Reply on RC2

Abstract

Development of accurate water quality modeling tools is necessary for integrated water quality management of river systems. The existing water quality models can simulate dissolved oxygen (DO) concentration quite well during high flow and phytoplankton blooms in rivers; however, there are discrepancies during the summer low-flow season that are assumed to be due to the uncertainties related to the organic matter contribution of the model boundary conditions. Therefore, we used the C-RIVE biogeochemical model to evaluate the influence of controlling parameters on DO simulations at low flow. Three Sobol sensitivity analyses (SA) were carried out based on a coarse model pre-analysis whose target was to develop SA scenarios providing a reduction in the number of model parameters and computation cost as well as hiding inter-parameter interactions. The parameters studied are related to bacterial (e.g., bacterial growth rate), organic matter (OM; repartition and degradation of OM into constituent fractions), and physical factors (e.g., reoxygenation of the river due to navigation and wind), whose variation ranges are selected based on a detailed literature review. Bacterial growth and mortality rates are found to be by far the two most influential parameters, followed by bacterial growth yield. More refined SA results indicate that the biodegradable fraction of dissolved organic matter (BDOM) and the bacterial growth yield are the most influential parameters under conditions of a high net bacterial growth rate (= growth rate – mortality rate), while bacterial growth yield is independently dominant in low net growth situations. Based on the results of this study, proposals are made for in situ measurement of BDOM under a dense and well-equipped urban area water quality monitoring network that could provide high-frequency data. The results also indicate the need for bacterial community monitoring in order to detect potential bacterial community shifts after transient events such as combined sewer overflows and post-infrastructure improvement in treatment plants. Furthermore, we discuss the integration of BDOM in data assimilation software for better estimation of BDOM contribution from boundary conditions, which would result in improved water quality modeling.