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Browsing by Author "Hoag, Dana L., committee member"

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    A multi criteria decision support system for watershed management under uncertain conditions
    (Colorado State University. Libraries, 2012) Ahmadi, Mahdi, author; Arabi, Mazdak, advisor; Ascough, James C., II, committee member; Fontane, Darrell G., committee member; Hoag, Dana L., committee member
    Nonpoint source (NPS) pollution is the primary cause of impaired water bodies in the United States and around the world. Elevated nutrient, sediment, and pesticide loads to waterways may negatively impact human health and aquatic ecosystems, increasing costs of pollutant mitigation and water treatment. Control of nonpoint source pollution is achievable through implementation of conservation practices, also known as Best Management Practices (BMPs). Watershed-scale NPS pollution control plans aim at minimizing the potential for water pollution and environmental degradation at minimum cost. Simulation models of the environment play a central role in successful implementation of watershed management programs by providing the means to assess the relative contribution of different sources to the impairment and water quality impact of conservation practices. While significant shifts in climatic patterns are evident worldwide, many natural processes, including precipitation and temperature, are affected. With projected changes in climatic conditions, significant changes in diffusive transport of nonpoint source pollutants, assimilative capacity of water bodies, and landscape positions of critical areas that should be targeted for implementation of conservation practices are also expected. The amount of investment on NPS pollution control programs makes it all but vital to assure the conservation benefits of practices will be sustained under the shifting climatic paradigms and challenges for adoption of the plans. Coupling of watershed models with regional climate projections can potentially provide answers to a variety of questions on the dynamic linkage between climate and ecologic health of water resources. The overarching goal of this dissertation is to develop a new analysis framework for the development of optimal NPS pollution control strategy at the regional scale under projected future climate conditions. Proposed frameworks were applied to a 24,800 ha watershed in the Eagle Creek Watershed in central Indiana. First, a computational framework was developed for incorporation of disparate information from observed hydrologic responses at multiple locations into the calibration of watershed models. This study highlighted the use of multiobjective approaches for proper calibration of watershed models that are used for pollutant source identification and watershed management. Second, an integrated simulation-optimization approach for targeted implementation of agricultural conservation practices was presented. A multiobjective genetic algorithm (NSGA-II) with mixed discrete-continuous decision variables was used to identify optimal types and locations of conservation practices for nutrient and pesticide control. This study showed that mixed discrete-continuous optimization method identifies better solutions than commonly used binary optimization methods. Third, the conclusion from application of NSGA-II optimization followed by development of a multi criteria decision analysis framework to identify near-optimal NPS pollution control plan using a priori knowledge about the system. The results suggested that the multi criteria decision analysis framework can be an effective and efficient substitute for optimization frameworks. Fourth, the hydrologic and water quality simulations driven by an extensive ensemble of climate projections were analyzed for their respective changes in basin average temperature and precipitation. The results revealed that the water yield and pollutants transport are likely to change substantially under different climatic paradigms. And finally, impact of projected climate change on performance of conservation practice and shifts in their optimal types and locations were analyzed. The results showed that performance of NPS control plans under different climatic projections will alter substantially; however, the optimal types and locations of conservation practices remained relatively unchanged.
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    Assessing best management practices for the remediation of selenium in surface water in an irrigated agricultural river valley: sampling, modeling, and multi-criteria decision analysis
    (Colorado State University. Libraries, 2016) Heesemann, Brent E., author; Gates, Timothy K., advisor; Bailey, Ryan T., advisor; Hoag, Dana L., committee member
    The ecological impacts of selenium have been studied for decades and regulatory standards established in an effort to mitigate them. Agricultural activities in regions with high levels of alluvial selenium can lead to in-stream levels that far exceed regulatory limits. Agricultural best management practices (BMPs) are being considered to reduce in-stream selenium concentrations, but exploring the potential effectiveness of these BMPs can only be done after gaining an understanding of the in-stream processes that govern the speciation and transport of selenium in response to loading from irrigation return flows. This study uses extensive field data enhanced by numerical modeling to achieve this. In-stream water and sediment selenium samples, collected over a period of eight years in a region of Colorado’s Lower Arkansas River Valley, were analyzed. A sensitivity analysis (SA) was performed on a two part steady-state water quality / solute transport numerical model capable of simulating in-stream selenium processes. The combination of field data and SA was then used to calibrate an unsteady flow version of the model representative of the region to which it was applied. Dissolved and precipitated selenium species concentrations were accurately predicted by the calibrated model. Model simulations indicated that reduced fertilization is the BMP most effective at reducing in-stream SeO4 and NO3 concentrations out of the four BMPs examined. Reduced irrigation, land fallowing, and canal sealing indicated increases in in-stream SeO4 concentrations, likely caused by a concentration of SeO4 in the adjacent aquifer. Model results also indicated that the tributaries are impacted more by surface runoff as compared to lateral groundwater flows, while the opposite is true for the River. Although reasonable results were obtained from the model, further investigation into the computational processes and calibrated parameter values is required as part of future work. This study also examines the socio-economic feasibility of various BMPs, through the issuing survey to stakeholders in the study region and its evaluation using analytic hierarchy process multi-criteria decision analysis (MCDA). Reduced irrigation was determined to be the most feasible BMP based on the MCDA, with stakeholders showing a clear preference for economic concerns and placing a higher importance on salinity over SeO4 or NO3 concentrations. With model results indicating the effectiveness of various BMPs, and MCDA survey results providing insight into which of the BMPs are most likely to be accepted by stakeholders, it was possible to assess which BMPs are most appropriate for implementation in this study region. In considering both the results from the modeling study and the MCDA, it was determined that reduced fertilization is likely the single best BMP. To date there have been few if any studies utilizing both field data, numerical modeling, and MCDA to so comprehensively describe in-stream selenium processes and the future prospects for selenium remediation in an agricultural region in the western United States.
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    Confronting input, parameter, structural, and measurement uncertainty in multi-site multiple-response watershed modeling using Bayesian inferences
    (Colorado State University. Libraries, 2012) Yen, Haw, author; Arabi, Mazdak, advisor; Fontane, Darrell G., committee member; Hoag, Dana L., committee member; Loftis, Jim C., committee member
    Simulation modeling is arguably one of the most powerful scientific tools available to address questions, assess alternatives, and support decision making for environmental management. Watershed models are used to describe and understand hydrologic and water quality responses of land and water systems under prevailing and projected conditions. Since the promulgation of the Clean Water Act of 1972 in the United States, models are increasingly used to evaluate potential impacts of mitigation strategies and support policy instruments for pollution control such as the Total Maximum Daily Load (TMDL) program. Generation, fate, and transport of water and contaminants within watershed systems comprise a highly complex network of interactions. It is difficult, if not impossible, to capture all important processes within a modeling framework. Although critical natural processes and management actions can be resolved at varying spatial and temporal scales, simulation models will always remain an approximation of the real system. As a result, the use of models with limited knowledge of the system and model structure is fraught with uncertainty. Wresting environmental decisions from model applications must consider factors that could conspire against credible model outcomes. The main goal of this study is to develop a novel Bayesian-based computational framework for characterization and incorporation of uncertainties from forcing inputs, model parameters, model structures, and measured responses in the parameter estimation process for multisite multiple-response watershed modeling. Specifically, the following objectives are defined: (i) to evaluate the effectiveness and efficiency of different computational strategies in sampling the model parameter space; (ii) to examine the role of measured responses at various locations in the stream network as well as intra-watershed processes in enhancing the model performance credibility; (iii) to facilitate combining predictions from competing model structures; and (iv) to develop a statistically rigorous procedure for incorporation of errors from input, parameter, structural and measurement sources in the parameter estimation process. The proposed framework was applied for simulating streamflow and total nitrogen at multiple locations within a 248 square kilometer watershed in the Midwestern United States using the Soil and Water Assessment Tool (SWAT). Results underlined the importance of simultaneous treatment of all sources of uncertainty for parameter estimation. In particular, it became evident that incorporation of input uncertainties was critical for determination of model structure for runoff generation and also representation of intra-watershed processes such as denitrification rate and dominant pathways for transport of nitrate within the system. The computational framework developed in this study can be implemented to establish credibility for modeling watershed processes. More importantly, the framework can reveal how collection of data from different responses at different locations within a watershed system of interest would enhance the predictive capability of watershed models by reducing input, parametric, structural, and measurement uncertainties.