Uncertainty and sensitivity analysis of a depth-averaged water quality model for evaluation of Escherichia Coli concentration in shallow estuaries

Sensitivity and uncertainty analysis investigate the robustness of numerical model predictions and provide information about the factors that contribute most to the variability of model output, identifying the most important parameters for model calibration. This paper presents a sensitivity and uncertainty analysis of a 2D depth-averaged water quality model applied to a shallow estuary. The model solves the mass transport equation for Escherichia Coli, including the effects of water temperature, salinity, solar radiation, turbulent diffusion and short wave dispersion. The sensitivity of the concentration of E. Coli in the estuary to input parameters and the different sources of uncertainty are studied using Global Sensitivity Analysis based on Monte Carlo simulation methods and sensitivity measures based on linear and non-linear regression analysis, in order to aid modellers in the calibration process and in the interpretation of model output. The extinction coefficient of light in water and the depth of the vertical layer over which the E. coli spread were found to be the most relevant parameters of the model. In the shallowest regions of the estuary errors in the bathymetry are also an important source of uncertainty on model output. Globally, the combination of these three parameters was found to be very effective for calibration purposes in the whole estuary.

► We present a numerical model for evaluation of E. coli concentration in estuaries.
► Global Sensitivity Analysis is used to identify the most relevant model parameters.
► Parameters related to solar radiation are the most relevant for model calibration.
► Bathymetry errors are also an important source of uncertainty on model output.
► Turbulence and salinity have a much lower impact on model results.