Spatial and temporal variability of in-stream water quality parameter influence on dissolved oxygen and nitrate within a regional stream network

Maintaining elevated aqueous concentrations of dissolved oxygen (DO) and decreased concentrations of nitrate (NO3) within stream environments is critical to sustaining aquatic life and the overall environmental health of a river system. Identifying system processes and system inputs that govern in-stream concentrations of DO and NO3 is paramount to achieving satisfactory concentrations or implementing efficient remediation methods. As these processes and inputs often depend on a multitude of climatic, environmental, and anthropogenic factors, it is essential to determine the spatio-temporal variability in their control of DO and NO3. In this study, a sensitivity analysis is applied to a regional-scale stream system of the Lower Arkansas River Basin in southeastern Colorado using a coupled QUAL2E-OTIS model to investigate the factors that govern DO and NO3 in space and time. Using the revised Morris scheme, a total of 34 model input factors (boundary conditions, flow and mass inputs, model parameters) are included in the analysis. Besides identifying the model input factors that govern DO and NO3 concentrations globally, the methodology also ascertains the influence of these factors according to location within the regional stream network and to season of the year. Results show that upstream solute concentrations, algal processes, channel roughness, groundwater discharge and solute mass loadings to the stream, and oxygen reaeration are the most influential processes and parameters in determining DO and NO3 concentrations. Many processes (algal growth and respiration, chemical kinetic reactions) have a time-varying influence due to seasonal changes in water temperature and solar radiation. Other processes (groundwater discharge and solute mass loading) are of moderate influence in the Arkansas River but of very strong influence in the tributaries. These results not only identify parameters and processes that should be targeted during field data collection and model calibration, but also highlight the possibility of implementing efficient remediation strategies that target processes at different locations and at different times of the year.