Articles | Volume 16, issue 24
https://doi.org/10.5194/gmd-16-7375-2023
© Author(s) 2023. 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-16-7375-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review and perspective paper
| 
19 Dec 2023
Review and perspective paper |
| 19 Dec 2023
| 19 Dec 2023
Perspectives of physics-based machine learning strategies for geoscientific applications governed by partial differential equations
Denise Degen
CORRESPONDING AUTHOR
Computational Geoscience, Geothermics and Reservoir Geophysics (CG3), RWTH Aachen University, Mathieustraße 30, 52074 Aachen, Germany
Daniel Caviedes Voullième
Forschungszentrum Jülich GmbH, Jülich Supercomputing Centre (JSC), Simulation and Data Lab. Terrestrial Systems, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
Forschungszentrum Jülich GmbH, Agrosphere, IBG-3, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
Susanne Buiter
RWTH Aachen University, Tectonics and Geodyamics (TAG), Lochnerstraße 4-20, 52064 Aachen, Germany
Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Harrie-Jan Hendricks Franssen
Forschungszentrum Jülich GmbH, Agrosphere, IBG-3, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
Centre for High-Performance Computing Terrestrial Systems, Geoverbund ABC/J, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
Harry Vereecken
Forschungszentrum Jülich GmbH, Agrosphere, IBG-3, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
Centre for High-Performance Computing Terrestrial Systems, Geoverbund ABC/J, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
Ana González-Nicolás
Forschungszentrum Jülich GmbH, Jülich Supercomputing Centre (JSC), Simulation and Data Lab. Terrestrial Systems, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
Florian Wellmann
Computational Geoscience, Geothermics and Reservoir Geophysics (CG3), RWTH Aachen University, Mathieustraße 30, 52074 Aachen, Germany
Fraunhofer Research Institution for Energy Infrastructures and Geothermal Systems (IEG), Am Hochschulcampus 1, 44801 Bochum, Germany
Related authors
Denise Degen, Moritz Ziegler, Oliver Heidbach, Andreas Henk, Karsten Reiter, and Florian Wellmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-2932, https://doi.org/10.5194/egusphere-2024-2932, 2024
Short summary
Short summary
Obtaining reliable estimates of the subsurface state distributions is essential to determine the location of e.g. potential nuclear waste disposal sites. However, providing these is challenging since it requires solving the problem numerous times yielding high computational cost. To overcome this, we use a physics-based machine learning method to construct surrogate models. We demonstrate how it produces physics-preserving predictions, which differentiates it from purely data-driven approaches.
Denise Degen, Cameron Spooner, Magdalena Scheck-Wenderoth, and Mauro Cacace
Geosci. Model Dev., 14, 7133–7153, https://doi.org/10.5194/gmd-14-7133-2021, https://doi.org/10.5194/gmd-14-7133-2021, 2021
Short summary
Short summary
In times of worldwide energy transitions, an understanding of the subsurface is increasingly important to provide renewable energy sources such as geothermal energy. To validate our understanding of the subsurface we require data. However, the data are usually not distributed equally and introduce a potential misinterpretation of the subsurface. Therefore, in this study we investigate the influence of measurements on temperature distribution in the European Alps.
Denise Degen and Mauro Cacace
Geosci. Model Dev., 14, 1699–1719, https://doi.org/10.5194/gmd-14-1699-2021, https://doi.org/10.5194/gmd-14-1699-2021, 2021
Short summary
Short summary
In this work, we focus on improving the understanding of subsurface processes with respect to interactions with climate dynamics. We present advanced, open-source mathematical methods that enable us to investigate the influence of various model properties on the final outcomes. By relying on our approach, we have been able to showcase their importance in improving our understanding of the subsurface and highlighting the current shortcomings of currently adopted models.
Denise Degen, Moritz Ziegler, Oliver Heidbach, Andreas Henk, Karsten Reiter, and Florian Wellmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-2932, https://doi.org/10.5194/egusphere-2024-2932, 2024
Short summary
Short summary
Obtaining reliable estimates of the subsurface state distributions is essential to determine the location of e.g. potential nuclear waste disposal sites. However, providing these is challenging since it requires solving the problem numerous times yielding high computational cost. To overcome this, we use a physics-based machine learning method to construct surrogate models. We demonstrate how it produces physics-preserving predictions, which differentiates it from purely data-driven approaches.
Manuela S. Kaufmann, Anja Klotzsche, Jan van der Kruk, Anke Langen, Harry Vereecken, and Lutz Weihermüller
EGUsphere, https://doi.org/10.5194/egusphere-2024-2889, https://doi.org/10.5194/egusphere-2024-2889, 2024
Short summary
Short summary
To use fertilizers more effectively, non-invasive geophysical methods can be used to understand nutrient distribution in the soil. We utilize in a long-term field study geophysical techniques to study soil properties and conditions under different fertilizer treatments. We compared the geophysical responds with soil samples and soil sensor data. Especially, electromagnetic induction and electrical resistivity tomography were effective in monitoring changes in nitrate levels over time.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
Hydrol. Earth Syst. Sci., 28, 4059–4063, https://doi.org/10.5194/hess-28-4059-2024, https://doi.org/10.5194/hess-28-4059-2024, 2024
Short summary
Short summary
Gao et al. (2023) question the importance of soil in hydrology, sparking debate. We acknowledge some valid points but critique their broad, unsubstantiated views on soil's role. Our response highlights three key areas: (1) the false divide between ecosystem-centric and soil-centric approaches, (2) the vital yet varied impact of soil properties, and (3) the call for a scale-aware framework. We aim to unify these perspectives, enhancing hydrology's comprehensive understanding.
Zhi Li, Gregor Rickert, Na Zheng, Zhibo Zhang, Ilhan Özgen-Xian, and Daniel Caviedes-Voullième
EGUsphere, https://doi.org/10.5194/egusphere-2024-2588, https://doi.org/10.5194/egusphere-2024-2588, 2024
Short summary
Short summary
We introduce SERGHEI-RE, a 3D subsurface flow simulator with performance-portable parallel computing capabilities. SERGHEI-RE performs effectively on various computational devices, from personal computers to advanced clusters. It allows users to solve flow equations with multiple numerical schemes, making it adaptable to various hydrological scenarios. Testing results show its accuracy and performance, confirming that SERGHEI-RE is a powerful tool for hydrological research.
Shahin Khosh Bin Ghomash, Heiko Apel, and Daniel Caviedes-Voullième
Nat. Hazards Earth Syst. Sci., 24, 2857–2874, https://doi.org/10.5194/nhess-24-2857-2024, https://doi.org/10.5194/nhess-24-2857-2024, 2024
Short summary
Short summary
Early warning is essential to minimise the impact of flash floods. We explore the use of highly detailed flood models to simulate the 2021 flood event in the lower Ahr valley (Germany). Using very high-resolution models resolving individual streets and buildings, we produce detailed, quantitative, and actionable information for early flood warning systems. Using state-of-the-art computational technology, these models can guarantee very fast forecasts which allow for sufficient time to respond.
Tobias Karl David Weber, Lutz Weihermüller, Attila Nemes, Michel Bechtold, Aurore Degré, Efstathios Diamantopoulos, Simone Fatichi, Vilim Filipović, Surya Gupta, Tobias L. Hohenbrink, Daniel R. Hirmas, Conrad Jackisch, Quirijn de Jong van Lier, John Koestel, Peter Lehmann, Toby R. Marthews, Budiman Minasny, Holger Pagel, Martine van der Ploeg, Shahab Aldin Shojaeezadeh, Simon Fiil Svane, Brigitta Szabó, Harry Vereecken, Anne Verhoef, Michael Young, Yijian Zeng, Yonggen Zhang, and Sara Bonetti
Hydrol. Earth Syst. Sci., 28, 3391–3433, https://doi.org/10.5194/hess-28-3391-2024, https://doi.org/10.5194/hess-28-3391-2024, 2024
Short summary
Short summary
Pedotransfer functions (PTFs) are used to predict parameters of models describing the hydraulic properties of soils. The appropriateness of these predictions critically relies on the nature of the datasets for training the PTFs and the physical comprehensiveness of the models. This roadmap paper is addressed to PTF developers and users and critically reflects the utility and future of PTFs. To this end, we present a manifesto aiming at a paradigm shift in PTF research.
Joschka Neumann, Nicolas Brüggemann, Patrick Chaumet, Normen Hermes, Jan Huwer, Peter Kirchner, Werner Lesmeister, Wilhelm August Mertens, Thomas Pütz, Jörg Wolters, Harry Vereecken, and Ghaleb Natour
EGUsphere, https://doi.org/10.5194/egusphere-2024-1598, https://doi.org/10.5194/egusphere-2024-1598, 2024
Short summary
Short summary
Climate change in combination with a steadily growing world population and a simultaneous decrease in agricultural land is one of the greatest global challenges facing mankind. In this context, Forschungszentrum Jülich established an "agricultural simulator" (AgraSim), which enables research into the effects of climate change on agricultural ecosystems and the optimization of agricultural cultivation and management strategies with the aid of combined experimental and numerical simulation.
Paolo Nasta, Günter Blöschl, Heye R. Bogena, Steffen Zacharias, Roland Baatz, Gabriëlle De Lannoy, Karsten H. Jensen, Salvatore Manfreda, Laurent Pfister, Ana M. Tarquis, Ilja van Meerveld, Marc Voltz, Yijian Zeng, William Kustas, Xin Li, Harry Vereecken, and Nunzio Romano
EGUsphere, https://doi.org/10.5194/egusphere-2024-1678, https://doi.org/10.5194/egusphere-2024-1678, 2024
Short summary
Short summary
The Unsolved Problems in Hydrology (UPH) initiative has emphasized the need to establish networks of multi-decadal hydrological observatories to tackle catchment-scale challenges on a global scale. This opinion paper provocatively discusses two end members of possible future hydrological observatory (HO) networks for a given hypothesized community budget: a comprehensive set of moderately instrumented observatories or, alternatively, a small number of highly instrumented super-sites.
Natalie Hummel, Susanne Buiter, and Zoltán Erdős
Solid Earth, 15, 567–587, https://doi.org/10.5194/se-15-567-2024, https://doi.org/10.5194/se-15-567-2024, 2024
Short summary
Short summary
Simulations of subducting tectonic plates often use material properties extrapolated from the behavior of small rock samples in a laboratory to conditions found in the Earth. We explore several typical approaches to simulating these extrapolated material properties and show that they produce very rigid subducting plates with unrealistic dynamics. Our findings imply that subducting plates deform by additional mechanisms that are less commonly implemented in simulations.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie Fisher, and Harrie-Jan Hendricks Franssen
EGUsphere, https://doi.org/10.5194/egusphere-2024-978, https://doi.org/10.5194/egusphere-2024-978, 2024
Short summary
Short summary
Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land Surface Models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes and variability of carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research on these processes.
Teng Xu, Sinan Xiao, Sebastian Reuschen, Nils Wildt, Harrie-Jan Hendricks Franssen, and Wolfgang Nowak
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-60, https://doi.org/10.5194/hess-2024-60, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
We provide a set of benchmarking scenarios for geostatistical inversion, and we encourage the scientific community to use these to compare their newly developed methods. To facilitate transparent, appropriate, and uncertainty-aware comparison of novel methods, we also provide accurate reference solutions, a high-end reference algorithm, and a diverse set of benchmarking metrics, all of which are publicly available. With this, we seek to foster more targeted and transparent progress in the field.
Lukas Strebel, Heye Bogena, Harry Vereecken, Mie Andreasen, Sergio Aranda-Barranco, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 28, 1001–1026, https://doi.org/10.5194/hess-28-1001-2024, https://doi.org/10.5194/hess-28-1001-2024, 2024
Short summary
Short summary
We present results from using soil water content measurements from 13 European forest sites in a state-of-the-art land surface model. We use data assimilation to perform a combination of observed and modeled soil water content and show the improvements in the representation of soil water content. However, we also look at the impact on evapotranspiration and see no corresponding improvements.
Bamidele Joseph Oloruntoba, Stefan Kollet, Carsten Montzka, Harry Vereecken, and Harrie-Jan Hendricks Franssen
EGUsphere, https://doi.org/10.5194/egusphere-2023-3132, https://doi.org/10.5194/egusphere-2023-3132, 2024
Short summary
Short summary
This study uses simulations to understand how the soil information across Africa affects the water balance, using 4 soil databases and 3 different rainfall datasets. Results show that the soil information impacts water balance estimates, especially with a higher rate of rainfall.
Michael Hillier, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard
Geosci. Model Dev., 16, 6987–7012, https://doi.org/10.5194/gmd-16-6987-2023, https://doi.org/10.5194/gmd-16-6987-2023, 2023
Short summary
Short summary
Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and structural orientations. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and improved data fitting techniques. These advances permit the modelling of more complex geology in diverse geological settings, different-sized areas, and various data regimes.
Theresa Boas, Heye Reemt Bogena, Dongryeol Ryu, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 27, 3143–3167, https://doi.org/10.5194/hess-27-3143-2023, https://doi.org/10.5194/hess-27-3143-2023, 2023
Short summary
Short summary
In our study, we tested the utility and skill of a state-of-the-art forecasting product for the prediction of regional crop productivity using a land surface model. Our results illustrate the potential value and skill of combining seasonal forecasts with modelling applications to generate variables of interest for stakeholders, such as annual crop yield for specific cash crops and regions. In addition, this study provides useful insights for future technical model evaluations and improvements.
Zbigniew P. Piotrowski, Jaro Hokkanen, Daniel Caviedes-Voullieme, Olaf Stein, and Stefan Kollet
EGUsphere, https://doi.org/10.5194/egusphere-2023-1079, https://doi.org/10.5194/egusphere-2023-1079, 2023
Preprint withdrawn
Short summary
Short summary
The computer programs capable of simulation of Earth system components evolve, adapting new fundamental science concepts and more observational data on more and more powerful computer hardware. Adaptation of a large scientific program to a new type of hardware is costly. In this work we propose cheap and simple but effective strategy that enable computation using graphic processing units, based on automated program code modification. This results in better resolution and/or longer predictions.
Mohammad Moulaeifard, Simon Bernard, and Florian Wellmann
Geosci. Model Dev., 16, 3565–3579, https://doi.org/10.5194/gmd-16-3565-2023, https://doi.org/10.5194/gmd-16-3565-2023, 2023
Short summary
Short summary
In this work, we propose a flexible framework to generate and interact with geological models using explicit surface representations. The essence of the work lies in the determination of the flexible control mesh, topologically similar to the main geological structure, watertight and controllable with few control points, to manage the geological structures. We exploited the subdivision surface method in our work, which is commonly used in the animation and gaming industry.
Nicolás Molnar and Susanne Buiter
Solid Earth, 14, 213–235, https://doi.org/10.5194/se-14-213-2023, https://doi.org/10.5194/se-14-213-2023, 2023
Short summary
Short summary
Progression of orogenic wedges over pre-existing extensional structures is common in nature, but deciphering the spatio-temporal evolution of deformation from the geological record remains challenging. Our laboratory experiments provide insights on how horizontal stresses are transferred across a heterogeneous crust, constrain which pre-shortening conditions can either favour or hinder the reactivatation of extensional structures, and explain what implications they have on critical taper theory.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
Short summary
Short summary
This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Frank Zwaan, Guido Schreurs, Susanne J. H. Buiter, Oriol Ferrer, Riccardo Reitano, Michael Rudolf, and Ernst Willingshofer
Solid Earth, 13, 1859–1905, https://doi.org/10.5194/se-13-1859-2022, https://doi.org/10.5194/se-13-1859-2022, 2022
Short summary
Short summary
When a sedimentary basin is subjected to compressional tectonic forces after its formation, it may be inverted. A thorough understanding of such
basin inversionis of great importance for scientific, societal, and economic reasons, and analogue tectonic models form a key part of our efforts to study these processes. We review the advances in the field of basin inversion modelling, showing how the modelling results can be applied, and we identify promising venues for future research.
Cosimo Brogi, Heye Reemt Bogena, Markus Köhli, Johan Alexander Huisman, Harrie-Jan Hendricks Franssen, and Olga Dombrowski
Geosci. Instrum. Method. Data Syst., 11, 451–469, https://doi.org/10.5194/gi-11-451-2022, https://doi.org/10.5194/gi-11-451-2022, 2022
Short summary
Short summary
Accurate monitoring of water in soil can improve irrigation efficiency, which is important considering climate change and the growing world population. Cosmic-ray neutrons sensors (CRNSs) are a promising tool in irrigation monitoring due to a larger sensed area and to lower maintenance than other ground-based sensors. Here, we analyse the feasibility of irrigation monitoring with CRNSs and the impact of the irrigated field dimensions, of the variations of water in soil, and of instrument design.
Michał P. Michalak, Lesław Teper, Florian Wellmann, Jerzy Żaba, Krzysztof Gaidzik, Marcin Kostur, Yuriy P. Maystrenko, and Paulina Leonowicz
Solid Earth, 13, 1697–1720, https://doi.org/10.5194/se-13-1697-2022, https://doi.org/10.5194/se-13-1697-2022, 2022
Short summary
Short summary
When characterizing geological/geophysical surfaces, various geometric attributes are calculated, such as dip angle (1D) or dip direction (2D). However, the boundaries between specific values may be subjective and without optimization significance, resulting from using default color palletes. This study proposes minimizing cosine distance among within-cluster observations to detect 3D anomalies. Our results suggest that the method holds promise for identification of megacylinders or megacones.
Olga Dombrowski, Cosimo Brogi, Harrie-Jan Hendricks Franssen, Damiano Zanotelli, and Heye Bogena
Geosci. Model Dev., 15, 5167–5193, https://doi.org/10.5194/gmd-15-5167-2022, https://doi.org/10.5194/gmd-15-5167-2022, 2022
Short summary
Short summary
Soil carbon storage and food production of fruit orchards will be influenced by climate change. However, they lack representation in models that study such processes. We developed and tested a new sub-model, CLM5-FruitTree, that describes growth, biomass distribution, and management practices in orchards. The model satisfactorily predicted yield and exchange of carbon, energy, and water in an apple orchard and can be used to study land surface processes in fruit orchards at different scales.
Jordan Bates, Francois Jonard, Rajina Bajracharya, Harry Vereecken, and Carsten Montzka
AGILE GIScience Ser., 3, 23, https://doi.org/10.5194/agile-giss-3-23-2022, https://doi.org/10.5194/agile-giss-3-23-2022, 2022
Wei Qu, Heye Bogena, Christoph Schüth, Harry Vereecken, Zongmei Li, and Stephan Schulz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-131, https://doi.org/10.5194/gmd-2022-131, 2022
Publication in GMD not foreseen
Short summary
Short summary
We applied the global sensitivity analysis LH-OAT to the integrated hydrology model ParFlow-CLM to investigate the sensitivity of the 12 parameters for different scenarios. And we found that the general patterns of the parameter sensitivities were consistent, however, for some parameters a significantly larger span of the sensitivities was observed, especially for the higher slope and in subarctic climatic scenarios.
Nicholas Jarvis, Jannis Groh, Elisabet Lewan, Katharina H. E. Meurer, Walter Durka, Cornelia Baessler, Thomas Pütz, Elvin Rufullayev, and Harry Vereecken
Hydrol. Earth Syst. Sci., 26, 2277–2299, https://doi.org/10.5194/hess-26-2277-2022, https://doi.org/10.5194/hess-26-2277-2022, 2022
Short summary
Short summary
We apply an eco-hydrological model to data on soil water balance and grassland growth obtained at two sites with contrasting climates. Our results show that the grassland in the drier climate had adapted by developing deeper roots, which maintained water supply to the plants in the face of severe drought. Our study emphasizes the importance of considering such plastic responses of plant traits to environmental stress in the modelling of soil water balance and plant growth under climate change.
Susanne J. H. Buiter, Sascha Brune, Derek Keir, and Gwenn Peron-Pinvidic
EGUsphere, https://doi.org/10.5194/egusphere-2022-139, https://doi.org/10.5194/egusphere-2022-139, 2022
Preprint archived
Short summary
Short summary
Continental rifts can form when and where continents are stretched. Rifts are characterised by faults, sedimentary basins, earthquakes and/or volcanism. If rifting can continue, a rift may break a continent into conjugate margins such as along the Atlantic and Indian Oceans. In some cases, however, rifting fails, such as in the West African Rift. We discuss continental rifting from inception to break-up, focussing on the processes at play, and illustrate these with several natural examples.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
Short summary
Short summary
Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Lukas Strebel, Heye R. Bogena, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 15, 395–411, https://doi.org/10.5194/gmd-15-395-2022, https://doi.org/10.5194/gmd-15-395-2022, 2022
Short summary
Short summary
We present the technical coupling between a land surface model (CLM5) and the Parallel Data Assimilation Framework (PDAF). This coupling enables measurement data to update simulated model states and parameters in a statistically optimal way. We demonstrate the viability of the model framework using an application in a forested catchment where the inclusion of soil water measurements significantly improved the simulation quality.
Veronika Forstner, Jannis Groh, Matevz Vremec, Markus Herndl, Harry Vereecken, Horst H. Gerke, Steffen Birk, and Thomas Pütz
Hydrol. Earth Syst. Sci., 25, 6087–6106, https://doi.org/10.5194/hess-25-6087-2021, https://doi.org/10.5194/hess-25-6087-2021, 2021
Short summary
Short summary
Lysimeter-based manipulative and observational experiments were used to identify responses of water fluxes and aboveground biomass (AGB) to climatic change in permanent grassland. Under energy-limited conditions, elevated temperature actual evapotranspiration (ETa) increased, while seepage, dew, and AGB decreased. Elevated CO2 mitigated the effect on ETa. Under water limitation, elevated temperature resulted in reduced ETa, and AGB was negatively correlated with an increasing aridity.
Denise Degen, Cameron Spooner, Magdalena Scheck-Wenderoth, and Mauro Cacace
Geosci. Model Dev., 14, 7133–7153, https://doi.org/10.5194/gmd-14-7133-2021, https://doi.org/10.5194/gmd-14-7133-2021, 2021
Short summary
Short summary
In times of worldwide energy transitions, an understanding of the subsurface is increasingly important to provide renewable energy sources such as geothermal energy. To validate our understanding of the subsurface we require data. However, the data are usually not distributed equally and introduce a potential misinterpretation of the subsurface. Therefore, in this study we investigate the influence of measurements on temperature distribution in the European Alps.
Yafei Huang, Jonas Weis, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-569, https://doi.org/10.5194/hess-2021-569, 2021
Manuscript not accepted for further review
Short summary
Short summary
Trends in agricultural droughts cannot be easily deduced from measurements. Here trends in agricultural droughts over 31 German and Dutch sites were calculated with model simulations and long-term observed meteorological data as input. We found that agricultural droughts are increasing although precipitation hardly decreases. The increase is driven by increase in evapotranspiration. The year 2018 was for half of the sites the year with the most extreme agricultural drought in the last 55 years.
Mengna Li, Yijian Zeng, Maciek W. Lubczynski, Jean Roy, Lianyu Yu, Hui Qian, Zhenyu Li, Jie Chen, Lei Han, Han Zheng, Tom Veldkamp, Jeroen M. Schoorl, Harrie-Jan Hendricks Franssen, Kai Hou, Qiying Zhang, Panpan Xu, Fan Li, Kai Lu, Yulin Li, and Zhongbo Su
Earth Syst. Sci. Data, 13, 4727–4757, https://doi.org/10.5194/essd-13-4727-2021, https://doi.org/10.5194/essd-13-4727-2021, 2021
Short summary
Short summary
The Tibetan Plateau is the source of most of Asia's major rivers and has been called the Asian Water Tower. Due to its remoteness and the harsh environment, there is a lack of field survey data to investigate its hydrogeology. Borehole core lithology analysis, an altitude survey, soil thickness measurement, hydrogeological surveys, and hydrogeophysical surveys were conducted in the Maqu catchment within the Yellow River source region to improve a full–picture understanding of the water cycle.
Hazel Gibson, Sam Illingworth, and Susanne Buiter
Geosci. Commun., 4, 437–451, https://doi.org/10.5194/gc-4-437-2021, https://doi.org/10.5194/gc-4-437-2021, 2021
Short summary
Short summary
In the spring of 2020, in response to the escalating global COVID-19 Coronavirus pandemic, the European Geosciences Union (EGU) moved its annual General Assembly online in a matter of weeks. This paper explores the feedback provided by participants who attended this experimental conference and identifies four key themes that emerged from analysis of the survey (connection, engagement, environment, and accessibility). The responses raise important questions about the format of future conferences.
Bernd Schalge, Gabriele Baroni, Barbara Haese, Daniel Erdal, Gernot Geppert, Pablo Saavedra, Vincent Haefliger, Harry Vereecken, Sabine Attinger, Harald Kunstmann, Olaf A. Cirpka, Felix Ament, Stefan Kollet, Insa Neuweiler, Harrie-Jan Hendricks Franssen, and Clemens Simmer
Earth Syst. Sci. Data, 13, 4437–4464, https://doi.org/10.5194/essd-13-4437-2021, https://doi.org/10.5194/essd-13-4437-2021, 2021
Short summary
Short summary
In this study, a 9-year simulation of complete model output of a coupled atmosphere–land-surface–subsurface model on the catchment scale is discussed. We used the Neckar catchment in SW Germany as the basis of this simulation. Since the dataset includes the full model output, it is not only possible to investigate model behavior and interactions between the component models but also use it as a virtual truth for comparison of, for example, data assimilation experiments.
Jan Vanderborght, Valentin Couvreur, Felicien Meunier, Andrea Schnepf, Harry Vereecken, Martin Bouda, and Mathieu Javaux
Hydrol. Earth Syst. Sci., 25, 4835–4860, https://doi.org/10.5194/hess-25-4835-2021, https://doi.org/10.5194/hess-25-4835-2021, 2021
Short summary
Short summary
Root water uptake is an important process in the terrestrial water cycle. How this process depends on soil water content, root distributions, and root properties is a soil–root hydraulic problem. We compare different approaches to implementing root hydraulics in macroscopic soil water flow and land surface models.
Alexander Schaaf, Miguel de la Varga, Florian Wellmann, and Clare E. Bond
Geosci. Model Dev., 14, 3899–3913, https://doi.org/10.5194/gmd-14-3899-2021, https://doi.org/10.5194/gmd-14-3899-2021, 2021
Short summary
Short summary
Uncertainty is an inherent property of any model of the subsurface. We show how geological topology information – how different regions of rocks in the subsurface are connected – can be used to train uncertain geological models to reduce uncertainty. More widely, the method demonstrates the use of probabilistic machine learning (Bayesian inference) to train structural geological models on auxiliary geological knowledge that can be encoded in graph structures.
Youri Rothfuss, Maria Quade, Nicolas Brüggemann, Alexander Graf, Harry Vereecken, and Maren Dubbert
Biogeosciences, 18, 3701–3732, https://doi.org/10.5194/bg-18-3701-2021, https://doi.org/10.5194/bg-18-3701-2021, 2021
Short summary
Short summary
The partitioning of evapotranspiration into evaporation from soil and transpiration from plants is crucial for a wide range of parties, from farmers to policymakers. In this work, we focus on a particular partitioning method, based on the stable isotopic analysis of water. In particular, we aim at highlighting the challenges that this method is currently facing and, in light of recent methodological developments, propose ways forward for the isotopic-partitioning community.
Cosimo Brogi, Johan A. Huisman, Lutz Weihermüller, Michael Herbst, and Harry Vereecken
SOIL, 7, 125–143, https://doi.org/10.5194/soil-7-125-2021, https://doi.org/10.5194/soil-7-125-2021, 2021
Short summary
Short summary
There is a need in agriculture for detailed soil maps that carry quantitative information. Geophysics-based soil maps have the potential to deliver such products, but their added value has not been fully investigated yet. In this study, we compare the use of a geophysics-based soil map with the use of two commonly available maps as input for crop growth simulations. The geophysics-based product results in better simulations, with improvements that depend on precipitation, soil, and crop type.
Denise Degen and Mauro Cacace
Geosci. Model Dev., 14, 1699–1719, https://doi.org/10.5194/gmd-14-1699-2021, https://doi.org/10.5194/gmd-14-1699-2021, 2021
Short summary
Short summary
In this work, we focus on improving the understanding of subsurface processes with respect to interactions with climate dynamics. We present advanced, open-source mathematical methods that enable us to investigate the influence of various model properties on the final outcomes. By relying on our approach, we have been able to showcase their importance in improving our understanding of the subsurface and highlighting the current shortcomings of currently adopted models.
Theresa Boas, Heye Bogena, Thomas Grünwald, Bernard Heinesch, Dongryeol Ryu, Marius Schmidt, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 14, 573–601, https://doi.org/10.5194/gmd-14-573-2021, https://doi.org/10.5194/gmd-14-573-2021, 2021
Short summary
Short summary
In this study we were able to significantly improve CLM5 model performance for European cropland sites by adding a winter wheat representation, specific plant parameterizations for important cash crops, and a cover-cropping and crop rotation subroutine to its crop module. Our modifications should be applied in future studies of CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes.
Benjamin Fersch, Till Francke, Maik Heistermann, Martin Schrön, Veronika Döpper, Jannis Jakobi, Gabriele Baroni, Theresa Blume, Heye Bogena, Christian Budach, Tobias Gränzig, Michael Förster, Andreas Güntner, Harrie-Jan Hendricks Franssen, Mandy Kasner, Markus Köhli, Birgit Kleinschmit, Harald Kunstmann, Amol Patil, Daniel Rasche, Lena Scheiffele, Ulrich Schmidt, Sandra Szulc-Seyfried, Jannis Weimar, Steffen Zacharias, Marek Zreda, Bernd Heber, Ralf Kiese, Vladimir Mares, Hannes Mollenhauer, Ingo Völksch, and Sascha Oswald
Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, https://doi.org/10.5194/essd-12-2289-2020, 2020
Stephanie Thiesen, Diego M. Vieira, Mirko Mälicke, Ralf Loritz, J. Florian Wellmann, and Uwe Ehret
Hydrol. Earth Syst. Sci., 24, 4523–4540, https://doi.org/10.5194/hess-24-4523-2020, https://doi.org/10.5194/hess-24-4523-2020, 2020
Short summary
Short summary
A spatial interpolator has been proposed for exploring the information content of the data in the light of geostatistics and information theory. It showed comparable results to traditional interpolators, with the advantage of presenting generalization properties. We discussed three different ways of combining distributions and their implications for the probabilistic results. By its construction, the method provides a suitable and flexible framework for uncertainty analysis and decision-making.
Jannis Groh, Jan Vanderborght, Thomas Pütz, Hans-Jörg Vogel, Ralf Gründling, Holger Rupp, Mehdi Rahmati, Michael Sommer, Harry Vereecken, and Horst H. Gerke
Hydrol. Earth Syst. Sci., 24, 1211–1225, https://doi.org/10.5194/hess-24-1211-2020, https://doi.org/10.5194/hess-24-1211-2020, 2020
Fabian Antonio Stamm, Miguel de la Varga, and Florian Wellmann
Solid Earth, 10, 2015–2043, https://doi.org/10.5194/se-10-2015-2019, https://doi.org/10.5194/se-10-2015-2019, 2019
Michael Paul Stockinger, Heye Reemt Bogena, Andreas Lücke, Christine Stumpp, and Harry Vereecken
Hydrol. Earth Syst. Sci., 23, 4333–4347, https://doi.org/10.5194/hess-23-4333-2019, https://doi.org/10.5194/hess-23-4333-2019, 2019
Short summary
Short summary
Precipitation moves through the soil to become stream water. The fraction of precipitation that becomes stream water after 3 months (Fyw) can be calculated with the stable isotopes of water. Previously, this was done for all the isotope data available, e.g., for several years. We used 1 year of data to calculate Fyw and moved this calculation time window over the time series. Results highlight that Fyw varies in time. Comparison studies of different regions should take this into account.
Frank Zwaan, Guido Schreurs, and Susanne J. H. Buiter
Solid Earth, 10, 1063–1097, https://doi.org/10.5194/se-10-1063-2019, https://doi.org/10.5194/se-10-1063-2019, 2019
Short summary
Short summary
This work was inspired by an effort to numerically reproduce laboratory models of extension tectonics. We tested various set-ups to find a suitable analogue model and in the process systematically charted the impact of set-ups and boundary conditions on model results, a topic poorly described in existing scientific literature. We hope that our model results and the discussion on which specific tectonic settings they could represent may serve as a guide for future (analogue) modeling studies.
Anne Klosterhalfen, Alexander Graf, Nicolas Brüggemann, Clemens Drüe, Odilia Esser, María P. González-Dugo, Günther Heinemann, Cor M. J. Jacobs, Matthias Mauder, Arnold F. Moene, Patrizia Ney, Thomas Pütz, Corinna Rebmann, Mario Ramos Rodríguez, Todd M. Scanlon, Marius Schmidt, Rainer Steinbrecher, Christoph K. Thomas, Veronika Valler, Matthias J. Zeeman, and Harry Vereecken
Biogeosciences, 16, 1111–1132, https://doi.org/10.5194/bg-16-1111-2019, https://doi.org/10.5194/bg-16-1111-2019, 2019
Short summary
Short summary
To obtain magnitudes of flux components of H2O and CO2 (e.g., transpiration, soil respiration), we applied source partitioning approaches after Scanlon and Kustas (2010) and after Thomas et al. (2008) to high-frequency eddy covariance measurements of 12 study sites covering various ecosystems (croplands, grasslands, and forests) in different climatic regions. We analyzed the interrelations among turbulence, site characteristics, and the performance of both partitioning methods.
Bibi S. Naz, Wolfgang Kurtz, Carsten Montzka, Wendy Sharples, Klaus Goergen, Jessica Keune, Huilin Gao, Anne Springer, Harrie-Jan Hendricks Franssen, and Stefan Kollet
Hydrol. Earth Syst. Sci., 23, 277–301, https://doi.org/10.5194/hess-23-277-2019, https://doi.org/10.5194/hess-23-277-2019, 2019
Short summary
Short summary
This study investigates the value of assimilating coarse-resolution remotely sensed soil moisture data into high-resolution land surface models for improving soil moisture and runoff modeling. The soil moisture estimates in this study, with complete spatio-temporal coverage and improved spatial resolution from the assimilation, offer a new reanalysis product for the monitoring of surface soil water content and other hydrological fluxes at 3 km resolution over Europe.
Miguel de la Varga, Alexander Schaaf, and Florian Wellmann
Geosci. Model Dev., 12, 1–32, https://doi.org/10.5194/gmd-12-1-2019, https://doi.org/10.5194/gmd-12-1-2019, 2019
Short summary
Short summary
GemPy is an open-source Python-based 3-D structural geological modeling software, which allows the implicit (i.e. automatic) creation of complex geological models from interface and orientation data. GemPy is implemented in the programming language Python, making use of a highly efficient underlying library, Theano, for efficient code generation that performs automatic differentiation. This enables the link to probabilistic machine-learning and Bayesian inference frameworks.
Nevil Quinn, Günter Blöschl, András Bárdossy, Attilio Castellarin, Martyn Clark, Christophe Cudennec, Demetris Koutsoyiannis, Upmanu Lall, Lubomir Lichner, Juraj Parajka, Christa D. Peters-Lidard, Graham Sander, Hubert Savenije, Keith Smettem, Harry Vereecken, Alberto Viglione, Patrick Willems, Andy Wood, Ross Woods, Chong-Yu Xu, and Erwin Zehe
Proc. IAHS, 380, 3–8, https://doi.org/10.5194/piahs-380-3-2018, https://doi.org/10.5194/piahs-380-3-2018, 2018
Nevil Quinn, Günter Blöschl, András Bárdossy, Attilio Castellarin, Martyn Clark, Christophe Cudennec, Demetris Koutsoyiannis, Upmanu Lall, Lubomir Lichner, Juraj Parajka, Christa D. Peters-Lidard, Graham Sander, Hubert Savenije, Keith Smettem, Harry Vereecken, Alberto Viglione, Patrick Willems, Andy Wood, Ross Woods, Chong-Yu Xu, and Erwin Zehe
Hydrol. Earth Syst. Sci., 22, 5735–5739, https://doi.org/10.5194/hess-22-5735-2018, https://doi.org/10.5194/hess-22-5735-2018, 2018
Mehdi Rahmati, Lutz Weihermüller, Jan Vanderborght, Yakov A. Pachepsky, Lili Mao, Seyed Hamidreza Sadeghi, Niloofar Moosavi, Hossein Kheirfam, Carsten Montzka, Kris Van Looy, Brigitta Toth, Zeinab Hazbavi, Wafa Al Yamani, Ammar A. Albalasmeh, Ma'in Z. Alghzawi, Rafael Angulo-Jaramillo, Antônio Celso Dantas Antonino, George Arampatzis, Robson André Armindo, Hossein Asadi, Yazidhi Bamutaze, Jordi Batlle-Aguilar, Béatrice Béchet, Fabian Becker, Günter Blöschl, Klaus Bohne, Isabelle Braud, Clara Castellano, Artemi Cerdà, Maha Chalhoub, Rogerio Cichota, Milena Císlerová, Brent Clothier, Yves Coquet, Wim Cornelis, Corrado Corradini, Artur Paiva Coutinho, Muriel Bastista de Oliveira, José Ronaldo de Macedo, Matheus Fonseca Durães, Hojat Emami, Iraj Eskandari, Asghar Farajnia, Alessia Flammini, Nándor Fodor, Mamoun Gharaibeh, Mohamad Hossein Ghavimipanah, Teamrat A. Ghezzehei, Simone Giertz, Evangelos G. Hatzigiannakis, Rainer Horn, Juan José Jiménez, Diederik Jacques, Saskia Deborah Keesstra, Hamid Kelishadi, Mahboobeh Kiani-Harchegani, Mehdi Kouselou, Madan Kumar Jha, Laurent Lassabatere, Xiaoyan Li, Mark A. Liebig, Lubomír Lichner, María Victoria López, Deepesh Machiwal, Dirk Mallants, Micael Stolben Mallmann, Jean Dalmo de Oliveira Marques, Miles R. Marshall, Jan Mertens, Félicien Meunier, Mohammad Hossein Mohammadi, Binayak P. Mohanty, Mansonia Pulido-Moncada, Suzana Montenegro, Renato Morbidelli, David Moret-Fernández, Ali Akbar Moosavi, Mohammad Reza Mosaddeghi, Seyed Bahman Mousavi, Hasan Mozaffari, Kamal Nabiollahi, Mohammad Reza Neyshabouri, Marta Vasconcelos Ottoni, Theophilo Benedicto Ottoni Filho, Mohammad Reza Pahlavan-Rad, Andreas Panagopoulos, Stephan Peth, Pierre-Emmanuel Peyneau, Tommaso Picciafuoco, Jean Poesen, Manuel Pulido, Dalvan José Reinert, Sabine Reinsch, Meisam Rezaei, Francis Parry Roberts, David Robinson, Jesús Rodrigo-Comino, Otto Corrêa Rotunno Filho, Tadaomi Saito, Hideki Suganuma, Carla Saltalippi, Renáta Sándor, Brigitta Schütt, Manuel Seeger, Nasrollah Sepehrnia, Ehsan Sharifi Moghaddam, Manoj Shukla, Shiraki Shutaro, Ricardo Sorando, Ajayi Asishana Stanley, Peter Strauss, Zhongbo Su, Ruhollah Taghizadeh-Mehrjardi, Encarnación Taguas, Wenceslau Geraldes Teixeira, Ali Reza Vaezi, Mehdi Vafakhah, Tomas Vogel, Iris Vogeler, Jana Votrubova, Steffen Werner, Thierry Winarski, Deniz Yilmaz, Michael H. Young, Steffen Zacharias, Yijian Zeng, Ying Zhao, Hong Zhao, and Harry Vereecken
Earth Syst. Sci. Data, 10, 1237–1263, https://doi.org/10.5194/essd-10-1237-2018, https://doi.org/10.5194/essd-10-1237-2018, 2018
Short summary
Short summary
This paper presents and analyzes a global database of soil infiltration data, the SWIG database, for the first time. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists or they were digitized from published articles. We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models.
Roland Baatz, Pamela L. Sullivan, Li Li, Samantha R. Weintraub, Henry W. Loescher, Michael Mirtl, Peter M. Groffman, Diana H. Wall, Michael Young, Tim White, Hang Wen, Steffen Zacharias, Ingolf Kühn, Jianwu Tang, Jérôme Gaillardet, Isabelle Braud, Alejandro N. Flores, Praveen Kumar, Henry Lin, Teamrat Ghezzehei, Julia Jones, Henry L. Gholz, Harry Vereecken, and Kris Van Looy
Earth Syst. Dynam., 9, 593–609, https://doi.org/10.5194/esd-9-593-2018, https://doi.org/10.5194/esd-9-593-2018, 2018
Short summary
Short summary
Focusing on the usage of integrated models and in situ Earth observatory networks, three challenges are identified to advance understanding of ESD, in particular to strengthen links between biotic and abiotic, and above- and below-ground processes. We propose developing a model platform for interdisciplinary usage, to formalize current network infrastructure based on complementarities and operational synergies, and to extend the reanalysis concept to the ecosystem and critical zone.
Gaochao Cai, Jan Vanderborght, Matthias Langensiepen, Andrea Schnepf, Hubert Hüging, and Harry Vereecken
Hydrol. Earth Syst. Sci., 22, 2449–2470, https://doi.org/10.5194/hess-22-2449-2018, https://doi.org/10.5194/hess-22-2449-2018, 2018
Short summary
Short summary
Different crop growths had consequences for the parameterization of root water uptake models. The root hydraulic parameters of the Couvreur model but not the water stress parameters of the Feddes–Jarvis model could be constrained by the field data measured from rhizotron facilities. The simulated differences in transpiration from the two soils and the different water treatments could be confirmed by sap flow measurements. The Couvreur model predicted the ratios of transpiration fluxes better.
Hanna Post, Harrie-Jan Hendricks Franssen, Xujun Han, Roland Baatz, Carsten Montzka, Marius Schmidt, and Harry Vereecken
Biogeosciences, 15, 187–208, https://doi.org/10.5194/bg-15-187-2018, https://doi.org/10.5194/bg-15-187-2018, 2018
Short summary
Short summary
Estimated values of selected key CLM4.5-BGC parameters obtained with the Markov chain Monte Carlo (MCMC) approach DREAM(zs) strongly altered catchment-scale NEE predictions in comparison to global default parameter values. The effect of perturbed meteorological input data on the uncertainty of the predicted carbon fluxes was notably higher for C3-grass and C3-crop than for coniferous and deciduous forest. A future distinction of different crop types including management is considered essential.
Hongjuan Zhang, Harrie-Jan Hendricks Franssen, Xujun Han, Jasper A. Vrugt, and Harry Vereecken
Hydrol. Earth Syst. Sci., 21, 4927–4958, https://doi.org/10.5194/hess-21-4927-2017, https://doi.org/10.5194/hess-21-4927-2017, 2017
Short summary
Short summary
Applications of data assimilation (DA) arise in many fields of geosciences, perhaps most importantly in weather forecasting and hydrology. We want to investigate the roles of data assimilation methods and land surface models (LSMs) in joint estimation of states and parameters in the assimilation experiments. We find that all DA methods can improve prediction of states, and that differences between DA methods were limited but that the differences between LSMs were much larger.
Raphael Schneeberger, Miguel de La Varga, Daniel Egli, Alfons Berger, Florian Kober, Florian Wellmann, and Marco Herwegh
Solid Earth, 8, 987–1002, https://doi.org/10.5194/se-8-987-2017, https://doi.org/10.5194/se-8-987-2017, 2017
Short summary
Short summary
Structural 3-D modelling has become a widely used technique within applied projects. We performed a typical modelling workflow for a study site with the occurrence of an underground facility. This exceptional setting enabled us to test the surface-based extrapolation of faults with the mapped faults underground. We estimated the extrapolation-related uncertainty with probabilistic 2-D interpolation. This research was conducted to improve structural 3-D modelling in less-constrained areas.
Carsten Montzka, Michael Herbst, Lutz Weihermüller, Anne Verhoef, and Harry Vereecken
Earth Syst. Sci. Data, 9, 529–543, https://doi.org/10.5194/essd-9-529-2017, https://doi.org/10.5194/essd-9-529-2017, 2017
Short summary
Short summary
Global climate models require adequate parameterization of soil hydraulic properties, but typical resampling to the model grid introduces uncertainties. Here we present a method to scale hydraulic parameters to individual model grids and provide a global data set that overcomes the problems. It preserves the information of sub-grid variability of the water retention curve by deriving local scaling parameters that enables modellers to perturb hydraulic parameters for model ensemble generation.
Roland Baatz, Harrie-Jan Hendricks Franssen, Xujun Han, Tim Hoar, Heye Reemt Bogena, and Harry Vereecken
Hydrol. Earth Syst. Sci., 21, 2509–2530, https://doi.org/10.5194/hess-21-2509-2017, https://doi.org/10.5194/hess-21-2509-2017, 2017
Short summary
Short summary
Soil moisture is a major variable that affects regional climate, weather and hydrologic processes on the Earth's surface. In this study, real-world data of a network of cosmic-ray sensors were assimilated into a regional land surface model to improve model states and soil hydraulic parameters. The results show the potential of these networks for improving model states and parameters. It is suggested to widen the number of observed variables and to increase the number of estimated parameters.
Xiaoqian Jiang, Roland Bol, Barbara J. Cade-Menun, Volker Nischwitz, Sabine Willbold, Sara L. Bauke, Harry Vereecken, Wulf Amelung, and Erwin Klumpp
Biogeosciences, 14, 1153–1164, https://doi.org/10.5194/bg-14-1153-2017, https://doi.org/10.5194/bg-14-1153-2017, 2017
Short summary
Short summary
It is the first study to distinguish the species of nano-sized (d=1−20 nm), small-sized (d=20−450 nm) colloidal P, and dissolved P (d<1 nm) of hydromorphic surface grassland soils from Cambisol, Stagnic Cambisol to Stagnosol using FFF and 31P-NMR. Evidence of nano-sized associations of OC–Fe(Al)–PO43/pyrophosphate in Stagnosol. Stagnic properties affect P speciation and availability by releasing dissolved inorganic and ester-bound P forms as well as nano-sized organic matter–Fe/Al–P colloids.
Bernd Schalge, Jehan Rihani, Gabriele Baroni, Daniel Erdal, Gernot Geppert, Vincent Haefliger, Barbara Haese, Pablo Saavedra, Insa Neuweiler, Harrie-Jan Hendricks Franssen, Felix Ament, Sabine Attinger, Olaf A. Cirpka, Stefan Kollet, Harald Kunstmann, Harry Vereecken, and Clemens Simmer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-557, https://doi.org/10.5194/hess-2016-557, 2016
Manuscript not accepted for further review
Short summary
Short summary
In this work we show how we used a coupled atmosphere-land surface-subsurface model at highest possible resolution to create a testbed for data assimilation. The model was able to capture all important processes and interactions between the compartments as well as showing realistic statistical behavior. This proves that using a model as a virtual truth is possible and it will enable us to develop data assimilation methods where states and parameters are updated across compartment.
Wei Qu, Heye R. Bogena, Johan A. Huisman, Marius Schmidt, Ralf Kunkel, Ansgar Weuthen, Henning Schiedung, Bernd Schilling, Jürgen Sorg, and Harry Vereecken
Earth Syst. Sci. Data, 8, 517–529, https://doi.org/10.5194/essd-8-517-2016, https://doi.org/10.5194/essd-8-517-2016, 2016
Short summary
Short summary
The Rollesbroich catchment is a hydrological observatory of the TERENO (Terrestrial Environmental Observatories) initiative. Hydrometeorological data and spatiotemporal variations in soil water content are measured at high temporal resolution and can be used for many purposes, e.g. validation of remote sensing retrievals, improving hydrological understanding, optimizing data assimilation and inverse modelling techniques. The data set is freely available online (http://www.tereno.net).
Wolfgang Kurtz, Guowei He, Stefan J. Kollet, Reed M. Maxwell, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 9, 1341–1360, https://doi.org/10.5194/gmd-9-1341-2016, https://doi.org/10.5194/gmd-9-1341-2016, 2016
Short summary
Short summary
This paper describes the development of a modular data assimilation (DA) system for the integrated Earth system model TerrSysMP with the help of the PDAF data assimilation library.
Currently, pressure and soil moisture data can be used to update model states and parameters in the subsurface compartment of TerrSysMP.
Results from an idealized twin experiment show that the developed DA system provides a good parallel performance and is also applicable for high-resolution modelling problems.
J. Florian Wellmann, Sam T. Thiele, Mark D. Lindsay, and Mark W. Jessell
Geosci. Model Dev., 9, 1019–1035, https://doi.org/10.5194/gmd-9-1019-2016, https://doi.org/10.5194/gmd-9-1019-2016, 2016
Short summary
Short summary
We often obtain knowledge about the subsurface in the form of structural geological models, as a basis for subsurface usage or resource extraction. Here, we provide a modelling code to construct such models on the basis of significant deformational events in geological history, encapsulated in kinematic equations. Our methods simplify complex dynamic processes, but enable us to evaluate how events interact, and finally how certain we are about predictions of structures in the subsurface.
X. Jiang, R. Bol, S. Willbold, H. Vereecken, and E. Klumpp
Biogeosciences, 12, 6443–6452, https://doi.org/10.5194/bg-12-6443-2015, https://doi.org/10.5194/bg-12-6443-2015, 2015
Short summary
Short summary
Overall P content increased with decreasing size of soil aggregate-sized fractions. The relative distribution and speciation of varying P forms were independent of particle size. The majority of alkaline extractable P was in the amorphous Fe/Al oxide fraction, most of which was orthophosphate. Significant amounts of monoester P were also bound to these oxides. Residual P contained similar amounts of P occluded in amorphous and crystalline Fe oxides. This P may be released by FeO dissolution.
Y. Rothfuss, S. Merz, J. Vanderborght, N. Hermes, A. Weuthen, A. Pohlmeier, H. Vereecken, and N. Brüggemann
Hydrol. Earth Syst. Sci., 19, 4067–4080, https://doi.org/10.5194/hess-19-4067-2015, https://doi.org/10.5194/hess-19-4067-2015, 2015
Short summary
Short summary
Profiles of soil water stable isotopes were followed non-destructively and with high precision for a period of 290 days in the laboratory
Rewatering at the end of the experiment led to instantaneous resetting of the isotope profiles, which could be closely followed with the new method
The evaporation depth dynamics was determined from isotope gradients calculation
Uncertainty associated with the determination of isotope kinetic fractionation where highlighted from inverse modeling.
X. Han, X. Li, G. He, P. Kumbhar, C. Montzka, S. Kollet, T. Miyoshi, R. Rosolem, Y. Zhang, H. Vereecken, and H.-J. H. Franssen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-8-7395-2015, https://doi.org/10.5194/gmdd-8-7395-2015, 2015
Revised manuscript not accepted
Short summary
Short summary
DasPy is a ready to use open source parallel multivariate land data assimilation framework with joint state and parameter estimation using Local Ensemble Transform Kalman Filter. The Community Land Model (4.5) was integrated as model operator. The Community Microwave Emission Modelling platform, COsmic-ray Soil Moisture Interaction Code and the Two-Source Formulation were integrated as observation operators for the multivariate assimilation of soil moisture and soil temperature, respectively.
S. Gebler, H.-J. Hendricks Franssen, T. Pütz, H. Post, M. Schmidt, and H. Vereecken
Hydrol. Earth Syst. Sci., 19, 2145–2161, https://doi.org/10.5194/hess-19-2145-2015, https://doi.org/10.5194/hess-19-2145-2015, 2015
H. Post, H. J. Hendricks Franssen, A. Graf, M. Schmidt, and H. Vereecken
Biogeosciences, 12, 1205–1221, https://doi.org/10.5194/bg-12-1205-2015, https://doi.org/10.5194/bg-12-1205-2015, 2015
Short summary
Short summary
This study introduces an extension of the classical two-tower approach for uncertainty estimation of measured net CO2 fluxes (NEE). Because land surface properties cannot be assumed identical at two eddy covariance towers, a correction for systematic flux differences is proposed to be added to the classical weather filter. With this extension, the overestimation of NEE uncertainty due to systematic flux differences (which are assumed to increase with tower distance) can considerably be reduced.
B. Scharnagl, S. C. Iden, W. Durner, H. Vereecken, and M. Herbst
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-12-2155-2015, https://doi.org/10.5194/hessd-12-2155-2015, 2015
Preprint withdrawn
X. Han, H.-J. H. Franssen, R. Rosolem, R. Jin, X. Li, and H. Vereecken
Hydrol. Earth Syst. Sci., 19, 615–629, https://doi.org/10.5194/hess-19-615-2015, https://doi.org/10.5194/hess-19-615-2015, 2015
Short summary
Short summary
This paper presents the joint assimilation of cosmic-ray neutron counts and land surface temperature with parameter estimation of leaf area index at an irrigated corn field. The results show that the data assimilation can reduce the systematic input errors due to the lack of irrigation data. The estimations of soil moisture, evapotranspiration and leaf area index can be improved in the joint assimilation framework.
J. L. Tetreault and S. J. H. Buiter
Solid Earth, 5, 1243–1275, https://doi.org/10.5194/se-5-1243-2014, https://doi.org/10.5194/se-5-1243-2014, 2014
Short summary
Short summary
Continents are composed of a collage of accreted terranes: tectonically sutured crustal units of various origins. This review covers the cycle of terrane accretion from the original entity (modern-day oceanic island arcs, oceanic plateaus, submarine ridges, seamounts, continental fragments, and microcontinents) to present-day examples of terrane accretion to finally allochthonous accreted terranes.
M. E. T. Quinquis and S. J. H. Buiter
Solid Earth, 5, 537–555, https://doi.org/10.5194/se-5-537-2014, https://doi.org/10.5194/se-5-537-2014, 2014
W. Kurtz, H.-J. Hendricks Franssen, P. Brunner, and H. Vereecken
Hydrol. Earth Syst. Sci., 17, 3795–3813, https://doi.org/10.5194/hess-17-3795-2013, https://doi.org/10.5194/hess-17-3795-2013, 2013
V. R. N. Pauwels, G. J. M. De Lannoy, H.-J. Hendricks Franssen, and H. Vereecken
Hydrol. Earth Syst. Sci., 17, 3499–3521, https://doi.org/10.5194/hess-17-3499-2013, https://doi.org/10.5194/hess-17-3499-2013, 2013
Related subject area
Numerical methods
The Measurement Error Proxy System Model: MEPSM v0.2
Numerical stabilization methods for level-set-based ice front migration
Modelling chemical advection during magma ascent
Consistent point data assimilation in Firedrake and Icepack
A Joint Reconstruction and Model Selection Approach for Large Scale Inverse Modeling
A computationally efficient parameterization of aerosol, cloud and precipitation pH for application at global and regional scale (EQSAM4Clim-v12)
Assessing the benefits of approximately exact step sizes for Picard and Newton solver in simulating ice flow (FEniCS-full-Stokes v.1.3.2)
Assessing effects of climate and technology uncertainties in large natural resource allocation problems
VISIR-2: ship weather routing in Python
Incremental analysis update (IAU) in the Model for Prediction Across Scales coupled with the Joint Effort for Data assimilation Integration (MPAS–JEDI 2.0.0)
Decision-making strategies implemented in SolFinder 1.0 to identify eco-efficient aircraft trajectories: application study in AirTraf 3.0
Developing meshing workflows in Gmsh v4.11 for the geologic uncertainty assessment of high-temperature aquifer thermal energy storage
Development and preliminary validation of a land surface image assimilation system based on the Common Land Model
NorSand4AI: a comprehensive triaxial test simulation database for NorSand constitutive model materials
Explicit stochastic advection algorithms for the regional scale particle-resolved atmospheric aerosol model WRF-PartMC (v1.0)
ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package
HETerogeneous vectorized or Parallel (HETPv1.0): an updated inorganic heterogeneous chemistry solver for the metastable-state NH4+–Na+–Ca2+–K+–Mg2+–SO42−–NO3−–Cl−–H2O system based on ISORROPIA II
Three-dimensional geological modelling of igneous intrusions in LoopStructural v1.5.10
Estimating volcanic ash emissions using retrieved satellite ash columns and inverse ash transport modeling using VolcanicAshInversion v1.2.1, within the operational eEMEP (emergency European Monitoring and Evaluation Programme) volcanic plume forecasting system (version rv4_17)
Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding
An automatic mesh generator for coupled 1D–2D hydrodynamic models
Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 1: Dust budget analyses and the impacts of a revised coupling scheme
Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 2: A semi-discrete error analysis framework for assessing coupling schemes
jsmetrics v0.2.0: a Python package for metrics and algorithms used to identify or characterise atmospheric jet streams
The Paleochrono-1.1 probabilistic model to derive optimized and consistent chronologies for several paleoclimatic sites
P3D-BRNS v1.0.0: a three-dimensional, multiphase, multicomponent, pore-scale reactive transport modelling package for simulating biogeochemical processes in subsurface environments
MinVoellmy v1: a lightweight model for simulating rapid mass movements based on a modified Voellmy rheology
Scalable Feature Extraction and Tracking (SCAFET): a general framework for feature extraction from large climate data sets
Sweep interpolation: a cost-effective semi-Lagrangian scheme in the Global Environmental Multiscale model
CHONK 1.0: landscape evolution framework: cellular automata meets graph theory
Calibration of absorbing boundary layers for geoacoustic wave modeling in pseudo-spectral time-domain methods
Assimilation of snow water equivalent from AMSR2 and IMS satellite data utilizing the local ensemble transform Kalman filter
GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling
A comparison of Eulerian and Lagrangian methods for vertical particle transport in the water column
AutoQS v1: automatic parametrization of QuickSampling based on training images analysis
Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions
A dynamical core based on a discontinuous Galerkin method for higher-order finite-element sea ice modeling
GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation
Leveraging Google's Tensor Processing Units for tsunami-risk mitigation planning in the Pacific Northwest and beyond
An improved subgrid channel model with upwind-form artificial diffusion for river hydrodynamics and floodplain inundation simulation
A model instability issue in the National Centers for Environmental Prediction Global Forecast System version 16 and potential solutions
A comparison of 3-D spherical shell thermal convection results at low to moderate Rayleigh number using ASPECT (version 2.2.0) and CitcomS (version 3.3.1)
LISFLOOD-FP 8.1: new GPU-accelerated solvers for faster fluvial/pluvial flood simulations
Fast approximate Barnes interpolation: illustrated by Python-Numba implementation fast-barnes-py v1.0
Strategies for conservative and non-conservative monotone remapping on the sphere
Modeling large‐scale landform evolution with a stream power law for glacial erosion (OpenLEM v37): benchmarking experiments against a more process-based description of ice flow (iSOSIA v3.4.3)
A mixed finite-element discretisation of the shallow-water equations
Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling
Massively parallel modeling and inversion of electrical resistivity tomography data using PFLOTRAN
Parallelized domain decomposition for multi-dimensional Lagrangian random walk mass-transfer particle tracking schemes
Matt J. Fischer
Geosci. Model Dev., 17, 6745–6760, https://doi.org/10.5194/gmd-17-6745-2024, https://doi.org/10.5194/gmd-17-6745-2024, 2024
Short summary
Short summary
In paleoclimate research, proxy system models (PSMs) model chemical or biological systems which receive environmental input and output (e.g., geochemical signals). The environmental inputs are rarely noiseless, which causes problems when calibrating multi-input PSMs. Here a PSM is developed which includes generalized noise in both model inputs and outputs and prior information. A quasi-Bayesian method enhances the stability of the solution of the Measurement Error Proxy System Model.
Gong Cheng, Mathieu Morlighem, and G. Hilmar Gudmundsson
Geosci. Model Dev., 17, 6227–6247, https://doi.org/10.5194/gmd-17-6227-2024, https://doi.org/10.5194/gmd-17-6227-2024, 2024
Short summary
Short summary
We conducted a comprehensive analysis of the stabilization and reinitialization techniques currently employed in ISSM and Úa for solving level-set equations, specifically those related to the dynamic representation of moving ice fronts within numerical ice sheet models. Our results demonstrate that the streamline upwind Petrov–Galerkin (SUPG) method outperforms the other approaches. We found that excessively frequent reinitialization can lead to exceptionally high errors in simulations.
Hugo Dominguez, Nicolas Riel, and Pierre Lanari
Geosci. Model Dev., 17, 6105–6122, https://doi.org/10.5194/gmd-17-6105-2024, https://doi.org/10.5194/gmd-17-6105-2024, 2024
Short summary
Short summary
Predicting the behaviour of magmatic systems is important for understanding Earth's matter and heat transport. Numerical modelling is a technique that can predict complex systems at different scales of space and time by solving equations using various techniques. This study tests four algorithms to find the best way to transport the melt composition. The "weighted essentially non-oscillatory" algorithm emerges as the best choice, minimising errors and preserving system mass well.
Reuben W. Nixon-Hill, Daniel Shapero, Colin J. Cotter, and David A. Ham
Geosci. Model Dev., 17, 5369–5386, https://doi.org/10.5194/gmd-17-5369-2024, https://doi.org/10.5194/gmd-17-5369-2024, 2024
Short summary
Short summary
Scientists often use models to study complex processes, like the movement of ice sheets, and compare them to measurements for estimating quantities that are hard to measure. We highlight an approach that ensures accurate results from point data sources (e.g. height measurements) by evaluating the numerical solution at true point locations. This method improves accuracy, aids communication between scientists, and is well-suited for integration with specialised software that automates processes.
Malena Sabaté Landman, Julianne Chung, Jiahua Jiang, Scot Miller, and Arvind Saibaba
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-90, https://doi.org/10.5194/gmd-2024-90, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Making an informed decision about what prior information to incorporate or discard in an inverse model is important yet very challenging, as it is often not straightforward to distinguish between informative and non-informative variables. In this study, we develop a new approach for incorporating prior information in an inverse model using predictor variables, while simultaneously selecting the relevant predictor variables for the estimation of the unknown quantity of interest.
Swen Metzger, Samuel Rémy, Jason E. Williams, Vincent Huijnen, and Johannes Flemming
Geosci. Model Dev., 17, 5009–5021, https://doi.org/10.5194/gmd-17-5009-2024, https://doi.org/10.5194/gmd-17-5009-2024, 2024
Short summary
Short summary
EQSAM4Clim has recently been revised to provide an accurate and efficient method for calculating the acidity of atmospheric particles. It is based on an analytical concept that is sufficiently fast and free of numerical noise, which makes it attractive for air quality forecasting. Version 12 allows the calculation of aerosol composition based on the gas–liquid–solid and the reduced gas–liquid partitioning with the associated water uptake for both cases, including the acidity of the aerosols.
Niko Schmidt, Angelika Humbert, and Thomas Slawig
Geosci. Model Dev., 17, 4943–4959, https://doi.org/10.5194/gmd-17-4943-2024, https://doi.org/10.5194/gmd-17-4943-2024, 2024
Short summary
Short summary
Future sea-level rise is of big significance for coastal regions. The melting and acceleration of glaciers plays a major role in sea-level change. Computer simulation of glaciers costs a lot of computational resources. In this publication, we test a new way of simulating glaciers. This approach produces the same results but has the advantage that it needs much less computation time. As simulations can be obtained with fewer computation resources, higher resolution and physics become affordable.
Jevgenijs Steinbuks, Yongyang Cai, Jonas Jaegermeyr, and Thomas W. Hertel
Geosci. Model Dev., 17, 4791–4819, https://doi.org/10.5194/gmd-17-4791-2024, https://doi.org/10.5194/gmd-17-4791-2024, 2024
Short summary
Short summary
This paper applies a cutting-edge numerical method, SCEQ, to show how uncertain climate change and technological progress affect the future utilization of the world's scarce land resources. The paper's key insight is to illustrate how much global cropland will expand when future crop yields are unknown. The study finds the range of outcomes for land use change to be smaller when using this novel method compared to existing deterministic models.
Gianandrea Mannarini, Mario Leonardo Salinas, Lorenzo Carelli, Nicola Petacco, and Josip Orović
Geosci. Model Dev., 17, 4355–4382, https://doi.org/10.5194/gmd-17-4355-2024, https://doi.org/10.5194/gmd-17-4355-2024, 2024
Short summary
Short summary
Ship weather routing has the potential to reduce CO2 emissions, but it currently lacks open and verifiable research. The Python-refactored VISIR-2 model considers currents, waves, and wind to optimise routes. The model was validated, and its computational performance is quasi-linear. For a ferry sailing in the Mediterranean Sea, VISIR-2 yields the largest percentage emission savings for upwind navigation. Given the vessel performance curve, the model is generalisable across various vessel types.
Soyoung Ha, Jonathan J. Guerrette, Ivette Hernández Baños, William C. Skamarock, and Michael G. Duda
Geosci. Model Dev., 17, 4199–4211, https://doi.org/10.5194/gmd-17-4199-2024, https://doi.org/10.5194/gmd-17-4199-2024, 2024
Short summary
Short summary
To mitigate the imbalances in the initial conditions, this study introduces our recent implementation of the incremental analysis update (IAU) in the Model for Prediction Across Scales – Atmospheric (MPAS-A) component coupled with the Joint Effort for Data assimilation Integration (JEDI) through the cycling system. A month-long cycling run demonstrates the successful implementation of the IAU capability in the MPAS–JEDI cycling system.
Federica Castino, Feijia Yin, Volker Grewe, Hiroshi Yamashita, Sigrun Matthes, Simone Dietmüller, Sabine Baumann, Manuel Soler, Abolfazl Simorgh, Maximilian Mendiguchia Meuser, Florian Linke, and Benjamin Lührs
Geosci. Model Dev., 17, 4031–4052, https://doi.org/10.5194/gmd-17-4031-2024, https://doi.org/10.5194/gmd-17-4031-2024, 2024
Short summary
Short summary
We introduce SolFinder 1.0, a decision-making tool to select trade-offs between different objective functions for optimal aircraft trajectories, including fuel use, flight time, NOx emissions, contrail distance, and climate impact. The module is included in the AirTraf 3.0 submodel and uses weather conditions simulated by the EMAC atmospheric model. This paper focuses on the ability of SolFinder to identify eco-efficient trajectories, reducing a flight's climate impact at limited cost penalties.
Ali Dashti, Jens C. Grimmer, Christophe Geuzaine, Florian Bauer, and Thomas Kohl
Geosci. Model Dev., 17, 3467–3485, https://doi.org/10.5194/gmd-17-3467-2024, https://doi.org/10.5194/gmd-17-3467-2024, 2024
Short summary
Short summary
This study developed new meshing workflows to enable the automatic generation of meshes that follow geological models. The workflow allows for importing several geological models as input for Gmsh and later exporting the same number of high-quality meshes. This way, geological uncertainty is directly included in the numerical simulations. This study evaluates the impact of the geological uncertainty on thermohydraulic performance of two reservoirs for high-temperature heat storage applications.
Wangbin Shen, Zhaohui Lin, Zhengkun Qin, and Juan Li
Geosci. Model Dev., 17, 3447–3465, https://doi.org/10.5194/gmd-17-3447-2024, https://doi.org/10.5194/gmd-17-3447-2024, 2024
Short summary
Short summary
In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The findings demonstrate that the assimilation of surface soil moisture observation images effectively and reasonably enhances the spatial structure of soil moisture analysis field.
Luan Carlos de Sena Monteiro Ozelim, Michéle Dal Toé Casagrande, and André Luís Brasil Cavalcante
Geosci. Model Dev., 17, 3175–3197, https://doi.org/10.5194/gmd-17-3175-2024, https://doi.org/10.5194/gmd-17-3175-2024, 2024
Short summary
Short summary
The paper addresses synthetic dataset challenges in nonlinear constitutive modeling of soils, providing two datasets: one with 2000 soil types, 40 test conditions each (160 000 triaxial tests), and a second with 2048 soil types, 42 test conditions each (172 032 triaxial tests). Each dataset is a 4000×10 matrix applicable for multivariate forecasting and geotechnical simulations. In addition, a new Python code is introduced, empowering researchers to leverage Python packages for NorSand analyses.
Jeffrey Henry Curtis, Nicole Riemer, and Matthew West
EGUsphere, https://doi.org/10.5194/egusphere-2024-825, https://doi.org/10.5194/egusphere-2024-825, 2024
Short summary
Short summary
This paper introduces a numerical method for simulating particle-based aerosol transport in atmospheric models. We detail the various advection order method’s numerical properties and demonstrate its implementation in a 3D weather prediction model (WRF) for the first time. Particle-based techniques improve the accuracy of aerosol size and composition predictions, which are key for aerosol-cloud and aerosol-radiation interactions.
Daniel Giles, Matthew M. Graham, Mosè Giordano, Tuomas Koskela, Alexandros Beskos, and Serge Guillas
Geosci. Model Dev., 17, 2427–2445, https://doi.org/10.5194/gmd-17-2427-2024, https://doi.org/10.5194/gmd-17-2427-2024, 2024
Short summary
Short summary
Digital twins of physical and human systems informed by real-time data are becoming ubiquitous across a wide range of settings. Progress for researchers is currently limited by a lack of tools to run these models effectively and efficiently. A key challenge is the optimal use of high-performance computing environments. The work presented here focuses on a developed open-source software platform which aims to improve this usage, with an emphasis placed on flexibility, efficiency, and scalability.
Stefan J. Miller, Paul A. Makar, and Colin J. Lee
Geosci. Model Dev., 17, 2197–2219, https://doi.org/10.5194/gmd-17-2197-2024, https://doi.org/10.5194/gmd-17-2197-2024, 2024
Short summary
Short summary
This work outlines a new solver written in Fortran to calculate the partitioning of metastable aerosols at thermodynamic equilibrium based on the forward algorithms of ISORROPIA II. The new code includes numerical improvements that decrease the computational speed (compared to ISORROPIA II) while improving the accuracy of the partitioning solution.
Fernanda Alvarado-Neves, Laurent Ailleres, Lachlan Grose, Alexander R. Cruden, and Robin Armit
Geosci. Model Dev., 17, 1975–1993, https://doi.org/10.5194/gmd-17-1975-2024, https://doi.org/10.5194/gmd-17-1975-2024, 2024
Short summary
Short summary
Previous work has demonstrated that adding geological knowledge to modelling methods creates more accurate and reliable models. Following this reasoning, we added constraints from magma emplacement mechanisms into existing modelling frameworks to improve the 3D characterisation of igneous intrusions. We tested the method on synthetic and real-world case studies, and the results show that our method can reproduce intrusion morphologies with no manual processing and using realistic datasets.
André R. Brodtkorb, Anna Benedictow, Heiko Klein, Arve Kylling, Agnes Nyiri, Alvaro Valdebenito, Espen Sollum, and Nina Kristiansen
Geosci. Model Dev., 17, 1957–1974, https://doi.org/10.5194/gmd-17-1957-2024, https://doi.org/10.5194/gmd-17-1957-2024, 2024
Short summary
Short summary
It is vital to know the extent and concentration of volcanic ash in the atmosphere during a volcanic eruption. Whilst satellite imagery may give an estimate of the ash right now (assuming no cloud coverage), we also need to know where it will be in the coming hours. This paper presents a method for estimating parameters for a volcanic eruption based on satellite observations of ash in the atmosphere. The software package is open source and applicable to similar inversion scenarios.
Kees Nederhoff, Maarten van Ormondt, Jay Veeramony, Ap van Dongeren, José Antonio Álvarez Antolínez, Tim Leijnse, and Dano Roelvink
Geosci. Model Dev., 17, 1789–1811, https://doi.org/10.5194/gmd-17-1789-2024, https://doi.org/10.5194/gmd-17-1789-2024, 2024
Short summary
Short summary
Forecasting tropical cyclones and their flooding impact is challenging. Our research introduces the Tropical Cyclone Forecasting Framework (TC-FF), enhancing cyclone predictions despite uncertainties. TC-FF generates global wind and flood scenarios, valuable even in data-limited regions. Applied to cases like Cyclone Idai, it showcases potential in bettering disaster preparation, marking progress in handling cyclone threats.
Younghun Kang and Ethan J. Kubatko
Geosci. Model Dev., 17, 1603–1625, https://doi.org/10.5194/gmd-17-1603-2024, https://doi.org/10.5194/gmd-17-1603-2024, 2024
Short summary
Short summary
Models used to simulate the flow of coastal and riverine waters, including flooding, require a geometric representation (or mesh) of geographic features that exhibit a range of disparate spatial scales from large, open waters to small, narrow channels. Representing these features in an accurate way without excessive computational overhead presents a challenge. Here, we develop an automatic mesh generation tool to help address this challenge. Our results demonstrate the efficacy of our approach.
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024, https://doi.org/10.5194/gmd-17-1387-2024, 2024
Short summary
Short summary
Sophisticated numerical models of the Earth's atmosphere include representations of many physical and chemical processes. In numerical simulations, these processes need to be calculated in a certain sequence. This study reveals the weaknesses of the sequence of calculations used for aerosol processes in a global atmosphere model. A revision of the sequence is proposed and its impacts on the simulated global aerosol climatology are evaluated.
Christopher J. Vogl, Hui Wan, Carol S. Woodward, and Quan M. Bui
Geosci. Model Dev., 17, 1409–1428, https://doi.org/10.5194/gmd-17-1409-2024, https://doi.org/10.5194/gmd-17-1409-2024, 2024
Short summary
Short summary
Generally speaking, accurate climate simulation requires an accurate evolution of the underlying mathematical equations on large computers. The equations are typically formulated and evolved in process groups. Process coupling refers to how the evolution of each group is combined with that of other groups to evolve the full set of equations for the whole atmosphere. This work presents a mathematical framework to evaluate methods without the need to first implement the methods.
Tom Keel, Chris Brierley, and Tamsin Edwards
Geosci. Model Dev., 17, 1229–1247, https://doi.org/10.5194/gmd-17-1229-2024, https://doi.org/10.5194/gmd-17-1229-2024, 2024
Short summary
Short summary
Jet streams are an important control on surface weather as their speed and shape can modify the properties of weather systems. Establishing trends in the operation of jet streams may provide some indication of the future of weather in a warming world. Despite this, it has not been easy to establish trends, as many methods have been used to characterise them in data. We introduce a tool containing various implementations of jet stream statistics and algorithms that works in a standardised manner.
Frédéric Parrenin, Marie Bouchet, Christo Buizert, Emilie Capron, Ellen Corrick, Russell Drysdale, Kenji Kawamura, Amaëlle Landais, Robert Mulvaney, Ikumi Oyabu, and Sune Rasmussen
EGUsphere, https://doi.org/10.5194/egusphere-2023-2911, https://doi.org/10.5194/egusphere-2023-2911, 2024
Short summary
Short summary
The Paleochrono1 probablistic dating model allows to derive a common and optimized chronology for several paleoclimatic sites from various archives (ice cores, speleothems, marine cores, lake cores, etc.). It combines prior sedimentation scenarios with chronological information such as dated horizons, dated intervals, stratigraphic links and (for ice cores) Delta-depth observations. Paleochrono1 is available under the MIT open-source license.
Amir Golparvar, Matthias Kästner, and Martin Thullner
Geosci. Model Dev., 17, 881–898, https://doi.org/10.5194/gmd-17-881-2024, https://doi.org/10.5194/gmd-17-881-2024, 2024
Short summary
Short summary
Coupled reaction transport modelling is an established and beneficial method for studying natural and synthetic porous material, with applications ranging from industrial processes to natural decompositions in terrestrial environments. Up to now, a framework that explicitly considers the porous structure (e.g. from µ-CT images) for modelling the transport of reactive species is missing. We presented a model that overcomes this limitation and represents a novel numerical simulation toolbox.
Stefan Hergarten
Geosci. Model Dev., 17, 781–794, https://doi.org/10.5194/gmd-17-781-2024, https://doi.org/10.5194/gmd-17-781-2024, 2024
Short summary
Short summary
The Voellmy rheology has been widely used for simulating snow and rock avalanches. Recently, a modified version of this rheology was proposed, which turned out to be able to predict the observed long runout of large rock avalanches theoretically. The software MinVoellmy presented here is the first numerical implementation of the modified rheology. It consists of MATLAB and Python classes, where simplicity and parsimony were the design goals.
Arjun Babu Nellikkattil, Danielle Lemmon, Travis Allen O'Brien, June-Yi Lee, and Jung-Eun Chu
Geosci. Model Dev., 17, 301–320, https://doi.org/10.5194/gmd-17-301-2024, https://doi.org/10.5194/gmd-17-301-2024, 2024
Short summary
Short summary
This study introduces a new computational framework called Scalable Feature Extraction and Tracking (SCAFET), designed to extract and track features in climate data. SCAFET stands out by using innovative shape-based metrics to identify features without relying on preconceived assumptions about the climate model or mean state. This approach allows more accurate comparisons between different models and scenarios.
Mohammad Mortezazadeh, Jean-François Cossette, Ashu Dastoor, Jean de Grandpré, Irena Ivanova, and Abdessamad Qaddouri
Geosci. Model Dev., 17, 335–346, https://doi.org/10.5194/gmd-17-335-2024, https://doi.org/10.5194/gmd-17-335-2024, 2024
Short summary
Short summary
The interpolation process is the most computationally expensive step of the semi-Lagrangian (SL) approach. In this paper we implement a new interpolation scheme into the semi-Lagrangian approach which has the same computational cost as a third-order polynomial scheme but with the accuracy of a fourth-order interpolation scheme. This improvement is achieved by using two third-order backward and forward polynomial interpolation schemes in two consecutive time steps.
Boris Gailleton, Luca C. Malatesta, Guillaume Cordonnier, and Jean Braun
Geosci. Model Dev., 17, 71–90, https://doi.org/10.5194/gmd-17-71-2024, https://doi.org/10.5194/gmd-17-71-2024, 2024
Short summary
Short summary
This contribution presents a new method to numerically explore the evolution of mountain ranges and surrounding areas. The method helps in monitoring with details on the timing and travel path of material eroded from the mountain ranges. It is particularly well suited to studies juxtaposing different domains – lakes or multiple rock types, for example – and enables the combination of different processes.
Carlos Spa, Otilio Rojas, and Josep de la Puente
Geosci. Model Dev., 16, 7237–7252, https://doi.org/10.5194/gmd-16-7237-2023, https://doi.org/10.5194/gmd-16-7237-2023, 2023
Short summary
Short summary
This paper develops a calibration methodology of all absorbing techniques typically used by Fourier pseudo-spectral time-domain (PSTD) methods for geoacoustic wave simulations. The main contributions of the paper are (a) an implementation and quantitative comparison of all absorbing techniques available for PSTD methods through a simple and robust numerical experiment, and (b) a validation of these absorbing techniques in several 3-D seismic scenarios with gradual heterogeneity complexities.
Joonlee Lee, Myong-In Lee, Sunlae Tak, Eunkyo Seo, and Yong-Keun Lee
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-221, https://doi.org/10.5194/gmd-2023-221, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
We developed a snow assimilation system using satellite data based on a land surface model. The snow states produced by the assimilation system demonstrate high performance in all regions, including transition regions, compared to the satellite data and land model. As snow significantly influences energy and water balance at the atmosphere-land boundary, this approach allows for a more accurate prediction of atmospheric conditions by realistically representing atmosphere-land interactions.
Michael Hillier, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard
Geosci. Model Dev., 16, 6987–7012, https://doi.org/10.5194/gmd-16-6987-2023, https://doi.org/10.5194/gmd-16-6987-2023, 2023
Short summary
Short summary
Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and structural orientations. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and improved data fitting techniques. These advances permit the modelling of more complex geology in diverse geological settings, different-sized areas, and various data regimes.
Tor Nordam, Ruben Kristiansen, Raymond Nepstad, Erik van Sebille, and Andy M. Booth
Geosci. Model Dev., 16, 5339–5363, https://doi.org/10.5194/gmd-16-5339-2023, https://doi.org/10.5194/gmd-16-5339-2023, 2023
Short summary
Short summary
We describe and compare two common methods, Eulerian and Lagrangian models, used to simulate the vertical transport of material in the ocean. They both solve the same transport problems but use different approaches for representing the underlying equations on the computer. The main focus of our study is on the numerical accuracy of the two approaches. Our results should be useful for other researchers creating or using these types of transport models.
Mathieu Gravey and Grégoire Mariethoz
Geosci. Model Dev., 16, 5265–5279, https://doi.org/10.5194/gmd-16-5265-2023, https://doi.org/10.5194/gmd-16-5265-2023, 2023
Short summary
Short summary
Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.
Siting Li, Ping Wang, Hong Wang, Yue Peng, Zhaodong Liu, Wenjie Zhang, Hongli Liu, Yaqiang Wang, Huizheng Che, and Xiaoye Zhang
Geosci. Model Dev., 16, 4171–4191, https://doi.org/10.5194/gmd-16-4171-2023, https://doi.org/10.5194/gmd-16-4171-2023, 2023
Short summary
Short summary
Optimizing the initial state of atmospheric chemistry model input is one of the most essential methods to improve forecast accuracy. Considering the large computational load of the model, we introduce an ensemble optimal interpolation scheme (EnOI) for operational use and efficient updating of the initial fields of chemical components. The results suggest that EnOI provides a practical and cost-effective technique for improving the accuracy of chemical weather numerical forecasts.
Thomas Richter, Véronique Dansereau, Christian Lessig, and Piotr Minakowski
Geosci. Model Dev., 16, 3907–3926, https://doi.org/10.5194/gmd-16-3907-2023, https://doi.org/10.5194/gmd-16-3907-2023, 2023
Short summary
Short summary
Sea ice covers not only the pole regions but affects the weather and climate globally. For example, its white surface reflects more sunlight than land. The oceans around the poles are therefore kept cool, which affects the circulation in the oceans worldwide. Simulating the behavior and changes in sea ice on a computer is, however, very difficult. We propose a new computer simulation that better models how cracks in the ice change over time and show this by comparing to other simulations.
Emma J. MacKie, Michael Field, Lijing Wang, Zhen Yin, Nathan Schoedl, Matthew Hibbs, and Allan Zhang
Geosci. Model Dev., 16, 3765–3783, https://doi.org/10.5194/gmd-16-3765-2023, https://doi.org/10.5194/gmd-16-3765-2023, 2023
Short summary
Short summary
Earth scientists often have to fill in spatial gaps in measurements. This gap-filling or interpolation can be accomplished with geostatistical methods, where the statistical relationships between measurements are used to inform how these gaps should be filled. Despite the broad utility of these methods, there are few freely available geostatistical software applications. We present GStatSim, a Python package for performing different geostatistical interpolation methods.
Ian Madden, Simone Marras, and Jenny Suckale
Geosci. Model Dev., 16, 3479–3500, https://doi.org/10.5194/gmd-16-3479-2023, https://doi.org/10.5194/gmd-16-3479-2023, 2023
Short summary
Short summary
To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance computing infrastructure, we provide an accessible software package able to rapidly model tsunami inundation over real topography by leveraging Google's Tensor Processing Unit, a high-performance hardware. Minimally trained users can take advantage of the rapid modeling abilities provided by this package via a web browser thanks to the ease of use of Google Cloud Platform.
Youtong Rong, Paul Bates, and Jeffrey Neal
Geosci. Model Dev., 16, 3291–3311, https://doi.org/10.5194/gmd-16-3291-2023, https://doi.org/10.5194/gmd-16-3291-2023, 2023
Short summary
Short summary
A novel subgrid channel (SGC) model is developed for river–floodplain modelling, allowing utilization of subgrid-scale bathymetric information while performing computations on relatively coarse grids. By including adaptive artificial diffusion, potential numerical instability, which the original SGC solver had, in low-friction regions such as urban areas is addressed. Evaluation of the new SGC model through structured tests confirmed that the accuracy and stability have improved.
Xiaqiong Zhou and Hann-Ming Henry Juang
Geosci. Model Dev., 16, 3263–3274, https://doi.org/10.5194/gmd-16-3263-2023, https://doi.org/10.5194/gmd-16-3263-2023, 2023
Short summary
Short summary
The National Centers for Environmental Prediction Global Forecast System version 16 experienced model instability failures in real-time runs resolved by increasing the minimum thickness depth parameter. Further investigation revealed that the issue was caused by the advection of geopotential heights at the model's layer interfaces. By replacing high-order boundary conditions with zero-gradient boundary conditions for interface-wind reconstruction, the instability was effectively addressed.
Grant T. Euen, Shangxin Liu, Rene Gassmöller, Timo Heister, and Scott D. King
Geosci. Model Dev., 16, 3221–3239, https://doi.org/10.5194/gmd-16-3221-2023, https://doi.org/10.5194/gmd-16-3221-2023, 2023
Short summary
Short summary
Due to the increasing availability of high-performance computing over the past few decades, numerical models have become an important tool for research. Here we test two geodynamic codes that produce such models: ASPECT, a newer code, and CitcomS, an older one. We show that they produce solutions that are extremely close. As methods and codes become more complex over time, showing reproducibility allows us to seamlessly link previously known information to modern methodologies.
Mohammad Kazem Sharifian, Georges Kesserwani, Alovya Ahmed Chowdhury, Jeffrey Neal, and Paul Bates
Geosci. Model Dev., 16, 2391–2413, https://doi.org/10.5194/gmd-16-2391-2023, https://doi.org/10.5194/gmd-16-2391-2023, 2023
Short summary
Short summary
This paper describes a new release of the LISFLOOD-FP model for fast and efficient flood simulations. It features a new non-uniform grid generator that uses multiwavelet analyses to sensibly coarsens the resolutions where the local topographic variations are smooth. Moreover, the model is parallelised on the graphical processing units (GPUs) to further boost computational efficiency. The performance of the model is assessed for five real-world case studies, noting its potential applications.
Bruno K. Zürcher
Geosci. Model Dev., 16, 1697–1711, https://doi.org/10.5194/gmd-16-1697-2023, https://doi.org/10.5194/gmd-16-1697-2023, 2023
Short summary
Short summary
We present a novel algorithm to efficiently compute Barnes interpolation, which is a method for transforming data values recorded at irregularly spaced points into a corresponding regular grid. In contrast to naive implementations with an algorithmic complexity that depends on the product of the number of sample points and the number of grid points, our approach reduces this dependency to their sum.
David H. Marsico and Paul A. Ullrich
Geosci. Model Dev., 16, 1537–1551, https://doi.org/10.5194/gmd-16-1537-2023, https://doi.org/10.5194/gmd-16-1537-2023, 2023
Short summary
Short summary
Climate models involve several different components, such as the atmosphere, ocean, and land models. Information needs to be exchanged, or remapped, between these models, and devising algorithms for performing this exchange is important for ensuring the accuracy of climate simulations. In this paper, we examine the efficacy of several traditional and novel approaches to remapping on the sphere and demonstrate where our approaches offer improvement.
Moritz Liebl, Jörg Robl, Stefan Hergarten, David Lundbek Egholm, and Kurt Stüwe
Geosci. Model Dev., 16, 1315–1343, https://doi.org/10.5194/gmd-16-1315-2023, https://doi.org/10.5194/gmd-16-1315-2023, 2023
Short summary
Short summary
In this study, we benchmark a topography-based model for glacier erosion (OpenLEM) with a well-established process-based model (iSOSIA). Our experiments show that large-scale erosion patterns and particularly the transformation of valley length geometry from fluvial to glacial conditions are very similar in both models. This finding enables the application of OpenLEM to study the influence of climate and tectonics on glaciated mountains with reasonable computational effort on standard PCs.
James Kent, Thomas Melvin, and Golo Albert Wimmer
Geosci. Model Dev., 16, 1265–1276, https://doi.org/10.5194/gmd-16-1265-2023, https://doi.org/10.5194/gmd-16-1265-2023, 2023
Short summary
Short summary
This paper introduces the Met Office's new shallow water model. The shallow water model is a building block towards the Met Office's new atmospheric dynamical core. The shallow water model is tested on a number of standard spherical shallow water test cases, including flow over mountains and unstable jets. Results show that the model produces similar results to other shallow water models in the literature.
Anthony Gruber, Max Gunzburger, Lili Ju, Rihui Lan, and Zhu Wang
Geosci. Model Dev., 16, 1213–1229, https://doi.org/10.5194/gmd-16-1213-2023, https://doi.org/10.5194/gmd-16-1213-2023, 2023
Short summary
Short summary
This work applies a novel technical tool, multifidelity Monte Carlo (MFMC) estimation, to three climate-related benchmark experiments involving oceanic, atmospheric, and glacial modeling. By considering useful quantities such as maximum sea height and total (kinetic) energy, we show that MFMC leads to predictions which are more accurate and less costly than those obtained by standard methods. This suggests MFMC as a potential drop-in replacement for estimation in realistic climate models.
Piyoosh Jaysaval, Glenn E. Hammond, and Timothy C. Johnson
Geosci. Model Dev., 16, 961–976, https://doi.org/10.5194/gmd-16-961-2023, https://doi.org/10.5194/gmd-16-961-2023, 2023
Short summary
Short summary
We present a robust and highly scalable implementation of numerical forward modeling and inversion algorithms for geophysical electrical resistivity tomography data. The implementation is publicly available and developed within the framework of PFLOTRAN (http://www.pflotran.org), an open-source, state-of-the-art massively parallel subsurface flow and transport simulation code. The paper details all the theoretical and implementation aspects of the new capabilities along with test examples.
Lucas Schauer, Michael J. Schmidt, Nicholas B. Engdahl, Stephen D. Pankavich, David A. Benson, and Diogo Bolster
Geosci. Model Dev., 16, 833–849, https://doi.org/10.5194/gmd-16-833-2023, https://doi.org/10.5194/gmd-16-833-2023, 2023
Short summary
Short summary
We develop a multi-dimensional, parallelized domain decomposition strategy for mass-transfer particle tracking methods in two and three dimensions, investigate different procedures for decomposing the domain, and prescribe an optimal tiling based on physical problem parameters and the number of available CPU cores. For an optimally subdivided diffusion problem, the parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run-up to thousands of cores.
Cited articles
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, https://www.tensorflow.org/ (last access: 24 September 2023), 2015. a
Abdi, D. S., Wilcox, L. C., Warburton, T. C., and Giraldo, F. X.: A GPU-accelerated continuous and discontinuous Galerkin non-hydrostatic atmospheric model, Int. J. High Perform. C., 33, 81–109, https://doi.org/10.1177/1094342017694427, 2017. a
Adams, B., Bohnhoff, W., Dalbey, K., Ebeida, M., Eddy, J., Eldred, M., Hooper, R., Hough, P., Hu, K., Jakeman, J., Khalil, M., Maupin, K., Monschke, J., Ridgway, E., Rushdi, A., Seidl, D., Stephens, J., Swiler, L., and Winokur, J.: DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.12 User's Manual, Sandia National Laboratories, Tech. Rep., SAND2020-12495, 2020. a, b, c
Afanasyev, A., Bianco, M., Mosimann, L., Osuna, C., Thaler, F., Vogt, H., Fuhrer, O., VandeVondele, J., and Schulthess, T. C.: GridTools: A framework for portable weather and climate applications, Software X, 15, 100707, https://doi.org/10.1016/j.softx.2021.100707, 2021. a
Alexander, F., Almgren, A., Bell, J., Bhattacharjee, A., Chen, J., Colella, P., Daniel, D., DeSlippe, J., Diachin, L., Draeger, E., Dubey, A., Dunning, T., Evans, T., Foster, I., Francois, M., Germann, T., Gordon, M., Habib, S., Halappanavar, M., Hamilton, S., Hart, W., Huang, Z. H., Hungerford, A., Kasen, D., Kent, P. R. C., Kolev, T., Kothe, D. B., Kronfeld, A., Luo, Y., Mackenzie, P., McCallen, D., Messer, B., Mniszewski, S., Oehmen, C., Perazzo, A., Perez, D., Richards, D., Rider, W. J., Rieben, R., Roche, K., Siegel, A., Sprague, M., Steefel, C., Stevens, R., Syamlal, M., Taylor, M., Turner, J., Vay, J.-L., Voter, A. F., Windus, T. L., and Yelick, K.: Exascale applications: skin in the game, Philos. T. Roy. Soc. A, 378, 20190056, https://doi.org/10.1098/rsta.2019.0056, 2020. a, b
Alexanderian, A.: Optimal experimental design for infinite-dimensional Bayesian inverse problems governed by PDEs: a review, Inverse Problems, 37, 043001, https://doi.org/10.1088/1361-6420/abe10c, 2021. a
Alnæs, M., Blechta, J., Hake, J., Johansson, A., Kehlet, B., Logg, A., Richardson, C., Ring, J., Rognes, M. E., and Wells, G. N.: The FEniCS project version 1.5, Archive of Numerical Software, 3, 9–23, https://doi.org/10.11588/ans.2015.100.20553, 2015. a
Al-Rfou, R., Alain, G., Almahairi, A., et al.: Theano: A Python framework for fast computation of mathematical expressions, arXiv [preprint], https://doi.org/10.48550/arXiv.1605.02688, 2016. a, b
Antoulas, A. C.: Approximation of Large-Scale Dynamical Systems, Society for Industrial and Applied Mathematics, ISBN 978-0-89871-658-0, eISBN 978-0-89871-871-3, https://doi.org/10.1137/1.9780898718713, 2005. a
Ardabili, S., Mosavi, A., Dehghani, M., and Várkonyi-Kóczy, A. R.: Deep Learning and Machine Learning in Hydrological Processes Climate Change and Earth Systems a Systematic Review, in: Lecture Notes in Networks and Systems, Springer International Publishing, 52–62, https://doi.org/10.1007/978-3-030-36841-8_5, 2020. a
Artigues, V., Kormann, K., Rampp, M., and Reuter, K.: Evaluation of performance portability frameworks for the implementation of a particle-in-cell code, Concurrency and Computation Practice and Experience, 32, e5640, https://doi.org/10.1002/cpe.5640, 2019. a, b, c
Asante-Duah, D. K.: Hazardous waste risk assessment, CRC Press, https://doi.org/10.1201/9781003070009, 2021. a
Audouze, C., De Vuyst, F., and Nair, P.: Reduced-order modeling of parameterized PDEs using time–space-parameter principal component analysis, Int. J. Numer. Meth. Eng., 80, 1025–1057, 2009. a
Audouze, C., De Vuyst, F., and Nair, P. B.: Nonintrusive reduced-order modeling of parametrized time-dependent partial differential equations, Numer. Meth. Part. D. E., 29, 1587–1628, 2013. a
Baig, F., Gao, C., Teng, D., Kong, J., and Wang, F.: Accelerating Spatial Cross-Matching on CPU-GPU Hybrid Platform With CUDA and OpenACC, Frontiers in Big Data, 3, 14, https://doi.org/10.3389/fdata.2020.00014, 2020. a, b
Baker, N., Alexander, F., Bremer, T., Hagberg, A., Kevrekidis, Y., Najm, H., Parashar, M., Patra, A., Sethian, J., Wild, S., Willcox, K., and Lee, S.: Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence, Tech. Rep., 1478744, https://doi.org/10.2172/1478744, 2019. a, b, c, d, e, f, g
Ballarin, F., Sartori, A., and Rozza, G.: RBniCS-reduced order modelling in FEniCS, http://mathlab.sissa.it/rbnics (last access: 23 September 2023), 2017. a
Bandai, T. and Ghezzehei, T. A.: Physics-Informed Neural Networks With Monotonicity Constraints for Richardson-Richards Equation: Estimation of Constitutive Relationships and Soil Water Flux Density From Volumetric Water Content Measurements, Water Resour. Res., 57, e2020WR027642, https://doi.org/10.1029/2020wr027642, 2021. a
Bangerth, W., Dannberg, J., Fraters, M., Gassmoeller, R., Glerum, A., Heister, T., Myhill, R., and Naliboff, J.: ASPECT v2.4.0 (v2.4.0), Zenodo [code], https://doi.org/10.5281/zenodo.6903424, 2022. a
Barrault, M., Maday, Y., Nguyen, N. C., and Patera, A. T.: An empirical interpolation method: application to efficient reduced-basis discretization of partial differential equations, C. R. Math., 339, 667–672, 2004. a
Bar-Sinai, Y., Hoyer, S., Hickey, J., and Brenner, M. P.: Learning data-driven discretizations for partial differential equations, P. Natl. Acad. Sci. USA, 116, 15344–15349, 2019. a
Baş, D. and Boyacı, I. H.: Modeling and optimization I: Usability of response surface methodology, J. Food Eng., 78, 836–845, 2007. a
Beckingsale, D. A., Burmark, J., Hornung, R., Jones, H., Killian, W., Kunen, A. J., Pearce, O., Robinson, P., Ryujin, B. S., and Scogland, T. R.: RAJA: Portable Performance for Large-Scale Scientific Applications, in: 2019 IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC (P3HPC), 71–81, https://doi.org/10.1109/p3hpc49587.2019.00012, 2019. a
Bentivoglio, R., Isufi, E., Jonkman, S. N., and Taormina, R.: Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2021-614, 2021. a
Bermúdez, M., Ntegeka, V., Wolfs, V., and Willems, P.: Development and Comparison of Two Fast Surrogate Models for Urban Pluvial Flood Simulations, Water Resour. Manage., 32, 2801–2815, https://doi.org/10.1007/s11269-018-1959-8, 2018. a
Bertagna, L., Deakin, M., Guba, O., Sunderland, D., Bradley, A. M., Tezaur, I. K., Taylor, M. A., and Salinger, A. G.: HOMMEXX 1.0: a performance-portable atmospheric dynamical core for the Energy Exascale Earth System Model, Geosci. Model Dev., 12, 1423–1441, https://doi.org/10.5194/gmd-12-1423-2019, 2019. a, b
Bertsimas, D. and Tsitsiklis, J. N.: Introduction to linear optimization, vol. 6, Athena Scientific Belmont, MA, 1997. a
Blöschl, G. and Sivapalan, M.: Scale issues in hydrological modelling: A review, Hydrol. Process., 9, 251–290, https://doi.org/10.1002/hyp.3360090305, 1995. a
Bondyopadhyay, P. K.: Moore's law governs the silicon revolution, P. IEEE, 86, 78–81, 1998. a
Buiter, S. and Ellis, S.: SULEC: Benchmarking a new ALE finite-element code, in: EGU General Assembly Conference Abstracts, 7528, https://ui.adsabs.harvard.edu/abs/2012EGUGA..14.7528B/abstract (last access: 8 December 2023), 2012. a
Busto, S., Stabile, G., Rozza, G., and Vázquez-Cendón, M. E.: POD–Galerkin reduced order methods for combined Navier–Stokes transport equations based on a hybrid FV-FE solver, Comput. Math. Appl., 79, 256–273, 2020. a
Carraro, T., Geiger, M., Rorkel, S., and Rannacher, R.: Multiple Shooting and Time Domain Decomposition Methods, Springer, ISBN 978-3-319-23320-8, 2015. a
Caviedes-Voullième, D., García-Navarro, P., and Murillo, J.: Verification, conservation, stability and efficiency of a finite volume method for the 1D Richards equation, J. Hydrol., 480, 69–84, 2013. a
Caviedes-Voullième, D., Gerhard, N., Sikstel, A., and Müller, S.: Multiwavelet-based mesh adaptivity with Discontinuous Galerkin schemes: Exploring 2D shallow water problems, Adv. Water Resour., 138, 103559, https://doi.org/10.1016/j.advwatres.2020.103559, 2020. a
Chen, J., Hoversten, G. M., Key, K., Nordquist, G., and Cumming, W.: Stochastic inversion of magnetotelluric data using a sharp boundary parameterization and application to a geothermal site, Geophysics, 77, E265–E279, 2012. a
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., and Zhang, Z.: MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems, arXiv [preprint], https://doi.org/10.48550/arXiv.1512.01274, 2015. a
Chen, W., Hesthaven, J. S., Junqiang, B., Qiu, Y., Yang, Z., and Tihao, Y.: Greedy nonintrusive reduced order model for fluid dynamics, AIAA Journal, 56, 4927–4943, 2018b. a
Chen, X., Duan, J., and Karniadakis, G. E.: Learning and meta-learning of stochastic advection-diffusion-reaction systems from sparse measurements, arXiv [preprint], https://doi.org/10.48550/arXiv.1910.09098, 2019. a
Chen, Y., Hesthaven, J. S., Maday, Y., and Rodríguez, J.: Certified reduced basis methods and output bounds for the harmonic Maxwell's equations, SIAM J. Sci. Comput., 32, 970–996, 2010. a
Chen, Y., Simon, K., and Behrens, J.: Extending legacy climate models by adaptive mesh refinement for single-component tracer transport: a case study with ECHAM6-HAMMOZ (ECHAM6.3-HAM2.3-MOZ1.0), Geosci. Model Dev., 14, 2289–2316, https://doi.org/10.5194/gmd-14-2289-2021, 2021. a
Chollet, F.: Keras, https://keras.io (last access: 23 September 2023), 2015. a
Chuang, P.-Y. and Barba, L. A.: Experience report of physics-informed neural networks in fluid simulations: pitfalls and frustration, arXiv [preprint], https://doi.org/10.48550/arXiv.2205.14249, 2022. a, b, c
Clement, V., Ferrachat, S., Fuhrer, O., Lapillonne, X., Osuna, C. E., Pincus, R., Rood, J., and Sawyer, W.: The CLAW DSL, in: PASC '18: Proceedings of the Platform for Advanced Scientific Computing Conference, ACM, Basel Switzerland, 2–4 July 2018, https://doi.org/10.1145/3218176.3218226, 2018. a
Condon, L. E., Kollet, S., Bierkens, M. F., Fogg, G. E., Maxwell, R. M., Hill, M. C., Fransen, H.-J. H., Verhoef, A., Van Loon, A. F., Sulis, M., and Abesser, C.: Global groundwater modeling and monitoring: Opportunities and challenges, Water Resour. Res., 57, e2020WR029500, https://doi.org/10.1029/2020WR029500, 2021. a
Conway, D., Simpson, J., Didana, Y., Rugari, J., and Heinson, G.: Probabilistic magnetotelluric inversion with adaptive regularisation using the no-U-turns sampler, Pure Appl. Geophys., 175, 2881–2894, 2018. a
Cressie, N.: The origins of kriging, Math. Geol., 22, 239–252, 1990. a
Dauxois, T., Peacock, T., Bauer, P., Caulfield, C. P., Cenedese, C., Gorlé, C., Haller, G., Ivey, G. N., Linden, P. F., Meiburg, E., Pinardi, N., Vriend, N. M., and Woods, A. W.: Confronting Grand Challenges in environmental fluid mechanics, Physical Review Fluids, 6, 020501, https://doi.org/10.1103/physrevfluids.6.020501, 2021. a
Dawson, C., Trahan, C. J., Kubatko, E. J., and Westerink, J. J.: A parallel local timestepping Runge–Kutta discontinuous Galerkin method with applications to coastal ocean modeling, Comput. Method. Appl. M., 259, 154–165, https://doi.org/10.1016/j.cma.2013.03.015, 2013. a
Dazzi, S., Vacondio, R., Palù, A. D., and Mignosa, P.: A local time stepping algorithm for GPU-accelerated 2D shallow water models, Adv. Water Resour., 111, 274–288, https://doi.org/10.1016/j.advwatres.2017.11.023, 2018. a
De Bézenac, E., Pajot, A., and Gallinari, P.: Deep learning for physical processes: Incorporating prior scientific knowledge, J. Stat. Mech-Theory E., 2019, 124009, https://doi.org/10.1088/1742-5468/ab3195, 2019. a
Degen, D., Veroy, K., and Wellmann, F.: cgre-aachen/DwarfElephant: DwarfElephant 1.0, Zenodo [code], https://doi.org/10.5281/zenodo.4074777, 2020b. a, b
Degen, D., Spooner, C., Scheck-Wenderoth, M., and Cacace, M.: How biased are our models? – a case study of the alpine region, Geosci. Model Dev., 14, 7133–7153, https://doi.org/10.5194/gmd-14-7133-2021, 2021a. a
Degen, D., Veroy, K., Freymark, J., Scheck-Wenderoth, M., Poulet, T., and Wellmann, F.: Global sensitivity analysis to optimize basin-scale conductive model calibration – A case study from the Upper Rhine Graben, Geothermics, 95, 102143, https://doi.org/10.1016/j.geothermics.2021.102143, 2021b. a, b, c, d
Degen, D., Cacace, M., and Wellmann, F.: 3D multi-physics uncertainty quantification using physics-based machine learning, Scientific Reports, 12, 17491, https://doi.org/10.1038/s41598-022-21739-7, 2022a. a, b
Degen, D., Veroy, K., Scheck-Wenderoth, M., and Wellmann, F.: Crustal-scale thermal models: Revisiting the influence of deep boundary conditions, Environ. Earth Sci., 81, 88, https://doi.org/10.1007/s12665-022-10202-5, 2022b. a, b
Degen, D., Veroy, K., and Wellmann, F.: Uncertainty quantification for basin-scale geothermal conduction models, Scientific Reports, 12, 4246, https://doi.org/10.1038/s41598-022-08017-2, 2022c. a, b
Degen, D., Caviedes Voullième, D., Buiter, S., Hendriks Franssen, H.-J., Vereecken, H., González-Nicolás, A., and Wellmann, F.: Non-Intrusive Reduced Basis Code for Geothermal, Geodynamic, and Hydrology Benchmarks, Zenodo [code and data set], https://doi.org/10.5281/zenodo.8369108, 2023. a
Degen, D. M.: Application of the reduced basis method in geophysical simulations: concepts, implementation, advantages, and limitations, PhD thesis, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, https://doi.org/10.18154/RWTH-2020-12042, 2020. a, b
Dwivedi, V. and Srinivasan, B.: Solution of Biharmonic Equation in Complicated Geometries With Physics Informed Extreme Learning Machine, J. Comput. Inf. Sci. Eng., 20, 061004, https://doi.org/10.1115/1.4046892, 2020. a
Edwards, H. C., Trott, C. R., and Sunderland, D.: Kokkos: Enabling manycore performance portability through polymorphic memory access patterns, J. Parallel Distr. Com., 74, 3202–3216, https://doi.org/10.1016/j.jpdc.2014.07.003, 2014. a
Ellis, S., Little, T., Wallace, L., Hacker, B., and Buiter, S.: Feedback between rifting and diapirism can exhume ultrahigh-pressure rocks, Earth Planet. Sc. Lett., 311, 427–438, 2011. a
Emmett, M. and Minion, M.: Toward an efficient parallel in time method for partial differential equations, Comm. App. Math. Com. Sc., 7, 105–132, 2012. a
Emmett, M. and Minion, M. L.: Efficient implementation of a multi-level parallel in time algorithm, in: Domain Decomposition Methods in Science and Engineering XXI, Springer, 359–366, ISBN 978-3-319-05788-0, 2014. a
Evans, T. M., Siegel, A., Draeger, E. W., Deslippe, J., Francois, M. M., Germann, T. C., Hart, W. E., and Martin, D. F.: A survey of software implementations used by application codes in the Exascale Computing Project, Int. J. High Perform. C., 36, 109434202110289, https://doi.org/10.1177/10943420211028940, 2021. a
Falkner, S., Klein, A., and Hutter, F.: BOHB: Robust and efficient hyperparameter optimization at scale, in: International Conference on Machine Learning, PMLR, Stockholm, Sweden, 10–15 July 2018, 1437–1446, https://proceedings.mlr.press/v80/falkner18a.html (last access: 22 September 2023), 2018. a, b
Fan, Y., Clark, M., Lawrence, D. M., Swenson, S., Band, L. E., Brantley, S. L., Brooks, P. D., Dietrich, W. E., Flores, A., Grant, G., Kirchner, J. W., Mackay, D. S., McDonnell, J. J., Milly, P. C. D., Sullivan, P. L., Tague, C., Ajami, H., Chaney, N., Hartmann, A., Hazenberg, P., McNamara, J., Pelletier, J., Perket, J., Rouholahnejad-Freund, E., Wagener, T., Zeng, X., Beighley, E., Buzan, J., Huang, M., Livneh, B., Mohanty, B. P., Nijssen, B., Safeeq, M., Shen, C., van Verseveld, W., Volk, J., and Yamazaki, D.: Hillslope Hydrology in Global Change Research and Earth System Modeling, Water Resour. Res., 55, 1737–1772, https://doi.org/10.1029/2018wr023903, 2019. a
Faroughi, S. A., Pawar, N., Fernandes, C., Das, S., Kalantari, N. K., and Mahjour, S. K.: Physics-guided, physics-informed, and physics-encoded neural networks in scientific computing, arXiv [preprint], https://doi.org/10.48550/arXiv.2211.07377, 2022. a, b, c
Fisher, M. and Gürol, S.: Parallelization in the time dimension of four-dimensional variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 1136–1147, 2017. a
Frangos, M., Marzouk, Y., Willcox, K., and van Bloemen Waanders, B.: Surrogate and reduced-order modeling: a comparison of approaches for large-scale statistical inverse problems, Chapt. 7, John Wiley & Sons. Print ISBN 9780470697436, Online ISBN 9780470685853, https://doi.org/10.1002/9780470685853, 2010. a
Frank, D. J., Dennard, R. H., Nowak, E., Solomon, P. M., Taur, Y., and Wong, H.-S. P.: Device scaling limits of Si MOSFETs and their application dependencies, P. IEEE, 89, 259–288, 2001. a
Freymark, J., Bott, J., Cacace, M., Ziegler, M., and Scheck-Wenderoth, M.: Influence of the Main Border Faults on the 3D Hydraulic Field of the Central Upper Rhine Graben, Geofluids, 2019, 7520714 https://doi.org/10.1155/2019/7520714, 2019. a
Gan, L., Fu, H., and Yang, G.: Translating novel HPC techniques into efficient geoscience solutions, Journal of Computational Science, 52, 101212, https://doi.org/10.1016/j.jocs.2020.101212, 2020. a, b
Gassner, G. J. and Winters, A. R.: A Novel Robust Strategy for Discontinuous Galerkin Methods in Computational Fluid Mechanics: Why? When? What? Where?, Frontiers in Physics, 8, 500690, https://doi.org/10.3389/fphy.2020.500690, 2021. a
Gelet, R., Loret, B., and Khalili, N.: A thermo-hydro-mechanical coupled model in local thermal non-equilibrium for fractured HDR reservoir with double porosity, J. Geophys. Res.-Sol. Ea., 117, B07205, https://doi.org/10.1029/2012JB009161, 2012. a, b, c
Geneva, N. and Zabaras, N.: Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks, J. Comput. Phys., 403, 109056, https://doi.org/10.1016/j.jcp.2019.109056, 2020. a
Gerhard, N., Caviedes-Voullième, D., Müller, S., and Kesserwani, G.: Multiwavelet-Based Grid Adaptation with Discontinuous Galerkin Schemes for Shallow Water Equations, J. Comput. Phys., 301, 265–288, https://doi.org/10.1016/j.jcp.2015.08.030, 2015. a
Germann, T. C.: Co-design in the Exascale Computing Project, Int. J. High Perform. C., 35, 503–507, https://doi.org/10.1177/10943420211059380, 2021. a
Götz, M., Debus, C., Coquelin, D., Krajsek, K., Comito, C., Knechtges, P., Hagemeier, B., Tarnawa, M., Hanselmann, S., Siggel, M., Basermann, A., and Streit, A.: HeAT – a Distributed and GPU-accelerated Tensor Framework for Data Analytics, in: Proceedings of the 19th IEEE International Conference on Big Data, IEEE, Atlanta, USA, 10–13 December 2020, 276–288, https://doi.org/10.1109/BigData50022.2020.9378050, 2020. a
Grepl, M.: Reduced-basis Approximation and A Posteriori Error Estimation for Parabolic Partial Differential Equations, PhD thesis, Massachusetts Institute of Technology, https://dspace.mit.edu/handle/1721.1/32387 (last access: 24 September 2023), 2005. a
Grete, P., Glines, F. W., and O′Shea, B. W.: K-Athena: A Performance Portable Structured Grid Finite Volume Magnetohydrodynamics Code, IEEE T. Parall. Distr., 32, 85–97, https://doi.org/10.1109/tpds.2020.3010016, 2021. a, b
Haghighat, E. and Juanes, R.: SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks, Comput. Method. Appl. M., 373, 113552, https://doi.org/10.1016/j.cma.2020.113552, 2021. a
Hammerschmidt, M., Herrmann, S., Pomplun, J., Zschiedrich, L., Burger, S., and Schmidt, F.: Reduced basis method for Maxwell's equations with resonance phenomena, in: Optical Systems Design 2015: Computational Optics, SPIE, vol. 9630, 138–151, https://doi.org/10.1117/12.2190425, 2015. a
Hamon, F. P., Schreiber, M., and Minion, M. L.: Parallel-in-time multi-level integration of the shallow-water equations on the rotating sphere, J. Comput. Phys., 407, 109210, https://doi.org/10.1016/j.jcp.2019.109210, 2020. a, b
He, Q., Barajas-Solano, D., Tartakovsky, G., and Tartakovsky, A. M.: Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport, Adv. Water Resour., 141, 103610, https://doi.org/10.1016/j.advwatres.2020.103610, 2020. a
Herman, J. and Usher, W.: SALib: An open-source Python library for Sensitivity Analysis, J. Open Source Softw., 2, 97, https://doi.org/10.21105/joss.00097, 2017. a, b
Hess, M. W. and Benner, P.: Fast evaluation of time–harmonic Maxwell's equations using the reduced basis method, IEEE T. Microw. Theory, 61, 2265–2274, 2013a. a
Hess, M. W. and Benner, P.: Reduced basis generation for maxwell’s equations by rigorous error estimation, in: 19th International Conference on the Computation of Electromagnetic Fields (COMPUMAG 2013), Budapest, Hungary, 30 June–4 July 2013, PD2–10, https://doi.org/10.1109/TMAG.2014.2299393, 2013b. a
Hess, M. W., Grundel, S., and Benner, P.: Estimating the inf-sup constant in reduced basis methods for time-harmonic Maxwell’s equations, IEEE T. Microw. Theory, 63, 3549–3557, 2015. a
Hokkanen, J., Kollet, S., Kraus, J., Herten, A., Hrywniak, M., and Pleiter, D.: Leveraging HPC accelerator architectures with modern techniques – hydrologic modeling on GPUs with ParFlow, Computat. Geosci., 25, 1579–1590, https://doi.org/10.1007/s10596-021-10051-4, 2021. a, b, c
Huang, P.: Investigating different formulations for hydrothermal convection in geothermal systems, in: Proceedings World Geothermal Congress 2020 Reykjavik, Iceland, 26 April–2 May 2020 https://www.researchgate.net/profile/Po_Wei_Huang4/publication/ (last access: 20 September 2023), 2018. a
Iglesias, M. and Stuart, A. M.: Inverse Problems and Uncertainty Quantification, SIAM News, 2–3, https://homepages.warwick.ac.uk/~masdr/BOOKCHAPTERS/stuart19c.pdf (last access: 21 September 2023), 2014. a
Jacquey, A. and Cacace, M.: Modelling fully-coupled Thermo-Hydro-Mechanical (THM) processes in fractured reservoirs using GOLEM: a massively parallel open-source simulator, in: EGU General Assembly Conference Abstracts, Vienna, Austria, April 2017, vol. 19, 15721, https://ui.adsabs.harvard.edu/abs/2017EGUGA..1915721J/abstract (last access: 22 September 2023), 2017. a, b
Jacquey, A. B. and Cacace, M.: Multiphysics modeling of a brittle-ductile lithosphere: 2. Semi-brittle, semi-ductile deformation and damage rheology, J. Geophys. Res.-Sol. Ea., 125, e2019JB018475, https://doi.org/10.1029/2019JB018475, 2020a. a, b
Jacquey, A. B. and Cacace, M.: Multiphysics Modeling of a Brittle-Ductile Lithosphere: 1. Explicit Visco-Elasto-Plastic Formulation and Its Numerical Implementation, J. Geophys. Res.-Sol. Ea., 125, e2019JB018474, https://doi.org/10.1029/2019JB018474, 2020b. a, b, c
Jagtap, A. D. and Karniadakis, G. E.: Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations, Commun. Comput. Phys., 28, 2002–2041, 2020. a
Jiang, S., Zheng, Y., and Solomatine, D.: Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning, Geophys. Res. Lett., 47, e2020GL088229, https://doi.org/10.1029/2020gl088229, 2020. a, b
Jones, A. G., Afonso, J. C., and Fullea, J.: Geochemical and geophysical constrains on the dynamic topography of the Southern African Plateau, Geochem. Geophy. Geosy., 18, 3556–3575, 2017. a
Jordan, M. I. and Mitchell, T. M.: Machine learning: Trends, perspectives, and prospects, Science, 349, 255–260, 2015. a
Jung, N.: Error Estimation for Parametric Model Order Reduction and Its Application (Doctoral dissertation, Technische Universität München), https://mediatum.ub.tum.de/?id=1080163 (last access: 18 September 2023), 2012. a
Karumuri, S., Tripathy, R., Bilionis, I., and Panchal, J.: Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks, J. Comput. Phys., 404, 109120, https://doi.org/10.1016/j.jcp.2019.109120, 2020. a
Kevlahan, N. K.-R.: Adaptive Wavelet Methods for Earth Systems Modelling, Fluids, 6, 236, https://doi.org/10.3390/fluids6070236, 2021. a
Koltzer, N., Scheck-Wenderoth, M., Bott, J., Cacace, M., Frick, M., Sass, I., Fritsche, J.-G., and Bär, K.: The Effects of Regional Fluid Flow on Deep Temperatures (Hesse, Germany), Energies, 12, 2081, https://doi.org/10.3390/en12112081, 2019. a
Kotsiantis, S. B., Zaharakis, I., and Pintelas, P.: Supervised machine learning: A review of classification techniques, Emerging artificial intelligence applications in computer engineering, 160, 3–24, 2007. a
Kuffour, B. N. O., Engdahl, N. B., Woodward, C. S., Condon, L. E., Kollet, S., and Maxwell, R. M.: Simulating coupled surface–subsurface flows with ParFlow v3.5.0: capabilities, applications, and ongoing development of an open-source, massively parallel, integrated hydrologic model, Geosci. Model Dev., 13, 1373–1397, https://doi.org/10.5194/gmd-13-1373-2020, 2020. a
Kumar, P., Debele, S. E., Sahani, J., Rawat, N., Marti-Cardona, B., Alfieri, S. M., Basu, B., Basu, A. S., Bowyer, P., Charizopoulos, N., Jaakko, J., Loupis, M., Menenti, M., Mickovski, S. B., Pfeiffer, J., Pilla, F., Pröll, J., Pulvirenti, B., Rutzinger, M., Sannigrahi, S., Spyrou, C., Tuomenvirta, H., Vojinovic, Z., and Zieher, T.: An overview of monitoring methods for assessing the performance of nature-based solutions against natural hazards, Earth-Sci. Rev., 217, 103603, https://doi.org/10.1016/j.earscirev.2021.103603, 2021. a, b
Kärnä, T., Kramer, S. C., Mitchell, L., Ham, D. A., Piggott, M. D., and Baptista, A. M.: Thetis coastal ocean model: discontinuous Galerkin discretization for the three-dimensional hydrostatic equations, Geosci. Model Dev., 11, 4359–4382, https://doi.org/10.5194/gmd-11-4359-2018, 2018. a
Käser, M. and Dumbser, M.: An arbitrary high-order discontinuous Galerkin method for elastic waves on unstructured meshes – I. The two-dimensional isotropic case with external source terms, Geophys. J. Int., 166, 855–877, https://doi.org/10.1111/j.1365-246x.2006.03051.x, 2006. a
Lawrence, B. N., Rezny, M., Budich, R., Bauer, P., Behrens, J., Carter, M., Deconinck, W., Ford, R., Maynard, C., Mullerworth, S., Osuna, C., Porter, A., Serradell, K., Valcke, S., Wedi, N., and Wilson, S.: Crossing the chasm: how to develop weather and climate models for next generation computers?, Geosci. Model Dev., 11, 1799–1821, https://doi.org/10.5194/gmd-11-1799-2018, 2018. a, b, c
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., and Anandkumar, A.: Fourier neural operator for parametric partial differential equations, arXiv [preprint], https://doi.org/10.48550/arXiv.2010.08895, 2020. a, b, c, d
Liang, J., Li, W., Bradford, S., and Šimůnek, J.: Physics-Informed Data-Driven Models to Predict Surface Runoff Water Quantity and Quality in Agricultural Fields, Water, 11, 200, https://doi.org/10.3390/w11020200, 2019. a
Liu, W. and Ramirez, A.: State of the art review of the environmental assessment and risks of underground geo-energy resources exploitation, Renew. Sust. Energ. Rev., 76, 628–644, 2017. a
Lu, L., Meng, X., Mao, Z., and Karniadakis, G. E.: DeepXDE: A deep learning library for solving differential equations, arXiv [preprint], https://doi.org/10.48550/arXiv.1907.04502, 2019. a
Lu, L., Meng, X., Cai, S., Mao, Z., Goswami, S., Zhang, Z., and Karniadakis, G. E.: A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data, Comput. Method. Appl. M., 393, 114778, https://doi.org/10.1016/j.cma.2022.114778, 2022. a, b
Mahesh, B.: Machine learning algorithms – a review, International Journal of Science and Research (IJSR), 9, 381–386, 2020. a
Mainini, L. and Willcox, K.: Surrogate modeling approach to support real-time structural assessment and decision making, AIAA Journal, 53, 1612–1626, 2015. a
Malek, A. and Beidokhti, R. S.: Numerical solution for high order differential equations using a hybrid neural network–optimization method, Appl. Math. Comput., 183, 260–271, 2006. a
Maxwell, R. M., Condon, L. E., and Melchior, P.: A Physics-Informed, Machine Learning Emulator of a 2D Surface Water Model: What Temporal Networks and Simulation-Based Inference Can Help Us Learn about Hydrologic Processes, Water, 13, 3633, https://doi.org/10.3390/w13243633, 2021. a
Meng, X. and Karniadakis, G. E.: A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems, J. Comput. Phys., 401, 109020, https://doi.org/10.1016/j.jcp.2019.109020, 2020. a
Meng, X., Li, Z., Zhang, D., and Karniadakis, G. E.: PPINN: Parareal physics-informed neural network for time-dependent PDEs, Comput. Method. Appl. M., 370, 113250, https://doi.org/10.1016/j.cma.2020.113250, 2020. a, b, c, d
Miao, T., Lu, W., Lin, J., Guo, J., and Liu, T.: Modeling and uncertainty analysis of seawater intrusion in coastal aquifers using a surrogate model: a case study in Longkou, China, Arab. J. Geosci., 12, 1, https://doi.org/10.1007/s12517-018-4128-8, 2019. a, b
Mohammadi, B.: A review on the applications of machine learning for runoff modeling, Sustainable Water Resources Management, 7, 98, https://doi.org/10.1007/s40899-021-00584-y, 2021. a
Morales-Hernández, M., Sharif, M. B., Gangrade, S., Dullo, T. T., Kao, S.-C., Kalyanapu, A., Ghafoor, S. K., Evans, K. J., Madadi-Kandjani, E., and Hodges, B. R.: High-performance computing in water resources hydrodynamics, J. Hydroinform., 22, 1217–1235, https://doi.org/10.2166/hydro.2020.163, 2020. a
Myers, R. H., Montgomery, D. C., and Anderson-Cook, C. M.: Response Surface Methodology: Process and Product Optimization Using Designed Experiments, John Wiley & Sons, ISBN 978-1-118-91601-8, 2016. a
Naliboff, J. B., Buiter, S. J., Péron-Pinvidic, G., Osmundsen, P. T., and Tetreault, J.: Complex fault interaction controls continental rifting, Nat. Commun., 8, 1179, https://doi.org/10.1038/s41467-017-00904-x, 2017. a
Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D., Frame, J. M., Prieto, C., and Gupta, H. V.: What Role Does Hydrological Science Play in the Age of Machine Learning?, Water Resour. Res., 57, e2020WR028091, https://doi.org/10.1029/2020wr028091, 2021. a, b
Nield, D. A. and Bejan, A.: Heat Transfer Through a Porous Medium, in: Convection in Porous Media, 37–55, Springer, https://doi.org/10.1007/978-1-4614-5541-7, 2017. a, b, c, d
Ortega-Gelabert, O., Zlotnik, S., Afonso, J. C., and Díez, P.: Fast stokes flow simulations for geophysical-geodynamic inverse problems and sensitivity analyses based on reduced order modeling, J. Geophys. Res.-Sol. Ea., 125, e2019JB018314, https://doi.org/10.1029/2019JB018314, 2020. a
Özgen-Xian, I., Kesserwani, G., Caviedes-Voullième, D., Molins, S., Xu, Z., Dwivedi, D., Moulton, J. D., and Steefel, C. I.: Wavelet-based local mesh refinement for rainfall–runoff simulations, J. Hydroinform., 22, 1059–1077, https://doi.org/10.2166/hydro.2020.198, 2020. a
Pang, G., D'Elia, M., Parks, M., and Karniadakis, G. E.: nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applications, J. Comput. Phys., 422, 109760, https://doi.org/10.1016/j.jcp.2020.109760, 2020. a, b
Paniconi, C. and Putti, M.: Physically based modeling in catchment hydrology at 50: Survey and outlook, Water Resour. Res., 51, 7090–7129, https://doi.org/10.1002/2015WR017780, 2015. a
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S.: PyTorch: An Imperative Style, High-Performance Deep Learning Library, in: Advances in Neural Information Processing Systems 32, edited by: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., and Garnett, R., Curran Associates, Inc., 8024–8035, http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (last access: 24 September 2023), 2019. a
Patil, A., Huard, D., and Fonnesbeck, C. J.: PyMC: Bayesian Stochastic Modelling in Python, J. Stat. Softw., 35, 1–81, https://doi.org/10.18637/jss.v035.i04, 2010. a, b
Peherstorfer, B. and Willcox, K.: Data-driven operator inference for nonintrusive projection-based model reduction, Comput. Method. Appl. M., 306, 196–215, https://doi.org/10.1016/j.cma.2016.03.025, 2016. a, b, c, d
Peherstorfer, B., Willcox, K., and Gunzburger, M.: Survey of multifidelity methods in uncertainty propagation, inference, and optimization, SIAM Rev., 60, 550–591, 2018. a
Pelletier, D., Fortin, A., and Camarero, R.: Are FEM solutions of incompressible flows really incompressible?(or how simple flows can cause headaches!), Int. J. Numer. Meth. Fl., 9, 99–112, 1989. a
Peng, W., Zhou, W., Zhang, J., and Yao, W.: Accelerating physics-informed neural network training with prior dictionaries, arXiv [preprint], https://doi.org/10.48550/arXiv.2004.08151, 2020. a
Permann, C. J., Gaston, D. R., Andrš, D., Carlsen, R. W., Kong, F., Lindsay, A. D., Miller, J. M., Peterson, J. W., Slaughter, A. E., Stogner, R. H., and Martineau, R. C.: MOOSE: Enabling massively parallel multiphysics simulation, SoftwareX, 11, 100430, https://doi.org/10.1016/j.softx.2020.100430, 2020. a, b, c
Piggott, M., Pain, C., Gorman, G., Power, P., and Goddard, A.: h, r, and hr adaptivity with applications in numerical ocean modelling, Ocean Model., 10, 95–113, https://doi.org/10.1016/j.ocemod.2004.07.007, 2005. a
Poulet, T., Veveakis, M., Paesold, M., and Regenauer-Lieb, K.: REDBACK: An open-source highly scalable simulation tool for rock mechanics with dissipative feedbacks, in: AGU Fall Meeting Abstracts, San Francisco, USA, 13–15 December 2014, https://ui.adsabs.harvard.edu/abs/2014AGUFM.H21M..02P/abstract (last access: 17 September 2023), 2014. a, b
Qian, E., Grepl, M., Veroy, K., and Willcox, K.: A certified trust region reduced basis approach to PDE-constrained optimization, SIAM J. Sci. Comput., 39, S434–S460, 2017. a
Rabiti, C., Alfonsi, A., Cogliati, J., Mandelli, D., Kinoshita, R., Sen, S., Wang, C., Talbot, P. W., Maljovec, D. P., and Chen, J.: RAVEN user manual, Tech. Rep., Idaho National Lab.(INL), Idaho Falls, ID (United States), https://doi.org/10.2172/1784874, 2020. a, b, c
Raissi, M. and Karniadakis, G. E.: Hidden physics models: Machine learning of nonlinear partial differential equations, J. Comput. Phys., 357, 125–141, 2018. a
Raissi, M., Perdikaris, P., and Karniadakis, G. E.: Numerical Gaussian processes for time-dependent and non-linear partial differential equations, arXiv [preprint], https://doi.org/10.48550/arXiv.1703.10230, 2017. a
Rannabauer, L., Dumbser, M., and Bader, M.: ADER-DG with a-posteriori finite-volume limiting to simulate tsunamis in a parallel adaptive mesh refinement framework, Comput. Fluids, 173, 299–306, https://doi.org/10.1016/j.compfluid.2018.01.031, 2018. a
Rao, C., Sun, H., and Liu, Y.: Hard encoding of physics for learning spatiotemporal dynamics, arXiv [preprint], https://doi.org/10.48550/arXiv.2105.00557, 2021. a, b, c
Rathgeber, F., Ham, D. A., Mitchell, L., Lange, M., Luporini, F., McRae, A. T., Bercea, G.-T., Markall, G. R., and Kelly, P. H.: Firedrake: automating the finite element method by composing abstractions, ACM T. Math. Software, 43, 1–27, https://doi.org/10.1145/2998441, 2016. a
Razavi, S., Tolson, B. A., and Burn, D. H.: Review of surrogate modeling in water resources, Water Resour. Res., 48, 7, https://doi.org/10.1029/2011WR011527, 2012. a
Regenauer-Lieb, K., Veveakis, M., Poulet, T., Wellmann, F., Karrech, A., Liu, J., Hauser, J., Schrank, C., Gaede, O., and Trefry, M.: Multiscale coupling and multiphysics approaches in earth sciences: Theory, Journal of Coupled Systems and Multiscale Dynamics, 1, 49–73, 2013. a
Richards, L. A.: Capillary conduction of liquids through porous mediums, Physics, 1, 318–333, 1931. a
Rizzo, C. B., de Barros, F. P., Perotto, S., Oldani, L., and Guadagnini, A.: Adaptive POD model reduction for solute transport in heterogeneous porous media, Comput. Geosci., 22, 297–308, https://doi.org/10.1007/s10596-017-9693-5, 2017. a, b
Rochlitz, R., Skibbe, N., and Günther, T.: custEM: Customizable finite-element simulation of complex controlled-source electromagnetic data, Geophysics, 84, F17–F33, https://doi.org/10.1190/geo2018-0208.1, 2019. a
Rogelj, J. and Knutti, R.: Geosciences after Paris, Nat. Geosci., 9, 187–189, 2016. a
Rosas-Carbajal, M., Linde, N., Kalscheuer, T., and Vrugt, J. A.: Two-dimensional probabilistic inversion of plane-wave electromagnetic data: methodology, model constraints and joint inversion with electrical resistivity data, Geophys. J. Int., 196, 1508–1524, 2014. a
Sabeena, J. and Reddy, P. V. S.: A Review of Weather forecasting schemes, i-manager's Journal on Pattern Recognition, 4, 27–30, https://doi.org/10.26634/jpr.4.2.13731, 2017. a, b
Santoso, R., Degen, D., Cacace, M., and Wellmann, F.: State-of-the-art physics-based machine learning for hydro-mechanical simulation in geothermal applications, European Geothermal Congress, Berlin, Germany, 17–21 October 2022, 1–10, https://www.researchgate.net/profile/Ryan-Santoso-2/publication/ (last access: 10 September 2023), 2022. a
Schaa, R., Gross, L., and du Plessis, J.: PDE-based geophysical modelling using finite elements: examples from 3D resistivity and 2D magnetotellurics, J. Geophys. Eng., 13, S59–S73, https://doi.org/10.1088/1742-2132/13/2/S59, 2016. a
Schenk, O. and Gärtner, K.: Solving unsymmetric sparse systems of linear equations with PARDISO, Future Gener. Comp. Sy., 20, 475–487, 2004. a
Shah, V., Joshi, A., Ghosal, S., Pokuri, B., Sarkar, S., Ganapathysubramanian, B., and Hegde, C.: Encoding invariances in deep generative models, arXiv [preprint], https://doi.org/10.48550/arXiv.1906.01626, 2019. a
Sharma, R., Farimani, A. B., Gomes, J., Eastman, P., and Pande, V.: Weakly-supervised deep learning of heat transport via physics informed loss, arXiv [preprint], https://doi.org/10.48550/arXiv.1807.11374, 2018. a
Shen, C., Chen, X., and Laloy, E.: Editorial: Broadening the Use of Machine Learning in Hydrology, Frontiers in Water, 3, 681023, https://doi.org/10.3389/frwa.2021.681023, 2021. a
Sit, M., Demiray, B. Z., Xiang, Z., Ewing, G. J., Sermet, Y., and Demir, I.: A comprehensive review of deep learning applications in hydrology and water resources, Water Sci. Technol., 82, 2635–2670, https://doi.org/10.2166/wst.2020.369, 2020. a, b
Smith, S., reedmaxwell, i-ferguson, Engdahl, N., FabianGasper, Avery, P., Chennault, C., Jourdain, S., grapp1, Condon, L., Kulkarni, K., Bansal, V., xy124, Bennett, A., basileh, Thompson, D., DrewLazzeriKitware, Swilley, J., Beisman, J., Beisman, J., alanquits, Coon, E., Bertolacci, I. M. S., Lührs, L., arezaii, aureliayang and cswoodward: parflow/parflow: ParFlow Version 3.10.0 (v3.10.0), Zenodo [code], https://doi.org/10.5281/zenodo.6413322, 2022. a
Song, X., Zhang, J., Zhan, C., Xuan, Y., Ye, M., and Xu, C.: Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications, J. Hydrol., 523, 739–757, 2015. a
Stefansson, I., Berre, I., and Keilegavlen, E.: A fully coupled numerical model of thermo-hydro-mechanical processes and fracture contact mechanics in porous media, arXiv [preprint], https://doi.org/10.48550/arXiv.2008.06289, 2020. a, b
Stewart, I. S. and Lewis, D.: Communicating contested geoscience to the public: Moving from “matters of fact” to “matters of concern”, Earth-Sci. Rev., 174, 122–133, 2017. a
Taron, J., Elsworth, D., and Min, K.-B.: Numerical simulation of thermal-hydrologic-mechanical-chemical processes in deformable, fractured porous media, Int. J. Rock Mech. Min., 46, 842–854, 2009. a
Tartakovsky, A. M., Marrero, C. O., Perdikaris, P., Tartakovsky, G. D., and Barajas-Solano, D.: Physics-Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems, Water Resour. Res., 56, e2019WR026731, https://doi.org/10.1029/2019wr026731, 2020. a
Tran, H., Leonarduzzi, E., la Fuente, L. D., Hull, R. B., Bansal, V., Chennault, C., Gentine, P., Melchior, P., Condon, L. E., and Maxwell, R. M.: Development of a Deep Learning Emulator for a Distributed Groundwater–Surface Water Model: ParFlow-ML, Water, 13, 3393, https://doi.org/10.3390/w13233393, 2021. a
Trott, C., Berger-Vergiat, L., Poliakoff, D., Rajamanickam, S., Lebrun-Grandie, D., Madsen, J., Awar, N. A., Gligoric, M., Shipman, G., and Womeldorff, G.: The Kokkos EcoSystem: Comprehensive Performance Portability for High Performance Computing, Comput. Sci. Eng., 23, 10–18, https://doi.org/10.1109/mcse.2021.3098509, 2021. a
Vallier, B., Magnenet, V., Schmittbuhl, J., and Fond, C.: THM modeling of hydrothermal circulation at Rittershoffen geothermal site, France, Geothermal Energy, 6, 22, https://doi.org/10.1186/s40517-018-0108-1, 2018. a
van Genuchten, M.: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892–898, 1980. a
Vermeulen, P. and Heemink, A.: Model-reduced variational data assimilation, Mon. Weather Rev., 134, 2888–2899, 2006. a
Vermeulen, P., Heemink, A. W., and Te Stroet, C. B.: Reduced models for linear groundwater flow models using empirical orthogonal functions, Adv. Water Resour., 27, 57–69, 2004. a
Veroy, K., Prud'homme, C., Rovas, D. V., and Patera, A. T.: A Posteriori Error Bounds for Reduced-Basis Approximation of Parametrized Noncoercive and Nonlinear Elliptic Partial Differential Equations, in: Proceedings of the 16th AIAA computational fluid dynamics conference, Orlando, FL, 23–26 June 2003, vol. 3847, https://doi.org/10.2514/6.2003-3847, 2003. a, b
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a, b
Volkwein, S.: Model reduction using proper orthogonal decomposition, Lecture Notes, Institute of Mathematics and Scientific Computing, University of Graz, https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=c8022ab24391f734cba5b41539f651e327c87b48 (last access: 22 September 2023), 2011. a, b
Wang, S., Yu, X., and Perdikaris, P.: When and why PINNs fail to train: A neural tangent kernel perspective, J. Comput. Phys., 449, 110768, https://doi.org/10.1016/j.jcp.2021.110768, 2022. a
Wellmann, J. F. and Reid, L. B.: Basin-scale geothermal model calibration: Experience from the Perth Basin, Australia, Energy Proced., 59, 382–389, 2014. a
Willard, J., Jia, X., Xu, S., Steinbach, M., and Kumar, V.: Integrating physics-based modeling with machine learning: A survey, arXiv [preprint], https://doi.org/10.48550/arXiv.2003.04919, 2020. a, b, c, d
Wirtz, D., Karajan, N., and Haasdonk, B.: Surrogate modeling of multiscale models using kernel methods, Int. J. Numer. Meth. Eng., 101, 1–28, https://doi.org/10.1002/nme.4767, 2015. a
Wu, J.-L., Kashinath, K., Albert, A., Chirila, D., Xiao, H., and Prabhat: Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems, J. Comput. Phys., 406, 109209, https://doi.org/10.1016/j.jcp.2019.109209, 2020. a
Yang, L., Zhang, D., and Karniadakis, G. E.: Physics-informed generative adversarial networks for stochastic differential equations, arXiv [preprint], https://doi.org/10.48550/arXiv.1811.02033, 2018. a
Yang, L., Meng, X., and Karniadakis, G. E.: B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data, J. Comput. Phys., 425, 109913, https://doi.org/10.1016/j.jcp.2020.109913, 2020. a, b
Yang, Y. and Perdikaris, P.: Physics-informed deep generative models, arXiv [preprint], https://doi.org/10.48550/arXiv.1812.03511, 2018. a
Young, C.-C., Liu, W.-C., and Wu, M.-C.: A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events, Appl. Soft Comput., 53, 205–216, https://doi.org/10.1016/j.asoc.2016.12.052, 2017. a, b
Yu, M., Yang, C., and Li, Y.: Big data in natural disaster management: a review, Geosciences, 8, 165, https://doi.org/10.3390/geosciences8050165, 2018. a, b
Zahura, F. T., Goodall, J. L., Sadler, J. M., Shen, Y., Morsy, M. M., and Behl, M.: Training Machine Learning Surrogate Models From a High-Fidelity Physics-Based Model: Application for Real-Time Street-Scale Flood Prediction in an Urban Coastal Community, Water Resour. Res., 56, e2019WR027038, https://doi.org/10.1029/2019wr027038, 2020. a
Zanchetta, A. D. L. and Coulibaly, P.: Hybrid Surrogate Model for Timely Prediction of Flash Flood Inundation Maps Caused by Rapid River Overflow, Forecasting, 4, 126–148, https://doi.org/10.3390/forecast4010007, 2022. a
Zenker, E., Worpitz, B., Widera, R., Huebl, A., Juckeland, G., Knüpfer, A., Nagel, W. E., and Bussmann, M.: Alpaka – An Abstraction Library for Parallel Kernel Acceleration, IEEE Computer Society, Chicago, USA, 23–27 May 2016, https://doi.org/10.1109/IPDPSW.2016.50, 2016. a
Zhang, E., Dao, M., Karniadakis, G. E., and Suresh, S.: Analyses of internal structures and defects in materials using physics-informed neural networks, Science Advances, 8, eabk0644, https://doi.org/10.1126/sciadv.abk0644, 2022. a
Zhu, Y., Zabaras, N., Koutsourelakis, P.-S., and Perdikaris, P.: Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data, J. Comput. Phys., 394, 56–81, 2019. a
Zounemat-Kermnai, M., Batelaan, O., Fadaee, M., and Hinkelmann, R.: Ensemble Machine Learning Paradigms in Hydrology: A Review, J. Hydrol., 598, 126266, https://doi.org/10.1016/j.jhydrol.2021.126266, 2021. a
Executive editor
This manuscript provides a review of physics-based machine learning methods, and provides a perspective on their use.
This manuscript provides a review of physics-based machine learning methods, and provides a...
Short summary
In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
In geosciences, we often use simulations based on physical laws. These simulations can be...
