@misc{12230,
  abstract     = {{Model ensembles have several benefits compared to single-model applications but are not frequently used within the lake modelling community. Setting up and running multiple lake models can be challenging and time consuming, despite the many similarities between the existing models (forcing data, hypsograph, etc.). Here we present an R package, LakeEnsemblR, that facilitates running ensembles of five different vertical one-dimensional hydrodynamic lake models (FLake, GLM, GOTM, Simstrat, MyLake). The package requires input in a standardised format and a single configuration file. LakeEnsemblR formats these files to the input required by each model, and provides functions to run and calibrate the models. The outputs of the different models are compiled into a single file, and several post-processing operations are supported. LakeEnsemblR's workflow standardisation can simplify model benchmarking and uncertainty quantification, and improve collaborations between scientists. We showcase the successful application of LakeEnsemblR for two different lakes.}},
  author       = {{Moore, Tadhg N. and Mesman, Jorrit P. and Ladwig, Robert and Feldbauer, Johannes and Olsson, Freya and Pilla, Rachel M. and Shatwell, Tom and Venkiteswaran, Jason J. and Delany, Austin D. and Dugan, Hilary and Rose, Kevin C. and Read, Jordan S.}},
  booktitle    = {{Environmental modelling & software with environment data news}},
  issn         = {{1873-6726}},
  keywords     = {{Ensemble modeling, Vertical one-dimensional lake model, R package, Calibration, Thermal structure, Hydrodynamics}},
  publisher    = {{Elsevier BV}},
  title        = {{{LakeEnsemblR: An R package that facilitates ensemble modelling of lakes}}},
  doi          = {{10.1016/j.envsoft.2021.105101}},
  volume       = {{143}},
  year         = {{2021}},
}

@misc{12239,
  abstract     = {{The modelling community has identified challenges for the integration and assessment of lake models due to the diversity of modelling approaches and lakes. In this study, we develop and assess a one-dimensional lake model and apply it to 32 lakes from a global observatory network. The data set included lakes over broad ranges in latitude, climatic zones, size, residence time, mixing regime and trophic level. Model performance was evaluated using several error assessment metrics, and a sensitivity analysis was conducted for nine parameters that governed the surface heat exchange and mixing efficiency. There was low correlation between input data uncertainty and model performance and predictions of temperature were less sensitive to model parameters than prediction of thermocline depth and Schmidt stability. The study provides guidance to where the general model approach and associated assumptions work, and cases where adjustments to model parameterisations and/or structure are required.}},
  author       = {{Bruce, Louise C. and Frassl, Marieke A. and Arhonditsis, George B. and Gal, Gideon and Hamilton, David P. and Hanson, Paul C. and Hetherington, Amy L. and Melack, John M. and Read, Jordan S. and Rinke, Karsten and Rigosi, Anna and Trolle, Dennis and Winslow, Luke and Adrian, Rita and Ayala, Ana I. and Bocaniov, Serghei A. and Boehrer, Bertram and Boon, Casper and Brookes, Justin D. and Bueche, Thomas and Busch, Brendan D. and Copetti, Diego and Cortés, Alicia and de Eyto, Elvira and Elliott, J. Alex and Gallina, Nicole and Gilboa, Yael and Guyennon, Nicolas and Huang, Lei and Kerimoglu, Onur and Lenters, John D. and MacIntyre, Sally and Makler-Pick, Vardit and McBride, Chris G. and Moreira, Santiago and Özkundakci, Deniz and Pilotti, Marco and Rueda, Francisco J. and Rusak, James A. and Samal, Nihar R. and Schmid, Martin and Shatwell, Tom and Snorthheim, Craig and Soulignac, Frédéric and Valerio, Giulia and van der Linden, Leon and Vetter, Mark and Vinçon-Leite, Brigitte and Wang, Junbo and Weber, Michael and Wickramaratne, Chaturangi and Woolway, R. Iestyn and Yao, Huaxia and Hipsey, Matthew R.}},
  booktitle    = {{Environmental modelling & software with environment data news }},
  issn         = {{1873-6726}},
  keywords     = {{Lake model, Stratification, GLM, Model assessment, Global observatory data, Network science}},
  number       = {{4}},
  pages        = {{274--291}},
  publisher    = {{Elsevier Science}},
  title        = {{{A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global observatory network}}},
  doi          = {{10.1016/j.envsoft.2017.11.016}},
  volume       = {{102}},
  year         = {{2018}},
}