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Browsing by Author "Labadie, John W., advisor"

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    ItemOpen Access
    A facilitated process and online toolset to analyze complex systems and coordinate active watershed development and transformation
    (Colorado State University. Libraries, 2014) Herzog, Margaret T., author; Labadie, John W., advisor; Grigg, Neil S., advisor; Sharvelle, Sybil, committee member; Lacy, Michael G., committee member; Clayshulte, Russell N., committee member
    Integrated Water Resources Management (IWRM) coordinates public, private, and nonprofit sectors in strategic resource development, while emphasizing holistic environmental protection. Without more integrated efforts, adverse human affects to water, other natural resources, and ecosystems services may worsen and cause more unintended cross-scale effects. Meanwhile, fragmented jurisdictional controls and competing demands continue to create new obstacles to shared solutions. Lack of coordination may accentuate negative impacts of extreme events, over-extraction, and other, often unrecognized threats to social-ecological systems integrity. To contend with these challenges, a research-based, facilitated process was used to design an online toolset to analyze complex systems more holistically, while exploring more ways to coordinate joint efforts. Although the focus of the research was the watershed scale, different scales of social-ecological problems may be amenable to this approach. The process builds on an adaptive co-management (ACM) framework. ACM promotes systems-wide, incremental improvements through cooperative action and reflection about complex issues affecting social-ecological systems at nested and overlapping scales. The resulting ACM Decision Support System (DSS) process may help reduce fragmentation in both habitat and social structure by recognizing and encouraging complex systems reintegration and reorganization to improve outcomes. The ACM DSS process incorporates resilience practice techniques to anticipate risks by monitoring drivers and thresholds and to build coordinated coping strategies. The Bear Creek Watershed Association (BCWA) served as a case study in nutrient management, which focused on understanding and mitigating the complex causes of cultural eutrophication in Bear Creek Reservoir - a flood control reservoir to which the entire watershed drains. The watershed lies in the Upper South Platte River Basin -the eastern mountain headwaters to metropolitan Denver, Colorado in the United States. To initiate Phase I of the ACM DSS process, qualitative data on issues, options, social ties, and current practices were triangulated through organizational interviews, document review, a systems design group, and ongoing BCWA, community, river basin, and state-level participation. The mixed methods approach employed geographic information systems (GIS) for spatial analysis, along with statistical analysis and modeling techniques to assess reported issues and potential options quantitatively. Social network analysis (SNA) was used systematically to evaluate organizational relationships, transactions, and to direct network expansion towards a more robust core-periphery network structure. Technical and local knowledge developed through these methods were complimented by ongoing academic literature review and analysis of related watershed efforts near and far. Concurrently, BCWA member organizations helped to incrementally design and test an online toolset for greater emphasis on ACM principles in watershed program management. To date, online components of the ACM DSS include issues reporting, interactive maps, monitoring data access, group search, a topical knowledge base, projects and options tracking, and watershed and lake management plan input. Online toolset development complimented assessment by formalizing what was learned together throughout the ACM DSS process to direct subsequent actions to align with this approach. Since the online system was designed using open source software and a flexible content management system, results can be readily adapted to serve a wider variety of purposes by adjusting the underlying datasets. The research produced several potentially useful results. A post-project survey averaged 9.3 on a 10-point satisfaction scale. The BCWA board adopted the resulting ACM DSS process as a permanent best management practice, funding a facilitator to continue its expansion. A network weaver to continually foster cooperation, a knowledge curator to expand shared knowledge resources, and a systems engineer to reduce uncertainty and ambiguity and dissect complexity were all found to be critical new roles for successful ACM implementation. Watershed program comparisons also revealed ten qualities that may promote ACM. The technical analysis of nutrient issues revealed that phosphorus enrichment from phosphorus desorption from fine sediments contributed to cultural eutrophication through several distinct mechanisms, which may be addressed through a wider range of non-point source controls and in-lake management options. Potential affects from floods, wildfires, and droughts were assessed, which has resulted in more coordinated, proactive plans and studies. Next steps include formulating multi-institutional, multi-level academic studies in the watershed, expanding community engagement efforts, and establishing innovation clusters. Multi-disciplinary research needs include studying nutrient exchange processes, piloting decentralized wastewater treatment systems, optimizing phosphorus removal processes, chemically blueprinting nutrient source streams, and developing an integrated modeling framework. At least four additional stages of development are planned to refine and mature the ACM DSS process over time. The ACM DSS process is also being considered for other places and IWRM problem sets.
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    A spatial decision support system for basin scale assessment of improved management of water quantity and quality in stream-aquifer systems
    (Colorado State University. Libraries, 2008) Triana, Enrique, author; Labadie, John W., advisor; Gates, Timothy K., advisor
    Challenges in river basin management have intensified over the years, with expanding competition among water demands and emerging environmental concerns, increasing the complexity of the decision making framework. A State-of-the-art spatial-decision support system (River GeoDSS) is developed herein to provide assistance in evaluating management alternatives towards optimal utilization of water resources, providing a comprehensive treatment of water quantity and quality objectives based on conjunctive surface and groundwater modeling within the complex administrative and legal framework of river basin management. The River GeoDSS provides sophisticated tools that allow accurate system simulations and evaluation of strategies while minimizing the technological burden on the user. A unique characteristic of the River GeoDSS is the integration of models, tools, user interfaces and modules, all seamlessly incorporated in a geographic information system (GIS) environment that encourages the user to focus on interpreting and understanding system behavior to better design remediation strategies and solutions. The River GeoDSS incorporates Geo-MODSIM, a fully functional implementation of MODSIM within the ArcMap interface (ESRI, Inc.), and Geo-MODFLOW, a new MODFLOW-MT3DMS results analysis tool in the ArcMap interface. The modeling system is complemented with a new artificial neural networks (ANN) module for natural and irrigation return flow quantity and quality evaluation and salt transport through reservoirs, as well as with a new water quality module (WQM) for conservative salt transport modeling of conjunctive use of surface water and groundwater resources in the river basin network. In this research, innovative methodologies are developed for applying ANNs in efficiently coupling surface and groundwater models for basin-scale modeling of stream-aquifer interactions. The core River GeoDSS is customized to provide comprehensive analysis of alternative solutions to achieving agricultural, environmental, and water savings goals in the Lower Arkansas River Basin in Colorado while assuring physical, legal and administrative compliance. The River GeoDSS applied to the Arkansas River Valley allowed comparing benefits and improvements of management strategies, illustrated their potential to reduce waterlogging and soil salinity, salt load to the river, and non-beneficial evapotranspiration in a strategic planning environment.
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    A tabu search evolutionary algorithm for multiobjective optimization: application to a bi-criterion aircraft structural reliability problem
    (Colorado State University. Libraries, 2015) Long, Kim Chenming, author; Duff, William S., advisor; Labadie, John W., advisor; Stansloski, Mitchell, committee member; Chong, Edwin K. P., committee member; Sampath, Walajabad S., committee member
    Real-world engineering optimization problems often require the consideration of multiple conflicting and noncommensurate objectives, subject to nonconvex constraint regions in a high-dimensional decision space. Further challenges occur for combinatorial multiobjective problems in which the decision variables are not continuous. Traditional multiobjective optimization methods of operations research, such as weighting and epsilon constraint methods, are ill-suited to solving these complex, multiobjective problems. This has given rise to the application of a wide range of metaheuristic optimization algorithms, such as evolutionary, particle swarm, simulated annealing, and ant colony methods, to multiobjective optimization. Several multiobjective evolutionary algorithms have been developed, including the strength Pareto evolutionary algorithm (SPEA) and the non-dominated sorting genetic algorithm (NSGA), for determining the Pareto-optimal set of non-dominated solutions. Although numerous researchers have developed a wide range of multiobjective optimization algorithms, there is a continuing need to construct computationally efficient algorithms with an improved ability to converge to globally non-dominated solutions along the Pareto-optimal front for complex, large-scale, multiobjective engineering optimization problems. This is particularly important when the multiple objective functions and constraints of the real-world system cannot be expressed in explicit mathematical representations. This research presents a novel metaheuristic evolutionary algorithm for complex multiobjective optimization problems, which combines the metaheuristic tabu search algorithm with the evolutionary algorithm (TSEA), as embodied in genetic algorithms. TSEA is successfully applied to bicriteria (i.e., structural reliability and retrofit cost) optimization of the aircraft tail structure fatigue life, which increases its reliability by prolonging fatigue life. A comparison for this application of the proposed algorithm, TSEA, with several state-of-the-art multiobjective optimization algorithms reveals that TSEA outperforms these algorithms by providing retrofit solutions with greater reliability for the same costs (i.e., closer to the Pareto-optimal front) after the algorithms are executed for the same number of generations. This research also demonstrates that TSEA competes with and, in some situations, outperforms state-of-the-art multiobjective optimization algorithms such as NSGA II and SPEA 2 when applied to classic bicriteria test problems in the technical literature and other complex, sizable real-world applications. The successful implementation of TSEA contributes to the safety of aeronautical structures by providing a systematic way to guide aircraft structural retrofitting efforts, as well as a potentially useful algorithm for a wide range of multiobjective optimization problems in engineering and other fields.
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    A value-function based method for incorporating ensemble forecasts in real-time optimal reservoir operations
    (Colorado State University. Libraries, 2020) Peacock, Matthew E., author; Labadie, John W., advisor; Ramirez, Jorge, committee member; Anderson, Chuck, committee member; Johnson, Lynn, committee member
    Increasing stress on water resource systems has led to a desire to seek methods of improving the performance of reservoir operations. Water managers face many challenges including changes in demand, variable hydrological input and new environmental pressures. These issues have led to an interest in using ensemble streamflow forecasts to improve the performance of a reservoir system. The currently available methods for using ensemble forecasts encounter difficulties as the resolution of the analysis increases in order to accurately model a real-world system. One of the difficulties is due to the "curse of dimensionality'' as computing time exponentially increases when the discretization of the state and action spaces becomes finer or when more state or action variables are considered. Another difficulty is the problem of delayed rewards. When the time step of the analysis becomes shorter than the travel time due to routing, rewards may not be realized in the same time step as the action which caused them. Current methods such as dynamic programming or scenario-tree based methods are not able to handle delayed rewards. This research presents a value function-based method which separates the problem into two subproblems: computing the state-value function in the no-forecast condition, and finding optimal sequences of decisions given the ensemble forecast with the state-value function providing information about the value at any state at the end of the forecast horizon. A continuous action deep reinforcement learning algorithm is used to overcome the problems of dimensionality and delayed rewards, and a particle swarm method is used to find optimal decisions during the forecast horizon. The method is applied to a case study in the Russian River basin and compared to an idealized operating rule. The results show that the reinforcement learning process is able to generate policies that skillfully operate the reservoir without forecasts. When forecasts are used, the method is able to produce non-dominated performance measures. When the water stress to the system is increased by removing a transbasin diversion, the method outperforms the idealized operations.
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    Integrated water and power modeling framework for renewable energy integration
    (Colorado State University. Libraries, 2012) Dozier, André, author; Labadie, John W., advisor; Zimmerle, Dan, committee member; Salas, Jose, committee member
    Increasing penetration of intermittent renewable energy sources into the bulk electricity system has caused new operational challenges requiring large ramping rate and reserve capacity as well as increased transmission congestion due to unscheduled flow. Contemporary literature and recent renewable energy integration studies indicate that more realism needs to be incorporated into renewable energy studies. Many detailed water and power models have been developed in their respective fields, but no free-of-charge integrated water and power system model that considers constraints and objectives in both systems jointly has been constructed. Therefore, an integrated water and power model structure that addresses some contemporary challenges is formulated as a long-term goal, but only a small portion of the model structure is actually implemented as software. A water network model called MODSIM is adapted using a conditional gradient method to be able to connect to an overarching optimization routine that decomposes the water and power problems. The water network model is connected to a simple power dispatch model that uses a linear programming approach to dispatch hydropower resources to mitigate power flows across a transmission line. The power dispatch model first decides optimal power injections from each of the hydropower reservoirs, which are then used as hydropower targets for the water network model to achieve. Any unsatisfied power demand or congested transmission line is assumed to be met by imported power. A case study was performed on the Mid-Columbia River in the U.S. to test the capabilities of the integrated water and power model. Results indicate that hydropower resources can accommodate transmission congestion and energy capacity on wind production up until a particular threshold on the penetration level, after which hydropower resources provide no added benefit to the system. Effects of operational decisions to mitigate wind power penetration level and transmission capacity on simulated total dissolved gases were negligible. Finally, future work on the integrated water and power model is discussed along with expected results from the fully implemented model and its potential applications.
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    Machine learning methods to facilitate optimal water allocation and management in irrigated river basins to comply with water law
    (Colorado State University. Libraries, 2019) Rohmat, Faizal Immaddudin Wira, author; Labadie, John W., advisor; Gates, Timothy K., advisor; Bailey, Ryan T., committee member; Anderson, Charles W., committee member
    The sustainability issues facing irrigated river basins are intensified by legal and institutional regulations imposed on the hydrologic system. Although solutions that would boost water savings and quality might prove to be feasible, such imposed institutional constraints could veto their implementation, rendering them legally ineffectual. The problems of basin-scale irrigation water management in a legally-constrained system are exemplified in the central alluvial valley of the Lower Arkansas River Basin (LARB) in Colorado, USA, and in the Tripa River Basin in Indonesia. In the LARB, water and land best management practices (BMPs) have been proposed to enhance the environment, conserve water, and boost productivity; however, the legal feasibility of their implementation in the basin hinder BMP adoption. In the Tripa river basin, the rapid growth of water demand pushes the proposal of new reservoir construction. However, inadequate water availability and the lack of water law enforcement requires the basin to seek water from adjacent basins, thereby raising legal and economic feasibility issues. To address these issues, an updated version of a decision support system (DSS) named River GeoDSS has been employed to model basin-scale behavior of the LARB for both historical (baseline) and BMP implementation scenarios. River GeoDSS uses GeoMODSIM as its water allocation component, which also handles water rights and uses a deep neural network (DNN) functionality to emulate calibrated regional MODFLOW-SFR2 models in modeling complex stream-aquifer interactions. The use of DNNs for emulation if critical for extrapolating the results of MODFLOW-SFR2 simulations to un-modeled portions of the basin and for compute-efficient analysis. The BMP implementations are found to introduce significant alterations to streamflows in the LARB, including shortages in flow deliveries to water right demands and in flow deficits at the Colorado-Kansas Stateline. To address this, an advanced Fuzzy-Mutation Linear Particle Swarm Optimization (Fuzzy-MLPSO) metaheuristic algorithm is applied to determine optimal operational policies for a new storage account in John Martin Reservoir for use in mitigating the side-effects of BMP implementation on water rights and the interstate compact. Prior to the implementation of Fuzzy-MLPSO, a dedicated study is conducted to develop the integration between MLPSO and GeoMODSIM, where it is applied in addressing the water allocation issue in the Tripa River Basin. The coupling of simulation (GeoMODSIM) and optimization (MLPSO) models provides optimal sizing of reservoirs and transbasin diversions along with optimal operation policies. Aside from that, this study shows that MLPSO converges faster compared to the original PSO with sufficiently smaller swarm size. The implementations of Fuzzy-MLPSO in the LARB provided optimal operational rules for a new storage account in John Martin Reservoir dedicated to abating the undesirable impacts of BMP implementation on water rights and Stateline flows. The Fuzzy-MLPSO processes inflow, storage, seasonal, and hydrologic states into divert-to-storage/release-from-storage decisions for the new storage account. Results show that concerns over shortages in meeting water rights demands and deficits to required Stateline flow due to otherwise beneficial BMP implementations can be addressed with optimized reservoir operations.
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    Optimal reservoir operations for riverine water quality improvement: a reinforcement learning strategy
    (Colorado State University. Libraries, 2011) Rieker, Jeffrey Donald, author; Labadie, John W., advisor; Fontane, Darrell G., committee member; Frevert, Donald K., committee member; Anderson, Charles W., committee member
    Complex water resources systems often involve a wide variety of competing objectives and purposes, including the improvement of water quality downstream of reservoirs. An increased focus on downstream water quality considerations in the operating strategies for reservoirs has given impetus to the need for tools to assist water resource managers in developing strategies for release of water for downstream water quality improvement, while considering other important project purposes. This study applies an artificial intelligence methodology known as reinforcement learning to the operation of reservoir systems for water quality enhancement through augmentation of instream flow. Reinforcement learning is a methodology that employs the concepts of agent control and evaluative feedback to develop improved reservoir operating strategies through direct interaction with a simulated river and reservoir environment driven by stochastic hydrology. Reinforcement learning methods have advantages over other more traditional stochastic optimization methods through implicit learning of the underlying stochastic structure through interaction with the simulated environment, rather than requiring a priori specification of probabilistic models. Reinforcement learning can also be coupled with various computing efficiency techniques as well as other machine learning methods such as artificial neural networks to mitigate the "curse of dimensionality" that is common to many optimization methodologies for solving sequential decision problems. A generalized mechanism is developed, tested, and evaluated for providing near-real time operational support to suggest releases of water from upstream reservoirs to improve water quality within a river using releases specifically designated for that purpose. The algorithm is designed to address a variable number of water quality constituents, with additional flexibility for adding new water quality requirements and learning updated operating strategies in a non-stationary environment. The generalized reinforcement learning algorithm is applied to the Truckee River in California and Nevada as a case study, where the federal and local governments are purchasing water rights for the purpose of augmenting Truckee River flows to improve water quality. Water associated with those acquired rights can be stored in upstream reservoirs on the Truckee River until needed for prevention of water quality standard violations in the lower reaches of the river. This study shows that in order for the water acquired for flow augmentation to be fully utilized as a part of a longer-term strategy for water quality management, increased flexibility is required as to how those waters are stored and how well the storage is protected from displacement through reservoir spill during times of high runoff. The results show that with those flexibilities, the reinforcement learning mechanism has the ability to produce both short-term and long-term strategies for the use of the water, with the long-term strategies capable of significantly improving water quality during times of drought over current and historic operating practices. The study also evaluates a number of variations and options for the application of reinforcement learning methods, as well as use of artificial neural networks for function generalization and approximation.