Applying Multi-criteria Evaluation Process in Predicting Water Quality of the Upper Guadalupe River, Central Texas

Authors: Tasnuva Udita*, Texas State University- San Marcos
Topics: Geographic Information Science and Systems, Land Use and Land Cover Change, Water Resources and Hydrology
Keywords: Guadalupe river, Geocomputation, Multi-criterial evaluation, Land use/ cover change, Water quality.
Session Type: Paper
Day: 4/14/2018
Start / End Time: 2:00 PM / 3:40 PM
Room: Poydras, Sheraton, 3rd Floor
Presentation File: No File Uploaded


The upper Guadalupe River is considered as an asset to the people of Central Texas due to its natural scenic beauty which attracts tourists every year. The increasing population of is triggering other forms of growth like structural developments in urbanization, industrialization, transportation networks etc. Rapid transitions of lands were observed within the last 25 years (Google Earth Images). They are developed mostly around water resources. Most of the transitions are from forested lands to urbanization, from wetlands to developed areas. More developments mean higher percentage of land use and vice versa. So, the intensity of land use can be a good predictor in stream channel condition. On the other hand, natural condition around the water bodies can predict to be maintaining the river ecological environment better. A lot of criteria that can be used but this study focuses on only the landscape matrices. The objectives of the study were: Identification of potential landscape factors contributing to the degradation of upper Guadalupe River’s ecological system, identification of potential areas of the stream networks that are vulnerable to river environment degradation based on those factors, perform a Multi-criteria evaluation (MCE) process and calculate the landscape matrices by a using a combination of Analytical Hierarchy Process (AHP) and Compromised Programming (CP) as two techniques, finding how human experts can affect the results in MCE in identifying those stream channels, show comparison between the maps generated for before and after human experts.

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