A considerable amount of research has been done to connect or integrate separate numerical models. The Environmental Modeling Research Laboratory (EMRL) has developed a generic file format, the eXtensible Model Data Format (XMDF). One of the objectives of the XMDF project is to facilitate the spatial interpolation and data-sharing necessary when linking models. The objective of this research is to investigate how model linking capabilities can be added to the XMDF by defining a Model Linkage Object (MLO) that is compatible with current model linking frameworks. A potential design for an MLO is defined in this thesis after a detailed review of existing model linking frameworks. The major model-linking systems studied in this research are FRAMES, DIAS, and OpenMI. Other software was also researched and a brief description of each is provided. To limit the scope of this research the MLO is analyzed for a two-dimensional mesh using only two of the model linking systems. FRAMES was chosen because the Army Corps of Engineers has already done extensive work with this software. OpenMI was selected because of its ability to link models during runtime.

Like other scientists, hydrologists encode mathematical formulations that simulate various hydrologic processes as computer programs so that problems with water resource management that would otherwise be manually intractable can be solved efficiently. These computer models are typically developed to answer specific questions within a specific study domain. For example, one computer model may be developed to solve for magnitudes of water flow and water levels in an aquifer while another may be developed to solve for magnitudes of water flow through a water distribution network of pipes and reservoirs. Interactions between different processes are often ignored or are approximated using overly simplistic assumptions. The increasing complexity of the water resources challenges society faces, including stresses from variable climate and land use change, means that some of these models need to be stitched together so that these challenges are not evaluated myopically from the perspective of a single research discipline or study domain. The research in this dissertation presents an investigation of the various approaches and technologies that can be used to support model integration. The research delves into some of the computational challenges associated with model integration and suggests approaches for dealing with these challenges. Finally, it advances new software that provides data structures that water resources modelers are more accustomed to and allows them to take advantage of advanced computing resources for efficient simulations.

Master of Landscape Architecture / Department of Landscape Architecture and Regional and Community Planning / Eric A. Bernard / The Kansas High Plains region is a key global agricultural production center (U.S. G.S, 2009). The High Plains physiography is ideal agricultural production landscape except for the semi-arid climate. Consequently, farmers mine vast groundwater resources from the High Plains Ogallala Aquifer formations to augment precipitation for crop production. Growing global population, current policy and subsidy programs, declining aquifer levels coupled with regional climatic changes call into question both short-term and long-term resilience of this agrarian landscape and food and water security. This project proposes a means to simulate future irrigated agriculture land use and crop cover patterns in the Kansas High Plains Aquifer region based on coupled modeling results from ongoing research at Kansas State University. A Cellular Automata (CA) modeling framework is used to simulate potential land use distribution, based on coupled modeling results from groundwater, economic, and crop models. The CA approach considers existing infrastructure resources, industrial and commercial systems, existing land use patterns, and suitability modeling results for agricultural production. The results of the distribution of irrigated land produced from the CA model provide necessary variable inputs for the next temporal coupled modeling iteration. For example, the groundwater model estimates water availability in saturated thickness and depth to water. The economic model projects which crops will be grown based on water availability and commodity prices at a county scale. The crop model estimates potential yield of a crop under specific soil, climate and growing conditions which further informs the economic model providing an estimate of profit, which informs regional economic and population models. Integrating the CA model into the coupled modeling system provides a key linkage to simulate spatial patterns of irrigated land use and crop type land cover based on coupled model results. Implementing the CA model in GIS offers visualization of coupled model components and results as well as the CA model land use and land cover. The project outcome hopes to afford decision-makers, including farmers, the ability to use the actual landscape data and the developed coupled modeling framework to strategically inform decisions with long-term resiliency.