Journal of Water Resources Planning and Management

The United Nations forecasts a world population growth of 1 billion in the next decade and 2 billion in the next two decades—growth that will place immense demands on the developing nations' water resources. The extent of these demands when placed against the constraints of time, water, and funds, and the lack of plans to manage droughts confirm that the world faces a serious water crisis. Yet, most actions proposed in today's debate offer questionable solutions, many built on faulty assumptions. Water markets and increases in irrigation efficiency will do little to meet the needs of these countries. Reallocations will have far-reaching social repercussions. The time constraints dictate that the situation be addressed with a greater sense of urgency and that proven measures, even if controversial, should be promptly implemented, while studies should concentrate on approaches that can realistically offer significant help in the following decades. An example of the consequences of the international community's confined if not distorted perspectives is offered by a case study of India's Sardar Sarovar Project.

Geographic information systems (GISs) offer data management and spatial analysis capabilities that can be useful in ground-water modeling. Many regional ground-water models require large, unwieldy data sets, and calibrating them has traditionally been a trial-and-error, hit-or-miss process. GIS provides automatic data collection, systematic model parameter assignment, spatial statistics generation, and the visual display of model results, all of which can improve and facilitate modeling. To utilize these abilities, however, GIS and ground-water models must be able to communicate. Researchers and practitioners have achieved this interface in three ways: (1) linking a GIS to a ground-water model through data-transfer programs; (2) integrating a model with a GIS database; and (3) embedding modeling capabilities within a GIS. This paper emphasizes the usefulness of GIS in ground-water modeling and evaluates these three methods of GIS-model interfacing. Current needs are identified, and suggestions for future work are made.

Geoelectric and well data are combined and used to delineate “best” well location areas. The best location is selected based on a compromise solution for two conflicting objectives: (1) maximum well yield (i.e., maximum sustainable pumping rate); and (2) maximum wellhead protection (i.e., maximum total travel time related to a fixed setback distance). The methodology combines hydraulic conductivities and layer thicknesses obtained from well logs, well-performance tests, and geoelectric measurements to estimate and map well yield and travel time. Hydraulic conductivities, layer thicknesses, and the estimated yields and total travel times are considered as spatially correlated random variables. Expected value and 75% reliability maps are developed for yield and total travel time. The best well location areas are identified based on a trade-off map that is calculated as a combination of the yield and travel time maps. The relative importance of well yield versus wellhead protection is incorporated in the trade-off relationship. The methodology is illustrated for a study area located near Ashland, Nebraska.

Application of stream water-quality models in decision making has been hampered by a lack of data appropriate for minimization of model-simulation uncertainty. A method for determining data needed to reduce model-prediction uncertainty is illustrated in this paper. First-order reliability analysis is applied to determine (1) the model parameters that significantly affect model-prediction uncertainty; and (2) the constituents for which model-prediction uncertainty is unacceptable. Additional data are required to reduce uncertainty in the parameters that significantly affect constituents with high prediction uncertainty and consequently in model prediction. The method is demonstrated for multiconstituent water-quality modeling on the Passaic River in New Jersey applying QUAL2E. The model-prediction uncertainty of dissolved oxygen, biochemical oxygen demand, ammonia, and chlorphyll a is considered. For this example, only the reaeration rate and the algal maximum-specific-growth rate contribute significant uncertainty to model prediction. The effect of reducing the uncertainty in the reaeration rate and algal maximum-specific-growth rate on the uncertainty on predicted dissolved oxygen and chlorphyll a , respectively, is demonstrated.

This paper presents an integration of a lumped parameter hydrologic model (Penn State Runoff Model) with a planning level geographic information system (GIS) in implementing a watershed-wide storm-water management plan. The integration is used to estimate physical input parameters of the model. The model is used to simulate runoff hydrographs for various durations and frequencies and process the hydrographs to create peak flow presentation and release rate tables. These tables provide information to create a watershed release rate map that is a practical tool for implementing a storm-water management plan. It is demonstrated that the Penn State Runoff Model integration successfully implements the requirements of the Stormwater Management Act of Pennsylvania. An innovative GIS integration approach is presented that employs both the vector and the raster GIS formats to take advantage of the best features of each. Cost effectiveness of GIS integration is discussed and recommendations are made for future research. The proposed approach is illustrated for one small and one large watershed in Pennsylvania.

A ground-water management model using a nonlinear programming algorithm was developed to find the minimum cost design of the combined pumping and treatment components of a pump-and-treat remediation system and includes the fixed costs of system construction and installation as well as operation and maintenance. The fixed-cost terms of the objective function are incorporated into the nonlinear programming formulation using a penalty coefficient method. Results of applying the model to an aquifer with homogeneous hydraulic conductivity show that a combined well field and treatment process model that includes fixed costs has a significant impact on the design and cost of these systems, reducing the cost by using fewer, larger-flow-rate wells. Previous pump-and-treat design formulations have resulted in systems with numerous, low-flow-rate wells due to the use of simplified cost functions that do not exhibit economies of scale or fixed costs. Two example aquifers with heterogeneous conductivity fields were also investigated, and system costs similar to the homogeneous case were obtained. However, the introduction of aquifer heterogeneity did affect the remediation design, for example, well locations and pumping rates. Trade-offs between total remediation system cost and cleanup standard, remediation period, and typical design parameters of air-stripping towers are examined through sensitivity analysis. Generally, the optimal injection concentration is found to be approximately 70–80% of the cleanup standard. Designs with remediation periods around 5–6 yr have the minimum cost for the case study presented in this paper. Remediation periods beyond 6 yr are not economically justified, since the tower operating costs become dominant and offset the reduction of capital cost.

A comparison is made between the formulation and computational performance of four numerical methods for modeling the transient behavior of water quality in drinking-water-distribution systems. Two are Eulerian-based (the finite-difference and discrete-volume methods) and two are Lagrangian-based (the time-driven and event-driven methods). The Eulerian approaches move water between fixed grid points or volume segments in pipes as time is advanced in uniform increments. The Lagrangian methods update conditions in variable-sized segments of water at either uniform time increments or only at times when a new segment reaches a downstream pipe junction. Each method is encoded into an existing distribution-system simulation model and run on several pipe networks of varying size under equal accuracy tolerances. Results show that the accuracies of the methods are comparable. The Lagrangian methods are more efficient for simulating chemical transport. For modeling water age, the time-driven Lagrangian method is the most-efficient while the Eulerian methods are more memory-efficient.