Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1267-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-1267-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
17 Mar 2020
Development and technical paper |
| 17 Mar 2020
| 17 Mar 2020
Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4
Theo Baracchini
Physics of Aquatic Systems Laboratory (APHYS) – Margaretha Kamprad
Chair, ENAC, EPFL, Lausanne, 1015, Switzerland
Philip Y. Chu
Great Lakes Environmental Research Laboratory, NOAA, Ann Arbor, MI
48108, USA
Jonas Šukys
Eawag, Swiss Federal Institute of Aquatic Science and Technology,
Systems Analysis, Integrated Assessment and Modelling, Dübendorf, 8600,
Switzerland
Gian Lieberherr
Oeschger Centre for Climate Change Research, Institute of Geography,
University of Bern, Bern, 3012, Switzerland
Stefan Wunderle
Oeschger Centre for Climate Change Research, Institute of Geography,
University of Bern, Bern, 3012, Switzerland
Alfred Wüest
Physics of Aquatic Systems Laboratory (APHYS) – Margaretha Kamprad
Chair, ENAC, EPFL, Lausanne, 1015, Switzerland
Eawag, Swiss Federal Institute of Aquatic Science and Technology,
Surface Waters – Research and Management, Kastanienbaum, 6047, Switzerland
Eawag, Swiss Federal Institute of Aquatic Science and Technology,
Surface Waters – Research and Management, Kastanienbaum, 6047, Switzerland
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- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. 10.5194/gmd-15-3555-2022
- An automated calibration framework and open source tools for 3D lake hydrodynamic models T. Baracchini et al. 10.1016/j.envsoft.2020.104787
- Assessing the thermal regime of poorly monitored reservoirs with a combined satellite and three-dimensional modeling approach N. Sharaf et al. 10.1080/20442041.2021.1913937
- Satellite and Machine Learning Monitoring of Optically Inactive Water Quality Variability in a Tropical River N. Li et al. 10.3390/rs14215466
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- Contributions from climate variation and human activities to flow regime change of Tonle Sap Lake from 2001 to 2020 K. Morovati et al. 10.1016/j.jhydrol.2022.128800
- Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects S. Piccolroaz et al. 10.1029/2023RG000816
- A Near‐Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time R. Thomas et al. 10.1029/2019WR026138
- Coupling remote sensing and particle tracking to estimate trajectories in large water bodies C. Li et al. 10.1016/j.jag.2022.102809
- Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends C. Gonzales-Inca et al. 10.3390/w14142211
- The role of wind-wave related processes in redistributing river-derived terrigenous sediments in Lake Turkana: A modelling study F. Zăinescu et al. 10.1016/j.jglr.2022.12.013
- Integrating Inland and Coastal Water Quality Data for Actionable Knowledge G. El Serafy et al. 10.3390/rs13152899
- Basin-scale gyres and mesoscale eddies in large lakes: a novel procedure for their detection and characterization, assessed in Lake Geneva S. Hamze-Ziabari et al. 10.5194/gmd-15-8785-2022
- Multi-scale evaluation of a 3D lake model forced by an atmospheric model against standard monitoring data M. Amadori et al. 10.1016/j.envsoft.2021.105017
- Assimilation of GPS-tracked drifter data to improve the Eulerian velocity fields in an estuary M. Khanarmuei et al. 10.1016/j.ecss.2021.107575
- Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir H. Wander et al. 10.1002/ecs2.4752
Latest update: 24 Apr 2024
Short summary
Lake physical processes occur at a wide range of spatiotemporal 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 a 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 spatiotemporal scales. 3D hydrodynamic lake...
