Graduation Semester and Year

2016

Language

English

Document Type

Thesis

Degree Name

Master of Landscape Architecture

Department

Landscape Architecture

First Advisor

Taner R. Özdil

Abstract

Landscape Architecture has recently seen a significant rise in evaluative studies among the scientific and professional community, perhaps as a result of growing concerns related to rapid urbanization, climate change, and environmental degradation (LAF, 2016; Ozdil, 2014). Several methods adopted in evaluative landscape architecture research have typically focused on landscape performance in post implementation conditions, with a relatively superficial use of technology to address contemporary urban problems. However, with the advancement of computer technologies, opportunities to assess and predict environmental landscape performance prior to design development in the site planning process have emerged. The purpose of this research is to apply a predictive modeling approach to assess and predict stormwater runoff and its impact on a site and watershed scale using Geographic Information System (GIS) and the Soil and Water Assessment Tool (SWAT2012), specifically during the design development stage of the site planning process. Recent technological developments in GIS have allowed the application of geoanalytical methods that challenge conventional data collection and analysis methods and broaden the design approach using quantifiable measures. These methods have also opened up the inquiry of scientific knowledge for research in urban areas within landscape architecture, planning, and other allied fields. This study utilizes quantitative predictive modeling methods and tools to study surface hydrological conditions prior to design development in an urban landscape context. The study adopts the case of the Southwestern Medical District in Dallas, Texas, which encompasses 350 hectares of the Headwaters Turtle Creek watershed. The study tests four hypothetical scenarios; pre-development, existing conditions, scenario 1, and scenario 2, using SWAT in order to understand the tool’s applicability and relevance to landscape architectural studies and practice. Predictive modeling is a method that utilizes computer simulation and monitoring data collected over time and space to visualize various land use changes (Gregersen et al., 2007). SWAT is an example of a predictive modeling tool, which presents opportunities for hydrological modeling in landscape architecture practice and research. This research is an attempt to investigate water quality and quantity in an urbanized watershed before project construction and completion. The research findings highlight the importance of predictive modeling in landscape architecture and planning, especially prior to design development. This scenario-based evaluation suggests that SWAT could be an effective predictive modeling tool that can inform landscape architecture planning and practice on impacts of design on water quality and quantity. The strength of the SWAT modeling tool lies in its ability to simulate water flow and quality at a given site, under various parameters that can be adjusted by the researcher. Results also suggest that the quantity and quality of water generated on a complex urban site, such as the Southwestern Medical District, can have an impact on watershed performance, if green infrastructure systems and low impact development strategies are applied. The research also illustrates the applicability and relevance of SWAT in today’s landscape architecture practice, and informs relevant professions about the capability of assessing stormwater runoff quality and quantity prior to design development using geospatial techniques and methods. Thus landscape architects and allied professions can have a more comprehensive and responsive approach that informs the built and natural environment in urban contexts.

Keywords

SWAT, GIS, Design development, Predictive modeling, Scenario planning

Disciplines

Architecture | Landscape Architecture

License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Comments

Degree granted by The University of Texas at Arlington

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