What Are the Key Catchment Characteristics Affecting Spatial Differences in Riverine Water Quality?. Issue 10 (4th October 2018)

• Record Type: Journal Article
• Authors: Lintern, A.; Webb, J. A.; Ryu, D.; Liu, S.; Waters, D.; Leahy, P.; Bende‐Michl, U.; Western, A. W.
• Is Part Of: Water resources research. Volume 54:Issue 10(2018)
• Page Start: 7252
• Page End: 7272
• Publication Date: 2018-10-04
• Subjects: water quality -- statistical model -- nutrients -- sediments -- spatial variability -- catchment characteristics; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2017WR022172
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: This study uses water‐quality data collected over 20 years, from 102 predominantly rural sites across Victoria, Australia, to further our understanding of spatial variability in riverine water quality. We focus on concentrations of total suspended solids, total phosphorus, filterable reactive phosphorus, total Kjeldahl nitrogen, nitrate/nitrite (NO x ), and electrical conductivity. We used an exhaustive search approach to identify the linear models that best link catchment characteristics to time‐averaged constituent concentrations. We ran additional analyses to (1) assess the performance of these models under drought conditions, and (2) understand the key drivers of site‐level variability (standard deviations) of constituent concentrations. Natural catchment characteristics appear to have a greater effect on spatial differences in average constituent concentrations. Performance of the statistical models of time‐averaged constituent concentrations varied, and spatial variability in mean electrical conductivity levels could be more readily explained by catchment characteristics compared to more reactive nutrients. Notwithstanding, the models performed relatively well under varying hydrologic conditions for most constituents. As such, these models provide an insight into the key factors affecting spatial variability in average stream water‐quality conditions. We also identified that hydrologic, climatic, and topographic characteristics of the catchment helped explain the spatial variability in temporal changes in constituents. After calibration and validation, these models of both average water quality and variability in water quality could be used to forecast stream water‐quality responses to future land use, climate, or soil and land management changes. Key Points: Human‐influenced (land use) and natural catchment characteristics (e.g., topography) affect spatial variability in stream water quality Spatial variability in electrical conductivity can be most easily explained by catchment characteristics There was minimal change in model performance when different hydrological periods were modeled