Journal of Water Resources Planning and Management

A hydrologic regionalization scheme is proposed for classification of watersheds at gauged sites. This scheme used 16 streamflow parameters estimated by a time series model to classify 94 watersheds into 6 regions by cluster analysis. The classified regions seem to be separated by physiographical boundaries, especially the two main clusters. Discriminant analysis tests the significance of the cluster difference; thus, each cluster represents one hydrologic region. Principal component analysis interprets the regional differences and similarities. The regional membership is mainly identified by the watershed variables of elevation, forest area, channel slope, and precipitation based on the calculation of the scores of canonical discriminant variates. This emphasizes the importance of the hydrologic regionalization and the identification of the specific characteristics in each region.

Multiple regression analysis (MRA) and a time series model (TSM) are developed and validated for using watershed characteristics to synthesize streamflow hydrographs. Relationships between the streamflow parameters and watershed variables are evaluated by canonical correlation analysis at 94 candidate watersheds. These relationships are constructed using MRA to predict streamflow parameters at six validation stations in two main hydrologic regions. The predicted streamflow parameters are applied to synthesizing specific monthly streamflows by using the developed TSM. The synthetic hydrographs are found to be mostly improved over those found from traditional simple regression equations. Statistical properties and reliability curves of the synthetic $Qs$ are compared with those of the historical records. The statistical properties seem to be well preserved, and the reliability curves are reasonable in one hydrologic region but somewhat biased in the other. The proposed regionalization scheme is validated and therefore considered feasible and reliable for estimating monthly streamflows at ungauged sites.

This paper explores the potential and limitations for southern California water markets using an economic-engineering network flow optimization model, CALVIN. CALVIN is used to estimate how a market would affect overall southern California water use, to preliminarily assess the economic benefit of more flexible water allocation policies, and to explore the characteristics of an ideal market. Results from CALVIN suggest substantial economic and reliability benefits exist for implementing water market or other transfer mechanisms, and these benefits could be achieved with relatively little reallocation of agricultural water. An ideal water market in southern California would reduce more costly urban water shortages, reducing the demand for increased imports from outside of southern California. Additionally, substantial economic benefits could accrue from expanding some conveyance and storage facilities, particularly the Colorado River Aqueduct and conjunctive use storage capacity.

This paper presents a systematic approach for solving the management problem of a contaminated groundwater supply system. The proposed methodology begins with the selection of a supply water quality criterion based on a stochastic health risk assessment and concludes with the establishment of a trade-off relationship between the increased management cost and the desired level of protection. In the uncertainty analysis, Gaussian quadrature numerical integration is used as an alternative to nested Monte Carlo simulation. Two management approaches—treating the contaminated groundwater with granular activated carbon and using imported water to lower the contamination level in the supply water—are used in a test problem. A random hydraulic conductivity field is used to produce the contamination variability at each extraction well. A trade-off relationship is obtained between the increased management cost and the level of protection by performing the uncertainty analysis at several supply water quality criteria for each of the two management approaches. This study demonstrates that despite the complexity in a contaminated groundwater supply system, a systematic approach can be used to quantify the many uncertainties associated with the management problem.

Water quality regulations often permit a “mixing zone” or “allocated impact zone” in which the initial dilution of a wastewater occurs rather imposing strict end-of-pipe criteria. Since wastewater treatment typically entails high marginal costs for removal of the last few percentages of contaminants from wastewater, the mixing zone concept allows for economically efficient use of the natural assimilative capacity of a water body. In practice, the regulatory mixing zone can encompass either the near field where discharge characteristics control mixing behavior or the far field where ambient conditions predominate the mixing process. Plume boundary interaction controls the transition between near-field and far-field mixing processes and strongly influences mixing behavior. Simulation of plume boundary interaction is accounted for in the schematization processes, a technique whereby ambient bathymetric geometry data is simplified while preserving the consequences of local boundaries on mixing behavior. Definitions of near-field and far-field mixing are given, followed by the current regulatory context of mixing zone analysis. Several examples of the schematization process are presented. The paper concludes with a case study example of the Cornell Mixing Zone Expert System (CORMIX) application in regulatory mixing zone analysis. CORMIX is a comprehensive rule-based decision support system and hydrodynamic simulation model developed to assist water quality managers with mixing zone analysis. The case study example highlights schematization and plume boundary interaction, shows graphical representations of the plume within the mixing zone, and illustrates the importance of ambient density stratification and density-current buoyant spreading in mixing zone regulatory analysis.

Obtaining optimal solutions for groundwater resources planning problems, while simultaneously considering both fixed costs and time-varying pumping rates, is a challenging task. Application of conventional optimization algorithms such as linear and nonlinear programming is difficult due to the discontinuity of the fixed cost function in the objective function and the combinatorial nature of assigning discrete well locations. Use of conventional discrete algorithms such as integer programming or discrete dynamic programming is hampered by the large computational burden caused by varying pumping rates over time. A novel procedure that integrates a genetic algorithm (GA) with constrained differential dynamic programming (CDDP) calculates optimal solutions for a groundwater resources planning problem while simultaneously considering fixed costs and time-varying pumping rates. The GA determines the number and locations of pumping wells with operating costs then evaluated using CDDP. This study demonstrates that fixed costs associated with installing wells significantly impact the optimal number and locations of wells.

A fuzzy rule-based control model for multipurpose real-time reservoir operation is constructed. A new, mathematically justified methodology for fuzzy inference—total fuzzy similarity—is used and compared with the more traditional Sugeno-style method. Specifically, the seasonal variation in both hydrological variables and operational targets is examined by considering the inputs as season-dependent relative values, instead of using absolute values. The inference drawn in several stages allows a simple, accessible model structure. The control model is illustrated using Lake Päijänne, a regulated lake in Finland. The model is calibrated to simulate the actual operation, but also to better fulfill the new multipurpose operational objectives determined by experts. Relatively similar results obtained with the inference methods and the strong mathematical background of total fuzzy similarity put fuzzy reasoning on a solid foundation.

A method is presented to optimize diversions of freshwater from multiple points in a well-mixed estuary. The objective is to prevent salinity concentrations at a point in the brackish water zone from exceeding levels that are stipulated for environmental defense. Optimization is achieved using an efficient gradient-based search algorithm (quasi-Newton with Broyden-Fletcher-Goldfarb-Shanno update). Time-invariant and time-variant diversion rates are considered as control parameters, and time-variant diversion rates are shown to permit larger diversions overall. The latter requires the identification of a vastly larger number of parameters in a more complex parameter space. Computations are carried out using a 1D model formulation applied to a series of hypothetical systems and the St. Jones estuary in Delaware.