Three-Dimensional Lake Water Quality Modeling: Sensitivity and Uncertainty Analyses

Abstract

Two sensitivity and uncertainty analysis methods are applied to a three-dimensional coupled hydrodynamic-ecological model (ELCOM-CAEDYM) of a morphologically complex lake. The primary goals of the analyses are to increase confidence in the model predictions, identify influential model parameters, quantify the uncertainty of model prediction, and explore the spatial and temporal variabilities of model predictions. The influence of model parameters on four model-predicted variables (model output) and the contributions of each of the model-predicted variables to the total variations in model output are presented. The contributions of predicted water temperature, dissolved oxygen, total phosphorus, and algal biomass contributed 3, 13, 26, and 58% of total model output variance, respectively. The fraction of variance resulting from model parameter uncertainty was calculated by two methods and used for evaluation and ranking of the most influential model parameters. Nine out of the top 10 parameters identified by each method agreed, but their ranks were different. Spatial and temporal changes of model uncertainty were investigated and visualized. Model uncertainty appeared to be concentrated around specific water depths and dates that corresponded to significant storm events. The results suggest that spatial and temporal variations in the predicted water quality variables are sensitive to the hydrodynamics of physical perturbations such as those caused by stream inflows generated by storm events. The sensitivity and uncertainty analyses identified the mineralization of dissolved organic carbon, sediment phosphorus release rate, algal metabolic loss rate, internal phosphorus concentration, and phosphorus uptake rate as the most influential model parameters.

A pollutant load hierarchical allocation method integrated in an environmental capacity management system for Zhushan Bay, Taihu Lake

“…The CVs of the RMSE are in reasonable ranges, i.e. in previous studies, the CVs of the RMSE were usually between 20% to 50% (Wu and Xu, 2011;Wang et al, 1999;Jia et al, 2010Jia et al, , 2011Missaghi et al, 2013). Some of the individual data points can cause the CV of the RMSE at some monitoring stations exceed 0.5, such as the TP concentration at Z3 (Fig.…”

“…In this study, the ratio method, which was recommended by and widely utilized in the TMDL program in the U.S., was used to calculate the margin of safety (Haire et al, 2009). Considering that the process of allowable pollutant load calculation is relatively reliable, the margin of safety was set to 5% of the allowable pollutant loads, as commonly applied in TMDL work (Bruce et al, 2006;Missaghi et al, 2013).…”

Constraining uncertainty and process-representation in an algal community lake model using high frequency in-lake observations

“…sampling required, but are critical for reducing the uncertainties associated with algal 670 modelling (Saloranta and Anderson, 2007;Missaghi et al, 2013). For some of the lake-671 years studied here modified diffuse P-river flow relationships were identified; the 672 relationships constrained were far from clear and were affected significantly by knock-on 673 effects from simulation errors.…”

Numerical Study of the Hydrodynamic and Sediment Transport Process in a Subtropical Water Reservoir: the Impacts of Storms and Winds

“…It is necessary to understand the spatial and temporal variation of the thermal structure and sediment transport processes, especially during and after storm events, since during these events there is more disruption to the lake systems [12][13][14]. Complex numerical models are often applied to fully capture these changing dynamics [12,15]. With decreased computational costs and improvements in computational power, coupled three-dimensional (3D) hydrodynamic and water quality models are increasingly being used [16].…”