• Complex Issues: Transdisciplinary Approaches To Generating Knowledge And Solutions

      Gupta, Hoshin; Harris, Susan; Gupta, Hoshin; Poupeau, Franck; Valdes, Juan (The University of Arizona., 2015)
      Over the past 50 years, complex scientific and social problems, defined as those for which the facts are uncertain, the risks great and the time frame for resolution short, have imposed demands on the scientific community to conduct scientific research that incorporates knowledge from stakeholders and expands beyond the boundaries of a single discipline. Responding to these demands, new scientific research concepts and methods have been developed and are still evolving. Beginning with the seminal work by Silvio Funtowicz and Jerome Ravetz on post normal science, this thesis traces the development of post normal science through the literature and examines the development and analysis of the three main components of transdisciplinarity: (a) problem identification; (b) knowledge production arising out of integrated research efforts by stakeholders and scientists from multiple disciplines; and, (c) the implementation of those research results. Transdisciplinarity will remain merely a concept and not a viable methodology until researchers are willing to engage in better ways to communicate, view, and process information, establish priorities and accept the methodologies of other disciplines. Further, transdisciplinarity will not realize its potential so long as researchers do not use the research process to both educate stakeholders and to be educated by stakeholders and also participate in the political arena in order to translate research into action.
    • Diagnostic Evaluation of Watershed Models

      Gupta, Hoshin V.; Martinez Baquero, Guillermo Felipe; Gupta, Hoshin V.; Troch, Peter; Valdes, Juan (The University of Arizona., 2007)
      With increasing model complexity there is a pressing need for new methods that can be used to mine information from large volumes of model results and available data. This work explores strategies to identify and evaluate the causes of discrepancy between models and data related to hydrologic processes, and to increase our knowledge about watershed input-output relationships. In this context, we evaluate the performance of the abcd monthly water balance model for 764 watersheds in the conterminous United States. The work required integration of the Hydro-Climatic Data Network dataset with various kinds of spatial information, and a diagnostic approach to relating model performance with assumptions and characteristics of the basins. The diagnostic process was implemented via classification of watersheds, evaluation of hydrologic signatures and the identification of dominant processes. Knowledge acquired during this process was used to test modifications of the model for hydrologic regions where the performance was "poor".
    • Using an Ensemble of Models to Design a Well Field Considering Regional Hydrologic Uncertainty

      Ferré, Paul "Ty"; Hundt, Stephen A.; Ferré, Paul A. "Ty"; Meixner, Thomas; Valdes, Juan (The University of Arizona., 2014)
      Groundwater models are often developed as tools for environmental decision-making. However, sparse data availability can limit a model's utility by confounding attempts to select a single structural representation of a system or to find a unique and optimal set of model parameters. As a result, estimates of prediction uncertainty and the value of further data collection may be important results of a modeling effort. The Discrimination/Inference to Reduce Expected Cost Technique (DIRECT) is a new method for developing an ensemble of models that collectively define prediction uncertainty in a manner that supports risk-based decision making and monitoring network design optimization. We apply aspects of DIRECT to a modeling investigation of an aquifer system in Central Utah where a major Coalbed Methane gas field is located and a new approach for stimulating gas production is being explored. In the first stage of this study we develop an ensemble of regional MODFLOW models and calculate their relative likelihood using a set of observation data. These regional results and likelihoods are then transferred to a regional MT3D residence time model and to a local advective transport model to provide further information for the well design. A cost function is applied to the transport results to assess the relative expected costs of several proposed well field designs. The set of hydrologic results and associated likelihoods from the ensemble are combined into cost curves that allow for the selection of designs that minimize expected costs. These curves were found to be a useful tool for visualizing the ways that design decisions and hydrologic results interact to generate costs. Furthermore, these curves reveal ways in which uncertainty can add to the cost of implementing a design. A final analysis explored the cost of having uncertain model results by applying and manipulating synthetic likelihood distributions to the transport results. These results suggest the value that may be added by reducing uncertainty through data collection. Overall, the application of DIRECT was found to provide a rich set of information that is not available when ensemble methods and cost consideration are omitted from a modeling study.