Articles | Volume 28, issue 8
https://doi.org/10.5194/hess-28-1791-2024
© Author(s) 2024. 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-28-1791-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Timing of spring events changes under modelled future climate scenarios in a mesotrophic lake
Department of Ecology and Genetics, Uppsala University, Uppsala, 75236, Sweden
Inmaculada C. Jiménez-Navarro
Department of Civil Engineering, Catholic University of San Antonio, Guadalupe, 30107, Spain
Ana I. Ayala
Department of Ecology and Genetics, Uppsala University, Uppsala, 75236, Sweden
Javier Senent-Aparicio
Department of Civil Engineering, Catholic University of San Antonio, Guadalupe, 30107, Spain
Dennis Trolle
WaterITech, Døjsøvej 1, Skanderborg, 8660, Denmark
Don C. Pierson
Department of Ecology and Genetics, Uppsala University, Uppsala, 75236, Sweden
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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.
Harriet L. Wilson, Ana I. Ayala, Ian D. Jones, Alec Rolston, Don Pierson, Elvira de Eyto, Hans-Peter Grossart, Marie-Elodie Perga, R. Iestyn Woolway, and Eleanor Jennings
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Lakes are often described in terms of vertical layers. The
epilimnionrefers to the warm surface layer that is homogeneous due to mixing. The depth of the epilimnion can influence air–water exchanges and the vertical distribution of biological variables. We compared various methods for defining the epilimnion layer and found large variability between methods. Certain methods may be better suited for applications such as multi-lake comparison and assessing the impact of climate change.
Cited articles
Adrian, R., Gerten, D., Huber, V., Wagner, C., and Schmidt, S. R.: Windows of change: temporal scale of analysis is decisive to detect ecosystem responses to climate change, Mar. Biol., 159, 2533–2542, https://doi.org/10.1007/s00227-012-1938-1, 2012.
Anneville, O., Souissi, S., Ibanez, F., Ginot, V., Druart, J. C., and Angeli, N.: Temporal mapping of phytoplankton assemblages in Lake Geneva: annual and interannual changes in their patterns of succession, Limnol. Oceanogr., 47, 1355–1366, 2002.
Beare, D. and McKenzie, E.: Connecting ecological and physical time-series: the potential role of changing seasonality, Mar. Ecol. Prog. Ser., 178, 307–309, https://doi.org/10.3354/meps178307, 1999.
Berger, S. A., Diehl, S., Stibor, H., Sebastian, P., and Scherz, A.: Separating effects of climatic drivers and biotic feedbacks on seasonal plankton dynamics: no sign of trophic mismatch, Freshwater Biol., 59, 2204–2220, https://doi.org/10.1111/fwb.12424, 2014.
Bieger, K., Arnold, J. G., Rathjens, H., White, M. J., Bosch, D. D., Allen, P. M., Volk, M., and Srinivasan, R.: Introduction to SWAT+, a completely restructured version of the soil and water assessment tool, J. Am. Water Resour. Assoc., 53, 115–130, https://doi.org/10.1111/1752-1688.12482, 2017.
Cavaliere, E., Fournier, I. B., Hazuková, V., Rue, G. P., Sadro, S., Berger, S. A., Cotner, J. B., Dugan, H. A., Hampton, S. E., Lottig, N. R., McMeans, B. C., Ozersky, T., Powers, S. M., Rautio, M., and O'Reilly, C. M.: The Lake Ice Continuum Concept: Influence of Winter Conditions on Energy and Ecosystem Dynamics, J. Geophys. Res.-Biogeo., 126, e2020JG006165, https://doi.org/10.1029/2020jg006165, 2021.
Chen, W., Nielsen, A., Andersen, T. K., Hu, F., Chou, Q., Søndergaard, M., Jeppesen, E., and Trolle, D.: Modeling the Ecological Response of a Temporarily Summer-Stratified Lake to Extreme Heatwaves, Water, 12, 94, https://doi.org/10.3390/w12010094, 2020.
Cortés, A., MacIntyre, S., and Sadro, S.: Flowpath and retention of snowmelt in an ice-covered arctic lake, Limnol. Oceanogr., 62, 2023–2044, https://doi.org/10.1002/lno.10549, 2017.
Donnelly, A., Caffarra, A., and O'Neill, B. F.: A review of climate-driven mismatches between interdependent phenophases in terrestrial and aquatic ecosystems, Int. J. Biometeorol., 55, 805–817, https://doi.org/10.1007/s00484-011-0426-5, 2011.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
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.
Findlay, H. S., Yool, A., Nodale, M., and Pitchford, J. W.: Modelling of autumn plankton bloom dynamics, J. Plankt. Res., 28, 209–220, https://doi.org/10.1093/plankt/fbi114, 2006.
Fink, G., Wessels, M., and Wüest, A.: Flood frequency matters: Why climate change degrades deep-water quality of peri-alpine lakes, J. Hydrol., 540, 457–468, https://doi.org/10.1016/j.jhydrol.2016.06.023, 2016.
Gronchi, E., Jöhnk, K. D., Straile, D., Diehl, S., and Peeters, F.: Local and continental-scale controls of the onset of spring phytoplankton blooms: Conclusions from a proxy-based model, Global Change Biol., 27, 1976–1990, https://doi.org/10.1111/gcb.15521, 2021.
Gronchi, E., Straile, D., Diehl, S., Jöhnk, K. D., and Peeters, F.: Impact of climate warming on phenological asynchrony of plankton dynamics across Europe, Ecol. Lett., 26, 717–728, https://doi.org/10.1111/ele.14190, 2023.
Hebert, M. P., Beisner, B. E., Rautio, M., and Fussmann, G. F.: Warming winters in lakes: Later ice onset promotes consumer overwintering and shapes springtime planktonic food webs, P. Natl. Acad. Sci. USA, 118, e2114840118, https://doi.org/10.1073/pnas.2114840118, 2021.
Helsel, D. R., Hirsch, R. M., Ryberg, K. R., Archfield, S. A., and Gilroy, E. J.: Chapter 3 of Section A, Statistical Analysis Book 4, Hydrologic Analysis and Interpretation [Supersedes USGS Techniques of Water-Resources Investigations, book 4, chap. A3, version 1.1.], in: Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, edited by: Helsel, D. R. and Hirsch, R. M., USGS, 458 pp., https://doi.org/10.3133/tm4a3, 2020.
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. Model. Softw., 128, 104697, https://doi.org/10.1016/j.envsoft.2020.104697, 2020.
Hrycik, A. R., Isles, P. D. F., Adrian, R., Albright, M., Bacon, L. C., Berger, S. A., Bhattacharya, R., Grossart, H. P., Hejzlar, J., Hetherington, A. L., Knoll, L. B., Laas, A., McDonald, C. P., Merrell, K., Nejstgaard, J. C., Nelson, K., Noges, P., Paterson, A. M., Pilla, R. M., Robertson, D. M., Rudstam, L. G., Rusak, J. A., Sadro, S., Silow, E. A., Stockwell, J. D., Yao, H., Yokota, K., and Pierson, D. C.: Earlier winter/spring runoff and snowmelt during warmer winters lead to lower summer chlorophyll-a in north temperate lakes, Global Change Biol., 27, 4615–4629, https://doi.org/10.1111/gcb.15797, 2021.
Huisman, J., van Oostveen, P., and Weissing, F. J.: Critical depth and critical turbulence: two different mechanisms for the development of phytoplankton blooms, Limnol. Oceanogr., 44, 1781–1787, https://doi.org/10.4319/lo.1999.44.7.1781, 1999.
IPCC: Climate Change 2021: The Physical Science Basis, in: Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, IPCC, Geneva, Switzerland, https://doi.org/10.1017/9781009157896, 2021.
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, Global Change Biol., 29, 1009–1023, https://doi.org/10.1111/gcb.16525, 2023.
Jeppesen, E., Mehner, T., Winfield, I. J., Kangur, K., Sarvala, J., Gerdeaux, D., Rask, M., Malmquist, H. J., Holmgren, K., Volta, P., Romo, S., Eckmann, R., Sandström, A., Blanco, S., Kangur, A., Ragnarsson Stabo, H., Tarvainen, M., Ventelä, A.-M., Søndergaard, M., Lauridsen, T. L., and Meerhoff, M.: Impacts of climate warming on the long-term dynamics of key fish species in 24 European lakes, Hydrobiologia, 694, 1–39, https://doi.org/10.1007/s10750-012-1182-1, 2012.
Jiménez-Navarro, I. C., Mesman, J. P., Pierson, D., Trolle, D., Nielsen, A., and Senent-Aparicio, J.: Application of an integrated catchment-lake model approach for simulating effects of climate change on lake inputs and biogeochemistry, Sci. Total Environ., 885, 163946, https://doi.org/10.1016/j.scitotenv.2023.163946, 2023.
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.
Kong, X., Ghaffar, S., Determann, M., Friese, K., Jomaa, S., Mi, C., Shatwell, T., Rinke, K., and Rode, M.: Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change, Water Res., 221, 118721, https://doi.org/10.1016/j.watres.2022.118721, 2022.
Ladwig, R., Appling, A. P., Delany, A., Dugan, H. A., Gao, Q., Lottig, N., Stachelek, J., and Hanson, P. C.: Long-term change in metabolism phenology in north temperate lakes, Limnol. Oceanogr., 67, 1502–1521, https://doi.org/10.1002/lno.12098, 2022.
Li, X., Peng, S., Xi, Y., Woolway, R. I., and Liu, G.: Earlier ice loss accelerates lake warming in the Northern Hemisphere, Nat. Commun., 13, 5156, https://doi.org/10.1038/s41467-022-32830-y, 2022.
Lyons, J., Rypel, A. L., Rasmussen, P. W., Burzynski, T. E., Eggold, B. T., Myers, J. T., Paoli, T. J., and McIntyre, P. B.: Trends in the Reproductive Phenology of two Great Lakes Fishes, Trans. Am. Fish. Soc., 144, 1263–1274, https://doi.org/10.1080/00028487.2015.1082502, 2015.
Magee, M. R. and Wu, C. H.: Response of water temperatures and stratification to changing climate in three lakes with different morphometry, Hydrol. Earth Syst. Sci., 21, 6253–6274, https://doi.org/10.5194/hess-21-6253-2017, 2017.
Meis, S., Thackeray, S. J., and Jones, I. D.: Effects of recent climate change on phytoplankton phenology in a temperate lake, Freshwater Biol., 54, 1888–1898, https://doi.org/10.1111/j.1365-2427.2009.02240.x, 2009.
Mesman, J. P., Jiménez-Navarro, I. C., Ayala, A. I., Senent-Aparicio, J., Trolle, D., and Pierson, D. C.: Timing of spring events changes under modelled future climate scenarios in a mesotrophic lake, WorkflowHub [code and data set], https://doi.org/10.48546/workflowhub.workflow.511.5, 2024.
Moras, S., Ayala, A. I., and Pierson, D. C.: Historical modelling of changes in Lake Erken thermal conditions, Hydrol. Earth Syst. Sci., 23, 5001–5016, https://doi.org/10.5194/hess-23-5001-2019, 2019.
Patakamuri, S. K. and O'Brien, N.: modifiedmk: Modified Versions of Mann Kendall and Spearman's Rho Trend Tests, R package version 1.6, CRAN [code], https://CRAN.R-project.org/package=modifiedmk (last access: 12 April 2024) 2021.
Peeters, F., Straile, D., Lorke, A., and Livingstone, D. M.: Earlier onset of the spring phytoplankton bloom in lakes of the temperate zone in a warmer climate, Global Change Biol., 13, 1898–1909, https://doi.org/10.1111/j.1365-2486.2007.01412.x, 2007a.
Peeters, F., Straile, D., Lorke, A., and Ollinger, D.: Turbulent mixing and phytoplankton spring bloom development in a deep lake, Limnol. Oceanogr., 52, 286–298, https://doi.org/10.4319/lo.2007.52.1.0286, 2007b.
Piccolroaz, S., Healey, N. C., Lenters, J. D., Schladow, S. G., Hook, S. J., Sahoo, G. B., and Toffolon, M.: On the predictability of lake surface temperature using air temperature in a changing climate: A case study for Lake Tahoe (U.S.A.), Limnol. Oceanogr., 63, 243–261, https://doi.org/10.1002/lno.10626, 2018.
Prowse, T. D. and Brown, K.: Hydro-ecological effects of changing Arctic river and lake ice covers: a review, Hydrol. Res., 41, 454–461, https://doi.org/10.2166/nh.2010.142, 2010.
R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing [code], https://www.R-project.org/ (last access: 12 April 2024), 2022.
Rouse, W. R., Douglas, M. S. V., Hecky, R. E., Hershey, A. E., Kling, G. W., Lesack, L., Marsh, P., McDonald, M., Nicholson, B. J., Roulet, N. T., and Smol, J. P.: Effects of Climate Change on the Freshwaters of Arctic and Subarctic North America, Hydrol. Process., 11, 873–902, https://doi.org/10.1002/(sici)1099-1085(19970630)11:8<873::Aid-hyp510>3.0.Co;2-6, 1997.
Runkel, R. and D Cicco, L.: rloadest: River Load Estimation, R package version 0.4.5, GitHub [code], https://github.com/USGS-R/rloadest (last access: 12 April 2024), 2017.
Runkel, R. L., Crawford, C. G., and Cohn, T. A.: Load Estimator (LOADEST): A FORTRAN program for estimating constituent loads in streams and rivers2328-7055, Report Series title: Techniques and Methods Book 4, Chap. A5, USGS, https://doi.org/10.3133/tm4A5, 2004.
Schmidt, W.: Über die Temperatur- und Stabilitätsverhältnisse Von Seen, Geograf. Ann., 10, 145–177, https://doi.org/10.1080/20014422.1928.11880475, 1928.
Schnedler-Meyer, N. A., Andersen, T. K., Hu, F. R. S., Bolding, K., Nielsen, A., and Trolle, D.: Water Ecosystems Tool (WET) 1.0 – a new generation of flexible aquatic ecosystem model, Geosci. Model Dev., 15, 3861–3878, https://doi.org/10.5194/gmd-15-3861-2022, 2022.
Schwefel, R., Gaudard, A., Wüest, A., and Bouffard, D.: Effects of climate change on deepwater oxygen and winter mixing in a deep lake (Lake Geneva): Comparing observational findings and modeling, Water Resour. Res., 52, 8811–8826, https://doi.org/10.1002/2016WR019194, 2016.
Sharma, S., Blagrave, K., Magnuson, J. J., O'Reilly, C. M., Oliver, S., Batt, R. D., Magee, M. R., Straile, D., Weyhenmeyer, G. A., Winslow, L., and Woolway, R. I.: Widespread loss of lake ice around the Northern Hemisphere in a warming world, Nat. Clim. Change, 9, 227–231, https://doi.org/10.1038/s41558-018-0393-5, 2019.
Shatwell, T., Thiery, W., and Kirillin, G.: Future projections of temperature and mixing regime of European temperate lakes, Hydrol. Earth Syst. Sci., 23, 1533–1551, https://doi.org/10.5194/hess-23-1533-2019, 2019.
SITES Data Portal: SITES Data Portal – Swedish Infrastructure for Ecosystem Science, SITES Data Portal [data set], https://doi.org/10.17616/R31NJNC5, 2022.
Sommer, U., Adrian, R., De Senerpont Domis, L., Elser, J. J., Gaedke, U., Ibelings, B., Jeppesen, E., Lürling, M., Molinero, J. C., Mooij, W. M., van Donk, E., and Winder, M.: Beyond the Plankton Ecology Group (PEG) Model: Mechanisms Driving Plankton Succession, Annu. Rev. Ecol. Evol. Systemat., 43, 429–448, https://doi.org/10.1146/annurev-ecolsys-110411-160251, 2012.
Straile, D.: Meteorological forcing of plankton dynamics in a large and deep continental European lake, Oecologia, 122, 44–50, https://doi.org/10.1007/PL00008834, 2000.
Straile, D.: Food webs in lakes – seasonal dynamics and the impact of climate variability, in: Aquatic food webs. An ecosystem approach, edited by: Belgrano, A., Scharler, U. M., Dunne, J., and Ulanowicz, R. E., Oxford University Press, New York, USA, 41–50, https://doi.org/10.1093/acprof:oso/9780198564836.003.0005, 2005.
Thackeray, S. J., Jones, I. D., and Maberly, S. C.: Long-term change in the phenology of spring phytoplankton: species-specific responses to nutrient enrichment and climatic change, J. Ecol., 96, 523–535, https://doi.org/10.1111/j.1365-2745.2008.01355.x, 2008.
Thackeray, S. J., Sparks, T. H., Frederiksen, M., Burthe, S., Bacon, P. J., Bell, J. R., Botham, M. S., Brereton, T. M., Bright, P. W., Carvalho, L., Clutton-Brock, T. I. M., Dawson, A., Edwards, M., Elliott, J. M., Harrington, R., Johns, D., Jones, I. D., Jones, J. T., Leech, D. I., Roy, D. B., Scott, W. A., Smith, M., Smithers, R. J., Winfield, I. J., and Wanless, S.: Trophic level asynchrony in rates of phenological change for marine, freshwater and terrestrial environments, Global Change Biol., 16, 3304–3313, https://doi.org/10.1111/j.1365-2486.2010.02165.x, 2010.
Thayne, M. W., Kraemer, B. M., Mesman, J. P., Ibelings, B. W., and Adrian, R.: Antecedent lake conditions shape resistance and resilience of a shallow lake ecosystem following extreme wind storms, Limnol. Oceanog., 67, S101–S120, https://doi.org/10.1002/lno.11859, 2021.
Toffolon, M. and Piccolroaz, S.: A hybrid model for river water temperature as a function of air temperature and discharge, Environ. Res. Lett., 10, 114011, https://doi.org/10.1088/1748-9326/10/11/114011, 2015.
Umlauf, L., Burchard, H., and Bolding, K.: GOTM: Sourcecode and Test Case Documentation, https://gotm.net/manual/stable/pdf/a4.pdf (last access: 12 April 2024), 2005.
Weyhenmeyer, G. A., Blenckner, T., and Pettersson, K.: Changes of the plankton spring outburst related to the North Atlantic Oscillation, Limnol. Oceanogr., 44, 1788–1792, https://doi.org/10.4319/lo.1999.44.7.1788, 1999.
Weyhenmeyer, G. A., Peter, H., and Willén, E.: Shifts in phytoplankton species richness and biomass along a latitudinal gradient – consequences for relationships between biodiversity and ecosystem functioning, Freshwater Biol., 58, 612–623, https://doi.org/10.1111/j.1365-2427.2012.02779.x, 2013.
Weyhenmeyer, G. A., Obertegger, U., Rudebeck, H., Jakobsson, E., Jansen, J., Zdorovennova, G., Bansal, S., Block, B. D., Carey, C. C., Doubek, J. P., Dugan, H., Erina, O., Fedorova, I., Fischer, J. M., Grinberga, L., Grossart, H. P., Kangur, K., Knoll, L. B., Laas, A., Lepori, F., Meier, J., Palshin, N., Peternell, M., Pulkkanen, M., Rusak, J. A., Sharma, S., Wain, D., and Zdorovennov, R.: Towards critical white ice conditions in lakes under global warming, Nat. Commun., 13, 4974, https://doi.org/10.1038/s41467-022-32633-1, 2022.
Wilson, H. L., Ayala, A. I., Jones, I. D., Rolston, A., Pierson, D., de Eyto, E., Grossart, H.-P., Perga, M.-E., Woolway, R. I., and Jennings, E.: Variability in epilimnion depth estimations in lakes, Hydrol. Earth Syst. Sci., 24, 5559–5577, https://doi.org/10.5194/hess-24-5559-2020, 2020.
Winder, M. and Schindler, D. E.: Climatic effects on the phenology of lake processes, Global Change Biol., 10, 1844–1856, https://doi.org/10.1111/j.1365-2486.2004.00849.x, 2004.
Winder, M. and Sommer, U.: Phytoplankton response to a changing climate, Hydrobiologia, 698, 5–16, https://doi.org/10.1007/s10750-012-1149-2, 2012.
Winslow, L. A., Read, J. S., Woolway, R. I., Brentrup, J. A., Leach, T., Zwart, J., Albers, S., and Collinge, D.: rLakeAnalyzer: Lake Physics Tools, R package version 1.11.4.1, CRAN [code], https://CRAN.R-project.org/package=rLakeAnalyzer (last access: 12 April 2024), 2019.
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, 2021.
Yang, Y., Stenger-Kovács, C., Padisák, J., and Pettersson, K.: Effects of winter severity on spring phytoplankton development in a temperate lake (Lake Erken, Sweden), Hydrobiologia, 780, 47–57, https://doi.org/10.1007/s10750-016-2777-8, 2016a.
Yang, Y., Pettersson, K., and Padisák, J.: Repetitive baselines of phytoplankton succession in an unstably stratified temperate lake (Lake Erken, Sweden): a long-term analysis, Hydrobiologia, 764, 211–227, https://doi.org/10.1007/s10750-015-2314-1, 2016b.
Zhan, Q., de Senerpont Domis, L. N., Lürling, M., Marcé, R., Heuts, T. S., and Teurlincx, S.: Process-based modeling for ecosystem service provisioning: Non-linear responses to restoration efforts in a quarry lake under climate change, J. Environ. Manage., 348, 119163, https://doi.org/10.1016/j.jenvman.2023.119163, 2023.
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.
Spring events in lakes are key processes for ecosystem functioning. We used a coupled...