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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-47
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-47
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 21 Mar 2019

Development and technical paper | 21 Mar 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Data assimilation of in-situ and satellite remote sensing data to 3D hydrodynamic lake models

Theo Baracchini1, Philip Yifei Chu2, Jonas Šukys3, Gian Lieberherr4, Stefan Wunderle4, Alfred Wüest1, and Damien Bouffard5 Theo Baracchini et al.
  • 1Physics of Aquatic Systems Laboratory (APHYS) – Margaretha Kamprad Chair, ENAC, EPFL, Lausanne, 1015,Switzerland
  • 2Great Lakes Environmental Research Laboratory, NOAA, Ann Arbor, MI 48108, USA
  • 3Eawag, Swiss Federal Institute of Aquatic Science and Technology,Systems Analysis, Integrated Assessment and Modelling, Dübendorf, 8600, Switzerland
  • 4Oeschger Centre for Climate Change Research, Institute of Geography, University of Bern, Bern, 3012, Switzerland
  • 5Eawag, Swiss Federal Institute of Aquatic Science and Technology, Surface Waters – Research and Management, Kastanienbaum, 6047, Switzerland

Abstract. The understanding of lakes physical dynamics is crucial to provide scientifically credible information for ecosystem management. We show how the combination of in-situ data, remote sensing observations and three-dimensional hydrodynamic numerical simulations is capable of delivering various spatio-temporal scales involved in lakes dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we present a flexible framework for DA into lakes three-dimensional hydrodynamic models. Using an Ensemble Kalman Filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in-situ and satellite remote sensing temperature data into a three-dimensional hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatio-temporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed for the constraints of operational systems and near real-time operations (e.g. integration into http://meteolakes.ch).

Theo Baracchini et al.
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Short summary
Lake physical processes occur at a wide range of spatio-temporal scales. 3D hydrodynamic lake models are the only information source capable of solving those scales, however they still need observations to be calibrated and to constrain their uncertainties. The optimal combination of 3D hydrodynamic model, in-situ measurements and remote sensing observations is achieved through data assimilation. Here we present a complete data assimilation experiment for lakes using open source tools.
Lake physical processes occur at a wide range of spatio-temporal scales. 3D hydrodynamic lake...
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