Session Topics

Session 1. Pore-scale processes of multiphase flow, transport, and geomechanics: modeling, model-data interaction, numerical algorithms, and upscaling.

Conveners: Yashar Mehmani (Pennsylvania State University), Ruby Fu (California Institute of Technology), Julien Maes (Heriot-Watt University), Sidian Chen (Stanford University)

Abstract: The optimal operation and design of porous media requires an in-depth understanding of the fundamental pore-scale processes that occur within them. These range from the flow of multiple fluid phases, their phase transitions, heat transfer, transport of passive/reactive or biological species, to the mechanical deformation and failure of the solid skeleton induced by external forcing or internal stresses. In this session, we solicit submissions that advance our basic understanding of these processes via novel computational tools. Emphasis is placed on new algorithmic methods, discretization schemes, multiphysics models, ways to interface traditional computing with data-driven techniques (e.g., machine learning), and reliable upscaling and/or downscaling of pore-scale information across multiple spatiotemporal scales. Submissions that consider validation against experiments or observations are strongly encouraged. Applications include, but certainly not limited to, geologic CO2 sequestration, vadose-zone or groundwater remediation, underground H2 storage, geothermal energy, gas hydrates, and manufactured devices for water treatment.

Session 2. Data-driven and physics-based machine learning methods for forecasting and knowledge discovery of surface hydrology.

Conveners: Jonathan Frame (Lynker Technologies), Dipankar Dwivedi (Lawrence Berkeley National Laboratory), Hernan Moreno (University of Texas at El Paso), Yang Yang (University of Hong Kong)

Abstract: This session invites innovative research that synergizes data-driven, physics-based, and/or combination (i.e., sequential or embedding) methods for understanding and forecasting surface hydrology. We seek contributions that demonstrate the integration of comprehensive hydrological data with machine learning and numerical modeling techniques, aiming to enhance forecasting accuracy and deepen our understanding of complex hydrological phenomena. We encourage the submission of research that explores the potential of these methods in capturing the intricate dynamics of entire hydrological systems, with a primary focus on surface processes. Contributions demonstrating the ability to blend large-scale data analysis with hydrological knowledge and insights, thereby pushing the boundaries of traditional hydrological modeling, are particularly welcome. Through this session, we aim to highlight cutting-edge research that offers novel working solutions and discoveries in surface hydrology. Our overarching goal is to foster a dialogue on the future of integrated and comprehensive hydrologic modeling, promoting advancements in the field.

Session 3. Data-driven and physics-based machine learning methods for forecasting and knowledge discovery of subsurface hydrology.

Conveners: Ahmed Elsheikh (Heriot-Watt University), Alex Tartakovsky (University of Illinois Urbana-Champaign), Andrew Bennett (University of Arizona), Gege Wen (Imperial College London)

Abstract: Join us at CMWR 2024 for a session dedicated to the fusion of data-driven and physics-based machine learning (ML) in subsurface hydrology. We invite contributions that demonstrate innovative applications of ML techniques for forecasting hydrological/flow behaviors, unraveling complex subsurface interactions, and enhancing our understanding of subsurface fluid flow systems. We especially encourage works that not only leverage machine learning with big data but also incorporate fundamental knowledge discovery. This session aims to serve as a platform for researchers to share their work around the intersection of cutting-edge ML  with essential hydrological problems, motivating future research in the realm of machine learning for subsurface hydrology and fluid flow. We welcome contributions in the following areas of ML: surrogate modeling and emulation of complex subsurface models; data assimilation and uncertainty quantification; reinforcement learning for reservoir optimization; differentiable modeling to incorporate physical knowledge; and explainable and Interpretable ML for physical models.

Session 4. Machine learning methods for remote sensing and satellite data.

Conveners: Ali Behrangi (University of Arizona), Beth Tellman (University of Arizona), Kuolin Hsu (University of California, Irvine).

Abstract: With the vast amounts of data generated by remote sensing, there arises a significant challenge in efficiently extracting meaningful information. In recent years, machine learning (ML) techniques have emerged as powerful tools for processing and analyzing remote sensing data, offering novel insights and solutions. We welcome presentations that include but not limited to (1) discussing successful applications of ML methods in advancing Earth science and applications using remote sensing data in various fields of the Earth Sciences, (2) highlighting challenges and opportunities in integrating ML with remote sensing data (e.g., dealing with too large or too small data sets, data heterogeneity, limited ground truth data, algorithm interpretability and transferability, computational speed and cost) along with strategies to address them, (3) incorporating uncertainty quantification techniques, and (4) discussing future directions and ideas to inspire further research and collaborations.

Session 5. Computational modeling of subsurface processes for climate mitigation and energy transition.

Conveners: Avinoam Rabinovich (Tel Aviv University), Inga Berre (University of Bergen), Birendra Jha (University of Southern California), Lauren Beckingham (Auburn University)

Abstract: Human activities have unequivocally caused global warming which, in turn, leads to widespread adverse impacts and related losses and damages to nature and people (IPCC report 2023). The subsurface has historically been a major contributor to the problem through fossil fuel production, however, it is now in a unique position to play a significant part in the solution. CO2 geosequestration has enormous potential for storing carbon and thus reducing anthropogenic emissions. Furthermore, subsurface compressed gas storage and hydrogen storage are developing technologies which can potentially act as large scale energy repositories used for dealing with the intermittency of renewables. Geothermal energy production and storage are receiving increased interest in many regions due to its potential for providing sustainable and reliable energy. This session is dedicated to modeling, quantitative analysis and physics-based investigations related to the use of the subsurface for mitigating climate change and for clean energy purposes.

Session 6. Model coupling, domain decomposition, and solvers for multiphysics problems.

Conveners: Nicola Castelletto (Lawrence Livermore National Laboratory), Jiamin Jiang (Chevron), Rainer Helmig (University of Stuttgart), Jakub Both (University of Bergen)

Abstract: This session aims to collect talks related to analysis of, and efficient numerical methods for, coupled problems arising in water resources.  Coupled problems may refer to either locally coupled problems or boundary coupled problems, including interaction between thermal, mechanical, flow and chemical processes. Of interest are efficient computational methods, including adaptivity, error estimates, and non-linear and linear solvers.

Session 7. Transport and mixing in heterogeneous porous and fractured media across scales.

Conveners: Ilenia Battiato (Stanford University), Jeffrey Hyman (Los Alamos National Lab), Marco Dentz (Spanish National Research Council)

Abstract: Flow and reactive transport in porous and fractured media are inherently multi-physical, multiscale processes that involve many coupled physics, including single and multiphase flows, homogeneous and heterogenous biogeochemical reactions, heat transfer, geomechanics, matrix alterations due to mineral precipitation, dissolution, clogging or fracturing. In addition, physical and chemical heterogeneity control dispersion, mixing and reactive transformations across a broad range of spatial scales in geological formations and governs applications ranging from long-term radioactive waste isolation, enhanced geothermal reservoir management, groundwater remediation, carbon sequestration, and hydrogen storage. This session invites recent contributions on experimental, computational (symbolic and numeric), data-driven and theoretical approaches to quantify transport, reaction, mixing phenomena and their feedbacks across scales, in porous and fractured media.

Session 8. Reactive transport modeling of hydrobiogeochemistry across scales.

Conveners: Xiaofan Yang (Beijing Normal University), Hang Deng (Peking University), Alexis Navarre-Sitchler (Colorado School of Mines), Matthew Winnick (University of Massachusetts, Amherst)

Abstract: Flow and reactive transport in porous and fractured media are governed by heterogeneity in physical and chemical properties that exists across scales. Reactive transport modeling is effective in describing and predicting coupled hydrobiogeochemical processes with applications to critical zone processes, groundwater management and remediation, geothermal energy production, and geologic storage of carbon and hydrogen. This session encourages submissions of reactive transport modeling studies across scales and applications. In particular, we seek submissions concerning: (1) pore-scale theoretical and numerical modeling studies to improve fundamental understanding of coupled flow, transport, and reactions in porous and fractured media, (2) studies that integrate observational data and reactive transport modeling in representative field site, and (3) emerging methods (e.g., AI-driven models) in upscaling. We seek to facilitate knowledge exchange and collaboration amongst all modelers from academia, agencies, and industry.

Session 9. Advances in integrating surface and subsurface hydrological modeling.

Conveners: Xingyuan Chen (PNNL), Jesus D Gomez Velez (Oak Ridge National Laboratory), Jean Martial Cohard (University of Grenoble)

Abstract: The stress of water resources globally has and will increase due to continuously growing demands and changing climate and land use. Central to addressing this challenge and proposing sustainable management strategies is using integrated models that represent the tight connection between surface and subsurface environments at scales ranging from individual channel reaches to continents. With the increase in computational resources and the advent of artificial intelligence (AI) and machine learning (ML) methods, we are at the cusp of major advances in physical, chemical, and biological representations of these models, in the integration with observations and in using these models for prediction, management, and decision making. This session welcomes contributions on advances in integrated hydrological modeling, including better representations and coupling of physical and biogeochemical processes (e.g., recharge processes, snowmelting, transport and transformation of nutrients and carbon), the integration of AI and ML, data assimilation, as well as case studies covering the wide range of scales where surface and subsurface integration plays a key role.

Session 10. Computational ecohydrology.

Conveners: Bin Peng (University of Illinois Urbana-Champaign), Xue Feng (University of Minnesota), Julia Green (University of Arizona)

Abstract: The study of ecohydrology concerns the interactions between the hydrological cycle and ecological systems, involving different biotic and abiotic components of the terrestrial biosphere. Representing such interactions in computational models can be challenging due to variations in their physical controls as well as nonlinearities in the system across a multitude of spatial and temporal scales. Improved modeling capacity of ecohydrological processes across scales can advance understanding  of ecosystem responses to disturbances or climatic stressors, improve predictions of the global water, energy, and carbon cycles, and help inform better ecosystem management under climate change. This session invites submissions that advances the modeling of ecohydrological processes from micro to macro scales through (i) novel insights into dominant ecohydrological pathways and scaling relationships, (ii) new developments in mathematical representations, (iii) model-data integration, (iv) computational methods, (v) data-driven or hybrid modeling leveraging artificial intelligence and deep learning, and (vi) advanced ecohydrological application of computational models in both natural and managed ecosystems. The topics of interest include but are not limited to:

• Watershed modeling, water resources, and water quality
• Land-atmosphere interactions
• Ecohydrology of cropping or urban systems
• Plants’ influence on aquatic systems, hydrodynamics, and sediment transport
• Plant xylem and phloem transport
• Root-rhizosphere interactions
• Belowground water, carbon, and nutrient dynamics
• Population dynamics, including competition
• Ecosystem responses to biotic and abiotic stressors and disturbances

Session 11. Advances in computational modeling of surface processes: debris flow, river ecosystems, and morphodynamics.

Conveners: Luke McGuire (University of Arizona), Jennifer Duan (University of Arizona), Hui Tang (German Research Center for Geosciences)

Abstract: Predicting interactions between fluid flow, sediment, and the land surface (e.g. river bed/banks) is critical for mitigating hazards and quantifying landscape change. Reliable simulations of geophysical flows, including floods, tsunamis and debris flows, over complex topography remains a challenge particularly when considering the unsteadiness and turbulence of river flow and the temporal evolution of the land surface. Quantifying the effects of climate and land use change on hazards and water resources would benefit from improvements in morphodynamic models for rivers, fans, deltas, and coasts. This session welcomes studies that address our ability to model geophysical flows, quantify sediment entrainment or deposition, or simulate the morphodynamics of rivers, fans, deltas, or coasts that develop over millennial time scales or in response to individual events. We aim to bring together a diverse group of researchers, including computational scientists, geomorphologists, hydrologists, and engineers, studying surface processes and geophysical flows from different perspectives.

Session 12. Advances in computational modeling of vadose zone processes.

Conveners: Hannes Bauser (University of Nevada, Las Vegas), Tissa Illangasekare (Colorado School of Mines), Bo Guo (University of Arizona)

Abstract: The vadose zone, located at the heart of the critical zone, extending from the water table to the land surface, has a strong control on various processes such as aquifer recharge, evapotranspiration, and chemical transport. Decades of research to address problems in soil physics and subsurface hydrology have advanced the modeling of vadose zone processes. However, the challenges associated with highly nonlinear dynamics, heterogeneity of soils at all scales, and a multitude of process interactions still remain, particularly in applications associated with the transport and retention of chemicals (especially the recent emergent contaminants) and land-atmospheric interaction that require higher spatially and temporally resolved modeling. Innovative computational methods are needed to develop models at multiple spatial scales, specifically in combination with limited measurement capabilities in the subsurface.

We invite abstracts that address these challenges and advance our understanding and computational representation of vadose zone processes. Topics include, but are not limited to, the transfer of small-scale understanding to larger scales, representation of heterogeneity across scales, effective representation through constitutive relationships, vadose zone modeling at regional scales, representation of vadose zone processes in climate and earth system models, experiments for improved computation, as well as improved numerical solutions including the use of machine learning. We particularly encourage abstracts that report failed attempts or highlight the limitations of current approaches.

Session 13. Computational modeling and model-data interaction for groundwater flow and contaminant transport.

Conveners: Tianyuan Zheng (Ocean University of China), Vittorio Di Federico (University of Bologna), Jian Luo (Georgia Tech), Jicai Zeng (University of Arizona)

Abstract: Effective groundwater contamination management, including control, remediation, and risk assessment, relies heavily on numerical models for simulating groundwater flow and contaminant transport in aquifers. However, the extensive computational requirements and challenges posed by sparse data can hinder the practical application of these models for tasks such as pollution source identification, remediation design, and uncertainty analysis. To address these challenges, the development of surrogate models or efficient computing methods that integrate model-data interaction is crucial. This session encourages interdisciplinary collaboration to advance computational modeling techniques and enhance model-data interaction for groundwater flow and contaminant transport.

Session 14. Digital rock and soil physics: challenges and advances in imaging and computing.

Conveners: Tom Bultreys (Ghent University), Lin Ma (University of Manchester)

Abstract: Improving the predictions of flow, transport and reactions in porous media depends in many cases on understanding the underlying microscopic dynamics at the pore scale. The investigation of these dynamics relies heavily on accurate pore-scale imaging and modelling techniques. Both come with methodological challenges, including trade-offs between image or model size and resolution, computational time versus accuracy/precision, temporal resolution limitations, large data handling, and precisely controlling the sample conditioning during experiments. In this session, we invite contributions on methods and case studies that address such challenges, including but not limited to:

• Multiphysics and image-based simulation algorithms
• Imaging/modelling multi-scale and heterogeneous pore systems
• In-situ micro-CT experiments
• Dynamic/time-lapse imaging
• Improved image processing and image analysis
• Validating pore-scale models with imaging data
• Upscaling from pore-scale to core-scale or beyond

Session 15. Microbial-mediated processes in hydrological systems.

Conveners: Lazaro Perez (Desert Research Institute), Yang Song (University of Arizona), Alexandra Kravchenko (Michigan State University), Judy Yang (University of Minnesota)

Abstract: The experimental characterization and modeling of microbial processes within porous media are crucial for advancements in soil ecology, bioremediation, oil recovery, hydrology, and global carbon and nutrient biogeochemical cycles. Microorganisms and biofilms, which are widespread within porous materials, play a significant role in affecting flow fields, reactive solute transport, and the physical properties of porous media. For instance, for the effective subsurface application of a bioremediation strategy, such as biofilm barriers for contaminant removal, a detailed understanding of microbial behavior and its influence on permeability, wettability, and reactive transport is essential. On the other hand, micro-environmental heterogeneity within the porous media significantly impacts community composition, metabolism, and functions of residing microbial communities, leading to complex interactive feedback. We invite contributions that offer new theoretical, experimental, and computational insights in fully or partially saturated conditions, focusing on biofilm hydrodynamics, clogging, colloid dynamics, biomineralization, reactive transport, microbial functioning on soil and hydrological environments, transport, and coupling across processes/scales. Relevant research methods include, but are not limited to, microfluidics, advanced imaging technologies, multi-omics, and novel numerical simulations that enable incorporating these technologies to advance the forecast of microorganism-environment interactions within porous media.

Session 16. Computational Methods for coupled human-natural systems and decision making.

Conveners: Jesse Dickenson (United States Geological Survey), Xiaogang He (National University of Singapore), Megan Konar (University of Illinois Urbana-Champaign)

Abstract: Advancements in computational methods to understand coupled human and natural systems (CHANS) are needed to improve decision-making under an uncertain future. This session welcomes submissions on a variety of water-related topics, such as computational methods to understand CHANS, potential for physics-informed machine learning to be used in CHANS, data-driven estimates of water use in society, agent-based modeling of human-nature interactions, variable selection and representation, uncertainty characterization, tradeoffs in CHANS, and risk assessment. We welcome submissions from researchers that cut across multiple disciplines, including hydrology, water resources engineering, computer science, statistics, social science, and environmental sciences, among others. The goal of the session is to present recent research on how to harness the latest advances in computational methods to improve decision-making in CHANS.

Topics of interest include the following, but are not limited to:

• Integrated modeling framework to characterize and frame CHANS
• Agent-based modeling of human-nature interactions
• Optimization techniques for complex water-centric system management that also involves energy and food elements
• Uncertainty quantification, scenario discovery, sensitivity analysis, and advanced data visualization techniques that can guide the understanding of CHANS
• Physics-informed machine learning for CHANS
• Computational approaches for water resources engineering, planning, and management
• Data-driven estimates of water use in society
• Computational methods for adaptation planning under climate change
• Irrigation scheduling and estimation for smart agriculture

Session 17. Leveraging computational advances for hydrologic science: Developments in high-performance computing, cloud platforms, and quantum computing.

Conveners: Nick Engdahl (Washington State University), David Moulton (Los Alamos National Laboratory), Glenn Hammond (Pacific Northwest National Laboratory)

Abstract: The technology available for simulating hydrologic systems is rapidly evolving, making this an exciting yet puzzling time. One of the major questions we face is how best to use recent computational advances, since there is no “one size fits all” approach to modeling and each tool requires tradeoffs to address scientific questions at their relevant scales. This session aims to highlight and discuss contemporary developments in computational hydrology including massively parallel computing, coupling of multiple chemical and/or physical processes across different scales, machine learning, geographically distributed and hybrid computing, and next generation methods and architectures like quantum computing. The overall objective being to understand the current state of these technologies and identify promising avenues for future research. Presentations are encouraged that focus on creating new capabilities, addressing deficiencies in computational technology, numerical methods or workflows, and case studies where the application of novel computing methods has enabled new insights.

Session 18. Inverse problems in hydrological modeling: from parameter estimation to uncertainty quantification and model identification.

Conveners: Anneli Guthke (University of Stuttgart), Velimir V Vesselinov (SmartTensors LLC), Tianfang Xu (Arizona State University), Marcel Schaap (University of Arizona)

Abstract: Inverse problems encompass a wide range of mathematical and computational techniques aimed at estimating model parameters, quantifying uncertainties, and identifying plausible structures of water resources models to improve their predictive capabilities. These problems arise when model outputs, e.g., streamflow, groundwater heads, soil moisture, or solute concentrations, are observed and used to infer the values of model parameters or plausibility of processes that are difficult or impossible to measure directly.

We welcome contributions along the following lines of research, both theoretical developments and practical applications:

• Bayesian methods for parameter inference, model selection/averaging, and uncertainty quantification
• Statistical methods for sensitivity analysis
• Approaches to infer most relevant processes and improve system understanding
• Concepts to identify and treat model structural errors
• Approaches to improve computational efficiency of solving inverse problems, including surrogate and ML-based techniques
• Methods to increase transparency and robustness of workflows and help communicate inference results

Session 19. Stochastic hydrogeology.

Conveners: Daniel Tartakovsky (Stanford University), Nicola Castelletto (Lawrence Livermore National Lab), Felipe De Barros (University of Southern California), Alberto Bellin (University of Trento)

Abstract: Stochastic hydrogeology has provided fundamental insights into subsurface processes (e.g., effective properties of heterogeneous media, nonlocal/anomalous behavior of solute transport, scale-dependence of flow and transport parameters) and yielded a suite of tools for their predictive simulations (e.g., efficient techniques for uncertainty quantification, data assimilation, and inverse modeling).  Dr. Francesca Boso, to whose vibrant life and untimely passing we dedicate this session, has made significant contributions to the field. We invite contributions in uncertainty quantification for models of subsurface flow and transport at local/regional/global scales, in models dealing with inherent stochasticity of climate forcing, in probabilistic forecasting of the resource availability constrained by climate change impacts, and in other related applications.