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Modeling Water Resources Management at the Basin Level: Methodology and Application to the Maipo River Basin
12 Feb 2007-
TL;DR: In this article, the authors presented the methodology development of an integrated economic-hydrologic river-basin model, as well as the application of this prototype to the Maipo River Basin in Chile.
Abstract: "With increasing competition for water across sectors and regions, the river basin has been recognized as the appropriate unit of analysis for addressing the challenges of water resources management. Modeling at this scale can provide essential information for policymakers in their resource allocation decisions. A river basin system is made up of water source components, instream and off-stream demand components, and intermediate (treatment and recycling) components. The river basin is thus characterized by natural and physical processes but also by human-made projects and management policies. The essential relations within each component and the interrelations among these components in the basin can be represented in an integrated modeling framework. Integrated hydrologic and economic models are well equipped to assess water management and policy issues in a river basin setting. McKinney et al. (1999) reviewed state-of-the-art modeling approaches to integrated water resources management at the river basin scale... This report presents the methodology development of an integrated economic–hydrologic river-basin model, as well as the application of this prototype to the Maipo River Basin in Chile. The model is based on a node-link river-basin network, including multiple source nodes (reservoirs, aquifers, river reaches, and so on) and demand sites, for municipal and industrial (M&I), hydropower, and agricultural water demands. from Authors' summary
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TL;DR: In this paper, the authors discuss the role of water for agriculture and food security, the challenges facing irrigated agriculture, and the range of policies, institutions, and investments needed to secure adequate access to water for food today and in the future.
Abstract: Irrigated agriculture is the main source of water withdrawals, accounting for around 70% of all the world’s freshwater withdrawals. The development of irrigated agriculture has boosted agricultural yields and contributed to price stability, making it possible to feed the world’s growing population. Rapidly increasing nonagricultural demands for water, changing food preferences, global climate change, and new demands for biofuel production place increasing pressure on scarce water resources. Challenges of growing water scarcity for agriculture are heightened by the increasing costs of developing new water, soil degradation, groundwater depletion, increasing water pollution, the degradation of water-related ecosystems, and wasteful use of already developed water supplies. This article discusses the role of water for agriculture and food security, the challenges facing irrigated agriculture, and the range of policies, institutions, and investments needed to secure adequate access to water for food today and in the future.
470 citations
TL;DR: The application of this methodology to the South Saskatchewan River Basin shows that CWAM can be utilized as a tool for promoting the understanding and cooperation of water users to achieve maximum welfare in a river basin and minimize the potential damage caused by water shortages.
187 citations
Cites background from "Modeling Water Resources Management..."
...Although many mathematical simulation and optimization models for water quantity, quality and economic management have been developed for use under various water rights systems (McKinney et al., 1999), most models and applications do not address the fairness issue, except for prior water allocation models (Fredericks et al....
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...…and optimization models for water quantity, quality and economic management have been developed for use under various water rights systems (McKinney et al., 1999), most models and applications do not address the fairness issue, except for prior water allocation models (Fredericks et al.,…...
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TL;DR: In this article, the authors present a decentralized optimization method known as constraint-based reasoning, which allows individual agents in a multi-agent system to optimize their behaviors over various alternatives and incorporates the optimization of all agents' objectives through an interaction scheme, in which the ith agent optimizes its objective with a selected priority for collaboration and forwards the solution and consequences to all agents that interact with it.
Abstract: [1] A watershed can be simulated as a multiagent system (MAS) composed of spatially distributed land and water users (agents) within a common defined environment. The watershed system is characterized by distributed decision processes at the agent level with a coordination mechanism organizing the interactions among individual decision processes at the system level. This paper presents a decentralized (distributed) optimization method known as constraint-based reasoning, which allows individual agents in an MAS to optimize their behaviors over various alternatives. The method incorporates the optimization of all agents' objectives through an interaction scheme, in which the ith agent optimizes its objective with a selected priority for collaboration and forwards the solution and consequences to all agents that interact with it. Agents are allowed to determine how important their own objectives are in comparison with the constraints, using a local interest factor (βi). A large βi value indicates a selfish agent who puts high priority on its own benefit and ignores collaboration requirements. This bottom-up problem-solving approach mimics real-world watershed management problems better than conventional “top-down” optimization methods in which it is assumed that individual agents will completely comply with any recommendations that the coordinator makes. The method is applied to a steady state hypothetical watershed with three off-stream human agents, one in-stream human agent (reservoir), and two ecological agents.
170 citations
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27 Jun 2011
TL;DR: This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability to help students develop an intuition on how to model uncertainty into mathematical problems.
Abstract: The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems.In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods.The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition:"The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make'Introduction to Stochastic Programming' an ideal textbook for the area." (Interfaces, 1998)
5,398 citations
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TL;DR: JuMP is an open-source modeling language that allows users to express a wide range of ideas in an easy-to-use manner.
Abstract: The most widely used is GAMS, which is specifically designed for Further details on GAMS can be found in the GAMS User's Guide. The GAMS User's Guide. Mathematical Programming, 87:153–176, 2000. (4). A. Brooke, D. Kendrick, and A. Meeraus. GAMS: A User's Guide. The Scientific Press, South San Francisco. JuMP is an open-source modeling language that allows users to express a wide D. Kendrick, A. Meeraus, and R. Raman, GAMS: A User's Guide, Scientific.
3,645 citations
TL;DR: An extensive literature review of all available salt tolerance data was undertaken to evaluate the current status of our knowledge of the salt tolerance of agricultural crops as mentioned in this paper, concluding that crops tolerate salinity up to a threshold level above which yields decrease approximately linearly as salt concentrations increase.
Abstract: An extensive literature review of all available salt tolerance data was undertaken to evaluate the current status of our knowledge of the salt tolerance of agricultural crops. In general, crops tolerate salinity up to a threshold level above which yields decrease approximately linearly as salt concentrations increase. Our best estimate of the threshold salinity level and yield decrease per unit salinity increase is presented for a large number of agricultural crops. The methods of measuring appropriate salinity and plant parameters to obtain meaningful salt tolerance data and the many plant, soil, water, and environmental factors influencing the plant's ability to tolerate salt are examined.
3,200 citations
31 Jan 1982
TL;DR: This article reviewed various studies which have provided a description of and possible explanation to patterns of innovation adoption in the agricultural sector and highlighted the diversity in observed patterns among various farmers' classes as well as differences in results from different studies in different socioeconomic environments.
Abstract: This paper is a revised version of Staff Working Paper 444 It reviews various studies which have provided a description of and possible explanation to patterns of innovation adoption in the agricultural sector It therefore covers both empirical and theoretical studies The discussion highlights the diversity in observed patterns among various farmers' classes as well as differences in results from different studies in different socio-economic environments, and reviews the attempts to rationalize such findings Special attention is given to the methodologies which are commonly used in studies of innovation adoption, and suggestions for improvements of such work through the use of appropriate economometric methods are provided The diversity of experiences with different innovations in different geographical and socio-cultural environments suggest that studies of adoption patterns should provide detailed information on attributes of the institutional, social and cultural setting and their interactions with economic factors These may be an important element in explaining conflicting experiences
3,145 citations