Articles | Volume 17, issue 8
https://doi.org/10.5194/gmd-17-3559-2024
© Author(s) 2024. 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-17-3559-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
02 May 2024
Development and technical paper |
| 02 May 2024
| 02 May 2024
HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model
Centre for Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Neuchatel, 2000, Switzerland
Hydrogeology, Department of Environmental Sciences, University of Basel, Basel, 4056, Switzerland
Hugo Delottier
Centre for Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Neuchatel, 2000, Switzerland
Wolfgang Kurtz
German Meteorological Service, Centre for Agrometeorological Research, Branch Office Weihenstephan, Freising, 85354, Germany
Lars Nerger
Alfred-Wegener-Institut, Helmholtz Zentrum für Polar- und Meeresforschung, Bremerhaven, 27570, Germany
Oliver S. Schilling
Centre for Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Neuchatel, 2000, Switzerland
Hydrogeology, Department of Environmental Sciences, University of Basel, Basel, 4056, Switzerland
Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, 8600, Switzerland
Philip Brunner
Centre for Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Neuchatel, 2000, Switzerland
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Short summary
We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
We have developed a new data assimilation framework by coupling an integrated hydrological model...
