TY - JOUR

T1 - Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

AU - Piccolroaz, S.

AU - Zhu, S.

AU - Ladwig, R.

AU - Carrea, L.

AU - Oliver, S.

AU - Piotrowski, A. P.

AU - Ptak, M.

AU - Shinohara, R.

AU - Sojka, M.

AU - Woolway, R. I.

AU - Zhu, D. Z.

N1 - Natural Science Research of Jiangsu Higher Education Institutions of China. Grant Number: 22KJB170023 National Natural Science Foundation of China. Grant Number: 52109099 United States National Science Foundation. Grant Number: DBI 1759865 UW-Madison Data Science Initiative grant. Grant Number: 1934633 Ministry of Education and Science of Poland. Grant Number: 3841/E-41/2023 European Space Agency Climate Change Initiative. Grant Number: 4000125030/18/I-NB Ministero dell’Istruzione, dell’Università e della Ricerca. Grant Number: L232/2016

PY - 2024/3

Y1 - 2024/3

N2 - Abstract Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process-based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process-based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications.

AB - Abstract Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process-based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process-based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications.

KW - thermal dynamics

KW - limnology

KW - deterministic models

KW - heat budget

KW - machine learning

KW - data sources

U2 - 10.1029/2023RG000816

DO - 10.1029/2023RG000816

M3 - Article

VL - 62

JO - Reviews of Geophysics

JF - Reviews of Geophysics

SN - 8755-1209

IS - 1

M1 - e2023RG000816

ER -