Research on stream and river temperatures is reviewed with particular attention being given to advances in understanding gained since 1990 and on investigations of fundamental controls on thermal behaviour, thermal heterogeneity at different spatial scales, the influence of human impacts and the nature of past and future trends. Copyright  2008 John Wiley & Sons, Ltd.

Recent advances in stream and river temperature research

We study approximations of optimization problems with probabilistic constraints in which the original distribution of the underlying random vector is replaced with an empirical distribution obtained from a random sample. We show that such a sample approximation problem with a risk level larger than the required risk level will yield a lower bound to the true optimal value with probability approaching one exponentially fast. This leads to an a priori estimate of the sample size required to have high confidence that the sample approximation will yield a lower bound. We then provide conditions under which solving a sample approximation problem with a risk level smaller than the required risk level will yield feasible solutions to the original problem with high probability. Once again, we obtain a priori estimates on the sample size required to obtain high confidence that the sample approximation problem will yield a feasible solution to the original problem. Finally, we present numerical illustrations of how these results can be used to obtain feasible solutions and optimality bounds for optimization problems with probabilistic constraints.

http://www.optimization-online.org/DB_FILE/2007/09/1779.pdf

A Sample Approximation Approach for Optimization with Probabilistic Constraints

During the last two decades, the water resources planning and management profession has seen a dramatic increase in the development and application of various types of evolutionary algorithms (EAs). This observation is especially true for application of genetic algorithms, arguably the most popular of the several types of EAs. Generally speaking, EAs repeatedly prove to be flexible and powerful tools in solving an array of complex water resources problems. This paper provides a comprehensive review of state-of-the-art methods and their applications in the field of water resources planning and management. A primary goal in this ASCE Task Committee effort is to identify in an organized fashion some of the seminal contributions of EAs in the areas of water distribution systems, urban drainage and sewer systems, water supply and wastewater treatment, hydrologic and fluvial modeling, groundwater systems, and parameter identification. The paper also identifies major challenges and opportunities for the future, ...

State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management

What are the actual barriers and drivers for sustainable building? A literature review, interviews and case studies are presented to address this question. Sustainable building is not hindered by a lack of technologies and assessment methods, but is instead beset with organizational and procedural difficulties entailed by the adoption of new methods. New technologies are resisted because they require process changes entailing risks and unforeseen costs. These hindrances can be reduced by learning what kind of decision-making phases, new tasks, actors, roles and ways of networking are needed. The barriers are outlined as steering mechanisms, economics, a lack of client understanding, process (procurement and tendering, timing, cooperation and networking), and underpinning knowledge (knowledge and common language, the availability of methods and tools, innovation). The most important actions to promote sustainable building are the development of the awareness of clients about the benefits of sustainable bui...

Barriers and drivers for sustainable building

The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

/pdf/evolutionary-algorithms-and-other-metaheuristics-in-water-395cpaoum8.pdf

Evolutionary algorithms and other metaheuristics in water resources

A new approach for reliability-based optimization of water distribution networks is presented. The approach links a genetic algorithm ~GA! as the optimization tool with the first-order reliability method ~FORM! for estimating network capacity reliability. Network capacity reliability in this case study refers to the probability of meeting minimum allowable pressure constraints across the network under uncertain nodal demands and uncertain pipe roughness conditions. The critical node capacity reliability approximation for network capacity reliability is closely examined and new methods for estimating the critical nodal and overall network capacity reliability using FORM are presented. FORM approximates Monte Carlo simulation reliabilities accurately and efficiently. In addition, FORM can be used to automatically determine the critical node location and corresponding capacity reliability. Network capacity reliability approxi- mations using FORM are improved by considering two failure modes. This research demonstrates the novel combination of a GA with FORM as an effective approach for reliability-based optimization of water distribution networks. Correlations between random variables are shown to significantly increase optimal network costs.

/pdf/genetic-algorithms-for-reliability-based-optimization-of-27cs7a9llz.pdf

Genetic Algorithms for Reliability-Based Optimization of Water Distribution Systems

Reliability, vulnerability, and resilience provide measures of the frequency, magnitude, and duration of the failure of water resources systems, respectively. Traditionally, these measures have been estimated using simulation. However, this can be computationally intensive, particularly when complex system-response models are used, when many estimates of the performance measures are required, and when persistence among the data needs to be taken into account. In this paper, an efficient method for estimating reliability, vulnerability, and resilience, which is based on the First-Order Reliability Method (FORM), is developed and demonstrated for the case study of managing water quality in the Willamette River, Oregon. Reliability, vulnerability, and resilience are determined for different dissolved oxygen (DO) standards. DO is simulated using a QUAL2EU water quality response model that has recently been developed for the Oregon Department of Environmental Quality (ODEQ) as part of the Willamette River Basin Water Quality Study (WRBWQS). The results obtained indicate that FORM can be used to efficiently estimate reliability, vulnerability, and resilience.

/pdf/first-order-reliability-method-for-estimating-reliability-g32s0z5niz.pdf

First‐order reliability method for estimating reliability, vulnerability, and resilience

At present, the performance of sustainable technologies in buildings is generally not assessed holistically, but rather from a primarily single issue perspective, e.g., only financially or environmentally. Such an approach is limited in that it ignores the interaction of the technologies within the physical facility itself as expressed through life cycle costs, the impact on the surrounding environment, design objectives of the project and its stakeholders whose value systems may conflict. This work identifies the primary cause-and-effect relationships of selected sustainable building technologies and illustrates elements of a framework for the systematic assessment of their performance from an environmental, social, economic, and technical perspective. Rooftop garden technology is used to demonstrate application of the framework. The primary goal of this work is to improve the understanding and decision-making capabilities of the building industry and government when faced with decisions regarding invest...

Assessing the performance of sustainable technologies for building projects

A recently developed framework to provide a logical structure for the a priori assessment of a particular technology with regard to sustainability concepts, performance, and relevancy to a project's construction process and operation is introduced. The framework is designed to assist project stakeholders and policy-makers in systematically identifying and evaluating the implications and relative merits of a range of alternative sustainable technologies. Building professionals were consulted throughout the development of the framework: to test the completeness of the framework; to capture the value systems of various industry participants in order to reflect them in the framework; and to gather information on the advantages and disadvantages of the green roof technology. The framework is applied to the example of the green roof technology, and the order-of-magnitude estimates are developed for performance measures identified as important from the perspective of industry professionals. The technology is eva...

Assessing the performance of sustainable technologies: a framework and its application

Traditional chance-constrained programming (CCP) and simulation-optimization methods of incorporating input information uncertainty in pollution management models are unsuitable for complex river systems with several critical water quality segments. Using the CCP method, characterization of the joint probability distribution of coefficients of the management model is often difficult because stream information is limited and the model formulation is generally difficult to understand and solve. For the simulation-optimization method most of the solutions produced are inferior. The multiple realization model, which includes several scenarios of design conditions simultaneously in an optimization model, overcomes such weaknesses by not requiring the joint probability distribution of the stochastic model coefficients and by producing noninferior solutions. Heuristic and neural network techniques are developed to reduce the computational time required to solve the multiple realization model, through identification and utilization of only potentially important stream and water quality information that influence the optimal solution. These techniques are applied to develop trade-off relationships between waste treatment cost and reliability of achieving dissolved oxygen objectives for an example river basin. Results show that the heuristic technique is computationally efficient when <1000 realizations are included in the model, while the neural network method is suitable when several thousand realizations are needed to adequately represent the stochastic water quality system.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/98WR02771

Barbara J. Lence

Papers

Genetic Algorithms for Reliability-Based Optimization of Water Distribution Systems

First‐order reliability method for estimating reliability, vulnerability, and resilience

Assessing the performance of sustainable technologies for building projects

Assessing the performance of sustainable technologies: a framework and its application

Surface water quality management using a multiple-realization chance constraint method