Spatial Statistical Approaches to Water Quality Modelling

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

Landscape and Anthropogenic Factors Affecting Spatial Patterns of Water Quality Trends in a Large River Basin, South Korea

Understanding changes in water quality over time and landscape and anthropogenic factors affecting them are of paramount importance to human and ecosystem health. We analyzed the seasonal trends of total nitrogen, total phosphorus, chemical oxygen demand, and total suspended solid (SS) in the Han River Basin (HRB) of South Korea using the Mann-Kendall test. We explored the effects of anthropogenic (land cover and population) and natural factors (topography and soil) on the trends by using Moran’s Eigenvector based spatial filtering regressions at four different spatial scales. Water quality of the HRB generally improved from the early 1990s to 2016 with decreasing summer nutrient and winter SS concentrations. Water quality trends were spatially autocorrelated with distinct spatial variations within the basin. Some stations close to the Seoul metropolitan area, however, still exhibited poor water quality conditions. Approximately 20–70 percent of spatial variation of different water quality trends were explained by some combination of current agricultural land cover, forest land cover, % area covered by water, change in those land covers and slope variations. The 100 m buffer and one-kilometer upstream scale analyses generally showed higher explanatory power than the sub-watershed scale analyses, while the effect of seasons differed for different parameters. The significant factors in each regression model typically differed among different scales but not among different seasons of the same scale. The spatial filtering approach removed the residual spatial autocorrelation and thus significantly increased the explanatory power of water quality trend models

Putting Space into Modeling Landscape and Water Quality Relationships in the Han River Basin, South Korea

When examining the relationship between landscape characteristics and water quality, most previous studies did not pay enough attention to the spatial aspects of landscape characteristics and water quality sampling stations. We analyzed the spatial pattern of total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and suspended solids (SS) in the Han River basin of South Korea to explore the role of different distance considerations and spatial statistical approaches to explaining the variation in water quality. Five-year (2012 through 2016) seasonal averages of those water quality attributes were used in the analysis as the response variables, while explanatory variables like land cover, elevation, slope, and hydrologic soil groups were subjected to different weighting treatments based on distance and flow accumulation. Moran\u27s Eigenvector-based spatial filters were used to consider spatial relations among water quality sampling sites and were used in regression models. Distinct spatial patterns of seasonal water quality exist, with the highest concentrations of TN, TP, COD, and SS in downstream urban areas and the lowest concentrations in upstream forest areas. TN concentrations are higher in dry winter than the wet summer season, while SS concentrations are higher in wet summer than the dry season. Spatial models substantially improved the model fit compared to aspatial models. The flow accumulation-based models performed best when the spatial filters were not used, but all models performed similarly when spatial filters were used. The distance weighting approaches were instrumental in understanding watershed level processes affecting source, mobilization, and delivery of physicochemical parameters that flow into the river water. We conclude that a consideration of the spatial aspects of sampling sites is as important as accounting for different distances and hydrological processes in modeling water quality

Environmental and Spatial Factors Affecting Surface Water Quality in a Himalayan Watershed, Central Nepal

Various spatial interrelationships among sampling stations are not well explored in the spatial modeling of water quality literature. This research explores the relationship between water quality and various social, demographic, and topographic factors in an urbanizing watershed of Nepal with a comparison of different connectivity matrices to conceptualize spatial interrelationships. We collected electrical conductivity and dissolved oxygen data from surface water bodies using a handheld probe and used the data to establish relationships with land use, topography, and population density-based explanatory variables at both watershed and 100-m buffer scales. The linear regression model was compared with different eigenvector-based spatial filtering models. These spatial filtering models were constructed using five different spatial conceptualizations based on different graph types generated from the geographic coordinates of the sampling sites. Population density, elevation, and percentage of sand in the watershed and riparian regions are most important in explaining dissolved oxygen concentration and electric conductivity. A human signature as population density and increased sand and gravel cover can be detected in this watershed impacting water quality. Among different graph types compared, the relative graph type provided the highest model strength signifying a stronger upstream-downstream relationship of dissolved oxygen, while k-nearest graph types with four neighbors provided the strongest model performance, indicating the impact of local factors on electrical conductivity. The relationships between socio-environmental factors and water quality and their spatial interrelationships identified in this work shed light on the source, mobilization, and transport of dissolved oxygen and electrical conductivity and can assist the water quality management endeavor

Geospatial datasets in support of high-resolution spatial assessment of population vulnerability to climate change in Nepal

We present a geographic information system (GIS) dataset with a nominal spatial resolution of one-kilometer composed of grid polygons originally derived and utilized in a high-resolution climate vulnerability model for Nepal. The different data sets described and shared in this article are processed and tailored to the specific objectives of our research paper entitled “High-resolution Spatial Assessment of Population Vulnerability to Climate Change in Nepal” (Mainali and Pricope, In press) [1]. We share these data recognizing that there is a significant gap in regards to data availability, the spatial patterns of different biophysical and socioeconomic variables, and the overall population vulnerability to climatic variability and disasters in Nepal. Individual variables, as well as the entire set presented in this dataset, can be used to better understand the spatial pattern of different physical, biological, climatic, and vulnerability characteristics in Nepal. The datasets presented in this article are sourced from different national and global databases and have been statistically treated to meet the needs of the article. The data are in GIS-ready ESRI shapefile file format of one-kilometer grid polygon with various fields (columns) for each dataset

Sources of Contaminated Flood Sediments in a Rural–urban Catchment: Johnson Creek, Oregon

This study investigated the delivery of contaminated sediments to the channel network by urban drainage systems in Johnson Creek in Oregon, USA. Concentrations of five heavy metal concentrations measured in 136 samples collected from 37 stormwater outfalls and 99 bed sampling points were analysed. While concentrations of zinc, cadmium and lead increased with distance downstream in Johnson Creek, this was not the case for chromium and copper. Zinc, copper, and cadmium concentrations in outfalls were significantly higher than those in the stream bed, indicating that stormwater runoff is responsible for delivering contaminated sediments to Johnson Creek. Zinc concentrations in outfalls were negatively associated with elevation and slope in the contributing subcatchment, and positively with impervious cover. However, no statistically significant relationships were found between the other heavy metal concentrations and sub-catchment variables. These findings demonstrate that relationships between sediment-related, heavy metal concentrations and subcatchment characteristics in this heterogeneous, rural-urban catchment are more complex than those found in situations where land-use is more segregated, questioning the applicability of commonly held assumptions regarding changes in the sources and delivery paths of flood-related, sediment-associated pollutants that accompany urbanisation

Stream Distance-Based Geographically Weighted Regression for Exploring Watershed Characteristics and Water Quality Relationships

We developed a novel spatial stream network geographically weighted regression ( SSN-GWR ) by incorporating stream-distance metrics into GWR. The model was tested for predicting seasonal total nitrogen (TN ) and total suspended solids ( TSS ) concentrations in relation to watershed characteristics for 108 sites in the Han River Basin, South Korea. The SSN-GWR model was run with the average seasonal water quality parameters from 2012 through 2016 and was validated with the data from 2017 through 2021. The model fit among ordinary least square regression, standard GWR ( STD-GWR ), and stream distance weighted SSN-GWR were compared based on their ability to explain the variation of seasonal water quality parameters. We also compared residual spatial autocorrelations as well as various error parameters from these models. Compared to the STD-GWR model, the SSN-GWR model generally provided better model fit, reduced residual spatial autocorrelation, and lessened overall modeling errors. Results show that the spatial patterns of model fit, as well as various coefficients from the upstream distance weighted regressions, capture local patterns as a product of upstream–downstream relations. We demonstrate that a successful model could be developed by integrating stream distance into the GWR, which not only improves model fit but also reveals realistic hydrological processes that relate watershed characteristics to water quality along with the stream network. The local variations in model fit derived from this work can be used to devise fine-scale interventions for water quality improvements in a spatially heterogeneous complex river basin

A Review of Spatial Statistical Approaches to Modeling Water Quality

We review different regression models related to water quality that incorporate spatial aspects in their model. Spatial aspects refer to the location of different sites and are usually characterized by the distance between different points and directions by which they are related to each other. We focus on spatial lag and error, spatial eigenvector-based, geographically weighted regression, and spatial-stream-network-based models. We evaluated different studies using these methods based on how they dealt with clustering (spatial autocorrelation) of response variables, incorporated those clustering in the error (residual spatial autocorrelation), used multi-scale processes, and improved the model performance. The water-quality-based regression modeling approaches are shifting from straight-line distance-based spatial relations to upstream–downstream relations. Calculation of spatial autocorrelation and residual spatial autocorrelation was dependent upon the type of spatial regression used. The weights matrix is used as available in the software and most of the studies did not attempt to modify it. Different scale processes like certain distance from rivers versus consideration of entire watersheds are dealt with separately in most of the studies. Generally, the capacity of the predictor variables to predict the response variable significantly improves when spatial regressions are used. We identify new research directions in terms of spatial considerations, weights matrix construction, inclusion of multi-scale processes, and identification of predictor variables in such models

A Review of Spatial Statistical Approaches to Modeling Water Quality

We review different regression models related to water quality that incorporate spatial aspects in their model. Spatial aspects refer to the location of different sites and are usually characterized by the distance between different points and directions by which they are related to each other. We focus on spatial lag and error, spatial eigenvector-based, geographically weighted regression, and spatial-stream-network-based models. We evaluated different studies using these methods based on how they dealt with clustering (spatial autocorrelation) of response variables, incorporated those clustering in the error (residual spatial autocorrelation), used multi-scale processes, and improved the model performance. The water-quality-based regression modeling approaches are shifting from straight-line distance-based spatial relations to upstream–downstream relations. Calculation of spatial autocorrelation and residual spatial autocorrelation was dependent upon the type of spatial regression used. The weights matrix is used as available in the software and most of the studies did not attempt to modify it. Different scale processes like certain distance from rivers versus consideration of entire watersheds are dealt with separately in most of the studies. Generally, the capacity of the predictor variables to predict the response variable significantly improves when spatial regressions are used. We identify new research directions in terms of spatial considerations, weights matrix construction, inclusion of multi-scale processes, and identification of predictor variables in such models

Stream Distance-Based Geographically Weighted Regression for Exploring Watershed Characteristics and Water Quality Relationships

We developed a novel spatial stream network geographically weighted regression (SSN-GWR) by incorporating stream-distance metrics into GWR. The model was tested for predicting seasonal total nitrogen (TN) and total suspended solids (TSS) concentrations in relation to watershed characteristics for 108 sites in the Han River Basin, South Korea. The SSN-GWR model was run with the average seasonal water quality parameters from 2012 through 2016 and was validated with the data from 2017 through 2021. The model fit among ordinary least square regression, standard GWR (STD-GWR), and stream distance weighted SSN-GWR were compared based on their ability to explain the variation of seasonal water quality parameters. We also compared residual spatial autocorrelations as well as various error parameters from these models. Compared to the STD-GWR model, the SSN-GWR model generally provided better model fit, reduced residual spatial autocorrelation, and lessened overall modeling errors. Results show that the spatial patterns of model fit, as well as various coefficients from the upstream distance weighted regressions, capture local patterns as a product of upstream–downstream relations. We demonstrate that a successful model could be developed by integrating stream distance into the GWR, which not only improves model fit but also reveals realistic hydrological processes that relate watershed characteristics to water quality along with the stream network. The local variations in model fit derived from this work can be used to devise fine-scale interventions for water quality improvements in a spatially heterogeneous complex river basin.</p