Reviews Open access

Modelling physical and ecological processes in medium-to-large deep European perialpine lakes: a review

Authors

Published
13 October 2021
Section
Reviews
DOI
10.4081/jlimnol.2021.2041

Abstract

In this paper we review a significant sample of the modelling studies carried out on medium-to-large deep European perialpine lakes (MLDEPLs). The reviewed bibliographic corpus was obtained querying Elsevier’s Scopus® database with a tailored search string on 8 January 2021. Results were filtered, accepting only journal papers written in English dealing with natural lakes having surface area > 10 km2. A list of 75 works was obtained, published between 1986 and 2021. Most studies have been carried out on Swiss lakes (44 out of 75 papers), Lake Geneva being the most investigated environment. A significant positive correlation was found between lake surface area and volume and the number of dedicated papers, suggesting that scientific attention is higher for environments characterised by large dimensions and relevant socio-economic interests. Both the number of papers and their citation count have experienced an exponential growth in time, pointing to a rising interest in quantitative modelling applications, but also to the increasing availability and ease of use of numerical modelling tools. Among the 75 selected papers, 55 employ a hydrodynamic driver, used alone or coupled with an ecological module, while the remnant 20 works adopt an ecological-only model. Among the papers employing hydrodynamic models, the use of three-dimensional (3D) drivers is surprisingly slightly more frequent (28 papers) than that of one-dimensional (1D) ones (26 papers), with most 3D applications having been published in the last 2011-2020 decade (24 papers). This reflects the interest on the hydrodynamic processes leading to the observed spatial heterogeneities in the biochemical properties of the MLDEPLs. However, coupling of ecological modules with 3D hydrodynamic drivers, to directly simulate these phenomena, is still restricted (2 papers) compared to that of 1D hydrodynamic drivers (8 papers), due to calibration and computational difficulties, which could be strongly reduced by future research achievements. Nevertheless, 1D models allow performing long-term prognoses considering multiple climate change and watershed management scenarios, due to their much smaller computational burden. The largest group of works dealing with ecological-only models (6 papers) is dedicated to applications of phosphorus budget models, which can above all be used to forecast variations in lake productivity in response to changes in the availability of the limiting nutrient.

Graphical abstract

2041-graphical-abstract800.jpg

Keywords

References

  1. Amadori M, Morini G, Piccolroaz S, Toffolon M, 2020. Involving citizens in hydrodynamic research: A combined local knowledge - numerical experiment on Lake Garda, Italy. Sci. Total Environ. 722:137720. DOI: https://doi.org/10.1016/j.scitotenv.2020.137720
  2. Amadori M, Piccolroaz S, Giovannini L, Zardi D, Toffolon M, 2018. Wind variability and Earth’s rotation as drivers of transport in a deep, elongated subalpine lake: The case of Lake Garda. J. Limnol. 77:505-521. DOI: https://doi.org/10.4081/jlimnol.2018.1814
  3. Ambrosetti W, Barbanti L, 1999. Deep water warming in lakes: an indicator of climatic change. J. Limnol. 58:1-9. DOI: https://doi.org/10.4081/jlimnol.1999.1
  4. Ambrosetti W, Barbanti L, Rolla A, Castellano L, Sala N, 2012. Hydraulic paths and estimation of the real residence time of the water in Lago Maggiore (N. Italy): application of massless markers transported in 3D motion fields. J. Limnol. 71:23-33. DOI: https://doi.org/10.4081/jlimnol.2012.e2
  5. Anneville O, Gammeter S, Straile D, 2005. Phosphorus decrease and climate variability: mediators of synchrony in phytoplankton changes among European peri‐alpine lakes. Freshwater Biol. 50:1731-1746. DOI: https://doi.org/10.1111/j.1365-2427.2005.01429.x
  6. Anneville O, Souissi S, Gammeter S, Straile D, 2004. Seasonal and inter‐annual scales of variability in phytoplankton assemblages: comparison of phytoplankton dynamics in three peri‐alpine lakes over a period of 28 years. Freshwater Biol. 49:98-115. DOI: https://doi.org/10.1046/j.1365-2426.2003.01167.x
  7. Baracchini T, Hummel S, Verlaan M, Cimatoribus A, Wüest A, Bouffard D, 2020. An automated calibration framework and open source tools for 3D lake hydrodynamic models. Environ. Modell. Softw. 134:104787. DOI: https://doi.org/10.1016/j.envsoft.2020.104787
  8. Barontini S, Grossi G, Kouwen N, Maran S, Scaroni P, Ranzi R, 2009. Impacts of climate change scenarios on runoff regimes in the southern Alps. Hydrol. Earth Syst. Sci. Discuss. 6:3089-3141. DOI: https://doi.org/10.5194/hessd-6-3089-2009
  9. Baudo R, 2002. Pollution and recovery of Lake Orta (Italy): Resilience at work? Aquat. Ecosyst. Health 5:71-78. DOI: https://doi.org/10.1080/14634980260199972
  10. Bodrato M, Vione D, 2014. APEX (Aqueous Photochemistry of Environmentally occurring Xenobiotics): a free software tool to predict the kinetics of photochemical processes in surface waters. Environ. Sci.-Proc. Imp. 16:732-740. DOI: https://doi.org/10.1039/C3EM00541K
  11. Bonvin F, Razmi AM, Barry DA, Kohn T, 2013. Micropollutant dynamics in Vidy Bay - A coupled hydrodynamic-photolysis model to assess the spatial extent of ecotoxicological risk. Environ. Sci. Technol. 47:9207-9216. DOI: https://doi.org/10.1021/es401294c
  12. Bresciani M, Cazzaniga I, Austoni M, Sforzi T, Buzzi F, Morabito G, Giardino C, 2018. Mapping phytoplankton blooms in deep subalpine lakes from Sentinel-2A and Landsat-8. Hydrobiologia 824:197-214. DOI: https://doi.org/10.1007/s10750-017-3462-2
  13. Bruce LC, Frassl MA, Arhonditsis GB, et al., 2018. A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global observatory network. Environ. Modell. Softw. 102:274-291. DOI: https://doi.org/10.1016/j.envsoft.2017.11.016
  14. Bryhn AC, Girel C, Paolini G, Jacquet S, 2010. Predicting future effects from nutrient abatement and climate change on phosphorus concentrations in Lake Bourget, France. Ecol. Model. 221:1440-1450. DOI: https://doi.org/10.1016/j.ecolmodel.2010.02.013
  15. Bueche T, Hamilton DP, Vetter M, 2017. Using the General Lake Model (GLM) to simulate water temperatures and ice cover of a medium-sized lake: a case study of Lake Ammersee, Germany. Environ. Earth Sci. 76:461. DOI: https://doi.org/10.1007/s12665-017-6790-7
  16. Bueche T, Vetter M, 2014a. Influence of groundwater inflow on water temperature simulations of Lake Ammersee using a one-dimensional hydrodynamic lake model. Erdkunde 68:19-31. DOI: https://doi.org/10.3112/erdkunde.2014.01.03
  17. Bueche T, Vetter M, 2014b. Simulating water temperatures and stratification of a pre-alpine lake with a hydrodynamic model: calibration and sensitivity analysis of climatic input parameters. Hydrol. Process. 28:1450-1464. DOI: https://doi.org/10.1002/hyp.9687
  18. Bueche T, Vetter M, 2015. Future alterations of thermal characteristics in a medium-sized lake simulated by coupling a regional climate model with a lake model. Clim. Dyn. 44:371-384. DOI: https://doi.org/10.1007/s00382-014-2259-5
  19. Bueche T, Wenk M, Poschlod B, Giadrossich F, Pirastru M, Vetter M, 2020. GlmGUI v1.0: an R-based graphical user interface and toolbox for GLM (General Lake Model) simulations. Geoscientific Model Dev. 13:565-580. DOI: https://doi.org/10.5194/gmd-13-565-2020
  20. Buerge IJ, Poiger T, Müller MD, Buser H-R, 2003. Caffeine, an anthropogenic marker for wastewater contamination of surface waters. Environ. Sci. Technol. 37:691-700. DOI: https://doi.org/10.1021/es020125z
  21. Burger DF, Hamilton DP, Pilditch CA, 2008. Modelling the relative importance of internal and external nutrient loads on water column nutrient concentrations and phytoplankton biomass in a shallow polymictic lake. Ecol. Model. 211: 411-423. DOI: https://doi.org/10.1016/j.ecolmodel.2007.09.028
  22. Buwalda F, 2020. Suitability of shallow water solving methods for GPU acceleration. M.Sc. Thesis in Applied Mathematics, Delft University of Technology, Delft, Netherlands: 146 pp.
  23. Calderoni A, Tartari GA, 2000. Evolution of the water chemistry of Lake Orta after liming. J. Limnol. 60:69-78. DOI: https://doi.org/10.4081/jlimnol.2001.69
  24. Canuto VM, Howard A, Cheng Y, Dubovikov MS, 2001. Ocean Turbulence. Part I: One-Point Closure Model—Momentum and Heat Vertical Diffusivities. J. Phys. Oceanogr. 31:1413-1426. DOI: https://doi.org/10.1175/1520-0485(2001)031<1413:OTPIOP>2.0.CO;2
  25. Caramatti I, Peeters F, Hamilton D, Hofmann H, 2020. Modelling inter-annual and spatial variability of ice cover in a temperate lake with complex morphology. Hydrol. Process. 34:691-704. DOI: https://doi.org/10.1002/hyp.13618
  26. Carraro E, Guyennon N, Hamilton D, Valsecchi L, Manfredi EC, Viviano G, Salerno F, Tartari G, Copetti D, 2012. Coupling high-resolution measurements to a three-dimensional lake model to assess the spatial and temporal dynamics of the cyanobacterium Planktothrix rubescens in a medium-sized lake. Hydrobiologia 698:77-95. DOI: https://doi.org/10.1007/s10750-012-1096-y
  27. Casulli V, Cheng RT, 1992. Semi‐implicit finite difference methods for three‐dimensional shallow water flow. Int. J. Numer. Meth. Fl. 15:629-648. DOI: https://doi.org/10.1002/fld.1650150602
  28. CH2011, 2011. Swiss climate change scenarios CH2011. C2SM, MeteoSwiss, ETH, NCCR Climate, and OcCC, Zurich, Switzerland: 88 pp.
  29. Chapra SC, Reckow KH, 1983. Engineering Approaches for Lake Management ‒ Volume 2: Mechanistic Modelling. Butterworth Publishers, Boston, USA: 492 pp.
  30. Christensen V, Pauly D, 1992. ECOPATH II — a software for balancing steady-state ecosystem models and calculating network characteristics. Ecol. Model. 61:169-185. DOI: https://doi.org/10.1016/0304-3800(92)90016-8
  31. Cole TM., Wells SA, 2013. CE-QUAL-W2: a two-dimensional, laterally averaged, hydrodynamic and water quality model, Version 3.71. User Manual, U.S. Army Corps of Engineers, Washington.
  32. Copetti D, Carniato L, Crise A, Guyennon N, Palmeri L, Pisacane G, Struglia M, Tartari G, 2013. Impacts of climate change on water quality, p. 307-332. In: A. Navarra and L. Tubiana (eds.), Regional Assessment of Climate Change in the Mediterranean. Springer. DOI: https://doi.org/10.1007/978-94-007-5781-3_10
  33. Copetti D, Guyennon N, Buzzi F, 2020. Generation and dispersion of chemical and biological gradients in a large-deep multi-basin lake (Lake Como, north Italy): The joint effect of external drivers and internal wave motions. Sci. Total Environ. 749:141587. DOI: https://doi.org/10.1016/j.scitotenv.2020.141587
  34. Costanza R, Duplisea D, Kautsky U, 1998. Ecological Modelling on modelling ecological and economic systems with STELLA. Ecol. Model. 110:1-4. DOI: https://doi.org/10.1016/S0304-3800(98)00039-8
  35. Cuypers Y, Vinçon-Leite B, Groleau A, Tassin B, Humbert J-F, 2011. Impact of internal waves on the spatial distribution of Planktothrix rubescens (cyanobacteria) in an alpine lake. ISME J. 5:580-589. DOI: https://doi.org/10.1038/ismej.2010.154
  36. Cyr H, 2017. Winds and the distribution of nearshore phytoplankton in a stratified lake. Water Res. 122:114-127. DOI: https://doi.org/10.1016/j.watres.2017.05.066
  37. Danis P-A, von Grafenstein U, Masson-Delmotte V, Planton S, Gerdeaux D, Moisselin J-M, 2004. Vulnerability of two European lakes in response to future climatic changes. Geophys. Res. Lett. 31:L21507. DOI: https://doi.org/10.1029/2004GL020833
  38. Derot J, Yajima H, Jacquet S, 2020. Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva. Harmful Algae 99:101906. DOI: https://doi.org/10.1016/j.hal.2020.101906
  39. Dietzel A, Mieleitner J, Kardaetz S, Reichert P, 2013. Effects of changes in the driving forces on water quality and plankton dynamics in three Swiss lakes – long-term simulations with BELAMO. Freshwater Biol. 58:10-35. DOI: https://doi.org/10.1111/fwb.12031
  40. Dietzel A, Reichert P, 2012. Calibration of computationally demanding and structurally uncertain models with an application to a lake water quality model. Environ. Modell. Softw. 38:129-146. DOI: https://doi.org/10.1016/j.envsoft.2012.05.007
  41. Dissanayake P, Hofmann H, Peeters F, 2019. Comparison of results from two 3D hydrodynamic models with field data: internal seiches and horizontal currents. Inland Waters 9:239-260. DOI: https://doi.org/10.1080/20442041.2019.1580079
  42. Dueri S, Castro-Jiménez J, Zaldívar J-M, 2009. Modelling the influence of thermal stratification and complete mixing on the distribution and fluxes of polychlorinated biphenyls in the water column of Ispra Bay (Lake Maggiore). Chemosphere 75:1266-1272. DOI: https://doi.org/10.1016/j.chemosphere.2009.01.066
  43. Enz CA, Müller R, Mbwenemo Bia M, Heeb J, 2002. A population dynamics model for evaluating mortality factors in whitefish (Coregonus suidteri) larvae in Lake Hallwil. Adv. Limnol. 57:343-358.
  44. Fenocchi A, Petaccia G, Sibilla S, 2016. Modelling flows in shallow (fluvial) lakes with prevailing circulations in the horizontal plane: limits of 2D compared to 3D models. J. Hydroinform. 18:928-945. DOI: https://doi.org/10.2166/hydro.2016.033
  45. Fenocchi A, Rogora M, Marchetto A, Sibilla S, Dresti C, 2020. Model simulations of the ecological dynamics induced by climate and nutrient load changes for deep subalpine Lake Maggiore (Italy/Switzerland). J. Limnol. 79:221-237. DOI: https://doi.org/10.4081/jlimnol.2020.1963
  46. Fenocchi A, Rogora M, Morabito G, Marchetto A, Sibilla S, Dresti C, 2019. Applicability of a one-dimensional coupled ecological-hydrodynamic numerical model to future projections in a very deep large lake (Lake Maggiore, Northern Italy/Southern Switzerland). Ecol. Model. 392:38-51. DOI: https://doi.org/10.1016/j.ecolmodel.2018.11.005
  47. Fenocchi A, Rogora M, Sibilla S, Ciampittiello M, Dresti C, 2018. Forecasting the evolution in the mixing regime of a deep subalpine lake under climate change scenarios through numerical modelling (Lake Maggiore, Northern Italy/Southern Switzerland). Clim. Dyn. 51:3521-3536. DOI: https://doi.org/10.1007/s00382-018-4094-6
  48. Fenocchi A, Rogora M, Sibilla S, Dresti C, 2017. Relevance of inflows on the thermodynamic structure and on the modeling of a deep subalpine lake (Lake Maggiore, Northern Italy/Southern Switzerland). Limnologica 63:42-56. DOI: https://doi.org/10.1016/j.limno.2017.01.006
  49. Finger D, Wüest A, Bossard P, 2013. Effects of oligotrophication on primary production in peri-alpine lakes. Water Resour. Res. 49:4700-4710. DOI: https://doi.org/10.1002/wrcr.20355
  50. Fornarelli R, Galelli S, Castelletti A, Antenucci JP, Marti CL, 2013. An empirical modeling approach to predict and understand phytoplankton dynamics in a reservoir affected by interbasin water transfers. Water Resour. Res. 49:3626-3641. DOI: https://doi.org/10.1002/wrcr.20268
  51. Fragoso CR, Motta Marques DML, Collischonn W, Tucci CEM, van Nes EH, 2008. Modelling spatial heterogeneity of phytoplankton in Lake Mangueira, a large shallow subtropical lake in South Brazil. Ecol. Model. 219:125-137. DOI: https://doi.org/10.1016/j.ecolmodel.2008.08.004
  52. Franchini F, Lepori F, Bruder A, 2017. Improving estimates of primary production in lakes: a test and a case study from a peri-alpine lake (Lake Lugano). Inland Waters 7:77-87. DOI: https://doi.org/10.1080/20442041.2017.1294351
  53. Fringer OB, Gerritsen M, Street RL, 2006. An unstructured-grid, finite-volume, nonhydrostatic, parallel coastal ocean simulator. Ocean Model. 14:139-173. DOI: https://doi.org/10.1016/j.ocemod.2006.03.006
  54. Gallina N, Beniston M, Jacquet S, 2017. Estimating future cyanobacterial occurrence and importance in lakes: a case study with Planktothrix rubescens in Lake Geneva. Aquat. Sci. 79:249-263. DOI: https://doi.org/10.1007/s00027-016-0494-z
  55. Gallina N, Salmaso N, Morabito G, Beniston M., 2013. Phytoplankton configuration in six deep lakes in the peri-Alpine region: are the key drivers related to eutrophication and climate? Aquat. Ecol. 47:177-193. DOI: https://doi.org/10.1007/s10452-013-9433-4
  56. García-Nieto PJ, García-Gonzalo E, Alonso Fernández JR, Díaz Muñiz C, 2018. Predictive modelling of eutrophication in the Pozón de la Dolores lake (Northern Spain) by using an evolutionary support vector machines approach. J. Math. Biol. 76:817-840. DOI: https://doi.org/10.1007/s00285-017-1161-2
  57. Gaudard A, Räman Vinnä L, Bärenbold F, Schmid M, Bouffard D, 2019a. 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. DOI: https://doi.org/10.5194/gmd-12-3955-2019
  58. Gaudard A, Wüest A, Schmid M, 2019b. Using lakes and rivers for extraction and disposal of heat: Estimate of regional potentials. Renew. Energy 134:330-342. DOI: https://doi.org/10.1016/j.renene.2018.10.095
  59. Goudsmit G, Burchard H, Peeters, F, Wüest A, 2002. Application of k‐ɛ turbulence models to enclosed basins: The role of internal seiches. J. Geophys. Res. 107:23. DOI: https://doi.org/10.1029/2001JC000954
  60. Gulliver JS, Stefan HG, 1982. Lake phytoplankton model with destratification. J. Env. Eng. Div.-ASCE 108:864-882. DOI: https://doi.org/10.1061/JEEGAV.0001348
  61. Guyennon N, Valerio G, Salerno F, Pilotti M, Tartari G, Copetti D, 2014. Internal wave weather heterogeneity in a deep multi-basin subalpine lake resulting from wavelet transform and numerical analysis. Adv. Water Resour. 71:149-161. DOI: https://doi.org/10.1016/j.advwatres.2014.06.013
  62. Hamilton DP, Schladow SG, 1997. Prediction of water quality in lakes and reservoirs. Part I — Model description. Ecol. Model. 96:91-110. DOI: https://doi.org/10.1016/S0304-3800(96)00062-2
  63. Henderson-Sellers B, 1986. Calculating the surface energy balance for lake and reservoir modelling: A review. Rev. Geophys. 24:625-649. DOI: https://doi.org/10.1029/RG024i003p00625
  64. Hipsey MR, Bruce LC, Boon C, Busch B, Carey CC, Hamilton DP, Hanson PC, Read JS, De Sousa E, Weber M, Winslow LA, 2019. 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. DOI: https://doi.org/10.5194/gmd-12-473-2019
  65. Hipsey MR, Bruce LC, Hamilton DP, 2013. Aquatic EcoDynamics (AED) model library: science manual. AED Report. The University of Western Australia, Perth, Australia: 34 pp.
  66. Hodges BR, Imberger J, Saggio A, Winters KB, 2000. Modeling basin-scale internal waves in a stratified lake. Limnol. Oceanogr. 45:1603-1620. DOI: https://doi.org/10.4319/lo.2000.45.7.1603
  67. Huang J, Gao J, Hörmann G, 2012. Hydrodynamic-phytoplankton model for short-term forecasts of phytoplankton in Lake Taihu, China. Limnologica 42:7-18. DOI: https://doi.org/10.1016/j.limno.2011.06.003
  68. Huang J, Gao J, Hörmann G, Fohrer N, 2014. Modeling the effects of environmental variables on short-term spatial changes in phytoplankton biomass in a large shallow lake, Lake Taihu. Environ. Earth Sci. 72:3609-3621. DOI: https://doi.org/10.1007/s12665-014-3272-z
  69. Huszar VLM., Caraco NF, Roland F, Cole J, 2006. Nutrient–chlorophyll relationships in tropical–subtropical lakes: do temperate models fit? Biogeochemistry 79:239-250. DOI: https://doi.org/10.1007/s10533-006-9007-9
  70. Hutter K, 1984. Hydrodynamics of lakes. CISM Courses and Lectures No. 286, Springer-Verlag, Wien: 341 pp. DOI: https://doi.org/10.1007/978-3-7091-2634-9
  71. Ibelings BW, Vonk M, Los HFJ, van der Molen DT, Mooij WM, 2003. Fuzzy modeling of cyanobacterial surface water blooms: validation with NOAA-AVHRR satellite images. Ecol. Appl. 13:1456-1472. DOI: https://doi.org/10.1890/01-5345
  72. Imberger J, Loh I, Hebbert B, Patterson J, 1978. Dynamics of reservoir of medium size. J. Hydraul. Eng. Div.-ASCE 104:725-743. DOI: https://doi.org/10.1061/JYCEAJ.0004997
  73. Imberger J, Patterson JC, 1981. A dynamic reservoir simulation model – DYRESM: 5, p. 310-361. In: H.B. Fischer (ed.), Transport Models for Inland and Coastal Waters. Academic Press. DOI: https://doi.org/10.1016/B978-0-12-258152-6.50014-2
  74. Jacob D, Bärring L, Christensen O, Christensen J, Castro M, Déqué M, Giorgi F, Hagermann S, Hirshi M, Jones R, Kjellström E, Lenderink G, Rockel B, Sànchez E, Schär C, Seneviratne SI, Somot S, val Ulden A, van den Hurk B, 2007. An inter-comparison of regional climate models for Europe: model performance in present-day climate. Clim. Change 81:31-52. DOI: https://doi.org/10.1007/s10584-006-9213-4
  75. Janjua MY, Gerdeaux D, 2009. Preliminary trophic network analysis of subalpine Lake Annecy (France) using an Ecopath model. Knowl. Manag. Aquat. Ec. 392:02. DOI: https://doi.org/10.1051/kmae/2009008
  76. Joehnk KD, Umlauf L, 2001. Modelling the metalimnetic oxygen minimum in a medium sized alpine lake. Ecol. Model. 136:67-80. DOI: https://doi.org/10.1016/S0304-3800(00)00381-1
  77. Jung N-C, Popescu I, Kelderman P, Solomatine DP, Price RK, 2010. Application of model trees and other machine learning techniques for algal growth prediction in Yongdam reservoir, Republic of Korea. J. Hydroinf. 12:262-274. DOI: https://doi.org/10.2166/hydro.2009.004
  78. Jurado E, Zaldívar J-M, Marinov D, Dachs J, 2007. Fate of persistent organic pollutants in the water column: Does turbulent mixing matter? Mar. Pollut. Bull. 54:441-451. DOI: https://doi.org/10.1016/j.marpolbul.2006.11.028
  79. Kara EM, Hanson P, Hamilton D, et al., 2012. Time-scale dependence in numerical simulations: assessment of physical, chemical and biological predictions in a stratified lake at temporal scales of hours to months. Environ. Modell. Softw. 35:104-121. DOI: https://doi.org/10.1016/j.envsoft.2012.02.014
  80. Kerimoglu O, Jacquet S, Vinçon-Leite B, Lemaire BJ, Rimet F, Soulignac F, Trévisan D, Anneville O, 2017. Modelling the plankton groups of the deep, peri-alpine Lake Bourget. Ecol. Model. 359:415-433. DOI: https://doi.org/10.1016/j.ecolmodel.2017.06.005
  81. Kiefer I, Odermatt D, Anneville O, Wüest A, Bouffard D, 2015. Application of remote sensing for the optimization of in-situ sampling for monitoring of phytoplankton abundance in a large lake. Sci. Total Environ. 527-528:493-506. DOI: https://doi.org/10.1016/j.scitotenv.2015.05.011
  82. Koschel R, Benndorf J, Proft G, Recknagel F, 1983. Calcite precipitation as a natural control mechanism of eutrophication. Arch. Hydrobiol. 98:380-408.
  83. Krishna S, Ulloa HN, Kerimoglu O, Minaudo C, Anneville O, Wüest A, 2021. Model-based data analysis of the effect of winter mixing on primary production in a lake under reoligotrophication. Ecol. Model. 440:109401. DOI: https://doi.org/10.1016/j.ecolmodel.2020.109401
  84. Laborde S, Antenucci JP, Copetti D, Imberger J, 2010. Inflow intrusions at multiple scales in a large temperate lake. Limnol. Oceanogr. 55:1301-1312. DOI: https://doi.org/10.4319/lo.2010.55.3.1301
  85. Laborde S, Imberger J, Toussaint S, 2012. Contributions of local knowledge to the physical limnology of Lake Como, Italy. Proc. Natl. Acad. Sci. U.S.A. 109:6441-6445. DOI: https://doi.org/10.1073/pnas.1113740109
  86. Lemaire M, Guillard J, Anneville O, Lobry J, 2020. Major biomass fluctuations in lake food webs – An example in the peri-alpine Lake Annecy. J. Great Lakes Res. 46:798-812. DOI: https://doi.org/10.1016/j.jglr.2020.04.007
  87. Lepori F, Roberts JJ, 2015. Past and future warming of a deep European lake (Lake Lugano): What are the climatic drivers? J. Great Lakes Res. 41:973-981. DOI: https://doi.org/10.1016/j.jglr.2015.08.004
  88. Lepori F, Roberts JJ, 2017. Effects of internal phosphorus loadings and food-web structure on the recovery of a deep lake from eutrophication. J. Great Lakes Res. 43:255-264. DOI: https://doi.org/10.1016/j.jglr.2017.01.008
  89. Lesser GR, Roelvink, JA, van Kester JATM, Stelling GS, 2004. Development and validation of a three-dimensional morphological model. Coast. Eng. 51:883-915. DOI: https://doi.org/10.1016/j.coastaleng.2004.07.014
  90. Livingstone DM, 2003. Impact of secular climate change on the thermal structure of a large temperate Central European lake. Clim. Change 57:205-225. DOI: https://doi.org/10.1023/A:1022119503144
  91. Los FJ, Villars MT, Van der Tol MWM, 2008. A 3-dimensional primary production model (BLOOM/GEM) and its applications to the (southern) North Sea (coupled physical–chemical–ecological model). J. Marine Syst. 74:259-294. DOI: https://doi.org/10.1016/j.jmarsys.2008.01.002
  92. Luyten PJ, Jones JE, Proctor R, Tabor A, Tett P, Wild-Allen K, 1999. COHERENS – A coupled hydrodynamical-ecological model for regional and shelf areas: User documentation. MUMM Report, Management Unit of the Mathematical Models of the North Sea, Brussels, Belgium: 914 pp.
  93. Mari L, Biotto C, Decoene A, Bonaventura L, 2009. A coupled ecological-hydrodynamic model for the spatial distribution of sessile aquatic species in thermally forced basins. Ecol. Model. 220:2310-2324. DOI: https://doi.org/10.1016/j.ecolmodel.2009.05.012
  94. Mellor GL, Yamada T, 1982. Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. 20:851-875. DOI: https://doi.org/10.1029/RG020i004p00851
  95. Messager ML, Lehner B, Grill G, Nedeva I, Schmitt O, 2016. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7:13603. DOI: https://doi.org/10.1038/ncomms13603
  96. Minella M, Leoni B, Salmaso N, Savoye L, Sommaruga R, Vione D, 2016. Long-term trends of chemical and modelled photochemical parameters in four Alpine lakes. Sci. Total Environ. 541:247-256. DOI: https://doi.org/10.1016/j.scitotenv.2015.08.149
  97. Mirbach S, Lang U, 2018. Density-driven underflows with suspended solids in Lake Constance. J. Soils Sed. 18:3145-3152. DOI: https://doi.org/10.1007/s11368-017-1753-x
  98. Morillo S, Imberger J, Antenucci JP, Copetti D, 2009. Using impellers to distribute local nutrient loadings in a stratified lake: Lake Como, Italy. J. Hydraul. Eng.-ASCE 135:564-574. DOI: https://doi.org/10.1061/(ASCE)HY.1943-7900.0000048
  99. Moschet C, Götz C, Longrée P, Hollender J, Singer H, 2013. Multi-level approach for the integrated assessment of polar organic micropollutants in an international lake catchment: The example of Lake Constance. Environ. Sci. Technol. 47:7028-7036. DOI: https://doi.org/10.1021/es304484w
  100. Müller B, Gächter R, Wüest A, 2014. Accelerated water quality improvement during oligotrophication in peri-alpine lakes. Environ. Sci. Technol. 48:6671-6677. DOI: https://doi.org/10.1021/es4040304
  101. Neumann T, Fennel W, Kremp C, 2002. Experimental simulations with an ecosystem model of the Baltic Sea: A nutrient load reduction experiment. Glob. Biogeochem. Cycles 16:7. DOI: https://doi.org/10.1029/2001GB001450
  102. Nouchi V, Kutser T, Wüest A, Müller B, Odermatt D, Baracchini T, Bouffard D, 2019. Resolving biogeochemical processes in lakes using remote sensing. Aquat. Sci. 81:27. DOI: https://doi.org/10.1007/s00027-019-0626-3
  103. Nyholm N, 1978. A simulation model for phytoplankton growth and nutrient cycling in eutrophic, shallow lakes. Ecol. Model. 4:279-310. DOI: https://doi.org/10.1016/0304-3800(78)90011-X
  104. Nürnberg GK, 1998. Prediction of annual and seasonal phosphorus concentrations in stratified and polymictic lakes. Limnol. Oceanogr. 43:1544-1552. DOI: https://doi.org/10.4319/lo.1998.43.7.1544
  105. Oman G, 1982. [Das Verhalten des geschichteten Zürichsees unter äusseren Windlasten]. [Report in German]. Mitteilung Nr. 60 der Versuchsanstalt für Wasserbau, Hydrologie und Glaziologie, ETH Zürich, Zürich, Switzerland: 202 pp.
  106. Omlin M, Brun R, Reichert P, 2001a. Biogeochemical model of Lake Zürich: sensitivity, identifiability and uncertainty analysis. Ecol. Model. 141:105-123. DOI: https://doi.org/10.1016/S0304-3800(01)00257-5
  107. Omlin M, Reichert P, Forster R, 2001b. Biogeochemical model of Lake Zürich: model equations and results. Ecol. Model. 141:77-103. DOI: https://doi.org/10.1016/S0304-3800(01)00256-3
  108. Oveisy A, Boegman L, Imberger J, 2012. Three-dimensional simulation of lake and ice dynamics during winter. Limnol. Oceanogr. 57:43-57. DOI: https://doi.org/10.4319/lo.2012.57.1.0043
  109. Panizzuti M, Tartari G, 1995. pH simulation model in a mixed layer of a lacustrine environment (the case of Lake Orta). Ecol. Model. 78:37-49. DOI: https://doi.org/10.1016/0304-3800(94)00116-Y
  110. Patterson JC, Hamblin PF, 1988. Thermal simulation of a lake with winter ice cover. Limnol. Oceanogr. 33:323-338. DOI: https://doi.org/10.4319/lo.1988.33.3.0323
  111. Paerl HW, Paul VJ, 2012. Climate change: Links to global expansion of harmful cyanobacteria. Water Res. 46:1349-1363. DOI: https://doi.org/10.1016/j.watres.2011.08.002
  112. Peeters F, Livingstone DM, Goudsmit G-H, Kipfer R, Forster R, 2002. Modeling 50 years of historical temperature profiles in a large central European lake. Limnol. Oceanogr. 47:186-197. DOI: https://doi.org/10.4319/lo.2002.47.1.0186
  113. Perroud M, Goyette S, Martynov A, Beniston M, Anneville O, 2009. Simulation of multiannual thermal profiles in deep Lake Geneva: A comparison of one-dimensional lake models. Limnol. Oceanogr. 54:1574-1594. DOI: https://doi.org/10.4319/lo.2009.54.5.1574
  114. Piccolroaz S, Amadori M, Toffolon M, Dijkstra HA, 2019. Importance of planetary rotation for ventilation processes in deep elongated lakes: Evidence from Lake Garda (Italy). Sci. Rep. 9:8290. DOI: https://doi.org/10.1038/s41598-019-44730-1
  115. Pilotti M, Simoncelli S, Valerio G, 2014a. A simple approach to the evaluation of the actual water renewal time of natural stratified lakes. Water Resour. Res. 50:2830-2849. DOI: https://doi.org/10.1002/2013WR014471
  116. Pilotti M, Valerio G, Gregorini L, Milanesi L, Hogg CAR, 2014b. Study of tributary inflows in Lake Iseo with a rotating physical model. J. Limnol. 73:115-129. DOI: https://doi.org/10.4081/jlimnol.2014.772
  117. Pinardi M, Fenocchi A, Giardino C, Sibilla S, Bartoli M, Bresciani M, 2015. Assessing potential algal blooms in a shallow fluvial lake by combining hydrodynamic modelling and remote-sensed images. Water 7:1921-1942. DOI: https://doi.org/10.3390/w7051921
  118. Posch T, Köster O, Salcher MM, Pernthaler J, 2012. Harmful filamentous cyanobacteria favoured by reduced water turnover with lake warming. Nat. Clim. Change 2:809-813. DOI: https://doi.org/10.1038/nclimate1581
  119. Råman Vinnå L, Bouffard D, Wüest A, Girardclos S, Dubois N, 2020. Assessing subaquatic mass movement hazards: an integrated observational and hydrodynamic modelling approach. Water Resour. Manag. 34:4133-4146. DOI: https://doi.org/10.1007/s11269-020-02660-y
  120. Råman Vinnå L, Wüest A, Bouffard D, 2017. Physical effects of thermal pollution in lakes. Water Resour. Res. 53:3968-3987. DOI: https://doi.org/10.1002/2016WR019686
  121. Razmi AM, Barry DA, Bakhtyar R, Le Dantec N, Dastgheib A, Lemmin U, Wüest A, 2013. Current variability in a wide and open lacustrine embayment in Lake Geneva (Switzerland). J. Great Lakes Res. 39:455-465. DOI: https://doi.org/10.1016/j.jglr.2013.06.011
  122. Razmi AM, Barry DA, Lemmin U, Bonvin F, Kohn T, Bakhtyar R, 2014. Direct effects of dominant winds on residence and travel times in the wide and open lacustrine embayment: Vidy Bay (Lake Geneva, Switzerland). Aquat. Sci. 76:59-71. DOI: https://doi.org/10.1007/s00027-013-0321-8
  123. Reichert P, 1994. AQUASIM – A tool for simulation and data analysis of aquatic systems. Water Sci. Technol. 30:21-30. DOI: https://doi.org/10.2166/wst.1994.0025
  124. Riley MJ, Stefan HG, 1988. Minlake: A dynamic lake water quality simulation model. Ecol. Model. 43:155-182. DOI: https://doi.org/10.1016/0304-3800(88)90002-6
  125. Rinke K, Yeates P, Rothhaupt K-O, 2010. A simulation study of the feedback of phytoplankton on thermal structure via light extinction. Freshwater Biol. 55:1674-1693. DOI: https://doi.org/10.1111/j.1365-2427.2010.02401.x
  126. Rogora M, Buzzi F, Dresti C, Leoni B, Lepori F, Mosello R, Patelli M, Salmaso N, 2018. Climatic effects on vertical mixing and deep-water oxygen content in the subalpine lakes in Italy. Hydrobiologia 824:33-50. DOI: https://doi.org/10.1007/s10750-018-3623-y
  127. Rossi G, Premazzi G, Marengo G, 1986. Correlation of a lake eutrophication model to field experiments. Ecol. Model. 34:167-189. DOI: https://doi.org/10.1016/0304-3800(86)90002-5
  128. Saha GC, Felix M, Li J, Thring RW, 2011. Seasonal stratification effects on risk quantification of contaminant spreading in a warm monomictic lake under different hydrodynamic conditions: a case study in Lake Constance, Germany. Int. J. Risk Assess. Manag. 15:79-93. DOI: https://doi.org/10.1504/IJRAM.2011.041005
  129. Salmaso N, 2019. Effects of habitat partitioning on the distribution of bacterioplankton in deep lakes. Front. Microbiol. 10:2257. DOI: https://doi.org/10.3389/fmicb.2019.02257
  130. Salmaso N, Anneville O, Straile D, Viaroli P, 2018. European large perialpine lakes under anthropogenic pressures and climate change: present status, research gaps and future challenges. Hydrobiologia 824:1-32. DOI: https://doi.org/10.1007/s10750-018-3758-x
  131. Salmaso N, Buzzi F, Capelli C, Cerasino L, Leoni B, Lepori F, Rogora M, 2020. Responses to local and global stressors in the large southern perialpine lakes: Present status and challenges for research and management. J. Great Lakes Res. 46:752-766. DOI: https://doi.org/10.1016/j.jglr.2020.01.017
  132. Salmaso N, Buzzi F, Cerasino L, Garibaldi L, Leoni B, Morabito G, Rogora M, Simona M, 2014. Influence of atmospheric modes of variability on the limnological characteristics of large lakes south of the Alps: a new emerging paradigm. Hydrobiologia 731:31-48. DOI: https://doi.org/10.1007/s10750-013-1659-6
  133. Salmaso N, Morabito G, Mosello R, Garibaldi L, Simona M, Buzzi F, Ruggiu D, 2003. A synoptic study of phytoplankton in the deep lakes south of the Alps (lakes Garda, Iseo, Como, Lugano and Maggiore). J. Limnol. 62:207-227. DOI: https://doi.org/10.4081/jlimnol.2003.207
  134. Salmaso N, Mosello R, Garibaldi L, Decet F, Brizzio MC, Cordella P, 2003. Vertical mixing as a determinant of trophic status in deep lakes: a case study from two lakes south of the Alps (Lake Garda and Lake Iseo). J. Limnol. 62:33-41. DOI: https://doi.org/10.4081/jlimnol.2003.s1.33
  135. Salmaso N, Mosello R, 2010. Limnological research in the deep southern subalpine lakes: synthesis, directions and perspectives. Adv. Oceanogr. Limn. 1:29-66. DOI: https://doi.org/10.1080/19475721003735773
  136. Salvadè G, Stocker K, Trösch J, Zamboni F, 1992. Hydrodynamics of Lake Lugano. Aquat. Sci. 54:187-204. DOI: https://doi.org/10.1007/BF00878136
  137. Schauser I, Hupfer M, Brüggemann R, 2004. SPIEL—a model for phosphorus diagenesis and its application to lake restoration. Ecol. Model. 176:389-407. DOI: https://doi.org/10.1016/j.ecolmodel.2003.10.033
  138. Schauser I, Hupfer M, Brüggemann R, 2006. Sensitivity analysis with a phosphorus diagenesis model (SPIEL). Ecol. Model. 190:87-98. DOI: https://doi.org/10.1016/j.ecolmodel.2005.03.024
  139. Scheu KR, Fong D, Monismith SG, Fringer OB, 2018. Modeling sedimentation dynamics of sediment-laden river intrusions in a rotationally-influenced, stratified lake. Water Resour. Res. 54:4084-4107. DOI: https://doi.org/10.1029/2017WR021533
  140. Schlabing D, Frassl MA, Eder MM, Rinke K, Bàrdossy A, 2014. Use of a weather generator for simulating climate change effects on ecosystems: A case study on Lake Constance. Environ. Modell. Softw. 61:326-338. DOI: https://doi.org/10.1016/j.envsoft.2014.06.028
  141. Schwefel R, Steinsberger T, Bouffard D, Bryant LD, Müller B, Wüest A, 2018. Using small-scale measurements to estimate hypolimnetic oxygen depletion in a deep lake. Limnol. Oceanogr. 63:S54-S67. DOI: https://doi.org/10.1002/lno.10723
  142. Simons TJ, 1981. The seasonal climate of the Upper Ocean: data analysis and model development. Technical Report. National Water Research Institute, Burlington: 72 pp.
  143. Skamarock WC, Klemp JB, 2008. A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys. 227:3465-3485. DOI: https://doi.org/10.1016/j.jcp.2007.01.037
  144. Soulignac F, Danis P-A, Bouffard D, Chanudet V, Dambrine E, Guénand Y, Harmel T, Ibelings BW, Trevisan D, Uittenbogaard R, Anneville O, 2018. Using 3D modeling and remote sensing capabilities for a better understanding of spatio-temporal heterogeneities of phytoplankton abundance in large lakes. J. Great Lakes Res. 44:756-764. DOI: https://doi.org/10.1016/j.jglr.2018.05.008
  145. Spekat A, Kreienkamp F, Enke W, 2010. An impact-oriented classification method for atmospheric patterns. Phys. Chem. Earth 35:352-359. DOI: https://doi.org/10.1016/j.pce.2010.03.042
  146. Spraggs JD, Street RL, 1975. Three-dimensional simulation of thermally-influenced hydrodynamic flows. Technical Report Nr. 190, Department of Civil Engineering, Stanford University, Stanford: 339 pp.
  147. Stelling GS, Duinmeijer SPA, 2003. A staggered conservative scheme for every Froude number in rapidly varied shallow water flows. Int. J. Numer. Meth. Fl. 43:1329-1354. DOI: https://doi.org/10.1002/fld.537
  148. Straile D, Kerimoglu O, Peeters F, 2015. Trophic mismatch requires seasonal heterogeneity of warming. Ecology 96:2794-2805. DOI: https://doi.org/10.1890/14-0839.1
  149. Tiberti R, Caroni R, Cannata M, Lami A, Manca D, Strigaro D, Rogora M, 2021. Automated high frequency monitoring of Lake Maggiore through in situ sensors: system design, field test and data quality control. J. Limnol. 80:2011. DOI: https://doi.org/10.4081/jlimnol.2021.2011
  150. Tolotti M, Dubois N, Milan M, Perga M-E, Straile D, Lami A, 2018. Large and deep perialpine lakes: a paleolimnological perspective for the advance of ecosystem science. Hydrobiologia 824:291-321. DOI: https://doi.org/10.1007/s10750-018-3677-x
  151. Trolle D, Hamilton DP, Hipsey MR, et al., 2012. A community-based framework for aquatic ecosystem models. Hydrobiologia 683:25-34. DOI: https://doi.org/10.1007/s10750-011-0957-0
  152. Trolle D, Jørgensen TB, Jeppesen E, 2008a. Predicting the effects of reduced external nitrogen loading on the nitrogen dynamics and ecological state of deep Lake Ravn, Denmark, using the DYRESM–CAEDYM model. Limnologica 38:220-232. DOI: https://doi.org/10.1016/j.limno.2008.05.009
  153. Trolle D, Skovgaard H, Jeppesen E, 2008b. The Water Framework Directive: Setting the phosphorus loading target for a deep lake in Denmark using the 1D lake ecosystem model DYRESM–CAEDYM. Ecol. Model. 219:138-152. DOI: https://doi.org/10.1016/j.ecolmodel.2008.08.005
  154. Ulloa HN, Constantinescu G, Chang K, Horna-Munoz D, Sepúlveda Steiner O, Bouffard D, Wüest A, 2019. Hydrodynamics of a periodically wind-forced small and narrow stratified basin: a large-eddy simulation experiment. Environ. Fluid Mechan. 19:667-698. DOI: https://doi.org/10.1007/s10652-018-9645-1
  155. Ulrich MM, Imboden DM, Schwarzenbach RP, 1995. MASAS—A user-friendly simulation tool for modeling the fate of anthropogenic substances in lakes. Environ. Softw. 10:177-198. DOI: https://doi.org/10.1016/0266-9838(95)00012-A
  156. Valerio G, Cantelli A, Monti P, Leuzzi G, 2017. A modeling approach to identify the effective forcing exerted by wind on a prealpine lake surrounded by a complex topography. Water Resour. Res. 53:4036-4052. DOI: https://doi.org/10.1002/2016WR020335
  157. Valerio G, Pilotti M, Barontini S, Leoni B, 2015. Sensitivity of the multiannual thermal dynamics of a deep pre-alpine lake to climatic change. Hydrol. Process. 29:767-779. DOI: https://doi.org/10.1002/hyp.10183
  158. Vilhena LC, Marti CL, Imberger J, 2013. The importance of nonlinear internal waves in a deep subalpine lake: Lake Iseo, Italy. Limnol. Oceanogr. 58:1871-1891. DOI: https://doi.org/10.4319/lo.2013.58.5.1871
  159. Vinçon-Leite B, 1991. [Contribution de la modélisation mathématique à l’étude de la qualité de l’eau dans les lacs sub-alpins: le lac du Bouget (Savoie)]. [Ph.D. Thesis in French]. Thèse de doctorat en Sciences Appliquées, Ecole Nationale des Ponts et Chaussées, Paris: 274 pp.
  160. Vinçon-Leite B, Casenave C, 2019. Modelling eutrophication in lake ecosystems: A review. Sci. Total Environ. 651:2985-3001. DOI: https://doi.org/10.1016/j.scitotenv.2018.09.320
  161. Vinçon-Leite B, Lemaire BJ, Khac VT, Tassin B, 2014. Long-term temperature evolution in a deep sub-alpine lake, Lake Bourget, France: how a one-dimensional model improves its trend assessment. Hydrobiologia 731:49-64. DOI: https://doi.org/10.1007/s10750-014-1818-4
  162. Vinçon-Leite B, Tassin B, Jaquet J-M, 1995. Contribution of mathematical modeling to lake ecosystem understanding: Lake Bourget (Savoy, France). Hydrobiologia 300-301:433-442. DOI: https://doi.org/10.1007/BF00024485
  163. Wadzuk BM, Hodges BR, 2009. Hydrostatic versus nonhydrostatic Euler-equation modeling of nonlinear internal waves. J. Eng. Mech.-ASCE 135:1069-1080. DOI: https://doi.org/10.1061/(ASCE)0733-9399(2009)135:10(1069)
  164. Weinberger S, Vetter M, 2012. Using the hydrodynamic model DYRESM based on results of a regional climate model to estimate water temperature changes at Lake Ammersee. Ecol. Model. 244:38-48. DOI: https://doi.org/10.1016/j.ecolmodel.2012.06.016
  165. Weinberger S, Vetter M, 2014. Lake heat content and stability variation due to climate change: coupled regional climate model (REMO)-lake model (DYRESM) analysis. J. Limnol. 73:93-105. DOI: https://doi.org/10.4081/jlimnol.2014.668
  166. Wüest A, Bouffard D, Guillard J, Ibelings BW, Lavanchy S, Perga M-E, Pasche N, 2021. LéXPLORE: A floating laboratory on Lake Geneva offering unique lake research opportunities. Wiley Interdiscip. Rev. Water 8:e1544. DOI: https://doi.org/10.1002/wat2.1544
  167. Yankova Y, Neuenschwander S, Köster O, Posch T, 2017. Abrupt stop of deep water turnover with lake warming: Drastic consequences for algal primary producers. Sci. Rep. 7:13770. DOI: https://doi.org/10.1038/s41598-017-13159-9

Metrics

Citations and online attention from Dimensions, Altmetric and PlumX.

PlumX Metrics

License

Copyright (c) 2021 The Author(s)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.