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 in- 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 that surface water temperatures in reservoirs differ significantly from those found in lakes, reinforcing 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 both in accuracy and in computational requirements, if applied to reservoirs with long-term observations available. A relationship between mean water retention times and the importance of inflows and outflows is established: reservoirs with the retention time shorter than ~100 days, if simulated without in- and outflow effects, tend to exhibit a statistically significant deviation in the computed surface temperatures regardless of their morphological characteristics.

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 intercomparisons (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 multi-model ensemble of empirical 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 datasets (e.g., weather forecasts; Mercado-Bettín et al, 2021) and parameter sets (e.g.…”