A data-based predictive model for spatiotemporal variability in stream water quality
AuthorGuo, D; Lintern, A; Webb, JA; Ryu, D; Bende-Michl, U; Liu, S; Western, AW
Source TitleHydrology and Earth System Sciences
PublisherCOPERNICUS GESELLSCHAFT MBH
University of Melbourne Author/sGuo, Danlu; Western, Andrew; Ryu, Dongryeol; Liu, Shuci; Liu, Shuci; Webb, James; Lintern, Anna
Document TypeJournal Article
CitationsGuo, D., Lintern, A., Webb, J. A., Ryu, D., Bende-Michl, U., Liu, S. & Western, A. W. (2020). A data-based predictive model for spatiotemporal variability in stream water quality. HYDROLOGY AND EARTH SYSTEM SCIENCES, 24 (2), pp.827-847. https://doi.org/10.5194/hess-24-827-2020.
Access StatusOpen Access
Abstract. Our current capacity to model stream water quality is limited – particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatiotemporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years and across 102 catchments (which span over 130 000 km2). The modeling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate–nitrite (NOx) and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which were identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explained (19.9 %), the model explains 38.2 % (NOx) to 88.6 % (EC) of the total spatiotemporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; the temporal variability remains largely unexplained across all catchments, although long-term trends are well captured. The model is best used to predict proportional changes in water quality on a Box–Cox-transformed scale, but it can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot spots and hot moments for waterway pollution; (2) predicting the effects of catchment changes on water quality, e.g., urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on the following: (1) alternative statistical model structures to improve fitting for truncated data (for constituents where a large amount of data fall below the detection limit); and (2) better representation of nonconservative constituents (e.g., FRP) by accounting for important biogeochemical processes.
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