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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Judith Eeckman, Brian De Grenus, Floreana Marie Miesen, James Thornton, Philip Brunner, and Nadav Peleg
Hydrol. Earth Syst. Sci., 29, 4093–4107, https://doi.org/10.5194/hess-29-4093-2025, https://doi.org/10.5194/hess-29-4093-2025, 2025
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The fate of liquid water from melting snow in winter and spring is difficult to understand in the mountains. This work uses a multi-instrumental network to accurately monitor the dynamics of snowmelt and infiltration at different depths in the ground and at different altitudes. The results show that melting snow quickly infiltrates into the upper layers of the soil but is also quickly transferred through the soil along the slopes towards the river.
Álvaro Pardo-Álvarez, Jan H. Fleckenstein, Kalliopi Koutantou, and Philip Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3521, https://doi.org/10.5194/egusphere-2025-3521, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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An upgraded version of a numerical solver is introduced to better capture the three-dimensional interactions between surface water and groundwater. Built using open-source software, it adds new features to handle the complexity of real environments, including the representation of subsurface geology and the simulation of diverse dynamic processes, such as solute transport and heat transfer, in both domains. A test case and a full description of the novel features are provided in this paper.
Alex Naoki Asato Kobayashi, Clément Roques, Daniel Hunkeler, Edward Mitchell, Robin Calisti, and Philip Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2025-816, https://doi.org/10.5194/egusphere-2025-816, 2025
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The increasing impact of climate change and human activities on greenhouse gas emissions highlights the need for effective monitoring, especially from the soil. Our design introduces a low-cost solution for measuring soil gas flux that is adaptable to various environments. Additionally, we propose a novel method for ensuring data quality before deploying these systems in the field.
Love Råman Vinnå, Vidushi Bigler, Oliver S. Schilling, and Jannis Epting
EGUsphere, https://doi.org/10.5194/egusphere-2024-3957, https://doi.org/10.5194/egusphere-2024-3957, 2025
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River water temperature is a key factor for water quality. Under climate change, inland water temperatures have increased, putting pressure on aquatic life and reducing the potential for human use. Here, future river water temperatures for Switzerland are studied. Results show that to the end of the 21st century, average river water temperatures will likely increase by 3.1±0.7 °C. This is likely to increases the thermal stress on sensitive aquatic species such as the brown trout.
Frauke Bunsen, Judith Hauck, Sinhué Torres-Valdés, and Lars Nerger
Ocean Sci., 21, 437–471, https://doi.org/10.5194/os-21-437-2025, https://doi.org/10.5194/os-21-437-2025, 2025
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Computer models are often used to estimate the ocean's CO2 uptake due to a lack of direct observations. Because such idealized models do not match precisely with the real world, we combine real-world observations of ocean temperature and salinity with a model and study the effect on the modeled air–sea CO2 flux (2010–2020). The corrections of temperature and salinity have their largest effect on regional CO2 fluxes in the Southern Ocean in winter and a small effect on the global CO2 uptake.
Friederike Currle, René Therrien, and Oliver S. Schilling
EGUsphere, https://doi.org/10.5194/egusphere-2025-372, https://doi.org/10.5194/egusphere-2025-372, 2025
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We present a new approach to simulate the transport of microbes in river-aquifer systems in the integrated hydrological model HydroGeoSphere. Compared to existing models, the advantage of the new implementation lies in the consideration of all relevant parts of the water budget and the flexibility to simulate in parallel the reactive transport of several microbial species and solutes. The new developed tool enables to improve our understanding of pathogen transport in river-groundwater systems.
Alexandre Gauvain, Ronan Abhervé, Alexandre Coche, Martin Le Mesnil, Clément Roques, Camille Bouchez, Jean Marçais, Sarah Leray, Etienne Marti, Ronny Figueroa, Etienne Bresciani, Camille Vautier, Bastien Boivin, June Sallou, Johan Bourcier, Benoit Combemale, Philip Brunner, Laurent Longuevergne, Luc Aquilina, and Jean-Raynald de Dreuzy
EGUsphere, https://doi.org/10.5194/egusphere-2024-3962, https://doi.org/10.5194/egusphere-2024-3962, 2025
Preprint archived
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HydroModPy is an open-source toolbox that makes it easier to study and model groundwater flow at catchment scale. By combining mapping tools with groundwater modeling, it automates the process of building, analyzing and deploying aquifer models. This allows researchers to simulate groundwater flow that sustains stream baseflows, providing insights for the hydrology community. Designed to be accessible and customizable, HydroModPy supports sustainable water management, research, and education.
Friederike Currle, René Therrien, and Oliver S. Schilling
EGUsphere, https://doi.org/10.5194/egusphere-2024-3787, https://doi.org/10.5194/egusphere-2024-3787, 2024
Preprint withdrawn
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We present a new approach to simulate the transport of microbes in river-aquifer systems in the integrated hydrological model HydroGeoSphere. Compared to existing models, the advantage of the new implementation lies in the consideration of all relevant parts of the water budget and the flexibility to simulate in parallel the reactive transport of several microbial species and solutes. The new developed tool enables to improve our understanding of pathogen transport in river-groundwater systems.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
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To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Yumeng Chen, Lars Nerger, and Amos S. Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-1078, https://doi.org/10.5194/egusphere-2024-1078, 2024
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In this paper, we present pyPDAF, a Python interface to the parallel data assimilation framework (PDAF) allowing for coupling with Python-based models. We demonstrate the capability and efficiency of pyPDAF under a coupled data assimilation setup.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
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This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
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Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, Andrew Ridout, and Lars Nerger
The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023, https://doi.org/10.5194/tc-17-2509-2023, 2023
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Observations show that the Arctic sea ice cover has reduced over the last 40 years. This study uses ensemble-based data assimilation in a stand-alone sea ice model to investigate the impacts of assimilating three different kinds of sea ice observation, including the novel assimilation of sea ice thickness distribution. We show that assimilating ice thickness distribution has a positive impact on thickness and volume estimates within the ice pack, especially for very thick ice.
Thomas Hermans, Pascal Goderniaux, Damien Jougnot, Jan H. Fleckenstein, Philip Brunner, Frédéric Nguyen, Niklas Linde, Johan Alexander Huisman, Olivier Bour, Jorge Lopez Alvis, Richard Hoffmann, Andrea Palacios, Anne-Karin Cooke, Álvaro Pardo-Álvarez, Lara Blazevic, Behzad Pouladi, Peleg Haruzi, Alejandro Fernandez Visentini, Guilherme E. H. Nogueira, Joel Tirado-Conde, Majken C. Looms, Meruyert Kenshilikova, Philippe Davy, and Tanguy Le Borgne
Hydrol. Earth Syst. Sci., 27, 255–287, https://doi.org/10.5194/hess-27-255-2023, https://doi.org/10.5194/hess-27-255-2023, 2023
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Although invisible, groundwater plays an essential role for society as a source of drinking water or for ecosystems but is also facing important challenges in terms of contamination. Characterizing groundwater reservoirs with their spatial heterogeneity and their temporal evolution is therefore crucial for their sustainable management. In this paper, we review some important challenges and recent innovations in imaging and modeling the 4D nature of the hydrogeological systems.
Hao-Cheng Yu, Yinglong Joseph Zhang, Lars Nerger, Carsten Lemmen, Jason C. S. Yu, Tzu-Yin Chou, Chi-Hao Chu, and Chuen-Teyr Terng
EGUsphere, https://doi.org/10.5194/egusphere-2022-114, https://doi.org/10.5194/egusphere-2022-114, 2022
Preprint archived
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We develop a new data assimilative approach by combining two parallel frameworks: PDAF and ESMF. This allows maximum flexibility and easy implementation of data assimilation for fully coupled earth system model applications. It is also validated by using a simple benchmark and applied to a realistic case simulation around Taiwan. The real case test shows significant improvement for temperature, velocity and surface elevation before, during and after typhoon events.
Guilherme E. H. Nogueira, Christian Schmidt, Daniel Partington, Philip Brunner, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 26, 1883–1905, https://doi.org/10.5194/hess-26-1883-2022, https://doi.org/10.5194/hess-26-1883-2022, 2022
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In near-stream aquifers, mixing between stream water and ambient groundwater can lead to dilution and the removal of substances that can be harmful to the water ecosystem at high concentrations. We used a numerical model to track the spatiotemporal evolution of different water sources and their mixing around a stream, which are rather difficult in the field. Results show that mixing mainly develops as narrow spots, varying In time and space, and is affected by magnitudes of discharge events.
K. Koutantou, G. Mazzotti, and P. Brunner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 477–484, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-477-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-477-2021, 2021
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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...
