Enhanced watershed modeling and data analysis with a fully coupled hydrologic model and cloud-based flow analysis

In today's world of increased water demand in the face of population growth and climate change, there are no simple answers. For this reason many municipalities, water resource engineers, and federal analyses turn to modeling watersheds for a better understanding of the possible outcomes of their water management actions. The physical processes that govern movement and transport of water and constituents are typically highly nonlinear. Therefore, improper characterization of a complex, integrated, processes like surface-subsurface water interaction can substantially impact water management decisions that are made based on existing models.;As a result many models are unable to accurately represent complex systems in which surface and subsurface processes are both important. Two popular watershed models have been used extensively to represent surface processes, SWAT (Arnold et al, 1998), and subsurface processes, MODFLOW (Harbaugh, 2005). The lack of comprehensive watershed simulation has led to a rise in uncertainty for managing water resources in complex surface-subsurface driven watersheds. For this reason, there have been multiple attempts to couple the SWAT and MODFLOW models for a more comprehensive watershed simulation (Perkins and Sophocleous, 1999; Menking, 2003; Galbiati et al., 2006; Kim et al., 2008); however, the previous couplings are typically monthly couplings with spatial restrictions for the two models. Additionally, most of these coupled SWAT-MODFLOW models are unavailable to the general public, unlike the constituent SWAT and MODFLOW models which are available. Furthermore, many of these couplings depend on a forced equal spatial discretization for computational units. This requires that one MODFLOW grid cell is the same size and location of one SWAT hydrologic response unit (HRU). Additionally, many of the previous couplings are based on a loose monthly average coupling which might be insufficient in natural spring and irrigated agricultural driven groundwater systems which can fluctuate on a sub-monthly time scale.;The primary goal of this work is to enhance the capacity for modeling watershed processes by fully coupling surface and subsurface hydrologic processes at a daily time step. The specific objectives of this work are 1) to examine and create a general spatial linkage between SWAT and MODFLOW allowing the use of spatially-different existing models for coupling; 2) to examine existing practices and address current weaknesses for coupling of the SWAT and MODFLOW models to develop an integrated modeling system; 3) to demonstrate the capacity of the enhanced model compared to the original SWAT and MODFLOW models on the North Fork of the Sprague River in the Upper Klamath Basin in Oregon.;The resulting generalized daily coupling between a spatially dis-similar SWAT and MODFLOW model on the North Fork of the Sprague River has resulted in a slightly more lower representation of monthly stream flow (monthly R 2 = 0.66, NS = 0.38) than the original SWAT model (monthly R 2 = 0.60, NS = 0.57) with no additional calibration. The Log10 results of stream flow illustrate an even greater improvement between SWAT-MODFLOW correlation (R2) but not the overall simulation (NS) (monthly R2 = 0.74, NS = -0.29) compared to the original SWAT (monthly R2 = 0.63, NS = 0.63) correlation (R2).;Additionally, with the increased complexity of environmental models there is a need to design and implement tools that are more accessible and computationally scalable; otherwise their use will remain limited to those that developed them. In light of advancements in cloud-computing technology a better implementation of modern desktop software packages would be the use of scalable cloud-based cyberinfrastructure, or cloud-based environmental modeling services. Cloud-based deployment of water data and modeling tools assist in a scalable as well as platform independent analysis; meaning a desktop, laptop, tablet, or smart phone can perform the same analyses. To utilize recent advancements in computer technology, a further focus of this work is to develop and demonstrate a scalable cloud-computing web-tool that facilitates access and analysis of stream flow data. The specific objectives are to 1) unify the various stream flow analysis topics into a single tool; 2) to assist in the access to data and inputs for current flow analysis methods; 3) to examine the scalability benefits of a cloud-based flow analysis tool.;Furthermore, the new Comprehensive Flow Analysis tool successfully combined time-series statistics, flood analysis, base-flow separation, drought analysis, duration curve analysis, and load estimation into a single web-based tool. Preliminary and secondary scalability testing has revealed that the CFA analyses are scalable in a cloud-based cyberinfrastructure environment to a request rate that is likely unrealistic for web tools. (Abstract shortened by UMI.).