David W. Watkins

Synthesis of System Dynamics Tools for Holistic Conceptualization of Water Resources Problems

Out-of-context analysis of water resources systems can result in unsustainable management strategies. To address this problem, systems thinking seeks to understand interactions among the subsystems driving a system’s overall behavior. System dynamics, a method for operationalizing systems thinking, facilitates holistic understanding of water resources systems, and strategic decision making. The approach also facilitates participatory modeling, and analysis of the system’s behavioral trends, essential to sustainable management. The field of water resources has not utilized the full capacity of system dynamics in the thinking phase of integrated water resources studies. We advocate that the thinking phase of modeling applications is critically important, and that system dynamics offers unique qualitative tools that improve understanding of complex problems. Thus, this paper describes the utility of system dynamics for holistic water resources planning and management by illustrating the fundamentals of the approach. Using tangible examples, we provide an overview of Causal Loop and Stock and Flow Diagrams, reference modes of dynamic behavior, and system archetypes to demonstrate the use of these qualitative tools for holistic conceptualization of water resources problems. Finally, we present a summary of the potential benefits as well as caveats of qualitative system dynamics for water resources decision making.

Solving Large Nonconvex Water Resources Management Models Using Generalized Benders Decomposition

Nonconvex nonlinear programming (NLP) problems arise frequently in water resources management, e.g., reservoir operations, groundwater remediation, and integrated water quantity and quality management. Such problems are usually large and sparse. Existing software for global optimization cannot cope with problems of this size, while current local sparse NLP solvers, e.g., MINOS (Murtagh and Saunders 1987), or CONOPT (Drud 1994) cannot guarantee a global solution. In this paper, we apply the Generalized Benders Decomposition (GBD) algorithm to two large nonconvex water resources models involving reservoir operations and water allocation in a river basin, using an approximation to the GBD cuts proposed by Floudas et al. (1989) and Floudas (1995). To ensure feasibility of the GBD subproblem, we relax its constraints by introducing elastic slack variables, penalizing these slacks in the objective function. This approach leads to solutions with excellent objective values in run times much less than the GAMS NLP solvers MINOS5 and CONOPT2, if the complicating variables are carefully selected. Using these solutions as initial points for MINOS5 or CONOPT2 often leads to further improvements.

Decomposition methods for water resources optimization models with fixed costs

This paper illustrates the application of two decomposition algorithms, generalized Benders decomposition (GBD) and outer approximation (OA), to water resources problems involving cost functions with both discrete and nonlinear terms. Each algorithm involves the solution of an alternating finite sequence of nonlinear programming subproblems and relaxed versions of a mixed-integer linear programming master problem. Three example models, involving capacity expansion of a conjunctively managed surface and groundwater system, are formulated and solved to demonstrate the performance of the algorithms. The results show that OA obtains solutions in far fewer iterations than GBD, but OA requires more computational resources per iteration. As a result, depending on the mixed-integer programming and nonlinear programming solvers available, GBD may be better suited for solving larger planning problems.

Educating engineers in the sustainable futures model with a global perspective

The solutions to the worlds current and future problems require that engineers and scientists design and construct ecologically and socially just systems within the carrying capacity of nature without compromising future generations. In addition, as governments move towards policies that promote an international marketplace, educators need to prepare students to succeed in the global economy. Young people entering the workforce in the upcoming decades will also have the opportunity to play a critical role in the eradication of poverty and hunger and facilitation of sustainable development, appropriate technology, beneficial infrastructure, and promotion of change that is environmentally and socially just. Many universities espouse the idea that discipline integration is a prerequisite for successful implementation of sustainability in education. However, few engineering curriculum have taken the step to integrate concepts of sustainable development with an international experience. This paper discusses the educational and global drivers for curricular change in this important area and demonstrates how several undergraduate and graduate programmes initiated at Michigan Technological University can provide a more interdisciplinary basis for educating engineers on global concepts of sustainability. To date, these programmes have taken place in 21 countries and reached approximately 300 students (49% women) that represent 11 engineering disciplines and nine non-engineering disciplines.

Cyber-physical systems for water sustainability: challenges and opportunities

Water plays a vital role in the proper functioning of the Earths ecosystems, and practically all human activities, such as agriculture, manufacturing, transportation, and energy production. The proliferation of industrial and agricultural activities in modern society, however, poses threats to water resources in the form of chemical, biological, and thermal pollution. On the other hand, tremendous advancements in science and technology offer valuable tools to address water sustainability challenges. Key technologies, including sensing technology, wireless communications and networking, hydrodynamic modeling, data analysis, and control, enable intelligently wireless networked water cyber-physical systems (CPS) with embedded sensors, processors, and actuators that can sense and interact with the water environment. This article provides an overview of water CPS for sustainability from four critical aspects: sensing and instrumentation; communications and networking; computing; and control. The article also explores opportunities and design challenges of relevant techniques.

Recent developments associated with decision support systems in water resources

In order to limit the scope of this review, a working definition of a decision support system is needed. L. Adelman has defined decision support systems (DSSs) as “interactive computer programs that utilize analytical methods, such as decision analysis, optimization algorithms, program scheduling routines, and so on, for developing models to help decision makers formulate alternatives, analyze their impacts, and interpret and select appropriate options for implementation” (Adelman [1992], p. 2). Another definition has been offered by S. J. Andriole, who defined decision support as consisting of “any and all data, information, expertise or activities that contribute to option selection“ (Andriole [1989], p. 3). A common idea explicit in each of these definitions is that DSSs integrate various technologies and aid in option selection. Implicit in each definition is that these are options for solving relatively large, unstructured problems. Thus, the following working definition of a DSS will be used in this review: A DSS is an integrated, interactive computer system, consisting of analytical tools and information management capabilities, designed to aid decision makers in solving relatively large, unstructured problems.

Performance-Based Evaluation of an Improved Robust Optimization Formulation

AbstractMuch progress has been made in the standardization of uncertainty analysis techniques for simulation modeling but less progress has been made in optimization modeling. Among the various techniques used for optimization modeling under uncertainty, robust optimization (RO) uniquely allows for evaluation and control of the various risks of poor system performance resulting from input parameter uncertainties in water-resources problems. A model formulation was developed that addresses an inadequacy in a previous RO formulation. The importance of evaluating, through postprocessing, RO model results with respect to a range of performance metrics, has been demonstrated rather than a single metric, as has been common in previous studies. An analysis of the tradeoffs between solution robustness (nearness to optimality across all scenarios) and feasibility robustness (nearness to feasibility across all scenarios) illustrates the importance of including these terms in multiobjective water resources decision ...

Quantifying health improvements from water quantity enhancement: an engineering perspective applied to rainwater harvesting in West Africa.

Knowledge of potential benefits resulting from technological interventions informs decision making and planning of water, sanitation, and hygiene programs. The public health field has built a body of literature showing health benefits from improvements in water quality. However, the connection between improvements in water quantity and health is not well documented. Understanding the connection between technological interventions and water use provides insight into this problem. We present a model predicting reductions in diarrhea disease burden when the water demands from hygiene and sanitation improvements are met by domestic rainwater harvesting (DRWH). The model is applied in a case study of 37 West African cities. For all cities, with a total population of over 10 million, we estimate that DRWH with 400 L storage capacity could result in a 9% reduction in disability-affected life years (DALYs). If DRWH is combined with point of use (POU) treatment, this potential impact is nearly doubled, to a 16% reduction in DALYs. Seasonal variability of diarrheal incidence may have a small to moderate effect on the effectiveness of DRWH, depending on the storage volume used. Similar predictions could be made for other interventions that improve water quantity in other locations where disease burden from diarrhea is known.

Continuous Hydrologic Modeling of Snow-Affected Watersheds in the Great Lakes Basin Using HEC-HMS

To reproduce historical stream flows, climate and land-use change studies require watershed models with physically based parameters, rather than empirical models that are simply calibrated. With this in mind, soil moisture accounting and the temperature index (degree-day) snowmelt models embodied in the Hydrologic Engineering Centers hydrologic modeling system (HEC-HMS) are applied to three Great Lakes watersheds—Kalamazoo, Maumee, and St. Louis—with different climatic and land-use characteristics. Watershed and subwatershed models are calibrated and validated on a daily time step using gauge precipitation measurements, observed snow water equiv- alent data, and physically based parameters estimated using geospatial databases. Results are compared with area-scaled outputs from the National Oceanic and Atmospheric Administration (NOAA) large basin runoff model (LBRM) for historical conditions. The results show modest improvements resulting from the increased spatial resolution of the HEC-HMS models, in addition to the benefits of the more process- based snow algorithm in HEC-HMS, particularly for the snow-dominated St. Louis watershed. However, both LBRM and HEC-HMS models had difficulty reproducing peaks in late winter and early spring runoff, and discrepancies could not be attributed to any systematic errors in the snowmelt models. DOI: 10.1061/(ASCE)HE.1943-5584.0000591.

Methodological Approaches to Projecting the Hydrologic Impacts of Climate Change

AbstractClimate change due to anthropogenic greenhouse gases (GHG) is expected to have important impacts on water resources, with a variety of societal impacts. Recent research has shown that applying different methodologies to assess hydrologic impacts can lead to widely diverging projections of water resources. The authors classify methods of projecting hydrologic impacts of climate change into those that estimate potential evapotranspiration (PET) based on air temperature and those that estimate PET based on components of the surface energy budget. In general, air temperature–based methods more frequently show reductions in measures of water resources (e.g., water yield or soil moisture) and greater sensitivity than those using energy budget–based methods. There are significant trade-offs between these two methods in terms of ease of use, input data required, applicability to specific locales, and adherence to fundamental physical constraints: namely, conservation of energy at the surface. Issues of un...