Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects

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.

Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

“…Likewise, Almeida et al. (2022) compared the prediction capabilities of FLake and the Hostetler models in reproducing LSWT against a ML algorithm in 24 reservoirs where temperature dynamics are substantially affected by inflows/outflows. They found that ML models may outperform process‐based physical models in terms of both accuracy and computational cost when long‐term observations were available.…”

A Multi‐Model Ensemble of Baseline and Process‐Based Models Improves the Predictive Skill of Near‐Term Lake Forecasts

“…To the best of our knowledge, no one has applied an MME approach to forecasting lake and reservoir temperatures with specified uncertainty. While MMEs for water temperatures have been applied to long‐term projections (Almeida et al., 2022; Feldbauer et al., 2022; La Fuente et al., 2022; Wynne et al., 2023), or as model inter‐comparisons (Golub et al., 2022), the utility of MMEs for real‐time water temperature forecasting remains unknown. This gap may exist because ensemble near‐term forecasts have, to date, focused on using ensembles of multiple driver data sets (e.g., weather forecasts; Mercado‐Bettín et al., 2021) and parameter sets (e.g., Thomas et al., 2020) to partition and quantify uncertainty (Clayer et al., 2023; Thomas et al., 2020), rather than using multiple models to generate more skillful operational forecasts.…”

A first assessment of ERA5 and ERA5‐Land reanalysis air temperature in Portugal

“…In recent years, the use of atmospheric reanalysis has increased significantly. Global reanalysis is now an established and frequently used source of climate data for a broad range of applications (e.g., Almeida et al, 2015; Almeida et al, 2022; Tarek et al, 2020; Vanella et al, 2020) including the energy sector (Kaspar et al, 2020). These datasets are generated by a global NWP model and a data assimilation system considering observed historical meteorological data with the aim of increasing weather condition consistency (Parker, 2016).…”