First Advisor

Heejun Chang

Term of Graduation

Summer 2020

Date of Publication

7-6-2020

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.) in Earth, Environment, & Society

Department

Earth, Environment, & Society

Language

English

Subjects

Water quality -- Mathematical models, Spatial analysis (Statistics), Autocorrelation (Statistics), Water quality -- Nepal -- Measurement -- Case studies

DOI

10.15760/etd.7383

Physical Description

1 online resource (xi, 135 pages)

Abstract

This dissertation aims to advance the existing knowledge related to spatial modeling of water quality by exploring and introducing innovative approaches to different spatial conceptualizations for water quality modeling and incorporating upstream-downstream relations in geographically weighted regression. By carrying out a systematic literature review of four different classes of spatial models in Chapter One, this dissertation identifies the following major research gaps: lack of incorporation of multiscale processes, not enough emphasis on spatial weights matrices, and unavailability of upstream-downstream relationships in geographically weighted regressions. Chapters Two and Three were designed to address these gaps in the literature. In Chapter Two, different spatial conceptualizations of sampling sites were compared based on their capacity to predict dissolved oxygen and electrical conductivity utilizing geographic information system derived explanatory variables in rivers of the Setikhola watershed in central Nepal. The model strengths are better while considering graph types close to the stream network structure for dissolved oxygen. The graph types that account for neighbors in all directions are better suited for electrical conductivity modeling. In Chapter Three, this dissertation demonstrates that a successful geographically weighted regression model could be developed using an upstream distance matrix that has comparable model strength with that of standard Euclidean distance weighted geographically weighted regression. The human impacts as population density and increased sand and gravel cover can be detected impacting water quality in the study watershed. The relationships between socio-environmental factors and water quality and their spatial interrelationships identified in the second chapter shed light on the source, mobilization, and transport of dissolved oxygen and electrical conductivity and can assist the water quality management endeavor. The local insights obtained from the upstream distance weighted geographically weighted regression of the third chapter help understand fine-scale impacts of socio-environmental and biophysical factors on water quality and assist in designing locally specific water quality management efforts.

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Persistent Identifier

https://archives.pdx.edu/ds/psu/33548

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