Articles | Volume 29, issue 4
https://doi.org/10.5194/hess-29-1183-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-1183-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Learning from a large-scale calibration effort of multiple lake temperature models
Johannes Feldbauer
CORRESPONDING AUTHOR
Institute of Hydrobiology, TU Dresden, Dresden, Germany
Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden
Tobias K. Andersen
National Institute of Aquatic Resources (DTU Aqua), Technical University of Denmark, Kongens Lyngby, Denmark
Robert Ladwig
Department of Ecoscience, Aarhus University, Aarhus, Denmark
Related authors
No articles found.
Katja Frieler, Stefan Lange, Jacob Schewe, Matthias Mengel, Simon Treu, Christian Otto, Jan Volkholz, Christopher P. O. Reyer, Stefanie Heinicke, Colin Jones, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Ryan Heneghan, Derek P. Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Dánnell Quesada Chacón, Kerry Emanuel, Chia-Ying Lee, Suzana J. Camargo, Jonas Jägermeyr, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Lisa Novak, Inga J. Sauer, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, Michel Bechtold, Robert Reinecke, Inge de Graaf, Jed O. Kaplan, Alexander Koch, and Matthieu Lengaigne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2103, https://doi.org/10.5194/egusphere-2025-2103, 2025
Short summary
Short summary
This paper describes the experiments and data sets necessary to run historic and future impact projections, and the underlying assumptions of future climate change as defined by the 3rd round of the ISIMIP Project (Inter-sectoral Impactmodel Intercomparison Project, isimip.org). ISIMIP provides a framework for cross-sectorally consistent climate impact simulations to contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
Jorrit P. Mesman, Inmaculada C. Jiménez-Navarro, Ana I. Ayala, Javier Senent-Aparicio, Dennis Trolle, and Don C. Pierson
Hydrol. Earth Syst. Sci., 28, 1791–1802, https://doi.org/10.5194/hess-28-1791-2024, https://doi.org/10.5194/hess-28-1791-2024, 2024
Short summary
Short summary
Spring events in lakes are key processes for ecosystem functioning. We used a coupled catchment–lake model to investigate future changes in the timing of spring discharge, ice-off, spring phytoplankton peak, and onset of stratification in a mesotrophic lake. We found a clear trend towards earlier occurrence under climate warming but also that relative shifts in the timing occurred, such as onset of stratification advancing more slowly than the other events.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Austin Delany, Robert Ladwig, Cal Buelo, Ellen Albright, and Paul C. Hanson
Biogeosciences, 20, 5211–5228, https://doi.org/10.5194/bg-20-5211-2023, https://doi.org/10.5194/bg-20-5211-2023, 2023
Short summary
Short summary
Internal and external sources of organic carbon (OC) in lakes can contribute to oxygen depletion, but their relative contributions remain in question. To study this, we built a two-layer model to recreate processes relevant to carbon for six Wisconsin lakes. We found that internal OC was more important than external OC in depleting oxygen. This shows that it is important to consider both the fast-paced cycling of internally produced OC and the slower cycling of external OC when studying lakes.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
Short summary
Short summary
The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
Short summary
Short summary
Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Robert Ladwig, Paul C. Hanson, Hilary A. Dugan, Cayelan C. Carey, Yu Zhang, Lele Shu, Christopher J. Duffy, and Kelly M. Cobourn
Hydrol. Earth Syst. Sci., 25, 1009–1032, https://doi.org/10.5194/hess-25-1009-2021, https://doi.org/10.5194/hess-25-1009-2021, 2021
Short summary
Short summary
Using a modeling framework applied to 37 years of dissolved oxygen time series data from Lake Mendota, we identified the timing and intensity of thermal energy stored in the lake water column, the lake's resilience to mixing, and surface primary production as the most important drivers of interannual dynamics of low oxygen concentrations at the lake bottom. Due to climate change, we expect an increase in the spatial and temporal extent of low oxygen concentrations in Lake Mendota.
Cited articles
Andersen, T. K., Bolding, K., Nielsen, A., Bruggeman, J., Jeppesen, E., and Trolle, D.: How morphology shapes the parameter sensitivity of lake ecosystem models, Environ. Modell. Softw., 136, 104945, https://doi.org/10.1016/j.envsoft.2020.104945, 2021. a, b, c, d
Audet, J., Neif, E. M., Cao, Y., Hoffmann, C. C., Lauridsen, T. L., Larsen, S. E., Søndergaard, M., Jeppesen, E., and Davidson, T. A.: Heat-wave effects on greenhouse gas emissions from shallow lake mesocosms, Freshwater Biol., 62, 1130–1142, https://doi.org/10.1111/fwb.12930, 2017. a
Ayala, A. I., Moras, S., and Pierson, D. C.: Simulations of future changes in thermal structure of Lake Erken: proof of concept for ISIMIP2b lake sector local simulation strategy, Hydrol. Earth Syst. Sci., 24, 3311–3330, https://doi.org/10.5194/hess-24-3311-2020, 2020. a, b
Borgonovo, E.: A new uncertainty importance measure, Reliab. Eng. Syst. Safe., 92, 771–784, https://doi.org/10.1016/j.ress.2006.04.015, 2007. a, b
Borgonovo, E., Lu, X., Plischke, E., Rakovec, O., and Hill, M. C.: Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box, Water Resour. Res., 53, 7933–7950, https://doi.org/10.1002/2017wr020767, 2017. a, b
Bruce, L. C., Frassl, M. A., Arhonditsis, G. B., Gal, G., Hamilton, D. P., Hanson, P. C., Hetherington, A. L., Melack, J. M., Read, J. S., Rinke, K., Rigosi, A., Trolle, D., Winslow, L., Adrian, R., Ayala, A. I., Bocaniov, S. A., Boehrer, B., Boon, C., Brookes, J. D., Bueche, T., Busch, B. D., Copetti, D., Cortés, A., de Eyto, E., Elliott, J. A., Gallina, N., Gilboa, Y., Guyennon, N., Huang, L., Kerimoglu, O., Lenters, J. D., MacIntyre, S., Makler-Pick, V., McBride, C. G., Moreira, S., Özkundakci, D., Pilotti, M., Rueda, F. J., Rusak, J. A. Samal, N. R. Schmid, M., Shatwell, T., Snorthheim, C., Soulignac, F., Valerio, G., van der Linden, L., Vetter, M., Vinçon-Leite, B., Wang, J., Weber, M., Wickramaratne, C., Woolway, R. I., Yao, H., and Hipsey, M. R.: A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a Global Observatory Network, Environ. Modell. Softw., 102, 274–291, https://doi.org/10.1016/j.envsoft.2017.11.016, 2018. a, b
Cengel, Y. A. and Ozisk, M. N.: Solar radiation absorption in solar ponds, Sol. Energy, 33, 581–591, 1984. a
Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H., and Buontempo, C.: WFDE5: bias-adjusted ERA5 reanalysis data for impact studies, Earth Syst. Sci. Data, 12, 2097–2120, https://doi.org/10.5194/essd-12-2097-2020, 2020. a
Dietze, M. C.: Prediction in ecology: a first-principles framework, Ecol. Appl., 27, 2048–2060, https://doi.org/10.1002/eap.1589, 2017. a
Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T., and Hanasaki, N.: GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land Surface, B. Am. Meteorol. Soc., 87, 1381–1398, https://doi.org/10.1175/bams-87-10-1381, 2006. a
Feldbauer, J. and Mesman, J.: aemon-j/isimip-sensitivity-analysis: revision, Zenodo [code], https://doi.org/10.5281/zenodo.13150422, 2024. a
Feldbauer, J., Ladwig, R., Mesman, J. P., Moore, T. N., Zündorf, H., Berendonk, T. U., and Petzoldt, T.: Ensemble of models shows coherent response of a reservoir’s stratification and ice cover to climate warming, Aquat. Sci., 84, 50, https://doi.org/10.1007/s00027-022-00883-2, 2022. a
Ford, D. E. and Stefan, H. G.: Thermal predictions using integral energy model, Journal of the Hydraulics Division, 106, 39–55, https://doi.org/10.1061/JYCEAJ.0005358, 1980. a
Frieler, K., Volkholz, J., Lange, S., Schewe, J., Mengel, M., del Rocío Rivas López, M., Otto, C., Reyer, C. P. O., Karger, D. N., Malle, J. T., Treu, S., Menz, C., Blanchard, J. L., Harrison, C. S., Petrik, C. M., Eddy, T. D., Ortega-Cisneros, K., Novaglio, C., Rousseau, Y., Watson, R. A., Stock, C., Liu, X., Heneghan, R., Tittensor, D., Maury, O., Büchner, M., Vogt, T., Wang, T., Sun, F., Sauer, I. J., Koch, J., Vanderkelen, I., Jägermeyr, J., Müller, C., Rabin, S., Klar, J., Vega del Valle, I. D., Lasslop, G., Chadburn, S., Burke, E., Gallego-Sala, A., Smith, N., Chang, J., Hantson, S., Burton, C., Gädeke, A., Li, F., Gosling, S. N., Müller Schmied, H., Hattermann, F., Wang, J., Yao, F., Hickler, T., Marcé, R., Pierson, D., Thiery, W., Mercado-Bettín, D., Ladwig, R., Ayala-Zamora, A. I., Forrest, M., and Bechtold, M.: Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a), Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, 2024. a
Gaudard, A., Råman Vinnå, L., Bärenbold, F., Schmid, M., and Bouffard, D.: Toward an open access to high-frequency lake modeling and statistics data for scientists and practitioners – the case of Swiss lakes using Simstrat v2.1, Geosci. Model Dev., 12, 3955–3974, https://doi.org/10.5194/gmd-12-3955-2019, 2019. a, b
Giorgi, F. and Mearns, L. O.: Calculation of Average, Uncertainty Range, and Reliability of Regional Climate Changes from AOGCM Simulations via the “Reliability Ensemble Averaging” (REA) Method, J. Climate, 15, 1141–1158, https://doi.org/10.1175/1520-0442(2002)015<1141:COAURA>2.0.CO;2, 2002. a
Golub, M., Thiery, W., Marcé, R., Pierson, D., Vanderkelen, I., Mercado-Bettin, D., Woolway, R. I., Grant, L., Jennings, E., Kraemer, B. M., Schewe, J., Zhao, F., Frieler, K., Mengel, M., Bogomolov, V. Y., Bouffard, D., Côté, M., Couture, R.-M., Debolskiy, A. V., Droppers, B., Gal, G., Guo, M., Janssen, A. B. G., Kirillin, G., Ladwig, R., Magee, M., Moore, T., Perroud, M., Piccolroaz, S., Raaman Vinnaa, L., Schmid, M., Shatwell, T., Stepanenko, V. M., Tan, Z., Woodward, B., Yao, H., Adrian, R., Allan, M., Anneville, O., Arvola, L., Atkins, K., Boegman, L., Carey, C., Christianson, K., de Eyto, E., DeGasperi, C., Grechushnikova, M., Hejzlar, J., Joehnk, K., Jones, I. D., Laas, A., Mackay, E. B., Mammarella, I., Markensten, H., McBride, C., Özkundakci, D., Potes, M., Rinke, K., Robertson, D., Rusak, J. A., Salgado, R., van der Linden, L., Verburg, P., Wain, D., Ward, N. K., Wollrab, S., and Zdorovennova, G.: A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector, Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, 2022. a, b, c, d, e, f
Goudsmit, G.-H., Burchard, H., Peeters, F., and Wüest, A.: Application of k-ϵ turbulence models to enclosed basins: The role of internal seiches, J. Geophys. Res.-Oceans, 107, 23-1–23-13, https://doi.org/10.1029/2001JC000954, 2002. a, b, c, d
Guo, M., Zhuang, Q., Yao, H., Golub, M., Leung, L. R., Pierson, D., and Tan, Z.: Validation and Sensitivity Analysis of a 1‐D Lake Model Across Global Lakes, J. Geophys. Res.-Atmos., 126, e2020JD033417, https://doi.org/10.1029/2020JD033417, 2021. a, b, c
Guseva, S., Bleninger, T., Jöhnk, K., Polli, B. A., Tan, Z., Thiery, W., Zhuang, Q., Rusak, J. A., Yao, H., Lorke, A., and Stepanenko, V.: Multimodel simulation of vertical gas transfer in a temperate lake, Hydrol. Earth Syst. Sci., 24, 697–715, https://doi.org/10.5194/hess-24-697-2020, 2020. a
Hanson, P. C., Weathers, K. C., and Kratz, T. K.: Networked lake science: how the Global Lake Ecological Observatory Network (GLEON) works to understand, predict, and communicate lake ecosystem response to global change, Inland Waters, 6, 543–554, https://doi.org/10.1080/IW-6.4.904, 2016. a
Herman, J. and Usher, W.: SALib: An open-source Python library for Sensitivity Analysis, The Journal of Open Source Software, 2, 97, https://doi.org/10.21105/joss.00097, 2017. a
Hipsey, M. R., Bruce, L. C., Boon, C., Busch, B., Carey, C. C., Hamilton, D. P., Hanson, P. C., Read, J. S., de Sousa, E., Weber, M., and Winslow, L. A.: A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON), Geosci. Model Dev., 12, 473–523, https://doi.org/10.5194/gmd-12-473-2019, 2019. a, b, c, d
Hipsey, M. R., Gal, G., Arhonditsis, G. B., Carey, C. C., Elliott, J. A., Frassl, M. A., Janse, J. H., de Mora, L., and Robson, B. J.: A system of metrics for the assessment and improvement of aquatic ecosystem models, Environ. Modell. Softw., 128, 104697, https://doi.org/10.1016/j.envsoft.2020.104697, 2020. a
Huisman, J., Codd, G. A., Paerl, H. W., Ibelings, B. W., Verspagen, J. M. H., and Visser, P. M.: Cyanobacterial blooms, Nat. Rev. Microbiol., 16, 471–483, https://doi.org/10.1038/s41579-018-0040-1, 2018. a
Håkanson, L.: Models to predict Secchi depth in small glacial lakes, Aquat. Sci., 57, 31–53, https://doi.org/10.1007/BF00878025, 1995. a
Iwanaga, T., Usher, W., and Herman, J.: Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses, Socio-Environmental Systems Modelling, 4, 18155, https://doi.org/10.18174/sesmo.18155, 2022. a
Jachner, S., van den Boogaart, K. G., and Petzoldt, T.: Statistical Methods for the Qualitative Assessment of Dynamic Models with Time Delay (R Package qualV), J. Stat. Softw., 22, 1–30, https://doi.org/10.18637/jss.v022.i08, 2007. a
Jane, S. F., Mincer, J. L., Lau, M. P., Lewis, A. S. L., Stetler, J. T., and Rose, K. C.: Longer duration of seasonal stratification contributes to widespread increases in lake hypoxia and anoxia, Glob. Change Biol., 29, 1009–1023, https://doi.org/10.1111/gcb.16525, 2023. a, b
Jansen, J., Woolway, R. I., Kraemer, B. M., Albergel, C., Bastviken, D., Weyhenmeyer, G. A., Marce, R., Sharma, S., Sobek, S., Tranvik, L. J., Perroud, M., Golub, M., Moore, T. N., Raman Vinna, L., La Fuente, S., Grant, L., Pierson, D. C., Thiery, W., and Jennings, E.: Global increase in methane production under future warming of lake bottom waters, Glob. Change Biol., 28, 5427–5440, https://doi.org/10.1111/gcb.16298, 2022. a
Kakouei, K., Kraemer, B. M., Anneville, O., Carvalho, L., Feuchtmayr, H., Graham, J. L., Higgins, S., Pomati, F., Rudstam, L. G., Stockwell, J. D., Thackeray, S. J., Vanni, M. J., and Adrian, R.: Phytoplankton and cyanobacteria abundances in mid-21st century lakes depend strongly on future land use and climate projections, Glob. Change Biol., 27, 6409–6422, https://doi.org/10.1111/gcb.15866, 2021. a
Kattel, G. R.: Climate warming in the Himalayas threatens biodiversity, ecosystem functioning and ecosystem services in the 21st century: is there a better solution?, Biodivers. Conserv., 31, 2017–2044, https://doi.org/10.1007/s10531-022-02417-6, 2022. a
Khorashadi Zadeh, F., Nossent, J., Sarrazin, F., Pianosi, F., van Griensven, A., Wagener, T., and Bauwens, W.: Comparison of variance-based and moment-independent global sensitivity analysis approaches by application to the SWAT model, Environ. Modell. Softw., 91, 210–222, https://doi.org/10.1016/j.envsoft.2017.02.001, 2017. a
Kim, H.: Global soil wetness project phase 3 atmospheric boundary conditions (experiment 1), Data Integration and Analysis System (DIAS) [data set], https://doi.org/10.20783/DIAS.501, 2017. a
Kitaigorodskii, S. A. and Miropolsky, Y. Z.: On the theory of the open ocean active layer, Izv. Akad. Nauk SSSR. Fizika Atmosfery i Okeana, 6, 178–188, 1970. a
Knoll, L. B., Sharma, S., Denfeld, B. A., Flaim, G., Hori, Y., Magnuson, J. J., Straile, D., and Weyhenmeyer, G. A.: Consequences of lake and river ice loss on cultural ecosystem services, Limnol. Oceanogr. Lett., 4, 119–131, https://doi.org/10.1002/lol2.10116, 2019. a
Koenings, J. P. and Edmundson, J. A.: Secchi disk and photometer estimates of light regimes in Alaskan lakes: Effects of yellow color and turbidity, Limnol. Oceanogr., 36, 91–105, https://doi.org/10.4319/lo.1991.36.1.0091, 1991. a
Ladwig, R., Rock, L. A., and Dugan, H. A.: Impact of salinization on lake stratification and spring mixing, Limnol. Oceanogr. Lett., 8, 93–102, https://doi.org/10.1002/lol2.10215, 2023. a
Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geosci. Model Dev., 12, 3055–3070, https://doi.org/10.5194/gmd-12-3055-2019, 2019. a
Lange, S. and Büchner, M.: ISIMIP3b bias-adjusted atmospheric climate input data, Version v1.0, ISIMIP Repository [data set], https://doi.org/10.48364/ISIMIP.842396, 2020. a
Lange, S., Menz, C., Gleixner, S., Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Schmied, H. M., Hersbach, H., Buontempo, C., and Cagnazzo, C.: WFDE5 over land merged with ERA5 over the ocean (W5E5 v2.0), ISIMIP Repository [data set], https://doi.org/10.48364/ISIMIP.342217, 2021. a
Mallard, M. S., Nolte, C. G., Bullock, O. R., Spero, T. L., and Gula, J.: Using a coupled lake model with WRF for dynamical downscaling, J. Geophys. Res.-Atmos., 119, 7193–7208, https://doi.org/10.1002/2014JD021785, 2014. a
Mckay, M. D., Beckman, R. J., and Conover, W. J.: A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code, Technometrics, 42, 55–61, https://doi.org/10.1080/00401706.2000.10485979, 2000. a
Mercado-Bettín, D.: ISIMIP_Local_Lakes, GitHub [code], https://github.com/icra/ISIMIP_Local_Lakes, last access: 24 February 2025. a
Mesman, J. and Feldbauer, J.: aemon-j/ISIMIP3_LER: revision, Zenodo [code], https://doi.org/10.5281/zenodo.13165427, 2025. a
Mironov, D. V.: Parameterization of lakes in numerical weather prediction. Description of a lake model, Technical Report 11, Deutscher Wetterdienst, Offenbach am Main, Germany, https://www.dwd.de/EN/ourservices/cosmo_technical_reports/pdf_einzelbaende/2019_11.pdf?__blob=publicationFile&v=2 (last access: 24 February 2025), 2008. a
Moore, T., Mesman, J., Feldbauer, J., Ladwig, R., Pilla, R., Read, J. S., Venkiteswaran, J., and Delany, A.: aemon-j/LakeEnsemblR: LakeEnsemblR v1.0.0, Version 1.0, Zenodo [code], https://doi.org/10.5281/zenodo.4146899, 2020. a
Moore, T. N., Mesman, J. P., Ladwig, R., Feldbauer, J., Olsson, F., Pilla, R. M., Shatwell, T., Venkiteswaran, J. J., Delany, A. D., Dugan, H., Rose, K. C., and Read, J. S.: LakeEnsemblR: An R package that facilitates ensemble modelling of lakes, Environ. Modell. Softw., 143, 105101, https://doi.org/10.1016/j.envsoft.2021.105101, 2021. a, b, c
O'Reilly, C. M., Sharma, S., Gray, D. K., Hampton, S. E., Read, J. S., Rowley, R. J., Schneider, P., Lenters, J. D., McIntyre, P. B., Kraemer, B. M., Weyhenmeyer, G. A., Straile, D., Dong, B., Adrian, R., Allan, M. G., Anneville, O., Arvola, L., Austin, J., Bailey, J. L., Baron, J. S., Brookes, J. D., de Eyto, E., Dokulil, M. T., Hamilton, D. P., Havens, K., Hetherington, A. L., Higgins, S. N., Hook, S., Izmest'eva, L. R., Jöhnk, K. D., Kangur, K., Kasprzak, P., Kumagai, M., Kuusisto, E., Leshkevich, G., Livingstone, D. M., MacIntyre, S., May, L., Melack, J. M., Mueller-Navarra, D. C., Naumenko, M., Noges, P., Noges, T., North, R. P., Plisnier, P.-D., Rigosi, A., Rimmer, A., Rogora, M., Rudstam, L. G., Rusak, J. A., Salmaso, N., Samal, N. R., Schindler, D. E., Schladow, S. G., Schmid, M., Schmidt, S. R., Silow, E., Soylu, M. E., Teubner, K., Verburg, P., Voutilainen, A., Watkinson, A., Williamson, C. E., and Zhang, G.: Rapid and highly variable warming of lake surface waters around the globe, Geophys. Res. Lett., 42, 10773–10781, https://doi.org/10.1002/2015gl066235, 2015. a
Piccolroaz, S., Zhu, S., Ladwig, R., Carrea, L., Oliver, S., Piotrowski, A., Ptak, M., Shinohara, R., Sojka, M., Woolway, R.-I., and Zhu, D. Z.: Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects, Rev. Geophys., 62, e2023RG000816, https://doi.org/10.1029/2023RG000816, 2024. a, b, c
Pilla, R. M., Williamson, C. E., Adamovich, B. V., Adrian, R., Anneville, O., Chandra, S., Colom-Montero, W., Devlin, S. P., Dix, M. A., Dokulil, M. T., Gaiser, E. E., Girdner, S. F., Hambright, K. D., Hamilton, D. P., Havens, K., Hessen, D. O., Higgins, S. N., Huttula, T. H., Huuskonen, H., Isles, P. D. F., Joehnk, K. D., Jones, I. D., Keller, W. B., Knoll, L. B., Korhonen, J., Kraemer, B. M., Leavitt, P. R., Lepori, F., Luger, M. S., Maberly, S. C., Melack, J. M., Melles, S. J., Muller-Navarra, D. C., Pierson, D. C., Pislegina, H. V., Plisnier, P. D., Richardson, D. C., Rimmer, A., Rogora, M., Rusak, J. A., Sadro, S., Salmaso, N., Saros, J. E., Saulnier-Talbot, E., Schindler, D. E., Schmid, M., Shimaraeva, S. V., Silow, E. A., Sitoki, L. M., Sommaruga, R., Straile, D., Strock, K. E., Thiery, W., Timofeyev, M. A., Verburg, P., Vinebrooke, R. D., Weyhenmeyer, G. A., and Zadereev, E.: Deeper waters are changing less consistently than surface waters in a global analysis of 102 lakes, Scientific Reports, 10, 20514, https://doi.org/10.1038/s41598-020-76873-x, 2020. a
Plischke, E., Borgonovo, E., and Smith, C. L.: Global sensitivity measures from given data, Eur. J. Oper. Res., 226, 536–550, https://doi.org/10.1016/j.ejor.2012.11.047, 2013. a, b
Read, J. S., Winslow, L. A., Hansen, G. J. A., Van Den Hoek, J., Hanson, P. C., Bruce, L. C., and Markfort, C. D.: Simulating 2368 temperate lakes reveals weak coherence in stratification phenology, Ecol. Model., 291, 142–150, https://doi.org/10.1016/j.ecolmodel.2014.07.029, 2014. a
Rodi, W.: Turbulence models and their application in hydraulics, International Association for Hydraulic Reasearch (IAHR), Monograph Series, IAHR, Delft, 104 pp., ISBN: 9789021270029, 1984. a
Saltelli, A., Tarantola, S., and Campolongo, F.: Sensitivity Analysis as an Ingredient of Modeling, Stat. Sci., 15, 377–395, https://www.jstor.org/stable/2676831 (last access: 24 February 2025), 2000. a
Scavia, D., Wang, Y.-C., Obenour, D. R., Apostel, A., Basile, S. J., Kalcic, M. M., Kirchhoff, C. J., Miralha, L., Muenich, R. L., and Steiner, A. L.: Quantifying uncertainty cascading from climate, watershed, and lake models in harmful algal bloom predictions, Sci. Total Environ., 759, 143487, https://doi.org/10.1016/j.scitotenv.2020.143487, 2021. a
Staehr, P. A., Bade, D., Van de Bogert, M. C., Koch, G. R., Williamson, C., Hanson, P., Cole, J. J., and Kratz, T.: Lake metabolism and the diel oxygen technique: state of the science, Limnol. Oceanogr.-Meth., 8, 628–644, https://doi.org/10.4319/lom.2010.8.0628, 2010. a
Stepanenko, V., Jöhnk, K. D., Machulskaya, E., Perroud, M., Subin, Z., Nordbo, A., Mammarella, I., and Mironov, D.: Simulation of surface energ fluxes and statification of a small boral lake by a set of one-dimensional models, Tellus A, 66, 21389, https://doi.org/10.3402/tellusa.v66.21389, 2014. a
Tan, S.-Q., Guo, H.-F., Liao, C.-H., Ma, J.-H., Tan, W.-Z., Peng, W.-Y., and Fan, J.-Z.: Collocation-analyzed multi-source ensembled wind speed data in lake district: a case study in Dongting Lake of China, Frontiers in Environmental Science, 11, 1287595, https://doi.org/10.3389/fenvs.2023.1287595, 2024. a
Thomas, R. Q., Figueiredo, R. J., Daneshmand, V., Bookout, B. J., Puckett, L. K., and Carey, C. C.: A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time, Water Resourc. Res., 56, e2019WR026138, https://doi.org/10.1029/2019WR026138, 2020. a
Umlauf, L., Bolding, K., and Burchard, H.: GOTM – Scientific Documentation, Leibniz-Institute for Baltic Sea Research, https://gotm.net/portfolio/documentation/ (last access: 24 February 2025), 2005. a
Vanderkelen, I., van Lipzig, N. P. M., Lawrence, D. M., Droppers, B., Golub, M., Gosling, S. N., Janssen, A. B. G., Marcé, R., Schmied, H. M., Perroud, M., Pierson, D., Pokhrel, Y., Satoh, Y., Schewe, J., Seneviratne, S. I., Stepanenko, V. M., Tan, Z., Woolway, R. I., and Thiery, W.: Global heat uptake by inland waters, Geophys. Res. Lett., 47, e2020GL087867, https://doi.org/10.1029/2020GL087867, 2020. a
Weber, M., Rinke, K., Hipsey, M., and Boehrer, B.: Optimizing withdrawal from drinking water reservoirs to reduce downstream temperature pollution and reservoir hypoxia, J. Environ. Manage., 197, 96–105, https://doi.org/10.1016/j.jenvman.2017.03.020, 2017. a
Weinstock, J.: Energy dissipation rates of turbulence in the stable free atmosphere, J. Atmos. Sci., 38, 880–883, https://doi.org/10.1175/1520-0469(1981)038<0880:EDROTI>2.0.CO;2, 1981. a
Woolway, R. I. and Merchant, C. J.: Worldwide alteration of lake mixing regimes in response to climate change, Nat. Geosci., 12, 271–276, https://doi.org/10.1038/s41561-019-0322-x, 2019. a
Woolway, R. I., Denfeld, B., Tan, Z., Jansen, J., Weyhenmeyer, G. A., and La Fuente, S.: Winter inverse lake stratification under historic and future climate change, Limnol. Oceanogr. Lett., 7, 302–311, https://doi.org/10.1002/lol2.10231, 2021a. a
Woolway, R. I., Sharma, S., Weyhenmeyer, G. A., Debolskiy, A., Golub, M., Mercado-Bettín, D., Perroud, M., Stepanenko, V., Tan, Z., Grant, L., Ladwig, R., Mesman, J., Moore, T. N., Shatwell, T., Vanderkelen, I., Austin, J. A., DeGasperi, C. L., Dokulil, M., La Fuente, S., Mackay, E. B., Schladow, S. G., Watanabe, S., Marcé, R., Pierson, D. C., Thiery, W., and Jennings, E.: Phenological shifts in lake stratification under climate change, Nat. Commun., 12, 2318, https://doi.org/10.1038/s41467-021-22657-4, 2021b. a
Wüest, A. and Lorke, A.: SMALL-SCALE HYDRODYNAMICS IN LAKES, Annu. Rev. Fluid Mech., 35, 373–412, https://doi.org/10.1146/annurev.fluid.35.101101.161220, 2003. a
Wüest, A., Piepke, G., and Van Senden, D. C.: Turbulent kinetic energy balance as a tool for estimating vertical diffusivity in wind‐forced stratified waters, Limnol. Oceanogr., 45, 1388–1400, https://doi.org/10.4319/lo.2000.45.6.1388, 2000. a
Yeates, P. and Imberger, J.: Pseudo two‐dimensional simulations of internal and boundary fluxes in stratified lakes and reservoirs, International Journal of River Basin Management, 1, 297–319, https://doi.org/10.1080/15715124.2003.9635214, 2003. a
Zhuang, Q., Guo, M., Melack, J. M., Lan, X., Tan, Z., Oh, Y., and Leung, L. R.: Current and Future Global Lake Methane Emissions: A Process‐Based Modeling Analysis, J. Geophys. Res.-Biogeo., 128, e2022JG007137, https://doi.org/10.1029/2022jg007137, 2023. a
Short summary
Models help to understand natural systems and are used to predict changes based on scenarios (e.g., climate change). To simulate water temperature and deduce impacts on water quality in lakes, 1D lake models are often used. There are several such models that differ regarding their assumptions and mathematical process description. This study examines the performance of four such models on a global dataset of 73 lakes and relates their performance to the model structure and lake characteristics.
Models help to understand natural systems and are used to predict changes based on scenarios...