An Introduction to Identifying Nonpoint Sources of Water Pollution Using a Modified Land Use Conflict Analysis Identification Strategy (LUCIS) Model, Non-point Source Identification Strategy: NPSIS

Persistent Link:
http://hdl.handle.net/10150/576109
Title:
An Introduction to Identifying Nonpoint Sources of Water Pollution Using a Modified Land Use Conflict Analysis Identification Strategy (LUCIS) Model, Non-point Source Identification Strategy: NPSIS
Author:
Cziesch, Jarrett
Issue Date:
2015
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Collection Information:
This item is part of the MS-GIST Master's Reports collection. For more information about items in this collection, please contact the UA Campus Repository at repository@u.library.arizona.edu.
Abstract:
This paper examines the Non-Point Source Identification Strategy (NPSIS); a modification of the Land Use Conflict Identification Strategy (LUCIS): NPSIS is a raster model useful for identifying non-point sources of water pollution from three known contributors (agriculture, domestic, and natural background). By using a standard operating procedure, developers are able to create standardized datasets useful for identifying non-point sources of water pollution throughout the contiguous United States. The NPSIS model process requires the use of three “non-point source water pollution” contributors. A contributor is termed as a Non-Point Category (NPC) that contains collective elements (i.e. nutrient applications for agricultural purposes and urban runoff from highly developed areas). Using a survey, water resource professionals familiar with chosen study areas rank each NPC element according to potential impact to water quality. Following the survey, raster datasets that represent each NPC and impact to water quality are created using a lowest to highest (“1-9”) ordinal rank system derived from survey results after which each dataset is normalized using a (“1-3”) ordinal rank. Finally, the normalized NPC datasets are combined into one final model useful for identifying each dominant NPC by rank and location within a specified USGS watershed. In conclusion, the modifications to the LUCIS method yields results beneficial for identifying non-point source loads of water pollution.
Type:
Reports (Electronic)
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Geographic Information Systems Technology
Degree Grantor:
University of Arizona

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleAn Introduction to Identifying Nonpoint Sources of Water Pollution Using a Modified Land Use Conflict Analysis Identification Strategy (LUCIS) Model, Non-point Source Identification Strategy: NPSISen_US
dc.contributor.authorCziesch, Jarretten
dc.date.issued2015en
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.description.collectioninformationThis item is part of the MS-GIST Master's Reports collection. For more information about items in this collection, please contact the UA Campus Repository at repository@u.library.arizona.edu.en
dc.description.abstractThis paper examines the Non-Point Source Identification Strategy (NPSIS); a modification of the Land Use Conflict Identification Strategy (LUCIS): NPSIS is a raster model useful for identifying non-point sources of water pollution from three known contributors (agriculture, domestic, and natural background). By using a standard operating procedure, developers are able to create standardized datasets useful for identifying non-point sources of water pollution throughout the contiguous United States. The NPSIS model process requires the use of three “non-point source water pollution” contributors. A contributor is termed as a Non-Point Category (NPC) that contains collective elements (i.e. nutrient applications for agricultural purposes and urban runoff from highly developed areas). Using a survey, water resource professionals familiar with chosen study areas rank each NPC element according to potential impact to water quality. Following the survey, raster datasets that represent each NPC and impact to water quality are created using a lowest to highest (“1-9”) ordinal rank system derived from survey results after which each dataset is normalized using a (“1-3”) ordinal rank. Finally, the normalized NPC datasets are combined into one final model useful for identifying each dominant NPC by rank and location within a specified USGS watershed. In conclusion, the modifications to the LUCIS method yields results beneficial for identifying non-point source loads of water pollution.en
dc.typeReports (Electronic)en
thesis.degree.nameM.S.en
thesis.degree.levelmastersen
thesis.degree.disciplineGeographic Information Systems Technologyen
thesis.degree.grantorUniversity of Arizonaen
dc.identifier.urihttp://hdl.handle.net/10150/576109en
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.