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Water Resource Systems Planning and Management: An Introduction to Methods, Models, and Applications

Resumen/Descripción – provisto por la editorial

No disponible.

Palabras clave – provistas por la editorial

hydrology; civil engineering; waste water technology; aquatic pollution; climate change; environmental engineering

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Información

Tipo de recurso:

libros

ISBN impreso

978-3-319-44232-7

ISBN electrónico

978-3-319-44234-1

Editor responsable

Springer Nature

País de edición

Reino Unido

Fecha de publicación

Tabla de contenidos

Water Resources Planning and Management: An Overview

Daniel P. Loucks; Eelco van Beek

Water resource systems have benefited both people and their economies for many centuries. The services provided by such systems are multiple. Yet in many regions of the world they are not able to meet even basic drinking water and sanitation needs. Nor can many of these water resource systems support and maintain resilient biodiverse ecosystems. Typical causes include inappropriate, inadequate and/or degraded infrastructure, excessive withdrawals of river flows, pollution from industrial and agricultural activities, eutrophication resulting from nutrient loadings, salinization from irrigation return flows, infestations of exotic plant and animals, excessive fish harvesting, flood plain and habitat alteration from development activities, and changes in water and sediment flow regimes.

Pp. 1-49

Water Resource Systems Modeling: Its Role in Planning and Management

Daniel P. Loucks; Eelco van Beek

Planning, designing, and managing water resource systems today inevitably involve impact prediction.

Pp. 51-72

Models for Identifying and Evaluating Alternatives

Daniel P. Loucks; Eelco van Beek

Water resources systems are characterized by multiple interdependent components that together produce multiple economic, environmental, ecological, and social impacts.

Pp. 73-91

An Introduction to Optimization Models and Methods

Daniel P. Loucks; Eelco van Beek

Water resource systems are characterized by multiple interdependent components that together produce multiple economic, environmental, ecological, and social impacts. As discussed in the previous chapter, planners and managers working toward improving the design and performance of these complex systems must identify and evaluate alternative designs and operating policies, comparing their predicted performance with desired goals or objectives. Typically, this identification and evaluation process is accomplished with the aid of optimization and simulation models. While optimization methods are designed to provide preferred values of system design and operating policy variables—values that will lead to the highest levels of system performance—they are often used to eliminate the clearly inferior options. Using optimization for a preliminary screening followed by more detailed and accurate simulation is the primary way we have, short of actually building physical models, of estimating effective system designs and operating policies. This chapter introduces and illustrates the art of optimization model development and use in analyzing water resources systems. The models and methods introduced in this chapter are extended in subsequent chapters.

Pp. 93-177

Data-Fitting, Evolutionary, and Qualitative Modeling

Daniel P. Loucks; Eelco van Beek

Clearly, all model outputs depend on model inputs. The optimization and simulation models discussed in the previous chapters are no exception. This chapter introduces some alternative modeling approaches that depend on observed data. These approaches include artificial neural networks and various evolutionary models. The chapter ends with some qualitative modeling. These data-driven models can serve as substitutes for more process-based models in applications where computational speed is critical or where the underlying relationships are poorly understood or too complex to be easily incorporated into calculus-based, linear, nonlinear, or dynamic programming models.

Pp. 179-211

An Introduction to Probability, Statistics, and Uncertainty

Daniel P. Loucks; Eelco van Beek

Processes that are not fully understood, and whose outcomes cannot be precisely predicted, are often called uncertain. Most of the inputs to, and processes that occur in, and outputs resulting from, water resource systems are not known with certainty.

Pp. 213-300

Modeling Uncertainty

Daniel P. Loucks; Eelco van Beek

Decision-makers are increasingly willing to consider the uncertainty associated with model predictions of the economic, environmental, or social impacts associated with possible decisions. Information on uncertainty does not make decision-making easier, but to ignore it is to ignore reality. Incorporating what is known about the uncertainty of input parameters and variables used in optimization and simulation models can help in quantifying the uncertainty in the resulting model output. This chapter outlines and illustrates some approaches for doing this.

Pp. 301-330

System Sensitivity and Uncertainty Analysis

Daniel P. Loucks; Eelco van Beek

The usefulness of any model is in part dependent on the accuracy and reliability of its output data. Yet, because all models are abstractions of reality, and because precise input data are rarely if ever available, all output values are subject to imprecision.

Pp. 331-374

Performance Criteria

Daniel P. Loucks; Eelco van Beek

Water resource systems typically provide a variety of economic, environmental, and ecological services.

Pp. 375-415

Water Quality Modeling and Prediction

Daniel P. Loucks; Eelco van Beek

The most fundamental human needs for water are for drinking, cooking, and personal hygiene. The quality of the water used to meet these needs must pose no risk to human health.

Pp. 417-467