Water Resources Systems Analysis Research Articles

Water Resources Systems Analysis: A Bright Past and a Challenging but Promising Future

The field of water resources systems analysis is now experiencing one of its most exciting eras where scientists, decision makers, and funding agencies want to apply systems approaches to solve varied, complex, uncertain, and interdisciplinary resource management problems. Solving these problems presents great opportunities for us to engage in complex, real-world decision-making and make positive changes. However, to capitalize on these opportunities, we as a field must also overcome several large challenges related to problem identification, integration, blind use of systems tools, a focus on optimality, and harnessing big data. To overcome, we must look back to find what we have accomplished, why we have sometimes failed, and how we can improve upon past work. In May 2013, we had the privilege to organize and facilitate a thought-provoking panel discussion at the Environmental Water Resources Institute (EWRI) Congress in Cincinnati, Ohio, where different researcher and practitioner panelists spanning multiple generations plus esteemed audience members discussed the past and future of water resources systems analysis. Here, we distill some of the key points that emerged during the discussion that we think should guide our systems analysis work and research in the years and decades ahead.

Sustainability Monitoring and Assessment: New Challenges Require New Thinking

Sustainability Monitoring and Assessment: New Challenges Require New Thinking

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.

Numerical assessment of metamodelling strategies in computationally intensive optimization

Metamodelling is an increasingly more popular approach for alleviating the computational burden associated with computationally intensive optimization/management problems in environmental and water resources systems. Some studies refer to the metamodelling approach as function approximation, surrogate modelling, response surface methodology or model emulation. A metamodel-enabled optimizer approximates the objective (or constraint) function in a way that eliminates the need to always evaluate this function via a computationally expensive simulation model. There is a sizeable body of literature developing and applying a variety of metamodelling strategies to various environmental and water resources related problems including environmental model calibration, water resources systems analysis and management, and water distribution network design and optimization. Overall, this literature generally implies metamodelling yields enhanced solution efficiency and (almost always) effectiveness of computationally intensive optimization problems. This paper initially develops a comparative assessment framework which presents a clear computational budget dependent definition for the success/failure of the metamodelling strategies, and then critically evaluates metamodelling strategies, through numerical experiments, against other common optimization strategies not involving metamodels. Three different metamodel-enabled optimizers involving radial basis functions, kriging, and neural networks are employed. A robust numerical assessment within different computational budget availability scenarios is conducted over four test functions commonly used in optimization as well as two real-world computationally intensive optimization problems in environmental and water resources systems. Numerical results show that metamodelling is not always an efficient and reliable approach to optimizing computationally intensive problems. For simpler response surfaces, metamodelling can be very efficient and effective. However, in some cases, and in particular for complex response surfaces when computational budget is not very limited, metamodelling can be misleading and a hindrance, and better solutions are achieved with optimizers not involving metamodels. Results also demonstrate that neural networks are not appropriate metamodelling tools for limited computational budgets while metamodels employing kriging and radial basis functions show comparable overall performance when the available computational budget is very limited.

Qatar Sustainable Water & Energy Utilization Initiative (QWE). Water and environmental research activities at TAMUQ

Texas A&M University at Qatar (TAMUQ) has very active and well-established environmental and water research and development program. TAMUQ has recently founded the Qatar Sustainable Water and Energy Utilization Initiative (QWE) which is a center of excellence for open and cooperative research, and capacity building established to address the sustainable environmental, water and energy efficiency issues relevant to Qatar. With current R&D projects worth in excess of USD 6 million, the QWE builds upon a strong scientific and technological base of direct relevance to Qatar. The QWE initiative aims to sustain and expand this critical knowledge resource to provide knowledge and technology transfer to stakeholders; to provide technical services; to provide continuity to the R&D efforts; to support long-term national programs; to engage in campaigns to promote the need for environmental protection and sustainable water and energy utilization to the wider public; and, of special importance, to develop the human capacity needed to address scientific and technical issues related to Qatar's current and future needs in these areas. The QWE team consists of twelve highly qualified research staff members is working on several research projects which are of direct benefit to the Qatar community. Current research projects cover the following areas: Environmental impact assessment and management Integrated water resources management Hybrid desalination systems and systems analysis for solar desalination Zero liquid discharge systems Water and energy systems analysis, integration and optimization Hazardous waste management Advanced water and wastewater treatment processes The presentation will outline the initiative and share results and insights developed across the numerous research projects carried out at the QWE. We will specifically focus on the direct link between research and capacity building activities and the development of tailored solutions to real problems observed in the region.

Factorial two-stage stochastic programming for water resources management

This study presents a factorial two-stage stochastic programming (FTSP) approach for supporting water resource management under uncertainty. FTSP is developed through the integration of factorial analysis and two-stage stochastic programming (TSP) methods into a general modeling framework. It can handle uncertainties expressed as probability distributions and interval numbers. This approach has two advantages in comparison to conventional inexact TSP methods. Firstly, FTSP inherits merits of conventional inexact two-stage optimization approaches. Secondly, it can provide detailed effects of uncertain parameters and their interactions on the system performance. The developed FTSP method is applied to a hypothetical case study of water resources systems analysis. The results indicate that significant factors and their interactions can be identified. They can be further analyzed for generating water allocation decision alternatives in municipal, industrial and agricultural sectors. Reasonable water allocation schemes can thus be formulated based on the resulting information of detailed effects from various impact factors and their interactions. Consequently, maximized net system benefit can be achieved.

Simulation and Analysis of Conjunctive Use with MODFLOW's Farm Process

The extension of MODFLOW onto the landscape with the Farm Process (MF-FMP) facilitates fully coupled simulation of the use and movement of water from precipitation, streamflow and runoff, groundwater flow, and consumption by natural and agricultural vegetation throughout the hydrologic system at all times. This allows for more complete analysis of conjunctive use water-resource systems than previously possible with MODFLOW by combining relevant aspects of the landscape with the groundwater and surface water components. This analysis is accomplished using distributed cell-by-cell supply-constrained and demand-driven components across the landscape within "water-balance subregions" comprised of one or more model cells that can represent a single farm, a group of farms, or other hydrologic or geopolitical entities. Simulation of micro-agriculture in the Pajaro Valley and macro-agriculture in the Central Valley are used to demonstrate the utility of MF-FMP. For Pajaro Valley, the simulation of an aquifer storage and recovery system and related coastal water distribution system to supplant coastal pumpage was analyzed subject to climate variations and additional supplemental sources such as local runoff. For the Central Valley, analysis of conjunctive use from different hydrologic settings of northern and southern subregions shows how and when precipitation, surface water, and groundwater are important to conjunctive use. The examples show that through MF-FMP's ability to simulate natural and anthropogenic components of the hydrologic cycle, the distribution and dynamics of supply and demand can be analyzed, understood, and managed. This analysis of conjunctive use would be difficult without embedding them in the simulation and are difficult to estimate a priori.

A new method for spatial and temporal analysis of risk in water resources management

Uncertainty in water resources management is in part about variability and in part about ambiguity. Both are associated with lack of clarity because of the behavior of all system components, lack of data, lack of detail, lack of structure to consider the water resources management problems, working and framing assumptions being used to consider the problems, known and unknown sources of bias, and ignorance about how much effort it is worth expending to clarify the management situation. The two major sources of variability are temporal and spatial heterogeneity. Temporal variability occurs when values fluctuate with time. Other values which are affected by spatial variability are dependent upon location of an area. A major part of the water resources management risk confusion relates to an inadequate distinction between the objective risk (real, physical) and subjective (perceived) risk. Because of the confusion between the two concepts, many characteristics of subjective risk are believed to be valid also for objective risk. The main objective of this paper is to initiate a discussion of the possible methodology for the reliability analysis of water resources systems that will be capable of: (a) addressing water resources uncertainty caused by variability and ambiguity, (b) integrating objective and subjective risk and (c) assisting the water resources management based on better understanding of temporal and spatial variability of risk.

Assessing the impacts of climate change on the water resources of the Seyhan River Basin in Turkey: Use of dynamically downscaled data for hydrologic simulations

Summary We explored the potential impacts of climate change on the hydrology and water resources of the Seyhan River Basin in Turkey. A dynamical downscaling method, referred to as the pseudo global warming method (PGWM), was used to connect the outputs of general circulation models (GCMs) and river basin hydrologic models. The GCMs used in this study were MRI-CGCM2 and CCSR/NIES/FRCGC-MIROC under the SRES A2 scenario, and the downscaled data covered two 10-year time slices corresponding to the present (1990s) and future (2070s). The hydrologic models along with a reservoir model were driven using the downscaled data for the present period. As a result, the temperature and precipitation, which were dynamically downscaled through bias-correction, were in good agreement with the observed data. The hydrologic simulation results also matched the observed flow, reservoir volume, and dam discharge. Therefore, we concluded that the PGWM combined with bias-correction is extremely useful to produce input data for hydrologic simulations. The simulation results for the future were compared with those for the present. The average annual temperature changes in the future relative to the present were projected to be +2.0 °C and +2.7 °C by MRI and CCSR, respectively. The annual precipitation decreased by 157 mm (25%) in MRI-future and by 182 mm (29%) in CCSR-future, and the annual evapotranspiration decreased by 36 mm (9%) in MRI-future and by 39 mm (10%) in CCSR-future; the annual runoff decreased by 118 mm (52%) in MRI-future and by 139 mm (61%) in CCSR-future. The analysis of water resource systems was conducted by using a simple scenario approach to take into account changes in water use. This analysis indicated that despite the impacts of climate change, water scarcity will not occur in the future if water demand does not increase. However, if the irrigated area is expanded in the future under the expectation of current flow, water scarcity will occur due to the combination of decreased inflow and increased water demand. Thus, in the Seyhan River Basin, water use and management will play more important roles than climate change in controlling future water resource conditions.

A New Method for Spatial Analysis of Risk in Water Resources Engineering Management

Uncertainty in water resources management is in part about variability, in part about ambiguity. Both are associated with lack of clarity because of the behavior of all system components, lack of data, lack of detail, lack of structure to consider the water resources management problems, working and framing assumptions being used to consider the problems, known and unknown sources of bias, and ignorance about how much effort it is worth expending to clarify the management situation. The two major sources of variability are temporal and spatial heterogeneity. Temporal variability occurs when values fluctuate with time. Other values which are affected by spatial variability are dependent upon location of an area. A major part of the water resources management risk confusion relates to an inadequate distinction between the objective risk (real, physical) and subjective (perceived) risk. Because of the confusion between the two concepts, many characteristics of subjective risk are believed to be valid also for objective risk. The main objective of this paper is to present the possible methodology for the reliability analysis of water resources systems that will be capable of: (a) addressing water resources uncertainty caused by variability and ambiguity; (b) integrating objective and subjective risk; and (c) assisting the water resources management based on better understanding of spatial variability of risk. Presented methodology is illustrated using flood reliability analysis of the Medway Creek floodplain in the City of London, Ontario, Canada.

A New Method for Spatial Analysis of Risk in Water Resources Engineering Management

Uncertainty in water resources management is in part about variability, in part about ambiguity. Both are associated with lack of clarity because of the behavior of all system components, lack of data, lack of detail, lack of structure to consider the water resources management problems, working and framing assumptions being used to consider the problems, known and unknown sources of bias, and ignorance about how much effort it is worth expending to clarify the management situation. The two major sources of variability are temporal and spatial heterogeneity. Temporal variability occurs when values fluctuate with time. Other values which are affected by spatial variability are dependent upon location of an area. A major part of the water resources management risk confusion relates to an inadequate distinction between the objective risk (real, physical) and subjective (perceived) risk. Because of the confusion between the two concepts, many characteristics of subjective risk are believed to be valid also for objective risk. The main objective of this paper is to present the possible methodology for the reliability analysis of water resources systems that will be capable of: (a) addressing water resources uncertainty caused by variability and ambiguity; (b) integrating objective and subjective risk; and (c) assisting the water resources management based on better understanding of spatial variability of risk. Presented methodology is illustrated using flood reliability analysis of the Medway Creek floodplain in the City of London, Ontario, Canada.

Fourier-PARMA Models and Their Application to River Flows

For analysis and design of water resource systems, it is sometimes useful to generate high-resolution (e.g., weekly) synthetic river flows. Periodic autoregressive moving average (PARMA) time series models provide a powerful tool for generating synthetic flows. Periodically stationary models are indicated when the basic statistics (mean, variance, and autocorrelation) of the time series exhibit significant seasonal variations. Parameter estimation for high-resolution PARMA models involves numerous parameters, which can lead to overfitting. Thus, this paper develops a parsimonious method of parameter fitting for high-resolution PARMA models, using discrete Fourier transforms to represent the set of periodic autoregressive and moving average model coefficients. Model parameters are computed via the innovations algorithm, and the asymptotic distributions of the discrete Fourier transform coefficients are obtained. Those asymptotic results are useful to determine the statistically significant Fourier coeffici...

Coupling of Engineering and Biological Models for Ecosystem Analysis

Robust ecosystem analysis of water resource systems remains elusive. A principle reason is the difficulty in linking engineering models used to simulate physicochemical processes associated with project design or operation with biological models used to simulate biological population attributes. A retrospective shows that each modeling tradition can be generally assigned (with exceptions) into either an Eulerian or Lagrangian reference framework. Eulerian and Lagrangian reference frameworks can be coupled to create a new synthesis, the Coupled Eulerian-Lagrangian Hybrid Ecological Modeling Concept (CEL Hybrid Concept), capable of simulating different ecosystem processes that range widely in spatial and temporal scale. The foundation of the CEL Hybrid Concept is the coupler, a collection of algorithms based on conservation principles that transform and conserve data in a way that allows the two frameworks to share a common information base. The coupling algorithm allows the simulation to aggregate, disaggregate, and translate information, as required by each framework, so that processes that differ substantially in scale can each be adequately simulated. The coupled system is illustrated by linking a fish swim path selection model with a hydrodynamic and water quality model.

Role of Evolutionary Computation in Environmental and Water Resources Systems Analysis

Since the 1960s, systems analytic methods have played a key role in environmental and water resources planning and management. An array of systematic search procedures as well as statistical methods continues to be the focus of investigation in environmental and water resources systems (EWRS). With the advent of modern and faster computing resources at a high degree of affordability, the system simulation models are now able to incorporate more processes and their interactions, resulting in relatively more complex model structures. Therefore, the coupling of simulation models with search methods for optimization have become increasingly challenging. In many cases, complexities that arise from, for example, nonlinearity, discontinuity, and discreteness in modern simulation models, limit the application of traditional search methods, e.g., mathematical programming procedures. Such limitations have been overcome recently by directly coupling the simulation models with heuristic search procedures. While this directly coupled simulation–optimization (S–O) approach is, in general, computationally demanding, it has proven to be a viable approach given cheaper and faster computational resources. Among the array of modern heuristic approaches (e.g., simulated annealing, tabu search, genetic algorithms, evolutionary strategies, particle swarm method, and ant colony optimization) that support an S–O framework for EWRS problems, the collection of methods—namely, genetic algorithms, evolutionary strategies, genetic programming, and evolutionary programming— encompassed within the broad category of evolutionary computation (EC) offers a multitude of capabilities. Starting in the early 1990s, evolutionary algorithms (including genetic algorithms and evolutionary strategies) have been applied and demonstrated for system optimization in the context of EWRS problems. Numerous studies over the past decade in areas that include groundwater monitoring and remediation design, water distribution network design, reservoir optimization, watershed management, and air pollution control show not only the viability of applying evolutionary algorithms to these challenging problems, but also the added benefits of using a directly coupled S–O approach enabled by these techniques. This special issue also represents a cross section of recent investigations related to EC methods and their applications in EWRS. In addition to system optimization, many EWRS problems pose several interesting scenarios that call for additional systems analytic capabilities. For example, most EWRS problems consider multiple competing objectives, requiring the systems analytic methods to provide multiobjective optimization capabilities. The structure of EC methods readily support efficient search for Pareto optimal solutions to a multiobjective optimization problem. This is currently an active area of research in the EC re-

Multiobjective water resources systems analysis using genetic algorithms - application to Chou-Shui River Basin, Taiwan

Multipurpose operation is adopted by most reservoirs in Taiwan in order to maximize the benefits of power generation, water supply, irrigation and recreational purposes. A multiobjective approach can be used to obtain trade-off curves among these multipurpose targets. The weighting method, in which different weighting factors are used for different purposes, was used in this research work. In Taiwan, most major reservoirs are operated by rule curves. Genetic algorithms with characteristics of artificial intelligence were applied to obtain the optimal rule curves of the multireservoir system under multipurpose operation in Chou-Shui River Basin in central Taiwan. The model results reveal that different shapes of rule curves under different weighting factors on targets can be efficiently obtained by genetic algorithms. Pareto optimal solutions for a trade-off between water supply and hydropower were obtained and analyzed.

A stochastic rainfall model for the assessment of regional water resource systems under changed climatic condition

Abstract. A stochastic model is developed for the synthesis of daily precipitation using conditioning by weather types. Daily precipitation statistics at multiple sites within the region of Yorkshire, UK, are linked to objective Lamb weather types (LWTs) and used to split the region into three distinct precipitation sub-regions. Using a variance minimisation criterion, the 27 LWTs are clustered into three physically realistic groups or ‘states'. A semi-Markov chain model is used to synthesise long sequences of weather states, maintaining the observed persistence and transition probabilities. The Neyman-Scott Rectangular Pulses (NSRP) model is then fitted for each weather state, using a defined summer and winter period. The combined model reproduces key aspects of the historic precipitation regime at temporal resolutions down to the hourly level. Long synthetic precipitation series are useful in the sensitivity analysis of water resource systems under current and changed climatic conditions. This methodology enables investigation of the impact of variations in weather type persistence or frequency. In addition, rainfall model statistics can be altered to simulate instances of increased intensity or proportion of dry days for example, for individual weather groups. The input of such data into a water resource model, simulating potential atmospheric circulation changes, will provide a valuable tool for future planning of water resource systems. The ability of the model to operate at an hourly level also allows its use in a wider range of hydrological impact studies, e.g. variations in river flows, flood risk estimation etc. Keywords: water resources; climate change; impacts; stochastic rainfall model; Lamb weather types

Simulating long series of streamflow using data from an atmospheric model

Studies using data from a general circulation model (GCM) and from numerical weather prediction models (NWP) as inputs to a hydrological model have produced useful results but were limited by the coarse resolution of the GCM data on the one hand and, on the other hand, by the relatively short period of high-resolution NWP data available. This paper describes an alternative approach using long-term (96 years) daily data from a high resolution atmospheric boundary layer model. The data from the atmospheric model were used as inputs to a daily distributed hydrological model of the Upper Columbia Basin in western Canada. The hydrological model was calibrated for five years and verified using streamflow and other data for a further 18 years. It was found that the hydrological model was able to simulate snowpack and streamflow data for the total period of 96 years of available atmospheric model output with good success. This method provides the opportunity for analysis of water resources systems under alternative climate and land-use scenarios.

Multiobjective Analysis of Multireservoir System

The explicit consideration of multiple objectives in decision making is becoming increasingly important in water resources systems analysis. Conflicting objectives like irrigation, flood control, hydropower production, and environmental preservation are inevitable while deriving the operating alternatives in an efficient way for any complex water resources system. In this paper, a linear multiobjective programming model has been developed and the constraint technique was used to derive the optimal releases for various purposes from a large‐scale multireservoir system consisting of five reservoirs in India. Maximization of irrigation releases and maximization of hydropower production have been considered as the twin objectives in the model subjected to constraints on physical limitations, environmental restrictions, and storage continuity. This model was applied to three series of inflow sequences representing the normal, drought, and excess‐flow conditions. The tradeoff analysis between the conflicting objectives of irrigation and hydropower production was also carried out and the transformation curve was plotted. The optimal point on this curve gives the best combination of the twin objectives considered in the model.

Water resources optimization—simulation in Argentina

This paper describes an integrated mathematical model, Oper, for use in the analysis and planning of multipurpose water resources systems. A typical system consists of reservoirs, hydropower stations, irrigated land, artificial and navigation channels, etc., over a reach of a river or a river basin. The model takes into account the hydrological, technical, sociopolitical and economic constraints that must be considered by planners and decision makers. The global model is composed of three different models: a preliminary design is obtained by means of the linear programming model Oper1; the corresponding hydrological and economic performances are tested through the simulation model Oper2; the optimal sequence of investments during the planning horizon is completed with the mixed integer programming model Oper4. The model has been implemented on a minicomputer and applied by the authors to the design of the water resources system on the Rio Negro basin, Argentina.

A linear programming approach to water-resources optimization

A linear-programming model for use in analysis and planning of multiobjective water resources systems is described in this paper. A typical system consists of reservoirs, hydropower stations, irrigated land, artificial and navigation channels, etc., over a reach of a river or a river basin.