Articles | Volume 17, issue 24
https://doi.org/10.5194/gmd-17-8955-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-8955-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
19 Dec 2024
Model description paper |
| 19 Dec 2024
| 19 Dec 2024
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
Katherine A. Muller
CORRESPONDING AUTHOR
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Peishi Jiang
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Glenn Hammond
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Tasneem Ahmadullah
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Hyun-Seob Song
Department of Biological Systems Engineering, University of Nebraska–Lincoln, Lincoln, NE, USA
Ravi Kukkadapu
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Nicholas Ward
Pacific Northwest National Laboratory, Sequim, WA 98382, USA
Madison Bowe
Pacific Northwest National Laboratory, Sequim, WA 98382, USA
Rosalie K. Chu
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Qian Zhao
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Vanessa A. Garayburu-Caruso
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Alan Roebuck
Pacific Northwest National Laboratory, Sequim, WA 98382, USA
Xingyuan Chen
Pacific Northwest National Laboratory, Richland, WA 99352, USA
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Subsurface injection of carbon dioxide (CO2) can be used for a variety of purposes including geologic carbon storage and enhanced oil recovery. Recently, CO2 injection into reactive host rocks has been explored as a way to transform CO2 into dense solid minerals. We present a simulation framework for modeling flow of CO2 due to injection and subsequent reactions that take place to mineralize CO2.
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Kristin Jones, Lenaïg G. Hemery, Nicholas D. Ward, Peter J. Regier, Mallory C. Ringham, and Matthew D. Eisaman
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Maggi M. Laan, Stephanie G. Fulton, Vanessa A. Garayburu-Caruso, Morgan E. Barnes, Mikayla A. Borton, Xingyuan Chen, Yuliya Farris, Brieanne Forbes, Amy E. Goldman, Samantha Grieger, Robert O. Hall Jr., Matthew H. Kaufman, Xinming Lin, Erin L. M. Zionce, Sophia A. McKever, Allison Myers-Pigg, Opal Otenburg, Aaron C. Pelly, Huiying Ren, Lupita Renteria, Timothy D. Scheibe, Kyongho Son, Jerry Tagestad, Joshua M. Torgeson, and James C. Stegen
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Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
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James Stegen, Amy J. Burgin, Michelle H. Busch, Joshua B. Fisher, Joshua Ladau, Jenna Abrahamson, Lauren Kinsman-Costello, Li Li, Xingyuan Chen, Thibault Datry, Nate McDowell, Corianne Tatariw, Anna Braswell, Jillian M. Deines, Julia A. Guimond, Peter Regier, Kenton Rod, Edward K. P. Bam, Etienne Fluet-Chouinard, Inke Forbrich, Kristin L. Jaeger, Teri O'Meara, Tim Scheibe, Erin Seybold, Jon N. Sweetman, Jianqiu Zheng, Daniel C. Allen, Elizabeth Herndon, Beth A. Middleton, Scott Painter, Kevin Roche, Julianne Scamardo, Ross Vander Vorste, Kristin Boye, Ellen Wohl, Margaret Zimmer, Kelly Hondula, Maggi Laan, Anna Marshall, and Kaizad F. Patel
Biogeosciences, 22, 995–1034, https://doi.org/10.5194/bg-22-995-2025, https://doi.org/10.5194/bg-22-995-2025, 2025
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Robert E. Danczak, Amy E. Goldman, Mikayla A. Borton, Rosalie K. Chu, Jason G. Toyoda, Vanessa A. Garayburu-Caruso, Emily B. Graham, Joseph W. Morad, Lupita Renteria, Jacqueline R. Hager, Shai Arnon, Scott Brooks, Edo Bar-Zeev, Michael Jones, Nikki Jones, Jorg Lewandowski, Christof Meile, Birgit M. Muller, John Schalles, Hanna Schulz, Adam Ward, and James C. Stegen
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William Kew, Allison Myers-Pigg, Christine H. Chang, Sean M. Colby, Josie Eder, Malak M. Tfaily, Jeffrey Hawkes, Rosalie K. Chu, and James C. Stegen
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Christian Lønborg, Cátia Carreira, Gwenaël Abril, Susana Agustí, Valentina Amaral, Agneta Andersson, Javier Arístegui, Punyasloke Bhadury, Mariana B. Bif, Alberto V. Borges, Steven Bouillon, Maria Ll. Calleja, Luiz C. Cotovicz Jr., Stefano Cozzi, Maryló Doval, Carlos M. Duarte, Bradley Eyre, Cédric G. Fichot, E. Elena García-Martín, Alexandra Garzon-Garcia, Michele Giani, Rafael Gonçalves-Araujo, Renee Gruber, Dennis A. Hansell, Fuminori Hashihama, Ding He, Johnna M. Holding, William R. Hunter, J. Severino P. Ibánhez, Valeria Ibello, Shan Jiang, Guebuem Kim, Katja Klun, Piotr Kowalczuk, Atsushi Kubo, Choon-Weng Lee, Cláudia B. Lopes, Federica Maggioni, Paolo Magni, Celia Marrase, Patrick Martin, S. Leigh McCallister, Roisin McCallum, Patricia M. Medeiros, Xosé Anxelu G. Morán, Frank E. Muller-Karger, Allison Myers-Pigg, Marit Norli, Joanne M. Oakes, Helena Osterholz, Hyekyung Park, Maria Lund Paulsen, Judith A. Rosentreter, Jeff D. Ross, Digna Rueda-Roa, Chiara Santinelli, Yuan Shen, Eva Teira, Tinkara Tinta, Guenther Uher, Masahide Wakita, Nicholas Ward, Kenta Watanabe, Yu Xin, Youhei Yamashita, Liyang Yang, Jacob Yeo, Huamao Yuan, Qiang Zheng, and Xosé Antón Álvarez-Salgado
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Stephanie G. Fulton, Morgan Barnes, Mikayla A. Borton, Xingyuan Chen, Yuliya Farris, Brieanne Forbes, Vanessa A. Garayburu-Caruso, Amy E. Goldman, Samantha Grieger, Robert Hall Jr., Matthew H. Kaufman, Xinming Lin, Erin McCann, Sophia A. McKever, Allison Myers-Pigg, Opal C. Otenburg, Aaron C. Pelly, Huiying Ren, Lupita Renteria, Timothy D. Scheibe, Kyongho Son, Jerry Tagestad, Joshua M. Torgeson, and James C. Stegen
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Preprint archived
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This research examines oxygen use in rivers, which is central to the carbon cycle and water quality. The study focused on an environmentally diverse river basin in the western United States and found that oxygen use in river water was very slow and influenced by factors like water temperature and concentrations of nutrients and carbon in the water. Results suggest that in the study system, most of the oxygen use occurs via mechanisms directly or indirectly associated with riverbed sediments.
Emily B. Graham, Hyun-Seob Song, Samantha Grieger, Vanessa A. Garayburu-Caruso, James C. Stegen, Kevin D. Bladon, and Allison N. Myers-Pigg
Biogeosciences, 20, 3449–3457, https://doi.org/10.5194/bg-20-3449-2023, https://doi.org/10.5194/bg-20-3449-2023, 2023
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Intensifying wildfires are increasing pyrogenic organic matter (PyOM) production and its impact on water quality. Recent work indicates that PyOM may have a greater impact on aquatic biogeochemistry than previously assumed, driven by higher bioavailability. We provide a full assessment of the potential bioavailability of PyOM across its chemical spectrum. We indicate that PyOM can be actively transformed within the river corridor and, therefore, may be a growing source of riverine C emissions.
Peishi Jiang, Pin Shuai, Alexander Sun, Maruti K. Mudunuru, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 27, 2621–2644, https://doi.org/10.5194/hess-27-2621-2023, https://doi.org/10.5194/hess-27-2621-2023, 2023
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We developed a novel deep learning approach to estimate the parameters of a computationally expensive hydrological model on only a few hundred realizations. Our approach leverages the knowledge obtained by data-driven analysis to guide the design of the deep learning model used for parameter estimation. We demonstrate this approach by calibrating a state-of-the-art hydrological model against streamflow and evapotranspiration observations at a snow-dominated watershed in Colorado.
James C. Stegen, Vanessa A. Garayburu-Caruso, Robert E. Danczak, Amy E. Goldman, Lupita Renteria, Joshua M. Torgeson, and Jacqueline Hager
Biogeosciences, 20, 2857–2867, https://doi.org/10.5194/bg-20-2857-2023, https://doi.org/10.5194/bg-20-2857-2023, 2023
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Chemical reactions in river sediments influence how clean the water is and how much greenhouse gas comes out of a river. Our study investigates why some sediments have higher rates of chemical reactions than others. We find that to achieve high rates, sediments need to have two things: only a few different kinds of molecules, but a lot of them. This result spans about 80 rivers such that it could be a general rule, helpful for predicting the future of rivers and our planet.
Heewon Jung, Hyun-Seob Song, and Christof Meile
Geosci. Model Dev., 16, 1683–1696, https://doi.org/10.5194/gmd-16-1683-2023, https://doi.org/10.5194/gmd-16-1683-2023, 2023
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Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
Piyoosh Jaysaval, Glenn E. Hammond, and Timothy C. Johnson
Geosci. Model Dev., 16, 961–976, https://doi.org/10.5194/gmd-16-961-2023, https://doi.org/10.5194/gmd-16-961-2023, 2023
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We present a robust and highly scalable implementation of numerical forward modeling and inversion algorithms for geophysical electrical resistivity tomography data. The implementation is publicly available and developed within the framework of PFLOTRAN (http://www.pflotran.org), an open-source, state-of-the-art massively parallel subsurface flow and transport simulation code. The paper details all the theoretical and implementation aspects of the new capabilities along with test examples.
Alexander Y. Sun, Peishi Jiang, Zong-Liang Yang, Yangxinyu Xie, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 5163–5184, https://doi.org/10.5194/hess-26-5163-2022, https://doi.org/10.5194/hess-26-5163-2022, 2022
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High-resolution river modeling is of great interest to local governments and stakeholders for flood-hazard mitigation. This work presents a physics-guided, machine learning (ML) framework for combining the strengths of high-resolution process-based river network models with a graph-based ML model capable of modeling spatiotemporal processes. Results show that the ML model can approximate the dynamics of the process model with high fidelity, and data fusion further improves the forecasting skill.
Manab Kumar Dutta, Krishnan Sreelash, Damodaran Padmalal, Nicholas D. Ward, and Thomas S. Bianchi
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-200, https://doi.org/10.5194/bg-2022-200, 2022
Revised manuscript not accepted
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Indian estuaries contribute to 2.62 % and 1.09 % of global riverine DIC and DOC export to the ocean, respectively. Major Indian estuaries emit ~9718 Gg yr-1 and 3.27 Gg yr-1 of CO2 and CH4 to the atmosphere, respectively, which contributes ~0.67 % and ~0.12 % to global CO2 and CH4 outgassing from estuaries.
James C. Stegen, Sarah J. Fansler, Malak M. Tfaily, Vanessa A. Garayburu-Caruso, Amy E. Goldman, Robert E. Danczak, Rosalie K. Chu, Lupita Renteria, Jerry Tagestad, and Jason Toyoda
Biogeosciences, 19, 3099–3110, https://doi.org/10.5194/bg-19-3099-2022, https://doi.org/10.5194/bg-19-3099-2022, 2022
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Rivers are vital to Earth, and in rivers, organic matter (OM) is an energy source for microbes that make greenhouse gas and remove contaminants. Predicting Earth’s future requires understanding how and why river OM is transformed. Our results help meet this need. We found that the processes influencing OM transformations diverge between river water and riverbed sediments. This can be used to build new models for predicting the future of rivers and, in turn, the Earth system.
Pin Shuai, Xingyuan Chen, Utkarsh Mital, Ethan T. Coon, and Dipankar Dwivedi
Hydrol. Earth Syst. Sci., 26, 2245–2276, https://doi.org/10.5194/hess-26-2245-2022, https://doi.org/10.5194/hess-26-2245-2022, 2022
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Using an integrated watershed model, we compared simulated watershed hydrologic variables driven by three publicly available gridded meteorological forcings (GMFs) at various spatial and temporal resolutions. Our results demonstrated that spatially distributed variables are sensitive to the spatial resolution of the GMF. The temporal resolution of the GMF impacts the dynamics of watershed responses. The choice of GMF depends on the quantity of interest and its spatial and temporal scales.
Huiying Ren, Erol Cromwell, Ben Kravitz, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 1727–1743, https://doi.org/10.5194/hess-26-1727-2022, https://doi.org/10.5194/hess-26-1727-2022, 2022
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We used a deep learning method called long short-term memory (LSTM) to fill gaps in data collected by hydrologic monitoring networks. LSTM accounted for correlations in space and time and nonlinear trends in data. Compared to a traditional regression-based time-series method, LSTM performed comparably when filling gaps in data with smooth patterns, while it better captured highly dynamic patterns in data. Capturing such dynamics is critical for understanding dynamic complex system behaviors.
Glenn E. Hammond
Geosci. Model Dev., 15, 1659–1676, https://doi.org/10.5194/gmd-15-1659-2022, https://doi.org/10.5194/gmd-15-1659-2022, 2022
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This paper describes a simplified interface for implementing and testing new chemical reactions within the reactive transport simulator PFLOTRAN. The paper describes the interface, providing example code for the interface. The paper includes several chemical reactions implemented through the interface.
Aditi Sengupta, Sarah J. Fansler, Rosalie K. Chu, Robert E. Danczak, Vanessa A. Garayburu-Caruso, Lupita Renteria, Hyun-Seob Song, Jason Toyoda, Jacqueline Hager, and James C. Stegen
Biogeosciences, 18, 4773–4789, https://doi.org/10.5194/bg-18-4773-2021, https://doi.org/10.5194/bg-18-4773-2021, 2021
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Conceptual models link microbes with the environment but are untested. We test a recent model using riverbed sediments. We exposed sediments to disturbances, going dry and becoming wet again. As the length of dry conditions got longer, there was a sudden shift in the ecology of microbes, chemistry of organic matter, and rates of microbial metabolism. We propose a new model based on feedbacks initiated by disturbance that cascade across biological, chemical, and functional aspects of the system.
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Dongwei Gui, Han Qiu, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci., 24, 4971–4996, https://doi.org/10.5194/hess-24-4971-2020, https://doi.org/10.5194/hess-24-4971-2020, 2020
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It is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models because of variant uncertainty sources and high computational cost. This work developed a new tool and demonstrate its implementation to a pilot example for comprehensive global sensitivity analysis of large-scale hydrological modelling. This method is mathematically rigorous and can be applied to other large-scale hydrological models.
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Short summary
The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to...
