A range of different statistical downscaling models was calibrated using both observed and general circulation model (GCM) generated daily precipitation time series and intercompared. The GCM used was the U.K. Meteorological Office, Hadley Centre's coupled ocean/atmosphere model (HadCM2) forced by combined CO2 and sulfate aerosol changes. Climate model results for 1980–1999 (present) and 2080–2099 (future) were used, for six regions across the United States. The downscaling methods compared were different weather generator techniques (the standard “WGEN” method, and a method based on spell-length durations), two different methods using grid point vorticity data as an atmospheric predictor variable (B-Circ and C-Circ), and two variations of an artificial neural network (ANN) transfer function technique using circulation data and circulation plus temperature data as predictor variables. Comparisons of results were facilitated by using standard sets of observed and GCM-derived predictor variables and by using a standard suite of diagnostic statistics. Significant differences in the level of skill were found among the downscaling methods. The weather generation techniques, which are able to fit a number of daily precipitation statistics exactly, yielded the smallest differences between observed and simulated daily precipitation. The ANN methods performed poorly because of a failure to simulate wet-day occurrence statistics adequately. Changes in precipitation between the present and future scenarios produced by the statistical downscaling methods were generally smaller than those produced directly by the GCM. Changes in daily precipitation produced by the GCM between 1980–1999 and 2080–2099 were therefore judged not to be due primarily to changes in atmospheric circulation. In the light of these results and detailed model comparisons, suggestions for future research and model refinements are presented.

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Statistical downscaling of general circulation model output: A comparison of methods

An inexact two-stage stochastic programming (ITSP) model is proposed for water resources management under uncertainty. The model is a hybrid of inexact optimization and two-stage stochastic programming. It can reflect not only uncertainties expressed as probability distributions but also those being available as intervals. The solution meth od for ITSP is computationally effective, which makes it applicable to practical problems. The ITSP is applied to a hypothetical case study of water resources system operation. The results indicate that reasonable solutions have been obtained. They are further analyzed and interpreted for generating decision alternatives and identifying significant factors that affect the system's performance. The information obtained through these post-optimality analyses can provide useful decision support for water managers.

An inexact two-stage stochastic programming model for water resources management under uncertainty

Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing

Paradigm shift in urban energy systems through distributed generation: Methods and models

In trying to make a satisfactory decision when imprecise and multicriteria situations are involved, a decision maker has to use a fuzzy multicriteria decision making method. "Fuzzy Multi-Criteria Decision Making" (MCDM) presents fuzzy multiattribute and multiobjective decision-making methodologies by distinguished MCDM researchers. In summarizing the concepts and results of the most popular fuzzy multicriteria methods, using numerical examples, this work examines all the fuzzy multicriteria methods recently developed, such as fuzzy AHP, fuzzy TOPSIS, interactive fuzzy multiobjective stochastic linear programming, fuzzy multiobjective dynamic programming, grey fuzzy multiobjective optimization, fuzzy multiobjective geometric programming, and more. Each of the 22 chapters includes practical applications along with new developments/results. This book may be used as a textbook in graduate operations research, industrial engineering, and economics courses. It will also be an excellent resource, providing new suggestions and directions for further research, for computer programmers, mathematicians, and scientists in a variety of disciplines where multicriteria decision making is needed.

Fuzzy Multi-Criteria Decision Making: Theory and Applications with Recent Developments

Incorporation of Inexact Dynamic Optimization with Fuzzy Relation Analysis for Integrated Climate Change Impact Study

In this study, a large-scale dynamic optimization model (University of Regina Energy Model, UREM) has been developed for supporting long-term energy systems planning in the Region of Waterloo. The model can describe energy management systems as networks of a series of energy flows, transferring extracted/imported energy resources to end users through a variety of conversion and transmission technologies over a number of periods. It can successfully incorporate optimization models, scenario development and policy analysis within a general framework. Complexities in energy management systems can be systematically reflected; thus, the applicability of the modeling process can be highly enhanced. Four scenarios (including a reference case) are considered based on different energy management policies and sustainable development strategies for in-depth analysis of interactions existing among energy, socio-economy and environment in the Region. Useful solutions for the planning of energy management systems have been generated, reflecting trade-offs among energy-related, environmental and economic considerations. They are helpful for supporting (a) adjustment or justification of the existing allocation patterns of energy resources and services, (b) allocations of renewable energy resources, (c) formulation of local policies regarding energy consumption, economic development and energy structure, and (d) analysis of interactions among economic cost, system efficiency, emission mitigation and energy-supply security. Results also indicate that UREM can help tackle dynamic and interactive characteristics of the energy management system in the Region of Waterloo and can address issues concerning cost-effective allocation of energy resources and services. Thus, it can be used by decision makers as an effective technique in examining and visualizing impacts of energy and environmental policies, regional/community development strategies and emission reduction measures within an integrated and dynamic framework. Copyright © 2008 John Wiley & Sons, Ltd.

Brad Bass

Papers

Incorporation of Inexact Dynamic Optimization with Fuzzy Relation Analysis for Integrated Climate Change Impact Study

Development of an optimization model for energy systems planning in the Region of Waterloo

Incorporating Climate Change into Risk Assessment Using Grey Mathematical Programming

Editorial: Climate Change and Variability, Uncertainty and Decision Making

Combination of Differentiated Prediction Approach and Interval Analysis for the Prediction of Weather Variables Under Uncertainty