Effects of dimensionality on the performance of hydrodynamic models for stratified lakes and reservoirs

Numerical models are an important tool for simulating temperature, hydrodynamics, and water quality in lakes and reservoirs. Existing models differ in dimensionality by considering spatial variations of simulated parameters (e.g., flow velocity and water temperature) in one (1D), two (2D) or three (3D) spatial dimensions. The different approaches are based on different levels of simplification in the description of hydrodynamic processes and result in different demands on computational power. The aim of this study is to compare three models with different dimensionalities and to analyze differences between model results in relation to model simplifications. We analyze simulations of thermal stratification, flow velocity and substance transport by density currents in a medium-sized drinking-water reservoir in the subtropical zone, using three widely used open-source models: GLM (1D), CE-QUAL-W2 (2D) and Delft3D (3D). The models were operated with identical initial and boundary conditions over a 1-year period. Their performance was assessed by comparing model results with measurements of temperature, flow velocity and turbulence. Our results show that all models were capable of simulating the seasonal changes in water temperature and stratification. Flow velocities, only available for the 2D and 3D approaches, were more challenging to reproduce, but 3D simulations showed closer agreement with observations. With increasing dimensionality, the quality of the simulations also increased in terms of error, correlation and variance. None of the models provided good agreement with observations in terms of mixed layer depth, which also affects the spreading of inflowing water as density currents and the results of water quality models that build on outputs of the hydrodynamic models.

Automated summary

Numerical models are valuable tools for simulating various aspects of lakes and reservoirs. The dimensionality of the models affects their computational requirements and accuracy. In this study, three models of different dimensions were compared and their simplifications were analyzed. The higher dimensional models showed better performance in simulating various parameters. The study highlights the importance of model dimensionality and its impact on model results.