\"Non-point source pollution is the leading cause of impairment in surface water in the Midwest. In this research, we seek to predict which watersheds are most vulnerable to point source pollution without field sampling using publically available GIS databases. Watersheds with higher vulnerability ratings can then be targeted for water quality monitoring, and funds used to improve watershed health can be distributed with greater efficacy. To better understand and target watershed vulnerability, we used three different approaches. In the first project, 35 sub-watersheds were sampled in the Lower Grand Watershed, which is a highly agricultural watershed in northern Missouri/southern Iowa. Statistical analyses were performed to determine which of these parameters were most correlated with water quality, and predictive relationships of water quality were developed. In the second project, a new methodology for watershed vulnerability to non-point source pollution was developed. Using the results from our first study to guide the weighting of different parameters, a weighted overlay and analytical hierarchy method was used to predict the vulnerability (poor water quality) of watersheds. This new vulnerability prediction method was tested on ten sub-watersheds within the Eagle Creek Watershed in central Indiana, which has a mixture of agricultural, forested, and urban land use. In the last project, the robustness of the new watershed vulnerability assessment method was tested using hydrological modeling. The Soil and Water Assessment Tool (SWAT) modeling program was used to model non-point source pollution in the Eagle Creek sub-watersheds. The results of these models provided a second method for verifying the robustness of the newly developed watershed vulnerability assessment method\"--Abstract, page iv.
","abstract_html":"<p>"Non-point source pollution is the leading cause of impairment in surface water in the Midwest. In this research, we seek to predict which watersheds are most vulnerable to point source pollution without field sampling using publically available GIS databases. Watersheds with higher vulnerability ratings can then be targeted for water quality monitoring, and funds used to improve watershed health can be distributed with greater efficacy. To better understand and target watershed vulnerability, we used three different approaches. In the first project, 35 sub-watersheds were sampled in the Lower Grand Watershed, which is a highly agricultural watershed in northern Missouri/southern Iowa. Statistical analyses were performed to determine which of these parameters were most correlated with water quality, and predictive relationships of water quality were developed. In the second project, a new methodology for watershed vulnerability to non-point source pollution was developed. Using the results from our first study to guide the weighting of different parameters, a weighted overlay and analytical hierarchy method was used to predict the vulnerability (poor water quality) of watersheds. This new vulnerability prediction method was tested on ten sub-watersheds within the Eagle Creek Watershed in central Indiana, which has a mixture of agricultural, forested, and urban land use. In the last project, the robustness of the new watershed vulnerability assessment method was tested using hydrological modeling. The Soil and Water Assessment Tool (SWAT) modeling program was used to model non-point source pollution in the Eagle Creek sub-watersheds. The results of these models provided a second method for verifying the robustness of the newly developed watershed vulnerability assessment method"--Abstract, page iv.</p>","abstract_has_math":false,"creators":["Jabbar, Fadhil K."],"institution":"Missouri University of Science and Technology","degree_name":"Ph. D. in Geological Engineering","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T03:18:26Z","subjects":["Agricultural pollution","Analytic hierarchy process","Land uses","Lower Grand River watershed","Vulnerability","Watershed health assessment","Geological Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsmine.mst.edu/doctoral_dissertations/2781","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Jabbar, Fadhil K."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. D. in Geological Engineering"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Missouri University of Science and Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Agricultural pollution","Analytic hierarchy process","Land uses","Lower Grand River watershed","Vulnerability","Watershed health assessment","Geological Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarsmine.mst.edu/doctoral_dissertations/2781"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["\"Non-point source pollution is the leading cause of impairment in surface water in the Midwest. In this research, we seek to predict which watersheds are most vulnerable to point source pollution without field sampling using publically available GIS databases. Watersheds with higher vulnerability ratings can then be targeted for water quality monitoring, and funds used to improve watershed health can be distributed with greater efficacy. To better understand and target watershed vulnerability, we used three different approaches. In the first project, 35 sub-watersheds were sampled in the Lower Grand Watershed, which is a highly agricultural watershed in northern Missouri/southern Iowa. Statistical analyses were performed to determine which of these parameters were most correlated with water quality, and predictive relationships of water quality were developed. In the second project, a new methodology for watershed vulnerability to non-point source pollution was developed. Using the results from our first study to guide the weighting of different parameters, a weighted overlay and analytical hierarchy method was used to predict the vulnerability (poor water quality) of watersheds. This new vulnerability prediction method was tested on ten sub-watersheds within the Eagle Creek Watershed in central Indiana, which has a mixture of agricultural, forested, and urban land use. In the last project, the robustness of the new watershed vulnerability assessment method was tested using hydrological modeling. The Soil and Water Assessment Tool (SWAT) modeling program was used to model non-point source pollution in the Eagle Creek sub-watersheds. The results of these models provided a second method for verifying the robustness of the newly developed watershed vulnerability assessment method\"--Abstract, page iv.
"]},{"key":"dc:title","label":"Title","values":["Assessment and prediction of surface water vulnerability from non-point source pollution in Midwestern watersheds"]}]}],"canonical_facts":{"dc:creator":["Jabbar, Fadhil K."],"dc:description.abstract":["\"Non-point source pollution is the leading cause of impairment in surface water in the Midwest. In this research, we seek to predict which watersheds are most vulnerable to point source pollution without field sampling using publically available GIS databases. Watersheds with higher vulnerability ratings can then be targeted for water quality monitoring, and funds used to improve watershed health can be distributed with greater efficacy. To better understand and target watershed vulnerability, we used three different approaches. In the first project, 35 sub-watersheds were sampled in the Lower Grand Watershed, which is a highly agricultural watershed in northern Missouri/southern Iowa. Statistical analyses were performed to determine which of these parameters were most correlated with water quality, and predictive relationships of water quality were developed. In the second project, a new methodology for watershed vulnerability to non-point source pollution was developed. Using the results from our first study to guide the weighting of different parameters, a weighted overlay and analytical hierarchy method was used to predict the vulnerability (poor water quality) of watersheds. This new vulnerability prediction method was tested on ten sub-watersheds within the Eagle Creek Watershed in central Indiana, which has a mixture of agricultural, forested, and urban land use. In the last project, the robustness of the new watershed vulnerability assessment method was tested using hydrological modeling. The Soil and Water Assessment Tool (SWAT) modeling program was used to model non-point source pollution in the Eagle Creek sub-watersheds. The results of these models provided a second method for verifying the robustness of the newly developed watershed vulnerability assessment method\"--Abstract, page iv.
"],"dc:identifier":["https://scholarsmine.mst.edu/doctoral_dissertations/2781"],"dc:subject":["Agricultural pollution","Analytic hierarchy process","Land uses","Lower Grand River watershed","Vulnerability","Watershed health assessment","Geological Engineering"],"dc:title":["Assessment and prediction of surface water vulnerability from non-point source pollution in Midwestern watersheds"],"dc:type":["Dissertation - Open Access"],"thesis:degree_name":["Ph. D. in Geological Engineering"],"thesis:institution_name":["Missouri University of Science and Technology"]},"updated_at":"2026-07-24T03:18:26Z"}