A multi-lake comparative analysis of the General Lake Model (GLM): stress-testing across a global observatory network

Bruce, Louise C.; Frassl, Marieke A.; Arhonditsis, George B.; Gal, Gideon; Hamilton, David P.; Hanson, Paul C.; Hetherington, Amy L.; Melack, John M.; Read, Jordan S.; Rinke, Karsten; Rigosi, Anna; Trolle, Dennis; Winslow, Luke; Adrian, Rita; Ayala, Ana I.; Bocaniov, Serghei A.; Boehrer, Bertram; Boon, Casper; Brookes, Justin D.; Bueche, Thomas; Busch, Brendan D.; Copetti, Diego; Cortés, Alicia; de Eyto, Elvira; Elliott, J. Alex; Gallina, Nicole; Gilboa, Yael; Guyennon, Nicolas; Huang, Lei; Kerimoglu, Onur; Lenters, John D.; MacIntyre, Sally; Makler-Pick, Vardit; McBride, Chris G.; Moreira, Santiago; Özkundakci, Deniz; Pilotti, Marco; Rueda, Francisco J.; Rusak, James A.; Samal, Nihar R.; Schmid, Martin; Shatwell, Tom; Snorthheim, Craig; Soulignac, Frédéric; Valerio, Giulia; van der Linden, Leon; Vetter, Mark; Vinçon-Leite, Brigitte; Wang, Junbo; Weber, Michael; Wickramaratne, Chaturangi; Woolway, R. Iestyn; Yao, Huaxia; Hipsey, Matthew R.. 2018 A multi-lake comparative analysis of the General Lake Model (GLM): stress-testing across a global observatory network. Environmental Modelling & Software, 102. 274-291. https://doi.org/10.1016/j.envsoft.2017.11.016

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Official URL: http://dx.doi.org/10.1016/j.envsoft.2017.11.016

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.envsoft.2017.11.016
UKCEH and CEH Sections/Science Areas:	Water Resources (Science Area 2017-)
ISSN:	1364-8152
Additional Keywords:	lake model, stratification, GLM, model assessment, global observatory data, network science
NORA Subject Terms:	Ecology and Environment Hydrology
Date made live:	28 Mar 2018 13:49 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/519713

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.envsoft.2017.11.016

UKCEH and CEH Sections/Science Areas:

Water Resources (Science Area 2017-)

ISSN:

1364-8152

Additional Keywords:

lake model, stratification, GLM, model assessment, global observatory data, network science

NORA Subject Terms:

Ecology and Environment
Hydrology