Quantifying spatially varying associations between water quality and sub-basin structural characteristics: a multiscale spatial approach
Abstract
Water quality patterns reflect spatially heterogeneous associations among topography, drainage connectivity, land use configuration, and soil hydrologic structure, whereas concentration-based assessments and global models may obscure where these relationships emerge and at which spatial scales they operate. Using monitoring data from 2020 to 2024, this study examined six water quality indicators: total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), dissolved oxygen (DO), pH, and electrical conductivity (EC) across 67 mid-basins in South Korea. Random forest (RF) screening identified relief ratio (R h ), drainage density (D d ), land use contagion (CONTAG land ), and soil patch density (PD soil ) as predictors representing basin morphometry, landscape configuration, and soil hydrologic heterogeneity. Multiscale geographically weighted regression (MGWR) was used to estimate basin-level associations and variable-specific bandwidths, and model fit was compared with that of ordinary least squares (OLS). MGWR produced higher in-sample R² values for all indicators and lower corrected Akaike information criterion (AICc) values for TOC, TN, TP, DO, and pH. Residual Moran’s I values were nonsignificant for five indicators, whereas EC retained significant negative residual spatial autocorrelation (Moran’s I = − 0.101, pseudo- p = 0.009), indicating that some parameter-specific spatial structure remained unaccounted for by the selected terrestrial basin predictors. Five-fold spatial block cross-validation showed parameter-dependent transportability, with the strongest performance for TN. Priority scores combining water quality conditions with MGWR-derived association magnitudes identified 11 priority basins, primarily in the Yeongsan River system, with additional basins in the Geum, Nakdong, and Seomjin River systems. These findings support parameter-specific interpretation and basin-targeted management.
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BibTeX
@article{choi2026quantifying,
title = {{Quantifying spatially varying associations between water quality and sub-basin structural characteristics: a multiscale spatial approach}},
author = {Seon Yeon Choi and Hun Kyun Bae and Chang Dae Jo and Heon Gak Kwon},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-74829-1},
url = {https://www.nature.com/articles/s41598-026-74829-1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Quantifying spatially varying associations between water quality and sub-basin structural characteristics: a multiscale spatial approach
AU - Seon Yeon Choi
AU - Hun Kyun Bae
AU - Chang Dae Jo
AU - Heon Gak Kwon
PY - 2026
DA - 2026/10/07
JO - Scientific Reports
DO - 10.1038/s41598-026-74829-1
UR - https://www.nature.com/articles/s41598-026-74829-1
AB - Water quality patterns reflect spatially heterogeneous associations among topography, drainage connectivity, land use configuration, and soil hydrologic structure, whereas concentration-based assessments and global models may obscure where these relationships emerge and at which spatial scales they operate. Using monitoring data from 2020 to 2024, this study examined six water quality indicators: total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), dissolved oxygen (DO), pH, and electrical conductivity (EC) across 67 mid-basins in South Korea. Random forest (RF) screening identified relief ratio (R h ), drainage density (D d ), land use contagion (CONTAG land ), and soil patch density (PD soil ) as predictors representing basin morphometry, landscape configuration, and soil hydrologic heterogeneity. Multiscale geographically weighted regression (MGWR) was used to estimate basin-level associations and variable-specific bandwidths, and model fit was compared with that of ordinary least squares (OLS). MGWR produced higher in-sample R² values for all indicators and lower corrected Akaike information criterion (AICc) values for TOC, TN, TP, DO, and pH. Residual Moran’s I values were nonsignificant for five indicators, whereas EC retained significant negative residual spatial autocorrelation (Moran’s I = − 0.101, pseudo- p = 0.009), indicating that some parameter-specific spatial structure remained unaccounted for by the selected terrestrial basin predictors. Five-fold spatial block cross-validation showed parameter-dependent transportability, with the strongest performance for TN. Priority scores combining water quality conditions with MGWR-derived association magnitudes identified 11 priority basins, primarily in the Yeongsan River system, with additional basins in the Geum, Nakdong, and Seomjin River systems. These findings support parameter-specific interpretation and basin-targeted management.
SN - 2045-2322
ER -