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MyLake—A multi-year lake simulation model code suitable for uncertainty and sensitivity analysis simulations

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  • Saloranta, Tuomo M.
  • Andersen, Tom

Abstract

Uncertainty and sensitivity analysis provide powerful means to enhance the performance of the models, to increase the transparency and credibility of the model results, and to allow the decision-maker to judge whether the model results are sufficiently accurate and precise to support decision-making or not. Uncertainty and sensitivity analysis of numerical models usually require hundreds, thousands, or even more, repeated model runs (e.g., in Monte Carlo simulation) as well as access to change the model parameters and variables on each model run. Consequently, many model codes, although widely used and well-formulated, are not well-suited for uncertainty and sensitivity analysis due to too long model execution time or due to lack of suitable interface to change model parameters and variables in an automated way. In this paper, we describe in details a lake model code, which aims to combine good simulation capabilities with efficient model execution time and easy application of numerical uncertainty and sensitivity analysis techniques. This model code is called MyLake (Multi-year Lake simulation model) and it is a one-dimensional process-based model code for simulation of daily vertical distribution of lake water temperature and thus density stratification, evolution of seasonal lake ice and snow cover, sediment–water interactions, and phosphorus-phytoplankton dynamics. After giving a detailed technical description of the different processes and algorithms included in the current version 1.2 of the MyLake model code, we present some results from a model application example including an Extended Fourier Amplitude Sensitivity Test (Extended FAST) sensitivity analysis and a simple Monte Carlo simulation based uncertainty analysis. Finally we discuss the performance of the MyLake code, especially in connection with numerical uncertainty and sensitivity analysis techniques.

Suggested Citation

  • Saloranta, Tuomo M. & Andersen, Tom, 2007. "MyLake—A multi-year lake simulation model code suitable for uncertainty and sensitivity analysis simulations," Ecological Modelling, Elsevier, vol. 207(1), pages 45-60.
  • Handle: RePEc:eee:ecomod:v:207:y:2007:i:1:p:45-60
    DOI: 10.1016/j.ecolmodel.2007.03.018
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    1. Dibike, Yonas & Marshall, Rebecca & de Rham, Laurent, 2024. "Climatic sensitivity of seasonal ice-cover, water temperature and biogeochemical cycling in Lake 239 of the Experimental Lakes Area (ELA), Ontario, Canada," Ecological Modelling, Elsevier, vol. 489(C).
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    3. Moe, S. Jannicke & Haande, Sigrid & Couture, Raoul-Marie, 2016. "Climate change, cyanobacteria blooms and ecological status of lakes: A Bayesian network approach," Ecological Modelling, Elsevier, vol. 337(C), pages 330-347.
    4. R. Iestyn Woolway & Pille Meinson & Peeter Nõges & Ian D. Jones & Alo Laas, 2017. "Atmospheric stilling leads to prolonged thermal stratification in a large shallow polymictic lake," Climatic Change, Springer, vol. 141(4), pages 759-773, April.
    5. Wang, Fugui & Mladenoff, David J. & Forrester, Jodi A. & Keough, Cindy & Parton, William J., 2013. "Global sensitivity analysis of a modified CENTURY model for simulating impacts of harvesting fine woody biomass for bioenergy," Ecological Modelling, Elsevier, vol. 259(C), pages 16-23.
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    7. Barton, D.N. & Saloranta, T. & Moe, S.J. & Eggestad, H.O. & Kuikka, S., 2008. "Bayesian belief networks as a meta-modelling tool in integrated river basin management -- Pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin," Ecological Economics, Elsevier, vol. 66(1), pages 91-104, May.
    8. Ratté-Fortin, Claudie & Chokmani, Karem & El Alem, Anas & Laurion, Isabelle, 2022. "A regional model to predict the occurrence of natural events: Application to phytoplankton blooms in continental waterbodies," Ecological Modelling, Elsevier, vol. 473(C).
    9. Jacobs, Bas & Tobi, Hilde & Hengeveld, Geerten M., 2024. "Linking error measures to model questions," Ecological Modelling, Elsevier, vol. 487(C).
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    11. Solomon Gebre & Netra Timalsina & Knut Alfredsen, 2014. "Some Aspects of Ice-Hydropower Interaction in a Changing Climate," Energies, MDPI, vol. 7(3), pages 1-15, March.
    12. Sadykova, Dinara & Skurdal, Jostein & Sadykov, Alexander & Taugbol, Trond & Hessen, Dag O., 2009. "Modelling crayfish population dynamics using catch data: A size-structured model," Ecological Modelling, Elsevier, vol. 220(20), pages 2727-2733.
    13. Janssen, Annette B.G. & Teurlincx, Sven & Beusen, Arthur H.W. & Huijbregts, Mark A.J. & Rost, Jasmijn & Schipper, Aafke M. & Seelen, Laura M.S. & Mooij, Wolf M. & Janse, Jan H., 2019. "PCLake+: A process-based ecological model to assess the trophic state of stratified and non-stratified freshwater lakes worldwide," Ecological Modelling, Elsevier, vol. 396(C), pages 23-32.
    14. Weissenberger, Sebastian & Lucotte, Marc & Houel, Stéphane & Soumis, Nicolas & Duchemin, Éric & Canuel, René, 2010. "Modeling the carbon dynamics of the La Grande hydroelectric complex in northern Quebec," Ecological Modelling, Elsevier, vol. 221(4), pages 610-620.
    15. Silvia Bossi & Luciano Blasi & Giacomo Cupertino & Ramiro dell’Erba & Angelo Cipollini & Saverio De Vito & Marco Santoro & Girolamo Di Francia & Giuseppe Marco Tina, 2024. "Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies," Sustainability, MDPI, vol. 16(19), pages 1-26, September.
    16. Guo Chen & Zhongyu Guo & Chihiro Yoshimura, 2023. "Integration of Photodegradation Process of Organic Micropollutants to a Vertically One-Dimensional Lake Model," Sustainability, MDPI, vol. 15(3), pages 1-17, January.

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