Abstract
Abstract. The complexity of the state-of-the-art climate models requires high computational resources and imposes rather simplified parameterization of inland waters. The effect of lakes and reservoirs on the local and regional climate is commonly parameterized in regional or global climate modeling as a function of surface water temperature estimated by atmosphere-coupled one-dimensional lake models. The latter typically neglect one of the major transport mechanisms specific to artificial reservoirs: heat and mass advection due to inflows and outflows. Incorporation of these essentially two-dimensional processes into lake parameterizations requires a trade-off between computational efficiency and physical soundness, which is addressed in this study. We evaluated the performance of the two most used lake parameterization schemes and a machine-learning approach on high-resolution historical water temperature records from 24 reservoirs. Simulations were also performed at both variable and constant water level to explore the thermal structure differences between lakes and reservoirs. Our results highlight the need to include anthropogenic inflow and outflow controls in regional and global climate models. Our findings also highlight the efficiency of the machine-learning approach, which may overperform process-based physical models in both accuracy and computational requirements if applied to reservoirs with long-term observations available. Overall, results suggest that the combined use of process-based physical models and machine-learning models will considerably improve the modeling of air–lake heat and moisture fluxes. A relationship between mean water retention times and the importance of inflows and outflows is established: reservoirs with a retention time shorter than ∼ 100 d, if simulated without inflow and outflow effects, tend to exhibit a statistically significant deviation in the computed surface temperatures regardless of their morphological characteristics.
Highlights
Numerical weather prediction (NWP) and climate modeling are essential tools in research and applied science applications (e.g., Bauer et al, 2015; Forster, 2017; Jacob et al, 2020)
We evaluate the importance of the energy transfers due to water inflows and outflows when modeling surface water energy fluxes in artificial reservoirs and elaborate a methodology to improve this essential aspect of regional climate models (RCMs) and general circulation models (GCMs)
The FLake model was developed for use in NWP and is currently implemented in several NWP models, for example the Consortium for Small-scale Modeling (COSMO) from the German Weather Service (Mironov et al, 2010), the High Resolution Limited Area Model (HIRLAM) from the Finnish Meteorological Institute, the Icosahedral Nonhydrostatic (ICON) model from the German Weather Service, and the Integrated Forecast System (IFS) from the European Centre for Medium-Range Weather Forecasts
Summary
Numerical weather prediction (NWP) and climate modeling are essential tools in research and applied science applications (e.g., Bauer et al, 2015; Forster, 2017; Jacob et al, 2020). In particular the evaporation rate, are affected by advection due to inflows and outflows (e.g., deepwater withdrawal) and by water level (WL) fluctuations (Rimmer et al, 2011; Friedrich et al, 2018) These fluctuations are usually much more pronounced in reservoirs than in natural lakes. Wang et al, 2019; Guseva et al, 2020) These intercomparison studies evaluated the model performance with application to one to three lakes, usually with very particular morphological characteristics (e.g., very deep or very shallow), over a limited time period. In view of the trade-off between results quality and computational efficiency, data-driven models have potential advantages in estimating the effect of lake inflows and outflows on SWT, which motivates their inclusion in model intercomparison studies. These are a 2-D model to define a calibrated and validated baseline scenario, two 1-D models without the parameterization of inflows or outflows, and an ANN to (ii) assess the modeling error in SWT of lakes (similar to a seepage lake) and reservoirs potentially associated with atmosphere–lake interactions and (iii) compare the performance and computational requirements of different approaches to predict the evolution of SWT in lakes (similar to a seepage lake) and reservoirs
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