Articles | Volume 16, issue 16
https://doi.org/10.5194/gmd-16-4767-2023
© 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-16-4767-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
24 Aug 2023
Development and technical paper |
| 24 Aug 2023
| 24 Aug 2023
Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0
Geothermal Energy and Geofluids Group, Institute of Geophysics, Department of Earth Sciences, ETH Zurich, Zurich, Switzerland
Bernd Flemisch
Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, Stuttgart, Germany
Chao-Zhong Qin
State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing, China
Martin O. Saar
Geothermal Energy and Geofluids Group, Institute of Geophysics, Department of Earth Sciences, ETH Zurich, Zurich, Switzerland
Department of Earth and Environmental Sciences, University of Minnesota, Minneapolis, USA
Anozie Ebigbo
CORRESPONDING AUTHOR
Chair of Hydromechanics, Helmut Schmidt University, Hamburg, Germany
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Xiaodong Ma, Marian Hertrich, Florian Amann, Kai Bröker, Nima Gholizadeh Doonechaly, Valentin Gischig, Rebecca Hochreutener, Philipp Kästli, Hannes Krietsch, Michèle Marti, Barbara Nägeli, Morteza Nejati, Anne Obermann, Katrin Plenkers, Antonio P. Rinaldi, Alexis Shakas, Linus Villiger, Quinn Wenning, Alba Zappone, Falko Bethmann, Raymi Castilla, Francisco Seberto, Peter Meier, Thomas Driesner, Simon Loew, Hansruedi Maurer, Martin O. Saar, Stefan Wiemer, and Domenico Giardini
Solid Earth, 13, 301–322, https://doi.org/10.5194/se-13-301-2022, https://doi.org/10.5194/se-13-301-2022, 2022
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Questions on issues such as anthropogenic earthquakes and deep geothermal energy developments require a better understanding of the fractured rock. Experiments conducted at reduced scales but with higher-resolution observations can shed some light. To this end, the BedrettoLab was recently established in an existing tunnel in Ticino, Switzerland, with preliminary efforts to characterize realistic rock mass behavior at the hectometer scale.
Roman Winter, Bernd Flemisch, Holger Class, and Rainer Merk
Saf. Nucl. Waste Disposal, 1, 31–31, https://doi.org/10.5194/sand-1-31-2021, https://doi.org/10.5194/sand-1-31-2021, 2021
Florian Amann, Valentin Gischig, Keith Evans, Joseph Doetsch, Reza Jalali, Benoît Valley, Hannes Krietsch, Nathan Dutler, Linus Villiger, Bernard Brixel, Maria Klepikova, Anniina Kittilä, Claudio Madonna, Stefan Wiemer, Martin O. Saar, Simon Loew, Thomas Driesner, Hansruedi Maurer, and Domenico Giardini
Solid Earth, 9, 115–137, https://doi.org/10.5194/se-9-115-2018, https://doi.org/10.5194/se-9-115-2018, 2018
Related subject area
Hydrology
Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0)
pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information
Representing the impact of Rhizophora mangroves on flow in a hydrodynamic model (COAWST_rh v1.0): the importance of three-dimensional root system structures
Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification
Enhancing the representation of water management in global hydrological models
NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process
Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations
GPEP v1.0: a Geospatial Probabilistic Estimation Package to support Earth Science applications
DynQual v1.0: a high-resolution global surface water quality model
GEMS v1.0: Generalizable empirical model of snow accumulation and melt based on daily snow mass changes in response to climate and topographic drivers
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
rSHUD v2.0: Advancing Unstructured Hydrological Modeling in the R Environment
Simulation of crop yield using the global hydrological model H08 (crp.v1)
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model
Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation (within CAESAR-Lisflood version 1.9j-WS)
Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)
Customized deep learning for precipitation bias correction and downscaling
Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain
Regional coupled surface–subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency domain discharge data
Operational water forecast ability of the HRRR-iSnobal combination: an evaluation to adapt into production environments
Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake
UniFHy v0.1.1: a community modelling framework for the terrestrial water cycle in Python
mesas.py v1.0: A flexible Python package for modeling solute transport and transit times using StorAge Selection functions
GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model
Basin-scale gyres and mesoscale eddies in large lakes: a novel procedure for their detection and characterization, assessed in Lake Geneva
SIMO v1.0: simplified model of the vertical temperature profile in a small, warm, monomictic lake
Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model
Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality
Coupling a large-scale hydrological model (CWatM v1.1) with a high-resolution groundwater flow model (MODFLOW 6) to assess the impact of irrigation at regional scale
RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling
Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest
Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation
A physically based distributed karst hydrological model (QMG model-V1.0) for flood simulations
Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability
CREST-VEC: a framework towards more accurate and realistic flood simulation across scales
Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains
The eWaterCycle platform for open and FAIR hydrological collaboration
Evaluating the Atibaia River hydrology using JULES6.1
A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector
CLIMFILL v0.9: a framework for intelligently gap filling Earth observations
Modeling subgrid lake energy balance in ORCHIDEE terrestrial scheme using the FLake lake model
Evaluating a reservoir parametrization in the vector-based global routing model mizuRoute (v2.0.1) for Earth system model coupling
Improved runoff simulations for a highly varying soil depth and complex terrain watershed in the Loess Plateau with the Community Land Model version 5
GSTools v1.3: a toolbox for geostatistical modelling in Python
AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
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We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
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We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
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Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
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Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
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NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
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We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-172, https://doi.org/10.5194/gmd-2023-172, 2023
Revised manuscript accepted for GMD
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Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed a Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
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DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-103, https://doi.org/10.5194/gmd-2023-103, 2023
Revised manuscript accepted for GMD
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We present a new snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in-situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in-situ data is limited.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
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Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Lele Shu, Paul Ullrich, Xianghong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-128, https://doi.org/10.5194/gmd-2023-128, 2023
Revised manuscript accepted for GMD
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Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290, https://doi.org/10.5194/gmd-16-3275-2023, https://doi.org/10.5194/gmd-16-3275-2023, 2023
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Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163, https://doi.org/10.5194/gmd-16-3137-2023, https://doi.org/10.5194/gmd-16-3137-2023, 2023
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Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows us to select the most influential input parameters, but it has to be adapted to spatial modelling. This study will identify relevant methods that can be transposed to any hydrological and water quality model and improve the fate of pesticide knowledge.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
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In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
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We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
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During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
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It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
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Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
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This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
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Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
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Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
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Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
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A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023, https://doi.org/10.5194/gmd-16-233-2023, 2023
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Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
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The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
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A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
Ciaran Harman and Esther Xu Fei
EGUsphere, https://doi.org/10.5194/egusphere-2022-1262, https://doi.org/10.5194/egusphere-2022-1262, 2022
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Over the last 10 years scientists have developed a new way of modeling how material is transported through complex systems, called StorAge Selection. Here we present some new code implementing this method that is easy to use, but also flexible and very accurate. We show that for cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other people's code to the right answer in an important way: it conserves mass.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-226, https://doi.org/10.5194/gmd-2022-226, 2022
Revised manuscript accepted for GMD
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This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1km at the equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for higher resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Seyed Mahmood Hamze-Ziabari, Ulrich Lemmin, Frédéric Soulignac, Mehrshad Foroughan, and David Andrew Barry
Geosci. Model Dev., 15, 8785–8807, https://doi.org/10.5194/gmd-15-8785-2022, https://doi.org/10.5194/gmd-15-8785-2022, 2022
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A procedure combining numerical simulations, remote sensing, and statistical analyses is developed to detect large-scale current systems in large lakes. By applying this novel procedure in Lake Geneva, strategies for detailed transect field studies of the gyres and eddies were developed. Unambiguous field evidence of 3D gyre/eddy structures in full agreement with predictions confirmed the robustness of the proposed procedure.
Kristina Šarović, Melita Burić, and Zvjezdana B. Klaić
Geosci. Model Dev., 15, 8349–8375, https://doi.org/10.5194/gmd-15-8349-2022, https://doi.org/10.5194/gmd-15-8349-2022, 2022
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We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
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Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Danielle S. Grogan, Shan Zuidema, Alex Prusevich, Wilfred M. Wollheim, Stanley Glidden, and Richard B. Lammers
Geosci. Model Dev., 15, 7287–7323, https://doi.org/10.5194/gmd-15-7287-2022, https://doi.org/10.5194/gmd-15-7287-2022, 2022
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This paper describes the University of New Hampshire's water balance model (WBM). This model simulates the land surface components of the global water cycle and includes water extractions for use by humans for agricultural, domestic, and industrial purposes. A new feature is described that permits water source tracking through the water cycle, which has implications for water resource management. This paper was written to describe a long-used model and presents its first open-source version.
Luca Guillaumot, Mikhail Smilovic, Peter Burek, Jens de Bruijn, Peter Greve, Taher Kahil, and Yoshihide Wada
Geosci. Model Dev., 15, 7099–7120, https://doi.org/10.5194/gmd-15-7099-2022, https://doi.org/10.5194/gmd-15-7099-2022, 2022
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We develop and test the first large-scale hydrological model at regional scale with a very high spatial resolution that includes a water management and groundwater flow model. This study infers the impact of surface and groundwater-based irrigation on groundwater recharge and on evapotranspiration in both irrigated and non-irrigated areas. We argue that water table recorded in boreholes can be used as validation data if water management is well implemented and spatial resolution is ≤ 100 m.
Robert Chlumsky, James R. Craig, Simon G. M. Lin, Sarah Grass, Leland Scantlebury, Genevieve Brown, and Rezgar Arabzadeh
Geosci. Model Dev., 15, 7017–7030, https://doi.org/10.5194/gmd-15-7017-2022, https://doi.org/10.5194/gmd-15-7017-2022, 2022
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We introduce the open-source RavenR package, which has been built to support the use of the hydrologic modelling framework Raven. The R package contains many functions that may be useful in each step of the model-building process, including preparing model input files, running the model, and analyzing the outputs. We present six reproducible use cases of the RavenR package for the Liard River basin in Canada to demonstrate how it may be deployed.
Bahar Bahrami, Anke Hildebrandt, Stephan Thober, Corinna Rebmann, Rico Fischer, Luis Samaniego, Oldrich Rakovec, and Rohini Kumar
Geosci. Model Dev., 15, 6957–6984, https://doi.org/10.5194/gmd-15-6957-2022, https://doi.org/10.5194/gmd-15-6957-2022, 2022
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Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.
Stefania Camici, Gabriele Giuliani, Luca Brocca, Christian Massari, Angelica Tarpanelli, Hassan Hashemi Farahani, Nico Sneeuw, Marco Restano, and Jérôme Benveniste
Geosci. Model Dev., 15, 6935–6956, https://doi.org/10.5194/gmd-15-6935-2022, https://doi.org/10.5194/gmd-15-6935-2022, 2022
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This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And Mapping), to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and terrestrial total water storage anomalies. Potentially useful for multiple operational and scientific applications, the added value of the STREAM approach is the ability to increase knowledge on the natural processes, human activities, and their interactions on the land.
Ji Li, Daoxian Yuan, Fuxi Zhang, Jiao Liu, and Mingguo Ma
Geosci. Model Dev., 15, 6581–6600, https://doi.org/10.5194/gmd-15-6581-2022, https://doi.org/10.5194/gmd-15-6581-2022, 2022
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A new karst hydrological model (the QMG model) is developed to simulate and predict the floods in karst trough valley basins. Unlike the complex structure and parameters of current karst groundwater models, this model has a simple double-layered structure with few parameters and decreases the demand for modeling data in karst areas. The flood simulation results based on the QMG model of the Qingmuguan karst trough valley basin are satisfactory, indicating the suitability of the model simulation.
Luca Trotter, Wouter J. M. Knoben, Keirnan J. A. Fowler, Margarita Saft, and Murray C. Peel
Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022, https://doi.org/10.5194/gmd-15-6359-2022, 2022
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MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It can be used to run and calibrate these models within a common environment as well as to easily modify them. We restructured and recoded MARRMoT in order to make the models run faster and to simplify their use, while also providing some new features. This new MARRMoT version runs models on average 3.6 times faster while maintaining very strong consistency in their outputs to the previous version.
Zhi Li, Shang Gao, Mengye Chen, Jonathan Gourley, Naoki Mizukami, and Yang Hong
Geosci. Model Dev., 15, 6181–6196, https://doi.org/10.5194/gmd-15-6181-2022, https://doi.org/10.5194/gmd-15-6181-2022, 2022
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Operational streamflow prediction at a continental scale is critical for national water resources management. However, limited computational resources often impede such processes, with streamflow routing being one of the most time-consuming parts. This study presents a recent development of a hydrologic system that incorporates a vector-based routing scheme with a lake module that markedly speeds up streamflow prediction. Moreover, accuracy is improved and flood false alarms are mitigated.
Suyeon Choi and Yeonjoo Kim
Geosci. Model Dev., 15, 5967–5985, https://doi.org/10.5194/gmd-15-5967-2022, https://doi.org/10.5194/gmd-15-5967-2022, 2022
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Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to predict a radar reflectivity map with a lead time of 10 min. Rad-cGAN showed superior performance at a lead time of up to 90 min compared with the reference models. Furthermore, we demonstrate the successful implementation of the transfer learning strategies using pre-trained Rad-cGAN to develop the models for different dam domains.
Rolf Hut, Niels Drost, Nick van de Giesen, Ben van Werkhoven, Banafsheh Abdollahi, Jerom Aerts, Thomas Albers, Fakhereh Alidoost, Bouwe Andela, Jaro Camphuijsen, Yifat Dzigan, Ronald van Haren, Eric Hutton, Peter Kalverla, Maarten van Meersbergen, Gijs van den Oord, Inti Pelupessy, Stef Smeets, Stefan Verhoeven, Martine de Vos, and Berend Weel
Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, https://doi.org/10.5194/gmd-15-5371-2022, 2022
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With the eWaterCycle platform, we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both open science and FAIR science. The eWatercyle platform gives easy access to well-known hydrological models, big datasets and example experiments. Using eWaterCycle hydrologists can easily compare the results from different models, couple models and do more complex hydrological computational research.
Hsi-Kai Chou, Ana Maria Heuminski de Avila, and Michaela Bray
Geosci. Model Dev., 15, 5233–5240, https://doi.org/10.5194/gmd-15-5233-2022, https://doi.org/10.5194/gmd-15-5233-2022, 2022
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Land surface models allow us to understand and investigate the cause and effect of environmental process changes. Therefore, this type of model is increasingly used for hydrological assessments. Here we explore the possibility of this approach using a case study in the Atibaia River basin, which serves as a major water supply for the metropolitan regions of Campinas and São Paulo, Brazil. We evaluated the model performance and use the model to simulate the basin hydrology.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
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Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Verena Bessenbacher, Sonia Isabelle Seneviratne, and Lukas Gudmundsson
Geosci. Model Dev., 15, 4569–4596, https://doi.org/10.5194/gmd-15-4569-2022, https://doi.org/10.5194/gmd-15-4569-2022, 2022
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Earth observations have many missing values. They are often filled using information from spatial and temporal contexts that mostly ignore information from related observed variables. We propose the gap-filling method CLIMFILL that additionally uses information from related variables. We test CLIMFILL using gap-free reanalysis data of variables related to soil–moisture climate interactions. CLIMFILL creates estimates for the missing values that recover the original dependence structure.
Anthony Bernus and Catherine Ottlé
Geosci. Model Dev., 15, 4275–4295, https://doi.org/10.5194/gmd-15-4275-2022, https://doi.org/10.5194/gmd-15-4275-2022, 2022
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The lake model FLake was coupled to the ORCHIDEE land surface model to simulate lake energy balance at global scale with a multi-tile approach. Several simulations were performed with various atmospheric reanalyses and different lake depth parameterizations. The simulated lake surface temperature showed good agreement with observations (RMSEs of the order of 3 °C). We showed the large impact of the atmospheric forcing on lake temperature. We highlighted systematic errors on ice cover phenology.
Inne Vanderkelen, Shervan Gharari, Naoki Mizukami, Martyn P. Clark, David M. Lawrence, Sean Swenson, Yadu Pokhrel, Naota Hanasaki, Ann van Griensven, and Wim Thiery
Geosci. Model Dev., 15, 4163–4192, https://doi.org/10.5194/gmd-15-4163-2022, https://doi.org/10.5194/gmd-15-4163-2022, 2022
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Human-controlled reservoirs have a large influence on the global water cycle. However, dam operations are rarely represented in Earth system models. We implement and evaluate a widely used reservoir parametrization in a global river-routing model. Using observations of individual reservoirs, the reservoir scheme outperforms the natural lake scheme. However, both schemes show a similar performance due to biases in runoff timing and magnitude when using simulated runoff.
Jiming Jin, Lei Wang, Jie Yang, Bingcheng Si, and Guo-Yue Niu
Geosci. Model Dev., 15, 3405–3416, https://doi.org/10.5194/gmd-15-3405-2022, https://doi.org/10.5194/gmd-15-3405-2022, 2022
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This study aimed to improve runoff simulations and explore deep soil hydrological processes for a highly varying soil depth and complex terrain watershed in the Loess Plateau, China. The actual soil depths and river channels were incorporated into the model to better simulate the runoff in this watershed. The soil evaporation scheme was modified to better describe the evaporation processes. Our results showed that the model significantly improved the runoff simulations.
Sebastian Müller, Lennart Schüler, Alraune Zech, and Falk Heße
Geosci. Model Dev., 15, 3161–3182, https://doi.org/10.5194/gmd-15-3161-2022, https://doi.org/10.5194/gmd-15-3161-2022, 2022
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The GSTools package provides a Python-based platform for geoostatistical applications. Salient features of GSTools are its random field generation, its kriging capabilities and its versatile covariance model. It is furthermore integrated with other Python packages, like PyKrige, ogs5py or scikit-gstat, and provides interfaces to meshio and PyVista. Four presented workflows showcase the abilities of GSTools.
Ather Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, and Kyung Hwa Cho
Geosci. Model Dev., 15, 3021–3039, https://doi.org/10.5194/gmd-15-3021-2022, https://doi.org/10.5194/gmd-15-3021-2022, 2022
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The field of artificial intelligence has shown promising results in a wide variety of fields including hydrological modeling. However, developing and testing hydrological models with artificial intelligence techniques require expertise from diverse fields. In this study, we developed an open-source framework based upon the python programming language to simplify the process of the development of hydrological models of time series data using machine learning.
Cited articles
Abd, A. S. and Abushaikha, A. S.: Reactive transport in porous media: a review
of recent mathematical efforts in modeling geochemical reactions in petroleum
subsurface reservoirs, SN Appl. Sci., 3, 401,
https://doi.org/10.1007/s42452-021-04396-9, 2021. a
Aftab, A., Hassanpouryouzband, A., Xie, Q., Machuca, L. L., and Sarmadivaleh,
M.: Toward a Fundamental Understanding of Geological Hydrogen Storage, Ind.
Eng. Chem. Res., 61, 3233–3253, https://doi.org/10.1021/acs.iecr.1c04380, 2022. a
Agartan, E., Trevisan, L., Cihan, A., Birkholzer, J., Zhou, Q., and
Illangasekare, T. H.: Experimental study on effects of geologic heterogeneity
in enhancing dissolution trapping of supercritical CO2, Water Resour.
Res., 51, 1635–1648, https://doi.org/10.1002/2014WR015778, 2015. a
Åkerlöf, G. and Teare, J.: A Note on the Density of Aqueous Solutions of
Hydrochloric Acid, J. Am. Chem. Soc., 60, 1226–1228,
https://doi.org/10.1021/ja01272a063, 1938. a
Almarcha, C., R'Honi, Y., De Decker, Y., Trevelyan, P. M. J., Eckert, K., and
De Wit, A.: Convective Mixing Induced by Acid–Base Reactions, J. Phys. Chem. B, 115, 9739–9744, https://doi.org/10.1021/jp202201e, 2011. a
Alnæs, M. S.: UFL: a finite element form language, in: Automated Solution
of Differential Equations by the Finite Element Method: The FEniCS Book,
edited by Logg, A., Mardal, K.-A., and Wells, G. N., Springer
Berlin Heidelberg, Berlin, Heidelberg, 303–338, https://doi.org/10.1007/978-3-642-23099-8_17,
2012. a
Alnæs, M. S., 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
Amarasinghe, W., Fjelde, I., Åge Rydland, J., and Guo, Y.: Effects of
permeability on CO2 dissolution and convection at reservoir temperature
and pressure conditions: A visualization study, Int. J. Greenh. Gas Con., 99,
103082, https://doi.org/10.1016/j.ijggc.2020.103082, 2020. a
Amestoy, P. R., Duff, I. S., L'Excellent, J.-Y., and Koster, J.: A Fully
Asynchronous Multifrontal Solver Using Distributed Dynamic Scheduling, SIAM
J. Matrix Anal. A., 23, 15–41, https://doi.org/10.1137/S0895479899358194, 2001. a
Amestoy, P. R., Buttari, A., L'Excellent, J.-Y., and Mary, T.: Performance and
Scalability of the Block Low-Rank Multifrontal Factorization on Multicore
Architectures, ACM T. Math. Software, 45, 1–26, https://doi.org/10.1145/3242094, 2019. a
Audigane, P., Gaus, I., Czernichowski-Lauriol, I., Pruess, K., and Xu, T.:
Two-dimensional reactive transport modeling of CO2 injection in a saline
aquifer at the Sleipner site, North Sea, Am. J. Sci., 307, 974–1008,
https://doi.org/10.2475/07.2007.02, 2007. a
Avnir, D. and Kagan, M.: Spatial structures generated by chemical reactions at
interfaces, Nature, 307, 717–720, https://doi.org/10.1038/307717a0, 1984. a
Azin, R., Raad, S. M. J., Osfouri, S., and Fatehi, R.: Onset of instability in
CO2 sequestration into saline aquifer: scaling relationship and the effect
of perturbed boundary, Heat Mass Transfer, 49, 1603–1612,
https://doi.org/10.1007/s00231-013-1199-7, 2013. a
Babaei, M. and Islam, A.: Convective-Reactive CO2 Dissolution in Aquifers
With Mass Transfer With Immobile Water, Water Resour. Res., 54, 9585–9604,
https://doi.org/10.1029/2018WR023150, 2018. a
Bacuta, C.: A Unified Approach for Uzawa Algorithms, SIAM J. Numer. Anal., 44,
2633–2649, https://doi.org/10.1137/050630714, 2006. a
Balay, S., Abhyankar, S., Adams, M. F., Benson, S., Brown, J., Brune, P.,
Buschelman, K., Constantinescu, E. M., Dalcin, L., Dener, A., Eijkhout, V.,
Gropp, W. D., Hapla, V., Isaac, T., Jolivet, P., Karpeev, D., Kaushik, D.,
Knepley, M. G., Kong, F., Kruger, S., May, D. A., McInnes, L. C., Mills,
R. T., Mitchell, L., Munson, T., Roman, J. E., Rupp, K., Sanan, P., Sarich,
J., Smith, B. F., Zampini, S., Zhang, H., Zhang, H., and Zhang, J.: PETSc
Web page,
urlhttps://petsc.org/ (last access: 3 November 2022), 2022. a
urlhttps://petsc.org/ (last access: 3 November 2022), 2022. a
Benzi, M., Golub, G. H., and Liesen, J.: Numerical solution of saddle point
problems, Acta Numer., 14, 1–137, https://doi.org/10.1017/S0962492904000212, 2005. a
Bethke, C. M.: Geochemical and Biogeochemical Reaction Modeling, Cambridge
University Press, 2nd edn., https://doi.org/10.1017/CBO9780511619670, 2007. a
Boffi, D., Brezzi, F., and Fortin, M.: Mixed Finite Element Methods and
Applications, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-36519-5,
2013. a
Bordeaux-Rego, F., Sanaei, A., and Sepehrnoori, K.: Enhancement of Simulation
CPU Time of Reactive-Transport Flow in Porous Media: Adaptive Tolerance and
Mixing Zone-Based Approach, Transport Porous Med., 143, 127–150,
https://doi.org/10.1007/s11242-022-01789-1, 2022. a
Boudreau, B. P., Meysman, F. J. R., and Middelburg, J. J.: Multicomponent ionic
diffusion in porewaters: Coulombic effects revisited, Earth Planet. Sc.
Lett., 22, 653–666, https://doi.org/10.1016/j.epsl.2004.02.034, 2004. a, b
Bożek, B., Sapa, L., and Danielewski, M.: Difference Methods to One and
Multidimensional Interdiffusion Models with Vegard Rule, Math. Model. Anal.,
24, 276–296, https://doi.org/10.3846/mma.2019.018, 2019. a
Bożek, B., Sapa, L., Tkacz-Śmiech, K., Zajusz, M., and Danielewski, M.:
Compendium About Multicomponent Interdiffusion in Two Dimensions, Metall.
Mater. Trans. A, 52, 3221–3231, https://doi.org/10.1007/s11661-021-06267-9, 2021. a
Bratsun, D., Kostarev, K., Mizev, A., and Mosheva, E.: Concentration-dependent
diffusion instability in reactive miscible fluids, Phys. Rev. E, 92,
011 003, https://doi.org/10.1103/PhysRevE.92.011003, 2015. a
Bratsun, D., Mizev, A., Mosheva, E., and Kostarev, K.: Shock-wave-like
structures induced by an exothermic neutralization reaction in miscible
fluids, Phys. Rev. E, 96, 053 106, https://doi.org/10.1103/PhysRevE.96.053106, 2017. a, b, c, d
Bratsun, D., Mizev, A., and Mosheva, E.: Extended classification of the
buoyancy-driven flows induced by a neutralization reaction in miscible
fluids. Part 2. Theoretical study, J. Fluid Mech., 916, A23,
https://doi.org/10.1017/jfm.2021.202, 2021. a, b, c, d
Bratsun, D. A., Oschepkov, V. O., Mosheva, E. A., and Siraev, R. R.: The effect
of concentration-dependent diffusion on double-diffusive instability, Phys.
Fluids, 34, 034 112, https://doi.org/10.1063/5.0079850, 2022. a, b
Brezzi, F., Douglad, J., and Marini, L. D.: Two families of mixed finite
elements for second order elliptic problems, Numerische Mathematik, 47,
217–235, https://doi.org/10.1007/BF01389710, 1985. a
Bringedal, C., Schollenberger, T., Pieters, G. J. M., van Duijn, C. J., and
Helmig, R.: Evaporation-Driven Density Instabilities in Saturated Porous
Media, Transport Porous Med., 143, 297–341,
https://doi.org/10.1007/s11242-022-01772-w, 2022. a
Brouzet, C., Méheust, Y., and Meunier, P.: CO2 convective
dissolution in a three-dimensional granular porous medium: An experimental
study, Phys. Rev. Fluids, 7, 033 802, https://doi.org/10.1103/PhysRevFluids.7.033802,
2022. a
Budroni, M. A.: Cross-diffusion-driven hydrodynamic instabilities in a
double-layer system: General classification and nonlinear simulations, Phys.
Rev. E, 92, 063007, https://doi.org/10.1103/PhysRevE.92.063007, 2015. a
Cappellen, P. V. and Gaillard, J.-F.: Chapter 8. Biogeochemical Dynamics in
Aquatic Sediments, in: Reactive Transport in Porous Media, edited by
Lichtner, P. C., Steefel, C. I., and Oelkers, E. H., De
Gruyter, 335–376, https://doi.org/10.1515/9781501509797-011, 1996. a
Carrayrou, J., Mosé, R., and Behra, P.: Operator-splitting procedures for
reactive transport and comparison of mass balance errors, J. Contam. Hydrol.,
68, 239–268, https://doi.org/10.1016/S0169-7722(03)00141-4, 2004. a
Carrera, J., Saaltink, M. W., Soler-Sagarra, J., Wang, J., and Valhondo, C.:
Reactive Transport: A Review of Basic Concepts with Emphasis on Biochemical
Processes, Energies, 15, https://doi.org/10.3390/en15030925, 2022. a
Chang, E., Brewer, A. W., Park, D. M., Jiao, Y., and Lammers, L. N.: Selective
Biosorption of Valuable Rare Earth Elements Among Co-Occurring Lanthanides,
Environ. Eng. Sci., 38, 154–164, https://doi.org/10.1089/ees.2020.0291, 2021. a
Cheng, C. and Milsch, H.: Permeability Variations in Illite-Bearing Sandstone:
Effects of Temperature and NaCl Fluid Salinity, J. Geophys. Res.-Sol. Ea.,
125, e2020JB020122, https://doi.org/10.1029/2020JB020122, 2020. a
Cherezov, I., Cardoso, S. S., and Kim, M. C.: Acceleration of convective
dissolution by an instantaneous chemical reaction: A comparison of
experimental and numerical results, Chem. Eng. Sci., 181, 298–310,
https://doi.org/10.1016/j.ces.2018.02.005, 2018. a
Citri, O., Kagan, M. L., Kosloff, R., and Avnir, D.: Evolution of chemically
induced unstable density gradients near horizontal reactive interfaces,
Langmuir, 6, 559–564, https://doi.org/10.1021/la00093a007, 1990. a
Class, H., Weishaupt, K., and Trötschler, O.: Experimental and Simulation
Study on Validating a Numerical Model for CO2 Density-Driven Dissolution
in Water, Water, 12, 738, https://doi.org/10.3390/w12030738, 2020. a
Cochepin, B., Trotignon, L., Bildstein, O., Steefel, C., Lagneau, V., and Van
der lee, J.: Approaches to modelling coupled flow and reaction in a 2D
cementation experiment, Adv. Water Resour., 31, 1540–1551,
https://doi.org/10.1016/j.advwatres.2008.05.007, 2008. a
Cogorno, J., Stolze, L., Muniruzzaman, M., and Rolle, M.: Dimensionality
effects on multicomponent ionic transport and surface complexation in porous
media, Geochim. Cosmochim. Ac., 318, 230–246,
https://doi.org/10.1016/j.gca.2021.11.037, 2022. a
Connolly, J. A. D.: The geodynamic equation of state: What and how, Geochem.
Geophy. Geosy., 10, Q10014, https://doi.org/10.1029/2009GC002540, 2009. a
Constantin, P., Ignatova, M., and Lee, F.-N.: Interior Electroneutrality in
Nernst–Planck–Navier–Stokes Systems,
Arch. Ration. Mech. An., 242, 1091–1118,
https://doi.org/10.1007/s00205-021-01700-0, 2021. a
Constantin, P., Ignatova, M., and Lee, F.-N.: Existence and stability of
nonequilibrium steady states of Nernst–Planck–Navier–Stokes systems,
Physica D, 442, 133536,
https://doi.org/10.1016/j.physd.2022.133536, 2022. a
Crameri, F.: Scientific colour maps, Zenodo [data set], https://doi.org/10.5281/zenodo.5501399, 2021. a
Damiani, L. H., Kosakowski, G., Glaus, M. A., and Churakov, S. V.: A framework
for reactive transport modeling using FEniCS–Reaktoro: governing equations
and benchmarking results, Comput. Geosci., 24, 1071–1085,
https://doi.org/10.1007/s10596-019-09919-3, 2020. a, b
Davis, M.: Palettable: Color palettes for Python,
https://jiffyclub.github.io/palettable (last access:
25 November 2022), 2019. a
De Lucia, M. and Kühn, M.: DecTree v1.0 – chemistry speedup in reactive transport simulations: purely data-driven and physics-based surrogates, Geosci. Model Dev., 14, 4713–4730, https://doi.org/10.5194/gmd-14-4713-2021, 2021. a
De Lucia, M., Kühn, M., Lindemann, A., Lübke, M., and Schnor, B.: POET (v0.1): speedup of many-core parallel reactive transport simulations with fast DHT lookups, Geosci. Model Dev., 14, 7391–7409, https://doi.org/10.5194/gmd-14-7391-2021, 2021. a
De Wit, A.: Chemo-hydrodynamic patterns in porous media, Philos.
T. Roy. Soc. A, 374, 20150419, https://doi.org/10.1098/rsta.2015.0419, 2016. a
De Wit, A.: Chemo-Hydrodynamic Patterns and Instabilities, Annu. Rev. Fluid
Mech., 52, 531–555, https://doi.org/10.1146/annurev-fluid-010719-060349, 2020. a
Dickinson, E. J. F., Limon-Peterson, J. G., and Compton, R. G.: The
electroneutrality assumption in electrochemistry, J. Solid State Electr., 15,
1335–1345, https://doi.org/10.1007/s10008-011-1323-x, 2011. a
Donea, J. and Huerta, A.: Finite Element Methods for Flow Problems, John Wiley
& Sons, https://doi.org/10.1002/0470013826, 2003. a
Dreyer, W., Guhlke, C., and Müller, R.: Overcoming the shortcomings of the
Nernst–Planck model, Phys. Chem. Chem. Phys., 15, 7075–7086,
https://doi.org/10.1039/C3CP44390F, 2013. a
Drummond, S.: Boiling and mixing of hydrothermal fluids: chemical effects on
mineral precipitation, PhD thesis, Pennsylvania State University,
https://www.proquest.com/docview/303157791 (last access:
21 November 2022), 1981. a
Eckert, K. and Grahn, A.: Plume and Finger Regimes Driven by an Exothermic
Interfacial Reaction, Phys. Rev. Lett., 82, 4436–4439,
https://doi.org/10.1103/PhysRevLett.82.4436, 1999. a
Eckert, K., Acker, M., and Shi, Y.: Chemical pattern formation driven by a
neutralization reaction. I. Mechanism and basic features, Phys. Fluids, 16,
385–399, https://doi.org/10.1063/1.1636160, 2004. a
Ezekiel, J., Adams, B. M., Saar, M. O., and Ebigbo, A.: Numerical analysis and
optimization of the performance of CO2-Plume Geothermal (CPG) production
wells and implications for electric power generation, Geothermics, 98,
102270, https://doi.org/10.1016/j.geothermics.2021.102270, 2022. a
Filipek, R., Kalita, P., Sapa, L., and Szyszkiewicz, K.: On local weak
solutions to Nernst–Planck–Poisson system, Appl. Anal., 96, 2316–2332,
https://doi.org/10.1080/00036811.2016.1221941, 2017. a
Flavell, A., Machen, M., Eisenberg, B., Kabre, J., Liu, C., and Li, X.: A
conservative finite difference scheme for Poisson–Nernst–Planck
equations, J. Comput. Phys., 13, 235–249, https://doi.org/10.1007/s10825-013-0506-3,
2014. a
Fleming, M. R., Adams, B. M., Kuehn, T. H., Bielicki, J. M., and Saar, M. O.:
Increased Power Generation due to Exothermic Water Exsolution in CO2 Plume
Geothermal (CPG) Power Plants, Geothermics, 88, 101865,
https://doi.org/10.1016/j.geothermics.2020.101865, 2020. a
Fortin, M. and Glowinski, R.: Augmented Lagrangian Methods in Quadratic
Programming, in: Augmented Lagrangian Methods: Applications to the Numerical
Solution of Boundary-Value Problems, edited by Fortin, M. and Glowinski, R.,
vol. 15 of Studies in Mathematics and Its Applications, chap. 1,
1–46, Elsevier, https://doi.org/10.1016/S0168-2024(08)70026-2, 1983. a, b
Frank, F., Ray, N., and Knabner, P.: Numerical investigation of homogenized
Stokes–Nernst–Planck–Poisson systems, Computing and Visualization in
Science, 14, 385–400, https://doi.org/10.1007/s00791-013-0189-0, 2011. a
Frizon, F., Lorente, S., Ollivier, J., and Thouvenot, P.: Transport model for
the nuclear decontamination of cementitious materials, Comp. Mater. Sci., 27,
507–516, https://doi.org/10.1016/S0927-0256(03)00051-X, 2003. a
Fu, B., Zhang, R., Liu, J., Cui, L., Zhu, X., and Hao, D.: Simulation of CO2
Rayleigh Convection in Aqueous Solutions of NaCl, KCl, MgCl2 and CaCl2
using Lattice Boltzmann Method, Int. J. Greenh. Gas Con., 98, 103066,
https://doi.org/10.1016/j.ijggc.2020.103066, 2020. a
Fu, B., Zhang, R., Xiao, R., Cui, L., Liu, J., Zhu, X., and Hao, D.: Simulation
of interfacial mass transfer process accompanied by Rayleigh convection in
NaCl solution, Int. J. Greenh. Gas Con., 106, 103281,
https://doi.org/10.1016/j.ijggc.2021.103281, 2021. a
Gamazo, P., Slooten, L. J., Carrera, J., Saaltink, M. W., Bea, S., and Soler,
J.: PROOST: object-oriented approach to multiphase reactive transport
modeling in porous media, J. Hydroinform., 18, 310–328,
https://doi.org/10.2166/hydro.2015.126, 2015. a
Giambalvo, E. R., Steefel, C. I., Fisher, A. T., Rosenberg, N. D., and Wheat,
C. G.: Effect of fluid-sediment reaction on hydrothermal fluxes of major
elements, eastern flank of the Juan de Fuca Ridge, Geochim. Cosmochim. Ac.,
66, 1739–1757, https://doi.org/10.1016/S0016-7037(01)00878-X, 2002. a
Gimmi, T. and Alt-Epping, P.: Simulating Donnan equilibria based on the
Nernst-Planck equation, Geochim. Cosmochim. Ac., 232, 1–13,
https://doi.org/10.1016/j.gca.2018.04.003, 2018. a
Glaus, M. A., Birgersson, M., Karnland, O., and Van Loon, L. R.: Seeming
Steady-State Uphill Diffusion of 22Na+ in Compacted Montmorillonite, Environ.
Sci. Technol., 47, 11522–11527, https://doi.org/10.1021/es401968c, 2013. a
Golparvar, A., Kästner, M., and Thullner, M.: P3D-BRNS v1.0.0: A Three-dimensional, Multiphase, Multicomponent, Pore-scale Reactive Transport Modelling Package for Simulating Biogeochemical Processes in Subsurface Environments, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2022-86, in review, 2022. a
Grimm Lima, M., Schädle, P., Green, C. P., Vogler, D., Saar, M. O., and Kong,
X.-Z.: Permeability Impairment and Salt Precipitation Patterns During CO2
Injection Into Single Natural Brine-Filled Fractures, Water Resour. Res., 56,
e2020WR027213, https://doi.org/10.1029/2020WR027213, 2020. a
Guo, R., Sun, H., Zhao, Q., Li, Z., Liu, Y., and Chen, C.: A Novel Experimental
Study on Density-Driven Instability and Convective Dissolution in Porous
Media, Geophys. Res. Lett., 48, e2021GL095619, https://doi.org/10.1029/2021GL095619,
2021. a
Harrower, M. and Brewer, C. A.: ColorBrewer.org: An Online Tool for Selecting
Colour Schemes for Maps, The Cartographic Journal, 40, 27–37,
https://doi.org/10.1179/000870403235002042, 2003. a
Hassanpouryouzband, A., Adie, K., Cowen, T., Thaysen, E. M., Heinemann, N.,
Butler, I. B., Wilkinson, M., and Edlmann, K.: Geological Hydrogen Storage:
Geochemical Reactivity of Hydrogen with Sandstone Reservoirs, ACS Energy
Letters, 7, 2203–2210, https://doi.org/10.1021/acsenergylett.2c01024, 2022. a
Hejazi, S. H. and Azaiez, J.: Nonlinear simulation of transverse flow
interactions with chemically driven convective mixing in porous media, Water
Resour. Res., 49, 4607–4618, https://doi.org/10.1002/wrcr.20298, 2013. a
Helgeson, H. C. and Kirkham, D. H.: Theoretical prediction of the thermodynamic
behavior of aqueous electrolytes at high pressures and temperatures; I,
Summary of the thermodynamic/electrostatic properties of the solvent, Am. J.
Sci., 274, 1089–1198, https://doi.org/10.2475/ajs.274.10.1089, 1974a. a
Helgeson, H. C. and Kirkham, D. H.: Theoretical prediction of the thermodynamic
behavior of aqueous electrolytes at high pressures and temperatures; II,
Debye-Huckel parameters for activity coefficients and relative partial molal
properties, Am. J. Sci., 274, 1199–1261, https://doi.org/10.2475/ajs.274.10.1199,
1974b. a
Helgeson, H. C. and Kirkham, D. H.: Theoretical prediction of the thermodynamic
properties of aqueous electrolytes at high pressures and temperatures. III.
Equation of state for aqueous species at infinite dilution, Am. J. Sci., 276,
97–240, https://doi.org/10.2475/ajs.276.2.97, 1976. a
Helgeson, H. C., Kirkham, D. H., and Flowers, G. C.: Theoretical prediction of
the thermodynamic behavior of aqueous electrolytes by high pressures and
temperatures; IV, Calculation of activity coefficients, osmotic coefficients,
and apparent molal and standard and relative partial molal properties to
600 ∘C and 5 KB, Am. J. Sci., 281, 1249–1516,
https://doi.org/10.2475/ajs.281.10.1249, 1981. a
Herz, M., Ray, N., and Knabner, P.: Existence and uniqueness of a global weak
solution of a Darcy-Nernst-Planck-Poisson system, GAMM-Mitteilungen, 35,
191–208, https://doi.org/10.1002/gamm.201210013, 2012. a
Hingerl, F. F.: Geothermal electrolyte solutions: thermodynamic model and
computational fitting framework development, PhD thesis, ETH Zurich,
https://doi.org/10.3929/ethz-a-009755479, 2012. a
Huang, P.-W.: pwhuang/RetroPy: First release of RetroPy (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7371384, 2022. a
Huang, Y., Shao, H., Wieland, E., Kolditz, O., and Kosakowski, G.: A new
approach to coupled two-phase reactive transport simulation for long-term
degradation of concrete, Constr. Build. Mater., 190, 805–829,
https://doi.org/10.1016/j.conbuildmat.2018.09.114, 2018. a
Huang, P.-W., Flemisch, B., Qin, C.-Z., Saar, M. O., and Ebigbo, A.: Data supplement for: Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0 (v1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7362225, 2022a. a
Huang, P.-W.,
Flemisch, B.,
Qin, C.-Z.,
Saar, M. O., and
Ebigbo, A.: Video supplement for: Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0, ETH Zürich [video], https://doi.org/10.3929/ethz-b-000579224, 2022b. a
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9,
90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Ignatova, M. and Shu, J.: Global Smooth Solutions of the
Nernst–Planck–Darcy System, J. Math. Fluid Mech., 24, 26,
https://doi.org/10.1007/s00021-022-00666-7, 2022. a
Illés, B., Medgyes, B., Dušek, K., Bušek, D., Skwarek, A., and Géczy, A.:
Numerical simulation of electrochemical migration of Cu based on the
Nernst-Plank equation, Int. J. Heat Mass Tran., 184, 122268,
https://doi.org/10.1016/j.ijheatmasstransfer.2021.122268, 2022. a
Izumoto, S., Huisman, J. A., Zimmermann, E., Heyman, J., Gomez, F., Tabuteau,
H., Laniel, R., Vereecken, H., Méheust, Y., and Le Borgne, T.: Pore-Scale
Mechanisms for Spectral Induced Polarization of Calcite Precipitation
Inferred from Geo-Electrical Millifluidics, Environ. Sci. Technol., 56,
4998–5008, https://doi.org/10.1021/acs.est.1c07742, 2022. a
Jasielec, J. J.: Electrodiffusion Phenomena in Neuroscience and the
Nernst–Planck–Poisson Equations, Electrochem., 2, 197–215,
https://doi.org/10.3390/electrochem2020014, 2021. a
Jiang, L., Wang, S., Liu, D., Zhang, W., Lu, G., Liu, Y., and Zhao, J.: Change
in Convection Mixing Properties with Salinity and Temperature: CO2 Storage
Application, Polymers, 12, 2084, https://doi.org/10.3390/polym12092084, 2020. a
Johnson, J. W., Oelkers, E. H., and Helgeson, H. C.: SUPCRT92: A software
package for calculating the standard molal thermodynamic properties of
minerals, gases, aqueous species, and reactions from 1 to 5000 bar and 0 to
1000 ∘C, Comput. Geosci., 18, 899–947,
https://doi.org/10.1016/0098-3004(92)90029-Q, 1992. a
Jotkar, M., De Wit, A., and Rongy, L.: Control of chemically driven convective
dissolution by differential diffusion effects, Phys. Rev. Fluids, 6,
053504, https://doi.org/10.1103/PhysRevFluids.6.053504, 2021. a
Jyoti, A. and Haese, R. R.: Validation of a multicomponent reactive-transport
model at pore scale based on the coupling of COMSOL and PhreeqC, Comput.
Geosci., 156, 104870, https://doi.org/10.1016/j.cageo.2021.104870, 2021. a
Kadeethum, T., Lee, S., Ballarin, F., Choo, J., and Nick, H.: A locally
conservative mixed finite element framework for coupled
hydro-mechanical–chemical processes in heterogeneous porous media, Comput.
Geosci., 152, 104774, https://doi.org/10.1016/j.cageo.2021.104774, 2021. a
Kim, M. C.: Effect of the irreversible
Kirby, B. J.: Species and Charge Transport, in: Micro- and Nanoscale Fluid
Mechanics: Transport in Microfluidic Devices, Cambridge University
Press, 250–264, https://doi.org/10.1017/CBO9780511760723.013, 2010. a, b
Kneafsey, T. J. and Pruess, K.: Laboratory Flow Experiments for Visualizing
Carbon Dioxide-Induced, Density-Driven Brine Convection, Transport Porous
Med., 82, 123–139, https://doi.org/10.1007/s11242-009-9482-2, 2010. a
Kontturi, K., Murtomäki, L., andManzanares, A. J.: Ionic Transport Processes:
in Electrochemistry and Membrane Science, Oxford University Press,
https://doi.org/10.1093/acprof:oso/9780199533817.001.0001, 2008. a
Kovtunenko, V. A. and Zubkova, A. V.: Existence and two-scale convergence of
the generalised Poisson–Nernst–Planck problem with non-linear interface
conditions, Eur. J. Appl. Math., 32, 683–710,
https://doi.org/10.1017/S095679252000025X, 2021. a
Kulik, D. A., Wagner, T., Dmytrieva, S. V., Kosakowski, G., Hingerl, F. F.,
Chudnenko, K. V., and Berner, U. R.: Theoretical prediction of the
thermodynamic properties of aqueous electrolytes at high pressures and
temperatures. III. Equation of state for aqueous species at infinite
dilution, Computat. Geosci., 17, 1–24, https://doi.org/10.1007/s10596-012-9310-6,
2012. a
Kwon, O., Herbert, B. E., and Kronenberg, A. K.: Permeability of illite-bearing
shale: 2. Influence of fluid chemistry on flow and functionally connected
pores, J. Geophys. Res.-Sol. Ea., 109, B10206, https://doi.org/10.1029/2004JB003055,
2004a. a
Kwon, O., Kronenberg, A. K., Gangi, A. F., Johnson, B., and Herbert, B. E.:
Permeability of illite-bearing shale: 1. Anisotropy and effects of clay
content and loading, J. Geophys. Res.-Sol. Ea., 109, B10205,
https://doi.org/10.1029/2004JB003052, 2004b. a
Kyas, S., Volpatto, D., Saar, M. O., and Leal, A. M. M.: Accelerated reactive
transport simulations in heterogeneous porous media using Reaktoro and
Firedrake, Computat. Geosci., 26, 295–327, https://doi.org/10.1007/s10596-021-10126-2,
2022. a, b
Laloy, E. and Jacques, D.: Speeding Up Reactive Transport Simulations in Cement
Systems by Surrogate Geochemical Modeling: Deep Neural Networks and k-Nearest
Neighbors, Transport Porous Med., 143, 433–462, https://doi.org/10.1007/s11242-022-01779-3, 2022. a
Leal, A. M., Blunt, M. J., and LaForce, T. C.: Efficient chemical equilibrium
calculations for geochemical speciation and reactive transport modelling,
Geochim. Cosmochim. Ac., 131, 301–322, https://doi.org/10.1016/j.gca.2014.01.038,
2014. a
Leal, A. M., Kulik, D. A., and Kosakowski, G.: Computational methods for
reactive transport modeling: A Gibbs energy minimization approach for
multiphase equilibrium calculations, Adv. Water Resour., 88, 231–240,
https://doi.org/10.1016/j.advwatres.2015.11.021, 2016. a
Leal, A. M. M.: Reaktoro for Python and C++,
https://reaktoro.org/ (last access: 21 November 2022), 2022. a
Leal, A. M. M., Kulik, D. A., Smith, W. R., and Saar, M. O.: An overview of
computational methods for chemical equilibrium and kinetic calculations for
geochemical and reactive transport modeling, Pure Appl. Chem., 89, 597–643,
https://doi.org/10.1515/pac-2016-1107, 2017. a
Leal, A. M. M., Kyas, S., Kulik, D. A., and Saar, M. O.: Accelerating
Reactive Transport Modeling: On-Demand Machine Learning Algorithm for
Chemical Equilibrium Calculations, Transport Porous Med., 133, 161–204,
https://doi.org/10.1007/s11242-020-01412-1, 2020. a
Lee, F.-N.: Global Regularity for Nernst–Planck–Navier–Stokes Systems with mixed boundary conditions, Nonlinearity, 36, 255–286,
https://doi.org/10.1088/1361-6544/aca50f, 2022. a
Lees, E., Rokkam, S., Shanbhag, S., and Gunzburger, M.: The electroneutrality
constraint in nonlocal models, J. Chem. Phys., 147, 124102,
https://doi.org/10.1063/1.5003915, 2017. a
Lemaigre, L., Budroni, M. A., Riolfo, L. A., Grosfils, P., and De Wit, A.:
Asymmetric Rayleigh-Taylor and double-diffusive fingers in reactive systems,
Phys. Fluids, 25, 014103, https://doi.org/10.1063/1.4774321, 2013. a, b, c, d
Lemmon, E. W. and Harvey, A. H.: Thermophysical Properties of Water and Steam,
in: CRC Handbook of Chemistry and Physics, edited by: Rumble, J. R., CRC
Press/Taylor & Francis, Boca Raton, FL, 102nd edn., 2021. a
Lichtner, P. C.: Continuum model for simultaneous chemical reactions and mass
transport in hydrothermal systems, Geochim. Cosmochim. Ac., 49, 779–800,
https://doi.org/10.1016/0016-7037(85)90172-3, 1985. a, b
Lichtner, P. C.: Principles and Practice of Reactive Transport Modeling, MRS
Proceedings, 353, 117–130, https://doi.org/10.1557/PROC-353-117, 1994. a
Ling, F. T., Plattenberger, D. A., Peters, C. A., and Clarens, A. F.: Sealing
Porous Media through Calcium Silicate Reactions with CO2 to Enhance the
Security of Geologic Carbon Sequestration, Environ. Eng. Sci., 38, 127–142,
https://doi.org/10.1089/ees.2020.0369, 2021. a
Liu, H. and Maimaitiyiming, W.: Efficient, Positive, and Energy Stable Schemes
for Multi-D Poisson–Nernst–Planck Systems, J. Sci. Comput., 148, 92,
https://doi.org/10.1007/s10915-021-01503-1, 2021. a
Liu, H., Wang, Z., Yin, P., and Yu, H.: Positivity-preserving third order DG
schemes for Poisson–Nernst–Planck equations, J. Comput. Phys., 452,
110777, https://doi.org/10.1016/j.jcp.2021.110777, 2022. a
López-Vizcaíno, R., Cabrera, V., Sprocati, R., Muniruzzaman, M., Rolle, M.,
Navarro, V., and Yustres, Á.: A modeling approach for electrokinetic
transport in double-porosity media, Electrochim. Acta, 431, 141139,
https://doi.org/10.1016/j.electacta.2022.141139, 2022. a
Luhmann, A. J., Kong, X.-Z., Tutolo, B. M., Ding, K., Saar, M. O., and
Seyfried, W. E. J.: Permeability Reduction Produced by Grain Reorganization
and Accumulation of Exsolved CO2 during Geologic Carbon Sequestration: A
New CO2 Trapping Mechanism, Environ. Sci. Technol., 47, 242–251,
https://doi.org/10.1021/es3031209, 2013. a
Maes, J. and Menke, H. P.: GeoChemFoam: Direct Modelling of Multiphase Reactive
Transport in Real Pore Geometries with Equilibrium Reactions, Transport
Porous Med., 139, 271–299, https://doi.org/10.1007/s11242-021-01661-8, 2021. a
Mahmoodpour, S., Rostami, B., Soltanian, M. R., and Amooie, M. A.: Effect of
brine composition on the onset of convection during CO2 dissolution in
brine, Comput. Geosci., 124, 1–13, https://doi.org/10.1016/j.cageo.2018.12.002, 2019. a
Mahmoodpour, S., Amooie, M. A., Rostami, B., and Bahrami, F.: Effect of gas
impurity on the convective dissolution of CO2 in porous media, Energy,
199, 117397, https://doi.org/10.1016/j.energy.2020.117397, 2020. a
Marini, L.: Chapter 4 – The Aqueous Electrolyte Solution, in: Geological
Sequestration of Carbon Dioxide, vol. 11 of Developments in
Geochemistry, Elsevier, 53–77, https://doi.org/10.1016/S0921-3198(06)80024-4,
2007. a
Martinez, M. J. and Hesse, M. A.: Two-phase convective CO2 dissolution in
saline aquifers, Water Resour. Res., 52, 585–599,
https://doi.org/10.1002/2015WR017085, 2016. a
Miron, G. D., Leal, A. M. M., and Yapparova, A.: Thermodynamic Properties of
Aqueous Species Calculated Using the HKF Model: How Do Different
Thermodynamic and Electrostatic Models for Solvent Water Affect Calculated
Aqueous Properties?, Geofluids, 2019, 5750390, https://doi.org/10.1155/2019/5750390, 2019. a
Moortgat, J., Li, M., Amooie, M. A., and Zhu, D.: A higher-order finite element
reactive transport model for unstructured and fractured grids, Sci.
Rep.-UK, 10, 15572, https://doi.org/10.1038/s41598-020-72354-3, 2020. a
Muniruzzaman, M. and Rolle, M.: Impact of multicomponent ionic transport on pH
fronts propagation in saturated porous media, Water Resour. Res., 51,
6739–6755, https://doi.org/10.1002/2015WR017134, 2015. a
Muniruzzaman, M. and Rolle, M.: Experimental investigation of the impact of
compound-specific dispersion and electrostatic interactions on transient
transport and solute breakthrough, Water Resour. Res., 53, 1189–1209,
https://doi.org/10.1002/2016WR019727, 2017. a
Muniruzzaman, M. and Rolle, M.: Multicomponent Ionic Transport Modeling in
Physically and Electrostatically Heterogeneous Porous Media With PhreeqcRM
Coupling for Geochemical Reactions, Water Resour. Res., 55, 11121–11143,
https://doi.org/10.1029/2019WR026373, 2019. a
Muniruzzaman, M., Haberer, C. M., Grathwohl, P., and Rolle, M.: Multicomponent
ionic dispersion during transport of electrolytes in heterogeneous porous
media: Experiments and model-based interpretation, Geochim. Cosmochim. Ac.,
141, 656–669, https://doi.org/10.1016/j.gca.2014.06.020, 2014. a
Naumov, D. Y., Bilke, L., Fischer, T., Rink, K., Wang, W., Watanabe, N., Lu,
R., Grunwald, N., Zill, F., Buchwald, J., Huang, Y., Bathmann, J., Chen, C.,
Chen, S., Meng, B., Shao, H., Kern, D., Yoshioka, K., Garibay Rodriguez, J.,
Miao, X., Parisio, F., Silbermann, C., Thiedau, J., Walther, M., Kaiser, S.,
Boog, J., Zheng, T., Meisel, T., and Ning, Z.: OpenGeoSys, Zenodo [data set],
https://doi.org/10.5281/zenodo.6405711, 2022. a
Neufeld, J. A., Hesse, M. A., Riaz, A., Hallworth, M. A., Tchelepi, H. A., and
Huppert, H. E.: Convective dissolution of carbon dioxide in saline aquifers,
Geophys. Res. Lett., 37, L22404, https://doi.org/10.1029/2010GL044728, 2010. a
Nishikata, E., Ishii, T., and Ohta, T.: Viscosities of aqueous hydrochloric
acid solutions, and densities and viscosities of aqueous hydroiodic acid
solutions, J. Chem. Eng. Data, 26, 254–256, https://doi.org/10.1021/je00025a008, 1981. a
Oliveira, T. D. S., Blunt, M. J., and Bijeljic, B.: Modelling of multispecies
reactive transport on pore-space images, Adv. Water Resour., 127, 192–208,
https://doi.org/10.1016/j.advwatres.2019.03.012, 2019. a
Onsager, L.: Theories and Problems of Liquid Diffusion, Ann. NY. Acad. Sci.,
46, 241–265, https://doi.org/10.1111/j.1749-6632.1945.tb36170.x, 1945. a
Pamukcu, S.: Electrochemical Transport and Transformations, in: Electrochemical
Remediation Technologies for Polluted Soils, Sediments and Groundwater,
edited by: Reddy, K. R. and Cameselle, C., John Wiley &
Sons, Ltd, chap. 2, 29–64, https://doi.org/10.1002/9780470523650.ch2, 2009. a
Parkhurst, D. L. and Appelo, C. A. J.: Description of input and examples for
PHREEQC version 3–A computer program for speciation, batch-reaction,
one-dimensional transport, and inverse geochemical calculations,
http://pubs.usgs.gov/tm/06/a43 (last access: 10 August 2023), 2013. 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
Petersen, S. and Hack, K.: The thermochemistry library ChemApp and its
applications, Int. J. Mater. Res., 98, 935–945, https://doi.org/10.3139/146.101551,
2007. a
Pogge von Strandmann, P. A. E., Burton, K. W., Snæbjörnsdóttir, S. O.,
Sigfússon, B., Aradóttir, E. S., Gunnarsson, I., Alfredsson, H. A., Mesfin,
K. G., Oelkers, E. H., and Gislason, S. R.: Rapid CO2 mineralisation
into calcite at the CarbFix storage site quantified using calcium isotopes,
Nat. Commun., 10, 1983, https://doi.org/10.1038/s41467-019-10003-8, 2019. a
Priya, P., Kuhlman, K. L., and Aluru, N. R.: Pore-Scale Modeling of
Electrokinetics in Geomaterials, Transport Porous Med., 137, 651–666,
https://doi.org/10.1007/s11242-021-01581-7, 2021. a
Randolph, J. B. and Saar, M. O.: Combining geothermal energy capture with
geologic carbon dioxide sequestration, Geophys. Res. Lett., 38, L10401,
https://doi.org/10.1029/2011GL047265, 2011. a
Rasouli, P., Steefel, C. I., Mayer, K. U., and Rolle, M.: Benchmarks for
multicomponent diffusion and electrochemical migration, Computat. Geosci.,
19, 523–533, https://doi.org/10.1007/s10596-015-9481-z, 2015. a
Rathgeber, F., Ham, D. A., Mitchell, L., Lange, M., Luporini, F., Mcrae, A.
T. T., Bercea, G.-T., Markall, G. R., and Kelly, P. H. J.: Firedrake:
Automating the Finite Element Method by Composing Abstractions, ACM Trans.
Math. Softw., 43, 24, https://doi.org/10.1145/2998441, 2016. a
Raviart, P. A. and Thomas, J. M.: A mixed finite element method for 2-nd order
elliptic problems, in: Mathematical Aspects of Finite Element Methods, edited
by Galligani, I. and Magenes, E., Springer Berlin Heidelberg,
Berlin, Heidelberg, 292–315, https://doi.org/10.1007/BFb0064470, 1977. a
Ray, N., Muntean, A., and Knabner, P.: Rigorous homogenization of a
Stokes–Nernst–Planck–Poisson system, J. Math. Anal. Appl., 390,
374–393, https://doi.org/10.1016/j.jmaa.2012.01.052, 2012a. a
Ray, N., van Noorden, T., Frank, F., and Knabner, P.: Multiscale Modeling of
Colloid and Fluid Dynamics in Porous Media Including an Evolving
Microstructure, Transport Porous Med., 95, 669–696,
https://doi.org/10.1007/s11242-012-0068-z, 2012b. a
Reddy, K. R. and Cameselle, C.: Electrochemical Remediation Technologies for
Polluted Soils, Sediments and Groundwater, John Wiley & Sons,
https://doi.org/10.1002/9780470523650, 2009. a
Reed, M. H.: Calculation of Simultaneous Chemical Equilibria in
Aqueous-Mineral-Gas Systems and its Application to Modeling Hydrothermal
Processes, in: Techniques in Hydrothermal Ore Deposits Geology, Society of
Economic Geologists, https://doi.org/10.5382/Rev.10.05, 1998. a
Revil, A. and Leroy, P.: Constitutive equations for ionic transport in porous
shales, J. Geophys. Res.-Sol. Ea., 109, B03208, https://doi.org/10.1029/2003JB002755, 2004. a
Rivera, F. F., Pérez, T., Castañeda, L. F., and Nava, J. L.: Mathematical
modeling and simulation of electrochemical reactors: A critical review, Chem.
Eng. Sci., 239, 116622, https://doi.org/10.1016/j.ces.2021.116622, 2021. a
Rolle, M., Muniruzzaman, M., Haberer, C. M., and Grathwohl, P.: Coulombic
effects in advection-dominated transport of electrolytes in porous media:
Multicomponent ionic dispersion, Geochim. Cosmochim. Ac., 120, 195–205,
https://doi.org/10.1016/j.gca.2013.06.031, 2013. a
Rolle, M., Sprocati, R., Masi, M., Jin, B., and Muniruzzaman, M.:
Nernst-Planck-based Description of Transport, Coulombic Interactions, and
Geochemical Reactions in Porous Media: Modeling Approach and Benchmark
Experiments, Water Resour. Res., 54, 3176–3195, https://doi.org/10.1002/2017WR022344,
2018. a
Rolle, M., Albrecht, M., and Sprocati, R.: Impact of solute charge and
diffusion coefficient on electromigration and mixing in porous media, J.
Contam. Hydrol., 244, 103933, https://doi.org/10.1016/j.jconhyd.2021.103933, 2022. a
Rubinstein, I.: Locally Electro-Neutral Electro-Diffusion without Electric
Current, in: Electro-Diffusion of Ions, Society for Industrial and Applied
Mathematics, chap. 3, 59–103, https://doi.org/10.1137/1.9781611970814.ch3, 1990. a
Samson, E. and Marchand, J.: Modeling the transport of ions in unsaturated
cement-based materials, Comput. Struct., 85, 1740–1756,
https://doi.org/10.1016/j.compstruc.2007.04.008, 2007. a
Sapa, L., Bożek, B., Tkacz-Śmiech, K., Zajusz, M., and Danielewski, M.:
Interdiffusion in many dimensions: mathematical models, numerical simulations
and experiment, Math. Mech. Solids, 25, 2178–2198,
https://doi.org/10.1177/1081286520923376, 2020. a
Savino, M., Lévy-Leduc, C., Leconte, M., and Cochepin, B.: An active learning
approach for improving the performance of equilibrium based chemical
simulations, Computat. Geosci., 26, 365–380,
https://doi.org/10.1007/s10596-022-10130-0, 2022. a
Shafabakhsh, P., Ataie-Ashtiani, B., Simmons, C. T., Younes, A., and Fahs, M.:
Convective-reactive transport of dissolved CO2 in fractured-geological
formations, Int. J. Greenh. Gas Con., 109, 103365,
https://doi.org/10.1016/j.ijggc.2021.103365, 2021. a
Shao, H., Dmytrieva, S. V., Kolditz, O., Kulik, D. A., Pfingsten, W., and
Kosakowski, G.: Modeling reactive transport in non-ideal aqueous–solid
solution system, Appl. Geochem., 24, 1287–1300,
https://doi.org/10.1016/j.apgeochem.2009.04.001, 2009. a
Shen, J. and Xu, J.: Unconditionally positivity preserving and energy
dissipative schemes for Poisson–Nernst–Planck equations, Numerische
Mathematik, 148, 671–697, https://doi.org/10.1007/s00211-021-01203-w, 2021. a
Sin, I. and Corvisier, J.: Multiphase Multicomponent Reactive Transport and
Flow Modeling, Rev. Mineral. Geochem., 85, 143–195,
https://pubs.geoscienceworld.org/msa/rimg/article/85/1/143/573304/Multiphase-Multicomponent-Reactive-Transport-and
(last access: 28 November 2022), 2019. a
Singh, M., Chaudhuri, A., Chu, S. P., Stauffer, P. H., and Pawar, R. J.:
Analysis of evolving capillary transition, gravitational fingering, and
dissolution trapping of CO2 in deep saline aquifers during continuous
injection of supercritical CO2, Int. J. Greenh. Gas Con., 82, 281–297,
https://doi.org/10.1016/j.ijggc.2019.01.014, 2019. a
Sipos, P. M., Hefter, G., and May, P. M.: Viscosities and Densities of Highly
Concentrated Aqueous MOH Solutions (M+ = Na+, K+, Li+, Cs+, (CH3)4N+) at 25.0 ∘C, J. Chem. Eng. Data, 45, 613–617, https://doi.org/10.1021/je000019h, 2000. a
Soltanian, M. R., Hajirezaie, S., Hosseini, S. A., Dashtian, H., Amooie, M. A.,
Meyal, A., Ershadnia, R., Ampomah, W., Islam, A., and Zhang, X.:
Multicomponent reactive transport of carbon dioxide in fluvial heterogeneous
aquifers, J. Nat. Gas Sci. Eng., 65, 212–223,
https://doi.org/10.1016/j.jngse.2019.03.011, 2019. a
Song, Z., Cao, X., and Huang, H.: Electroneutral models for dynamic
Poisson-Nernst-Planck systems, Phys. Rev. E, 97, 012411,
https://doi.org/10.1103/PhysRevE.97.012411, 2018. a
Sprocati, R. and Rolle, M.: Integrating Process-Based Reactive Transport
Modeling and Machine Learning for Electrokinetic Remediation of Contaminated
Groundwater, Water Resour. Res., 57, e2021WR029959,
https://doi.org/10.1029/2021WR029959, 2021. a
Sprocati, R. and Rolle, M.: On the interplay between electromigration and
electroosmosis during electrokinetic transport in heterogeneous porous media,
Water Res., 213, 118161, https://doi.org/10.1016/j.watres.2022.118161, 2022. a
Sprocati, R., Masi, M., Muniruzzaman, M., and Rolle, M.: Modeling
electrokinetic transport and biogeochemical reactions in porous media: A
multidimensional Nernst–Planck–Poisson approach with PHREEQC coupling,
Adv. Water Resour., 127, 134–147, https://doi.org/10.1016/j.advwatres.2019.03.011,
2019. a
Steefel, C. I. and MacQuarrie, K. T. B.: Chapter 2. Approaches to modeling of
reactive transport in porous media, in: Reactive Transport in Porous Media,
edited by Lichtner, P. C., Steefel, C. I., and Oelkers, E. H.,
De Gruyter, 83–130, https://doi.org/10.1515/9781501509797-005, 2018. a
Tabrizinejadas, S., Carrayrou, J., Saaltink, M. W., Baalousha, H. M., and Fahs,
M.: On the Validity of the Null Current Assumption for Modeling Sorptive
Reactive Transport and Electro-Diffusion in Porous Media, Water, 13, 2221,
https://doi.org/10.3390/w13162221, 2021. a
Teng, Y., Wang, P., Jiang, L., Liu, Y., and Wei, Y.: New Spectrophotometric
Method for Quantitative Characterization of Density-Driven Convective
Instability, Polymers, 13, 661, https://doi.org/10.3390/polym13040661, 2021. a
Thieulot, C. and Bangerth, W.: On the choice of finite element for applications in geodynamics, Solid Earth, 13, 229–249, https://doi.org/10.5194/se-13-229-2022, 2022. a
Thomas, C., Dehaeck, S., and De Wit, A.: Convective dissolution of CO2 in
water and salt solutions, Int. J. Greenh. Gas Con., 72, 105–116,
https://doi.org/10.1016/j.ijggc.2018.01.019, 2018. a
Tournassat, C. and Steefel, C. I.: Modeling diffusion processes in the presence
of a diffuse layer at charged mineral surfaces: a benchmark exercise,
Computat. Geosci., 25, 1319–1336, https://doi.org/10.1007/s10596-019-09845-4, 2021. a
Tournassat, C., Steefel, C. I., and Gimmi, T.: Solving the Nernst-Planck
Equation in Heterogeneous Porous Media With Finite Volume Methods: Averaging
Approaches at Interfaces, Water Resour. Res., 56, e2019WR026832,
https://doi.org/10.1029/2019WR026832, 2020. a, b, c
Trevelyan, P. M. J., Almarcha, C., and De Wit, A.: Buoyancy-driven
instabilities of miscible two-layer stratifications in porous media and
Hele-Shaw cells, J. Fluid Mech., 670, 38–65,
https://doi.org/10.1017/S0022112010005008, 2011. a
Tsinober, A., Rosenzweig, R., Class, H., Helmig, R., and Shavit, U.: The Role
of Mixed Convection and Hydrodynamic Dispersion During CO2 Dissolution in
Saline Aquifers: A Numerical Study, Water Resour. Res., 58, e2021WR030494,
https://doi.org/10.1029/2021WR030494, 2022. a
Tutolo, B. M., Luhmann, A. J., Kong, X.-Z., Saar, M. O., and Seyfried, W.
E. J.: Experimental Observation of Permeability Changes In Dolomite at CO2
Sequestration Conditions, Environ. Sci. Technol., 48, 2445–2452,
https://doi.org/10.1021/es4036946, 2014. a
Uzawa, H.: Iterative methods for concave programming, in: Studies in linear and
nonlinear progamming, edited by: Arrow, K. J., Hurwicz, L., and Uzawa, H., Standford University Press,
15, 1–46, 1958. a
Vanýsek, P.: Ionic Conductivity and Diffusion at Infinite Dilution, in: CRC
Handbook of Chemistry and Physics, edited by: Rumble, J. R., CRC Press/Taylor
& Francis, Boca Raton, FL, 102nd edn., 2021. a
Wang, S., Cheng, Z., Zhang, Y., Jiang, L., Liu, Y., and Song, Y.: Unstable
Density-Driven Convection of CO2 in Homogeneous and Heterogeneous Porous
Media With Implications for Deep Saline Aquifers, Water Resour. Res., 57,
e2020WR028132, https://doi.org/10.1029/2020WR028132, 2021. a
Weller, H. G., Tabor, G., Jasak, H., and Fureby, C.: A tensorial approach to
computational continuum mechanics using object-oriented techniques, Comput.
Phys., 12, 620–631, https://doi.org/10.1063/1.168744, 1998. a
Wilkins, A., Green, C. P., and Ennis-King, J.: An open-source multiphysics
simulation code for coupled problems in porous media, Comput. Geosci., 154,
104820, https://doi.org/10.1016/j.cageo.2021.104820, 2021. a
Wolery, T. J.: EQ3/6, a software package for geochemical modeling of aqueous
systems: Package overview and installation guide (Version 7.0),
https://doi.org/10.2172/138894, 1992.
a
Yan, D., Pugh, M., and Dawson, F.: Adaptive time-stepping schemes for the
solution of the Poisson-Nernst-Planck equations, Appl. Numer. Math., 163,
254–269, https://doi.org/10.1016/j.apnum.2021.01.018, 2021. a
Yang, C. and Gu, Y.: Accelerated Mass Transfer of CO2 in Reservoir Brine Due
to Density-Driven Natural Convection at High Pressures and Elevated
Temperatures, Ind. Eng. Chem. Res., 45, 2430–2436, https://doi.org/10.1021/ie050497r,
2006. a
Yapparova, A., Miron, G. D., Kulik, D. A., Kosakowski, G., and Driesner, T.: An
advanced reactive transport simulation scheme for hydrothermal systems
modelling, Geothermics, 78, 138–153,
https://doi.org/10.1016/j.geothermics.2018.12.003, 2019. a
Yekta, A., Salinas, P., Hajirezaie, S., Amooie, M. A., Pain, C. C., Jackson,
M. D., Jacquemyn, C., and Soltanian, M. R.: Reactive transport modeling in
heterogeneous porous media with dynamic mesh optimization, Computat. Geosci.,
25, 357–372, https://doi.org/10.1007/s10596-020-10009-y, 2021. a
Yu, Y., Gao, W., Castel, A., Liu, A., Feng, Y., Chen, X., and Mukherjee, A.:
Modelling steel corrosion under concrete non-uniformity and structural
defects, Cement Conrete Res., 135, 106109,
https://doi.org/10.1016/j.cemconres.2020.106109, 2020. a
Zalts, A., El Hasi, C., Rubio, D., Ureña, A., and D'Onofrio, A.: Pattern
formation driven by an acid-base neutralization reaction in aqueous media in
a gravitational field, Phys. Rev. E, 77, 015304,
https://doi.org/10.1103/PhysRevE.77.015304, 2008. a
Zaytsev, I. D. and Aseyev, G. G.: Properties of Aqueous Solutions of
Electrolytes, CRC Press, Boca Raton, FL, ISBN 9780849393143, 1992. a
Zeebe, R. E.: On the molecular diffusion coefficients of dissolved CO2,
Zhang, Q., Tu, B., Fang, Q., and Lu, B.: A structure-preserving finite element
discretization for the time-dependent Nernst-Planck equation, Journal of
Applied Mathematics and Computing, 68, 1545–1564,
https://doi.org/10.1007/s12190-021-01571-4, 2022. a
Zhang, W., Li, Y., and Omambia, A. N.: Reactive transport modeling of effects
of convective mixing on long-term CO2 geological storage in deep saline
formations, Int. J. Greenh. Gas Con., 5, 241–256,
https://doi.org/10.1016/j.ijggc.2010.10.007, 2011. a
Zhang, Z., Fu, Q., Zhang, H., Yuan, X., and Yu, K.-T.: Experimental and
Numerical Investigation on Interfacial Mass Transfer Mechanism for Rayleigh
Convection in Hele-Shaw Cell, Ind. Eng. Chem. Res., 59, 10195–10209,
https://doi.org/10.1021/acs.iecr.0c01345, 2020. a
Short summary
Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Water in natural environments consists of many ions. Ions are electrically charged and exert...
