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.

[1]  R. Avtar,et al.  Impact of population growth and land use and land cover (LULC) changes on water quality in tourism-dependent economies using a geographically weighted regression approach , 2021, Environmental Science and Pollution Research.

[2]  Yasuyo K. Makido,et al.  Effects of land use change, wetland fragmentation, and best management practices on total suspended sediment concentrations in an urbanizing Oregon watershed, USA. , 2021, Journal of environmental management.

[3]  Heejun Chang,et al.  Environmental and spatial factors affecting surface water quality in a Himalayan watershed, Central Nepal , 2021 .

[4]  Eduard Szöcs,et al.  Preparing GIS data for analysis of stream monitoring data: The R package openSTARS , 2020, PloS one.

[5]  E. D’Amico,et al.  Variation in stream network relationships and geospatial predictions of watershed conductivity , 2020, Freshwater Science.

[6]  Dawen Yang,et al.  Spatiotemporal variation in nitrogen loads and their impacts on river water quality in the upper Yangtze River basin , 2020 .

[7]  Chaosheng Zhang,et al.  Investigating spatially varying relationships between total organic carbon contents and pH values in European agricultural soil using geographically weighted regression. , 2020, The Science of the total environment.

[8]  B. Majone,et al.  Combining Hydrologic Simulations and Stream‐network Models to Reveal Flow‐ecology Relationships in a Large Alpine Catchment , 2020, Water Resources Research.

[9]  Heejun Chang,et al.  Putting space into modeling landscape and water quality relationships in the Han River basin, South Korea , 2020, Comput. Environ. Urban Syst..

[10]  Y. Freund.  2019 in review. , 2019, European journal of emergency medicine : official journal of the European Society for Emergency Medicine.

[11]  G. Zolezzi,et al.  Testing the River Continuum Concept with geostatistical stream-network models , 2019, Ecological Complexity.

[12]  Frank C. Curriero,et al.  Development and Evaluation of Geostatistical Methods for Non-Euclidean-Based Spatial Covariance Matrices , 2019, Mathematical Geosciences.

[13]  Y. Chun,et al.  A review of spatial statistical approaches to modeling water quality , 2019, Progress in Physical Geography: Earth and Environment.

[14]  Cristina García,et al.  A spatial stream-network approach assists in managing the remnant genetic diversity of riparian forests , 2019, Scientific Reports.

[15]  Heejun Chang,et al.  Comparing the functional recognition of aesthetics, hydrology, and quality in urban stream restoration through the framework of environmental perception , 2019, River Research and Applications.

[16]  Xiaojun Deng.  Correlations between water quality and the structure and connectivity of the river network in the Southern Jiangsu Plain, Eastern China. , 2019, The Science of the total environment.

[17]  Stephen A. Jarvis,et al.  Embedding road networks and travel time into distance metrics for urban modelling , 2018, Int. J. Geogr. Inf. Sci..

[18]  A. Western,et al.  What Are the Key Catchment Characteristics Affecting Spatial Differences in Riverine Water Quality? , 2018, Water Resources Research.

[19]  Heejun Chang,et al.  Landscape and anthropogenic factors affecting spatial patterns of water quality trends in a large river basin, South Korea , 2018, Journal of Hydrology.

[20]  Alexis J. Comber,et al.  Distance metric choice can both reduce and induce collinearity in geographically weighted regression , 2018, Environment and Planning B: Urban Analytics and City Science.

[21]  Nina S. N. Lam,et al.  Geographically Weighted Elastic Net: A Variable-Selection and Modeling Method under the Spatially Nonstationary Condition , 2018 .

[22]  L. Avery,et al.  Using spatial-stream-network models and long-term data to understand and predict dynamics of faecal contamination in a mixed land-use catchment. , 2018, The Science of the total environment.

[23]  Tomoki Nakaya,et al.  The Importance of Scale in Spatially Varying Coefficient Modeling , 2017, Annals of the American Association of Geographers.

[24]  A. Western,et al.  Key factors influencing differences in stream water quality across space , 2018 .

[25]  A. Stewart Fotheringham,et al.  Multiscale Geographically Weighted Regression (MGWR) , 2017 .

[26]  C. Nietch,et al.  Improving Predictive Models of In‐Stream Phosphorus Concentration Based on Nationally‐Available Spatial Data Coverages , 2017, Journal of the American Water Resources Association.

[27]  Guoyan Pan,et al.  Spatial scale and seasonal dependence of land use impacts on riverine water quality in the Huai River basin, China , 2017, Environmental Science and Pollution Research.

[28]  Martin Charlton,et al.  Geographically weighted regression with parameter-specific distance metrics , 2017, Int. J. Geogr. Inf. Sci..

[29]  Eun-Sung Chung,et al.  Multi-Criteria Assessment of Spatial Robust Water Resource Vulnerability Using the TOPSIS Method Coupled with Objective and Subjective Weights in the Han River Basin , 2016 .

[30]  Minghua Zhang,et al.  Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression. , 2016, The Science of the total environment.

[31]  E. Peterson,et al.  To aggregate or not? Capturing the spatio-temporal complexity of the thermal regime , 2016 .

[32]  R. Mendes.  R: The R Project for Statistical Computing , 2016 .

[33]  Zhenyao Shen,et al.  Impact of landscape pattern at multiple spatial scales on water quality: A case study in a typical urbanised watershed in China , 2015 .

[34]  Yanwei Sun,et al.  Scale Effects on Spatially Varying Relationships Between Urban Landscape Patterns and Water Quality , 2014, Environmental Management.

[35]  Jay M. Ver Hoef,et al.  Applications of spatial statistical network models to stream data , 2014 .

[36]  Martin Charlton,et al.  Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data , 2014, Int. J. Geogr. Inf. Sci..

[37]  Jay M. Ver Hoef,et al.  STARS: An ArcGIS Toolset Used to Calculate the Spatial Information Needed to Fit Spatial Statistical Models to Stream Network Data , 2014 .

[38]  Martin Charlton,et al.  The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models , 2013, Geo spatial Inf. Sci..

[39]  Heejun Chang,et al.  Local landscape predictors of maximum stream temperature and thermal sensitivity in the Columbia River Basin, USA. , 2013, The Science of the total environment.

[40]  Marie-Josée Fortin,et al.  Modelling dendritic ecological networks in space: an integrated network perspective. , 2013, Ecology letters.

[41]  M. G. Estes,et al.  Estimating ground-level PM(2.5) concentrations in the southeastern U.S. using geographically weighted regression. , 2013, Environmental research.

[42]  Heejun Chang,et al.  Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales. , 2012, Journal of hazardous materials.

[43]  S. Kaushal,et al.  The urban watershed continuum: evolving spatial and temporal dimensions , 2012, Urban Ecosystems.

[44]  A. Lyapustin,et al.  Estimating Ground-Level PM2.5 Concentrations in the Southeastern U.S. using MAIAC AOD Retrievals , 2011 .

[45]  J. Tu.  Spatially varying relationships between land use and water quality across an urbanization gradient explored by geographically weighted regression. , 2011 .

[46]  Martin Charlton,et al.  Geographically Weighted Regression Using a Non-Euclidean Distance Metric with a Study on London House Price Data , 2011 .

[47]  Marie-Josée Fortin,et al.  From Graphs to Spatial Graphs , 2010 .

[48]  Xiaoying Yang,et al.  GIS-based spatial regression and prediction of water quality in river networks: a case study in Iowa. , 2010, Journal of environmental management.

[49]  A. Stewart Fotheringham,et al.  Robust Geographically Weighted Regression: A Technique for Quantifying Spatial Relationships Between Freshwater Acidification Critical Loads and Catchment Attributes , 2010 .

[50]  Erin E. Peterson,et al.  A Moving Average Approach for Spatial Statistical Models of Stream Networks , 2010 .

[51]  Jay M Ver Hoef,et al.  A mixed-model moving-average approach to geostatistical modeling in stream networks. , 2010, Ecology.

[52]  M. Charlton,et al.  GEOGRAPHICALLY WEIGHTED REGRESSION WHITE PAPER , 2009 .

[53]  J. Tu,et al.  Examining spatially varying relationships between land use and water quality using geographically weighted regression I: model design and evaluation. , 2008, The Science of the total environment.

[54]  Heejun Chang,et al.  Spatial analysis of water quality trends in the Han River basin, South Korea. , 2008, Water research.

[55]  Paul J. A. Withers,et al.  Transport and delivery of suspended solids, nitrogen and phosphorus from various sources to freshwaters in the UK , 2008 .

[56]  Daniel A. Griffith,et al.  Semiparametric Filtering of Spatial Autocorrelation: The Eigenvector Approach , 2007 .

[57]  J. Hoef,et al.  Spatial statistical models that use flow and stream distance , 2006, Environmental and Ecological Statistics.

[58]  Eric R. Ziegel,et al.  Geographically Weighted Regression , 2006, Technometrics.

[59]  Lisa M. Ganio,et al.  A geostatistical approach for describing spatial pattern in stream networks , 2005 .

[60]  David O'Sullivan,et al.  Geographic Information Analysis , 2002 .

[61]  S. Fotheringham,et al.  Geographically Weighted Regression , 1998 .

[62]  J. LeSage.  Spatial Econometrics , 1998 .

[63]  N. Cressie.  Spatial prediction and ordinary kriging , 1988 .

[64]  G. Minshall,et al.  The River Continuum Concept , 1980 .