Water Practice and Technology (May 2024)
Comparative analysis of hydrodynamic flowrate sources as drivers of water quality models for nitrogenous compounds in complex ungauged South African rivers
Abstract
Water quality modelling is a critical tool for managing the health of river ecosystems, particularly in regions impacted by point source pollution activities. This study investigates the influence of different hydrodynamic data sources on the performance of two river water quality models, the Basic Model (BM) and the Water Quality Analysis Simulation Programme (WASP) for modelling nitrogenous compounds in a complex river system including wastewater treatment plant effluent discharges. Four diverse hydrodynamic data input types were considered. These included measured station data, altered station data, rainfall-generated flow, and the WRSM/Pitman model estimate. Findings revealed trends, analysis of variance (ANOVA), and t-test analyses consistently demonstrated significant disparities between model predictions and measured data in specific river segments, indicating a need for segment-specific modelling approaches. An increase in Root Mean Square Error (RMSE) and Mean Square Error (MSE) values in certain segments pointed to a decline in model accuracy when confronted with distinct hydrodynamic conditions. Additionally, application of four diverse hydrodynamic data input sources yielded similar performance for BM and WASP against measured data. The research findings indicated a complex interplay between river hydrodynamics and water quality modelling, resulting in a recommendation for tailored modelling strategies that account for unique characteristics of river segments. HIGHLIGHTS Hydrodynamic data input sources yielded similar performance for the Basic Model and WASP against measured data for nitrogenous compounds.; Reduced performance of models farther from boundary was detected.; Altered station hydrology showed comparable impact on WASP and Basic Model.; Examination of segment-specific model accuracy disparities across varied inputs and models.;
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