LakeEnsemblR: An R package that facilitates ensemble modelling of lakes

Tadhg N. Moore; Jorrit P. Mesman; Robert Ladwig; Johannes Feldbauer; Freya Olsson; Rachel M. Pilla; Tom Shatwell; Jason J. Venkiteswaran; Austin D. Delany; Hilary Dugan; Kevin C. Rose; Jordan S. Read

doi:10.1016/j.envsoft.2021.105101

LakeEnsemblR: An R package that facilitates ensemble modelling of lakes

Tadhg N. Moore, Jorrit P. Mesman, Robert Ladwig, Johannes Feldbauer, Freya Olsson, Rachel M. Pilla, Tom Shatwell, Jason J. Venkiteswaran, Austin D. Delany, Hilary Dugan, Kevin C. Rose, Jordan S. Read

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

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.

Original language	English
Article number	105101
Journal	Environmental Modelling and Software
Volume	143
DOIs	https://doi.org/10.1016/j.envsoft.2021.105101
State	Published - Sep 2021
Externally published	Yes

Funding

J.F. was funded by the European Social Fund and co-financed by tax funds based on the budget approved by the members of the Saxon State Parliament. R.M.P. was funded by Sentinel North Research Internship Scholarship program for foreign students at Université Laval and student travel award support from GLEON . T.N.M. was funded by: the WATExR project which is part of ERA4CS , an ERA-NET initiated by JPI Climate, and funded by MINECO ( ES ), FORMAS ( SE ), BMBF ( DE ), EPA ( IE ), RCN ( NO ), and IFD ( DK ), with co-funding by the European Union (Grant number: 690,462 ) and also by NSF grants DEB-1926050 and DBI-1933016 . R.L. was funded through a National Science Foundation ABI development grant ( #DBI 1759865 ). J.J.V. was supported by a Natural Sciences and Engineering Research Council of Canada Discovery Grant ( RGPIN-2018-06389 ). K.C.R. was funded by National Science Foundation grants 1754265 , 1638704 , and 1761805 . J.P.M. was funded by the European Union'sHorizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement no. 722518 (MANTEL ITN). T.N.M. was funded by: the WATExR project which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by MINECO (ES), FORMAS (SE), BMBF (DE), EPA (IE), RCN (NO), and IFD (DK), with co-funding by the European Union (Grant number: 690,462) and also by NSF grants DEB-1926050 and DBI-1933016.J.P.M. was funded by the European Union'sHorizon 2020Research and Innovation Programme under the Marie Sk?odowska-Curie grant agreement no. 722518 (MANTEL ITN).R.L. was funded through a National Science Foundation ABI development grant (#DBI 1759865).J.F. was funded by the European Social Fund and co-financed by tax funds based on the budget approved by the members of the Saxon State Parliament.R.M.P. was funded by Sentinel North Research Internship Scholarship program for foreign students at Universit? Laval and student travel award support from GLEON.T.S. was supported by German Science Foundation grants DFG KI 853/13?1 and CDZ 1259.J.J.V. was supported by a Natural Sciences and Engineering Research Council of Canada Discovery Grant (RGPIN-2018-06389).K.C.R. was funded by National Science Foundation grants 1754265, 1638704, and 1761805. T.S. was supported by German Science Foundation grants DFG KI 853/13–1 and CDZ 1259 .

Funders	Funder number
European Union'sHorizon 2020 Research and Innovation Programme
IFD
Marie Skłodowska-Curie
RCN
Saxon State Parliament
Saxon State Parliament.R.M.P
National Science Foundation	DEB-1926050, DBI-1933016, 1759865
Université Laval
Horizon 2020 Framework Programme	722518
Global Lake Ecological Observatory Network
Natural Sciences and Engineering Research Council of Canada	1638704, 1754265, 1761805, RGPIN-2018-06389
European Commission	690,462
Environmental Protection Agency
Deutsche Forschungsgemeinschaft	CDZ 1259, DFG KI 853/13–1
Svenska Forskningsrådet Formas
Bundesministerium für Bildung und Forschung
Ministerio de Economía y Competitividad
European Social Fund

Keywords

Calibration
Ensemble modeling
Hydrodynamics
R package
Thermal structure
Vertical one-dimensional lake model

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10.1016/j.envsoft.2021.105101

Cite this

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title = "LakeEnsemblR: An R package that facilitates ensemble modelling of lakes",

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.",

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author = "Moore, {Tadhg N.} and Mesman, {Jorrit P.} and Robert Ladwig and Johannes Feldbauer and Freya Olsson and Pilla, {Rachel M.} and Tom Shatwell and Venkiteswaran, {Jason J.} and Delany, {Austin D.} and Hilary Dugan and Rose, {Kevin C.} and Read, {Jordan S.}",

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month = sep,

doi = "10.1016/j.envsoft.2021.105101",

language = "English",

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AU - Feldbauer, Johannes

AU - Olsson, Freya

AU - Pilla, Rachel M.

AU - Shatwell, Tom

AU - Venkiteswaran, Jason J.

AU - Delany, Austin D.

AU - Dugan, Hilary

AU - Rose, Kevin C.

AU - Read, Jordan S.

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N2 - 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.

AB - 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.

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LakeEnsemblR: An R package that facilitates ensemble modelling of lakes

Abstract

Funding

Keywords

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