Journal of Water Resources Planning and Management

This paper considers the problem of optimal water distribution to several retention reservoirs in an urban sewer network during rainfall. The goal of the control actions is the minimization of overflows and eventually the reduction of their polluting impact on receiving waters. Decomposition of the overall control task into a local‐flow control layer and a global coordinating control layer is assumed. For the global control task, a linear multivariable feedback regulator is developed via a systematic design procedure including a simplified model approach, a quadratic minimization criterion and subsequent application of the linear quadratic optimization method. The proposed control design guidelines facilitate quick and efficient regulator design for a broad class of sewer‐network control problems. A simulation study was performed for a particular network by use of an accurate hydrodynamic simulation model. Simulation results indicate significant overflow reductions achieved by the application of the multivariable controller as compared to fixed local control measures.

The multipurpose use of water in all types of human activities and the intensive development and urbanization of settlements have resulted in a considerable increase in water demand and water pollution. This is why it is necessary to build complex, spatially scattered, multipurpose water‐resources systems. The multiple objectives and limitations encountered in the planning and management of such systems result in the multidimensional mathematical formulation of the problem and in the application of complex models that need to be simplified. Their simplification often makes the obtained results and the influence of different criteria and limitations on the choice of a final solution somewhat unclear. To eliminate these weak points in the planning of complex water‐resources systems, a multiphase optimization procedure is developed, leading to the development of complex, multipurpose water‐resources systems.

Water‐supply planners and managers must continually evaluate the ability of surface water sources to meet current and future demands of public water‐supply systems they serve. Water demand changes over time, increasing with population growth and industrial development. However, continuing sedimentation in surface water reservoirs reduces their capacities and expected future yields. A method has been developed to calculate future reservoir capacity losses due to the sedimentation process. The method allows for consolidation or compaction of sediment constituents with time. Mitigatory measures for increasing the availability of water to deficient systems are investigated. These measures for maintaining adequate, reliable water supplies are devised to achieve optimal use of water resources with minimal adverse impacts on stream and riverine ecology. All surface water‐supply systems in Illinois are investigated with respect to their future demands, yields of their water sources, any present and future inadequacies for specified drought frequencies, and feasible and economical mitigatory measures.

A procedure for the economic optimization of hydrothermal power systems operation has been developed. The procedure is suitable for on‐line system utilization on a daily basis. To alleviate the inherent high dimensionality of the optimization problem, a time decomposition is used. The weekly and daily cyclic nature of load demand makes it convenient to specify operational periods in terms of weeks, days, and hours. A set of three models, i.e., monthly (four weeks), daily, and hourly, is developed. The three individual models are coupled to perform an overall optimization. A feedback mechanism is developed through the periodic monitoring of system states and the updating of the higher‐level models with a periodicity corresponding to the normal periods of the particular model. The procedure considers the seasonal variations of inflow to the system reservoirs, the variation of transmission losses with hydro and thermal plant loadings, and thermal unit commitment and dispatch. Application is made to a moderately sized hydro‐thermal system in the People's Republic of China. It was shown that the developed procedure is feasible and practical for on‐line daily operations. Significant reductions in the operating costs for the Chinese system have been obtained.

Although multiple linear regression has been used for many years to predict seasonal streamflow volumes, typical practice has not realized the maximum accuracy obtainable from regression. Several techniques can help provide superior forecast accuracy using regression models: (1) Using only data known at forecast time; (2) principal components regression; (3) cross validation; and (4) systematic searching for optimal or near‐optimal combinations of variables. Using no future data requires that a separate equation be used each month that forecasts are made rather than using a single equation throughout the forecast season. Consistency of month‐to‐month forecasts can be obtained by judicious selection of variables to maintain a high degree of similarity in the monthly equations. Results for the South Fork Boise River at Anderson Ranch Dam and other basins in the West indicate that these new regression procedures can give substantial improvements in forecast accuracy over existing procedures without sacrificing month‐to‐month forecast consistency.

Statistical analysis showed that monsoon reduced the variabilities of rainfall occurrences in Rechna Doab, an important agricultural region of Pakistan; but besides rainfall depths, it had little effect on other rainfall properties. Among the point‐process models explored, two continuous‐time models (Neyman‐Scott clustering or white noise [NSWN] and Rectangular Pulses models, [RPM]) and a discrete‐time model (Markov renewal [MRM]), NSWN is non‐Markovian, RPM is Markovian, and MRM is semi‐Markovian. Based on the variance ratio, variance at an arbitrary time scale to variance at daily time scale, none of the models calibrated (three out of six cases reported) was consistently better than others. However, after recalibrating them with data from six stations mixed together, only MRM predicted variance ratios that are consistent with their empirical counterparts. This is probably because MRM could account for the time discreteness of the rainfall sample process and accommodate data of high variabilities. Applying continuous point‐process models to time scales higher than daily and different from those used in calibration is discouraged, particularly the latter.