponding to warm or cool and wet or dry years increases these differences. Current reservoir management practices cannot accommodate historical climate variability. Substantial gain in resilience to climate variability is shown to result when the reservoir is operated by a control scheme which uses reliable forecasts and accounts for their uncertainty. This study shows that such coupled forecast-control decision systems can mitigate adverse effects of climatic forcing on regional water resources.

Medium-range flow prediction for the Nile : a comparison of stochastic and deterministic methods

Abstract Due to its great importance, the availability of long flow records, contemporary as well as older, and the additional historical information of its behaviour, the Nile is an ideal test case for identifying and understanding hydrological behaviours, and for model development. Such behaviours include the long-term persistence, which historically has motivated the discovery of the Hurst phenomenon and has put into question classical statistical results and typical stochastic models. Based on the empirical evidence from the exploration of the Nile flows and on the theoretical insights provided by the principle of maximum entropy, a concept newly employed in hydrological stochastic modelling, an advanced yet simple stochastic methodology is developed. The approach is focused on the prediction of the Nile flow a month ahead, but the methodology is general and can be applied to any type of stochastic prediction. The stochastic methodology is also compared with deterministic approaches, specifically an analogue (local nonlinear chaotic) model and a connectionist (artificial neural network) model based on the same flow record. All models have good performance with the stochastic model outperforming in prediction skills and the analogue model in simplicity. In addition, the stochastic model has other elements of superiority such as the ability to provide long-term simulations and to improve understanding of natural behaviours.

Decision support systems for integrated water resources management with an application to the nile basin

Integrated water-resource management (IWRM) is the process of formulating and implementing shared-vision planning and management strategies for sustainable water-resource development and utilization with due consideration to all spatial and temporal interdependencies among natural processes and human and ecological water uses. Public policy actors develop consensus and decide on shared-vision strategies based on information generated and communicated by decision support systems (DSSs) and associated processes. Thus, the role of DSS is to leverage current scientific and technological advances in developing and evaluating specific policy options for possible adoption by the IWRM process. DSSs are developed and used by research institutions, government agencies, consultants, and the information technology industry. The greatest challenge in the development and effective use of integrated decision-support systems is the availability of qualified water-resources professionals. A comprehensive professional training and capacity-building program must be part and parcel of DSS development. Sufficient training, retention of qualified personnel, continuing education, and long-term capacity building must all be part of a general educational strategy.

Western water and climate change.

The western United States is a region long defined by water challenges. Climate change adds to those historical challenges, but does not, for the most part, introduce entirely new challenges; rather climate change is likely to stress water supplies and resources already in many cases stretched to, or beyond, natural limits. Projections are for continued and, likely, increased warming trends across the region, with a near certainty of continuing changes in seasonality of snowmelt and streamflows, and a strong potential for attendant increases in evaporative demands. Projections of future precipitation are less conclusive, although likely the northern-most West will see precipitation increases while the southernmost West sees declines. However, most of the region lies in a broad area where some climate models project precipitation increases while others project declines, so that only increases in precipitation uncertainties can be projected with any confidence. Changes in annual and seasonal hydrographs are likely to challenge water managers, users, and attempts to protect or restore environmental flows, even where annual volumes change little. Other impacts from climate change (e.g., floods and water-quality changes) are poorly understood and will likely be location dependent. In this context, four iconic river basins offer glimpses into specific challenges that climate change may bring to the West. The Colorado River is a system in which overuse and growing demands are projected to be even more challenging than climate-change-induced flow reductions. The Rio Grande offers the best example of how climate-change-induced flow declines might sink a major system into permanent drought. The Klamath is currently projected to face the more benign precipitation future, but fisheries and irrigation management may face dire straits due to warming air temperatures, rising irrigation demands, and warming waters in a basin already hobbled by tensions between endangered fisheries and agricultural demands. Finally, Californias Bay-Delta system is a remarkably localized and severe weakness at the heart of the regions trillion-dollar economy. It is threatened by the full range of potential climate-change impacts expected across the West, along with major vulnerabilities to increased flooding and rising sea levels.

Optimal control of the activated sludge process

Abstract This paper describes an application of modern optimal control theory techniques in the regulation of the activated sludge process. The process is simulated using multicomponent biological reactor and dynamical thickening models. The control problem is formulated with recycle and wastage rates as control variables and is characterized by high dimensionality and system model nonlinearity. The solution is obtained by a Newton-type successive approximations control method. In the case study, this approach is compared with (1) the commonly proposed recycle ratio control strategy and (2) a constant recycle and wastage rate stategy. Wastage regulation is shown to be a much more effective control input than recycle for reducing effluent organic variability. The control framework proposed can be extended to include other regulation schemes and account for process and input uncertainties.

Optimal control of the activated sludge process: effect of sludge storage

Abstract This paper describes an application of an optimal control method in the regulation of the activated sludge process including a biological reactor, a variable volume sludge storage tank and a settler. The control variables represent flowrates (1) from the settler to the storage tank, (2) from the storage tank to the biological reactor and (3) from the storage tank to wastage. The control problem is to determine optimal control variable sequences that minimize effluent organics variability on a daily horizon. The results indicate that the presence of the storage tank significantly improves process regulation with the relative control efficiency reduced with increasing storage capacity. The researched methodology lends itself to on-line process control implementation.

Joint variable spatial downscaling

Joint Variable Spatial Downscaling (JVSD), a new statistical technique for downscaling gridded climatic variables, is developed to generate high resolution gridded datasets for regional watershed modeling and assessments. The proposed approach differs from previous statistical downscaling methods in that multiple climatic variables are downscaled simultaneously and consistently to produce realistic climate projections. In the bias correction step, JVSD uses a differencing process to create stationary joint cumulative frequency statistics of the variables being downscaled. The functional relationship between these statistics and those of the historical observation period is subsequently used to remove GCM bias. The original variables are recovered through summation of bias corrected differenced sequences. In the spatial disaggregation step, JVSD uses a historical analogue approach, with historical analogues identified simultaneously for all atmospheric fields and over all areas of the basin under study. Analysis and comparisons are performed for 20th Century Climate in Coupled Models (20C3M), broadly available for most GCMs. The results show that the proposed downscaling method is able to reproduce the sub-grid climatic features as well as their temporal/spatial variability in the historical periods. Comparisons are also performed for precipitation and temperature with other statistical and dynamic downscaling methods over the southeastern US and show that JVSD performs favorably. The downscaled sequences are used to assess the implications of GCM scenarios for the Apalachicola-Chattahoochee-Flint river basin as part of a comprehensive climate change impact assessment.

Operational trade‐offs in reservoir control

Reservoir operation decisions require constant reevaluation in the face of conflicting objectives, varying hydrologic conditions, and frequent operational policy changes. Optimality is a relative concept very much dependent on the circumstances under which a decision is made. More than anything else, reservoir management authorities need the means to assess the impacts of various operational options. It is their responsibility to define what is desirable after a thorough evaluation of the existing circumstances. This article presents a model designed to generate operational trade-offs common among reservoir systems. The model avoids an all-encompassing problem formulation and distinguishes three operational modes (levels) corresponding to normal, drought, and flood operations. Each level addresses only relevant system elements and uses a static and a dynamic control module to optimize turbine performance within each planning period and temporally. The model is used for planning the operation of the Savannah River System.

Control models for hydroelectric energy optimization

The optimization of hydroelectric energy is addressed via a new multilevel control model, which is used to derive estimates of system firm energy with or without dependable capacity commitments. The model is able to optimize individual turbine operation as well as overall system operation on an hourly and daily basis. The mechanism by which the various models are linked and exchange information ensures full compatibility among the control levels and guarantees operational consistency across all timescales. The model is applied to the Lanier-Allatoona-Carters system, located in the southeastern United States, and is suitable for planning as well as operational applications.