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
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Susanne J. H. Buiter, Sascha Brune, Derek Keir, and Gwenn Peron-Pinvidic
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
Positive matrix factorization of large real-time atmospheric mass spectrometry datasets using error-weighted randomized hierarchical alternating least squares
CLAQC v1.0 – Country Level Air Quality Calculator: an empirical modeling approach
Hydro-geomorphological modelling of leaky wooden dam efficacy from reach to catchment scale with CAESAR-Lisflood 1.9j
Stabilized two-phase material point method for hydromechanical coupling problems in solid-fluid porous media
Enhancing single precision with quasi-double precision: achieving double-precision accuracy in the Model for Prediction Across Scales – Atmosphere (MPAS-A) version 8.2.1
Advances in land surface forecasting: a comparison of LSTM, gradient boosting, and feed-forward neural networks as prognostic state emulators in a case study with ecLand
Subgrid corrections for the linear inertial equations of a compound flood model – a case study using SFINCS 2.1.1 Dollerup release
Introducing Iterative Model Calibration (IMC) v1.0: a generalizable framework for numerical model calibration with a CAESAR-Lisflood case study
Development of a high-order global dynamical core using the discontinuous Galerkin method for an atmospheric large-eddy simulation (LES) and proposal of test cases: SCALE-DG v0.8.0
Optimized step size control within the Rosenbrock solvers for stiff chemical ODE systems in KPP version 2.2.3_rs4
A joint reconstruction and model selection approach for large-scale linear inverse modeling (msHyBR v2)
Assimilation of snow water equivalent from AMSR2 and IMS satellite data utilizing the local ensemble transform Kalman filter
The Paleochrono-1.1 probabilistic model to derive a common age model for several paleoclimatic sites using absolute and relative dating constraints
asQ: parallel-in-time finite element simulations using ParaDiag for geoscientific models and beyond
Explicit stochastic advection algorithms for the regional-scale particle-resolved atmospheric aerosol model WRF-PartMC (v1.0)
Potential based Thermodynamics with Consistent Conservative Cascade Transport for Implicit Large Eddy Simulation: PTerodaC3TILES version 1.0
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 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
Isogeometric analysis of the lithosphere under topographic loading: Igalith v1.0.0
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
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
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
GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling
Parameterization and tuning of the Bay of Biscay Atlantis model v1
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
Benjamin C. Sapper, Sean Youn, Daven K. Henze, Manjula Canagaratna, Harald Stark, and Jose L. Jimenez
Geosci. Model Dev., 18, 2891–2919, https://doi.org/10.5194/gmd-18-2891-2025, https://doi.org/10.5194/gmd-18-2891-2025, 2025
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Positive matrix factorization (PMF) has been used by atmospheric scientists to extract underlying factors present in large datasets. This paper presents a new technique for error-weighted PMF that drastically reduces the computational costs of previously developed algorithms. We use this technique to deliver interpretable factors and solution diagnostics from an atmospheric chemistry dataset.
Stefania Renna, Francesco Granella, Lara Aleluia Reis, and Paulina Schulz-Antipa
Geosci. Model Dev., 18, 2373–2408, https://doi.org/10.5194/gmd-18-2373-2025, https://doi.org/10.5194/gmd-18-2373-2025, 2025
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The Country Level Air Quality Calculator (CLAQC) is a new fast modeling tool that predicts globally country-level monthly and annual concentrations of two major air pollutants, fine particulate matter (PM2.5) and tropospheric ozone (O3). It was designed to inform national and regional climate and pollution mitigation policies. It is easy to use and computationally efficient, allowing for the simulation of a large number of emission scenarios for policy assessments and optimization frameworks.
Joshua M. Wolstenholme, Christopher J. Skinner, David Milan, Robert E. Thomas, and Daniel R. Parsons
Geosci. Model Dev., 18, 1395–1411, https://doi.org/10.5194/gmd-18-1395-2025, https://doi.org/10.5194/gmd-18-1395-2025, 2025
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Leaky wooden dams are a type of natural flood management intervention that aims to reduce flood risk downstream by temporarily holding back water during a storm event and releasing it afterwards. These structures alter the river hydrology, and therefore the geomorphology, yet often this is excluded from numerical models. Here we show that by not simulating geomorphology, we are currently underestimating the efficacy of these structures to reduce the flood peak and store water.
Xiong Tang, Wei Liu, Siming He, Lei Zhu, Michel Jaboyedoff, Huanhuan Zhang, Yuqing Sun, and Zenan Huo
EGUsphere, https://doi.org/10.5194/egusphere-2025-707, https://doi.org/10.5194/egusphere-2025-707, 2025
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This manuscript presents a numerical model about an explicit stabilized coupled two phase material point method. The novelty lies in the employment of stabilized techniques including the strain smoothing method and the multi-field variational principleto to mitigate the spurious pore pressure and maintain the numerical stability. The proposed formulation provides an effective and reliable approach for simulating solid-fluid porous media under static and dynamic conditions.
Jiayi Lai, Lanning Wang, Qizhong Wu, Yizhou Yang, and Fang Wang
Geosci. Model Dev., 18, 1089–1102, https://doi.org/10.5194/gmd-18-1089-2025, https://doi.org/10.5194/gmd-18-1089-2025, 2025
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High-performance computing limitations often hinder numerical model development. Traditional models use double precision for accuracy, which is computationally expensive. Lower precision reduces costs but can introduce errors. The quasi-double-precision (QDP) algorithm helps mitigate these errors. This study applies the QDP algorithm to the Model for Prediction Across Scales – Atmosphere, showing reduced errors and computational time, making it an efficient solution for large-scale simulations.
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington, Margarita Choulga, Souhail Boussetta, Maria Kalweit, Joschka Bödecker, Carsten F. Dormann, Florian Pappenberger, and Gianpaolo Balsamo
Geosci. Model Dev., 18, 921–937, https://doi.org/10.5194/gmd-18-921-2025, https://doi.org/10.5194/gmd-18-921-2025, 2025
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We compared spatiotemporal forecasts of three machine learning models that learned water and energy
states on the land surface from a physical model scheme. The forecasting models were developed with reanalysis data and simulations on a European scale and transferred to the globe. We found that all approaches deliver highly accurate approximations of the physical dynamic at long time horizons, implying their usefulness to advance land surface forecasting with synthetic data.
states on the land surface from a physical model scheme. The forecasting models were developed with reanalysis data and simulations on a European scale and transferred to the globe. We found that all approaches deliver highly accurate approximations of the physical dynamic at long time horizons, implying their usefulness to advance land surface forecasting with synthetic data.
Maarten van Ormondt, Tim Leijnse, Roel de Goede, Kees Nederhoff, and Ap van Dongeren
Geosci. Model Dev., 18, 843–861, https://doi.org/10.5194/gmd-18-843-2025, https://doi.org/10.5194/gmd-18-843-2025, 2025
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Accurate flood risk assessments are crucial for storm protection. To achieve efficiency, computational costs must be minimized. This paper introduces a novel subgrid approach for linear inertial equations (LIEs) with bed level and friction variations, implemented in the Super-Fast INundation of CoastS (SFINCS) model. Pre-processed lookup tables enhance simulation precision with lower costs. Validations show significant accuracy improvement even at coarser resolutions.
Chayan Banerjee, Kien Nguyen, Clinton Fookes, Gregory Hancock, and Thomas Coulthard
Geosci. Model Dev., 18, 803–818, https://doi.org/10.5194/gmd-18-803-2025, https://doi.org/10.5194/gmd-18-803-2025, 2025
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In geosciences, the reliance on numerical models necessitates the precise calibration of their parameters to effectively translate information from observed to unobserved settings. We introduce a generalizable framework for calibrating numerical models, with a case study of the geomorphological model CAESAR-Lisflood. This approach efficiently identifies the optimal set of parameters for a given numerical model, enabling retrospective and prospective analyses at various temporal resolutions.
Yuta Kawai and Hirofumi Tomita
Geosci. Model Dev., 18, 725–762, https://doi.org/10.5194/gmd-18-725-2025, https://doi.org/10.5194/gmd-18-725-2025, 2025
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When considering future high-resolution atmospheric simulations with O(10–100 m) grid spacing, such as global large-eddy simulations, numerical errors with the conventional low-order dynamical cores can contaminate the effects of turbulent parameterization. To achieve high-order discretization, we developed a global atmospheric dynamical core using the discontinuous Galerkin method (DGM). By conducting several validation tests, we discussed the impact of a high-order DGM on atmospheric flows.
Raphael Dreger, Timo Kirfel, Andrea Pozzer, Simon Rosanka, Rolf Sander, and Domenico Taraborrelli
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-166, https://doi.org/10.5194/gmd-2024-166, 2025
Revised manuscript accepted for GMD
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Model simulations are essentials for understanding the interactions between atmospheric composition and weather. However, models including chemistry are very slow. Hence, any computation speedup of such models is important for advancing the understanding of interactions within the Earth System. In this study we analysed and optimized the time stepping for chemistry calculations. Our results show that atmospheric chemistry models could be run notably faster without any loss in the accuracy.
Malena Sabaté Landman, Julianne Chung, Jiahua Jiang, Scot M. Miller, and Arvind K. Saibaba
Geosci. Model Dev., 17, 8853–8872, https://doi.org/10.5194/gmd-17-8853-2024, https://doi.org/10.5194/gmd-17-8853-2024, 2024
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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.
Joonlee Lee, Myong-In Lee, Sunlae Tak, Eunkyo Seo, and Yong-Keun Lee
Geosci. Model Dev., 17, 8799–8816, https://doi.org/10.5194/gmd-17-8799-2024, https://doi.org/10.5194/gmd-17-8799-2024, 2024
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We developed an advanced snow water equivalent (SWE) data assimilation framework using satellite data based on a land surface model. The results of this study highlight the beneficial impact of data assimilation by effectively combining land surface model and satellite-derived data according to their relative uncertainty, thereby controlling not only transitional regions but also the regions with heavy snow accumulation that are difficult to detect by satellite.
Frédéric Parrenin, Marie Bouchet, Christo Buizert, Emilie Capron, Ellen Corrick, Russell Drysdale, Kenji Kawamura, Amaëlle Landais, Robert Mulvaney, Ikumi Oyabu, and Sune Olander Rasmussen
Geosci. Model Dev., 17, 8735–8750, https://doi.org/10.5194/gmd-17-8735-2024, https://doi.org/10.5194/gmd-17-8735-2024, 2024
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The Paleochrono-1.1 probabilistic dating model allows users 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) Δdepth observations. Paleochrono-1.1 is available under an open-source license.
Joshua Hope-Collins, Abdalaziz Hamdan, Werner Bauer, Lawrence Mitchell, and Colin Cotter
EGUsphere, https://doi.org/https://doi.org/10.48550/arXiv.2409.18792, https://doi.org/https://doi.org/10.48550/arXiv.2409.18792, 2024
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Effectively using modern supercomputers requires massively parallel algorithms. Time-parallel algorithms calculate the system state (e.g. the atmosphere) at multiple times simultaneously and have exciting potential, but are tricky to implement and still require development. We have developed software to simplify implementing and testing the ParaDiag algorithm on supercomputers. We show that for some atmospheric problems it can enable faster or more accurate solutions than traditional techniques.
Jeffrey H. Curtis, Nicole Riemer, and Matthew West
Geosci. Model Dev., 17, 8399–8420, https://doi.org/10.5194/gmd-17-8399-2024, https://doi.org/10.5194/gmd-17-8399-2024, 2024
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This paper introduces a numerical method for simulating particle-based aerosol transport in atmospheric models. We detail the various numerical properties of the advection order method 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.
John Thuburn
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-153, https://doi.org/10.5194/gmd-2024-153, 2024
Revised manuscript accepted for GMD
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A new computational fluid dynamics code for simulating the atmospheric boundary layer and convection is presented. Moist thermodynamics is formulated via thermodynamic potentials, avoiding inconsistencies that can be introduced with conventional approaches. Numerical methods typical of weather and climate models are used, with no explicit subgrid scheme. Results highlight some advantages (e.g., large time steps) and disadvantages (e.g., weak vertical fluxes near the surface) of this approach.
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
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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
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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
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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
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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.
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
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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
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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
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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.
Rozan Rosandi, Yudi Rosandi, and Bernd Simeon
EGUsphere, https://doi.org/10.5194/egusphere-2024-1093, https://doi.org/10.5194/egusphere-2024-1093, 2024
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We model Earth's lithosphere as a thin elastic shell and present numerical methods of isogeometric finite element analysis to simulate its deformation in isostatic equilibrium using technologies from computer-aided design. The simulations also serve as a basis for identifying parameters of the model that are most plausible to explain observed data. This research has been done to showcase the capabilities of isogeometric analysis in solving higher-order problems in geoscientific applications.
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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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.
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
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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.
Ane Lopez de Gamiz-Zearra, Cecilie Hansen, Xavier Corrales, Iñaki Quincoces, Izaskun Preciado, and Eider Andonegi
EGUsphere, https://doi.org/10.5194/egusphere-2023-1368, https://doi.org/10.5194/egusphere-2023-1368, 2023
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This paper describes the development of the first calibrated end-to-end Atlantis model for the Bay of Biscay that will help us improve the comprehension of the spatial functioning of the Bay of Biscay ecosystem and help establishing management measures of human activities. Our results highlighted the importance of lower trophic levels to the pelagic system and demonstrate the importance of having accurate and precise data for biological processes.
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
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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
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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
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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.
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...
