H005
Advances in Data Assimilation and Uncertainty Quantification of Water Resources Management

Session ID#: 158627

Session Description:
Uncertainty quantification is the key to understanding the reliability of hydrological models and making intelligent water management decisions. Many new data sources have been available to water resources management in recent decades, such as satellite data, geophysical surveys, and geochemical/hydrological campaigns. At the same time, computing capabilities have been advanced significantly, including statistical and machine learning methods to assimilate various datasets as well as to simulate hydrological processes. Integrating multiple spatial and temporal datasets enables more accurate predictions and uncertainty reduction in hydrological models. This session invites contributions to both theory and applications of data assimilation, inversion problems, uncertainty quantification, and sequential decision-making in water resources problems. Specific topics of interest include:

  • Spatial data integration
  • Bayesian inversion
  • AI-based surrogate modeling
  • Hybrid data-driven and physical modeling
  • Uncertainty quantification in river/groundwater systems
  • Water contaminations and remediations
Index Terms:

1846 Model calibration [HYDROLOGY]
1849 Numerical approximations and analysis [HYDROLOGY]
1873 Uncertainty assessment [HYDROLOGY]
1880 Water management [HYDROLOGY]

Primary Convener:  Lijing Wang, Stanford University, Department of Geological Sciences, Stanford, CA, United States
Conveners:  Alex Miltenberger, Stanford University, Stanford, CA, United States, Philippe Renard, University of Neuchâtel, Centre for Hydrogeology and Geothermics, Neuchâtel, Switzerland and Haruko M Wainwright, Lawrence Berkeley National Laboratory, Berkeley, CA, United States; Massachusetts Institute of Technology, Cambridge, United States
Student/Early Career Convener:  Lijing Wang, Stanford University, Department of Geological Sciences, Stanford, CA, United States
 
Uncertainty Analysis Framework in the Water Balance Equation Using Bayesian Statistical Modeling Approach (1167993)
Hyunglok Kim and Wade Crow, USDA ARS, Beltsville, United States
 
Redefining automatic karst hydrograph recession analysis: introducing variable matrix fractions and parameter uncertainty (1168866)
Tunde Olarinoye1, Michael Rinderer2, Tom Gleeson3 and Andreas J Hartmann2,4, (1)University of Freiburg, Chair of Hydrological Modeling and Water Resources, Freiberg, Germany, (2)University of Freiburg, Chair of Hydrological Modeling and Water Resources, Freiburg, Germany, (3)University of Victoria, Department of Civil Engineering, Victoria, BC, Canada, (4)Technical University of Dresden, Institute of Groundwater Management, Dresden, Germany
 
An Integrated Remote Sensing, Geophysical and Stable Isotope Approach for Groundwater Assessment in the northern area of the Western Desert, Egypt. (1184600)
Doaa Hegazy, National Authority for Remote sensing and Space Sciences, Geoscience department, Cairo, Egypt, Abotalib Farag, University of Southern California, Electrical Engineering, Los Angeles, United States, Mohamed Sultan, Western Michigan University, Department of Geological and Environmental Sciences, Kalamazoo, United States, Mohammed El Bastawesy, Kingston, ON, Canada; National Authority for Remote Sensing and Space Sciences, Cairo, Egypt, Karim Abdelmalik, Ain Shams University, department of geology, Cairo, Egypt, Mohamed Attwa, Zagazig University, Department of Geology, Zagazig, Egypt and Sayed Medany, National Authority of remote sensing and space science, Agricultural applications, Soils and Marine Division, Cairo, Egypt
 
Approximation of Gradient Flows in Stein Variational Gradient Descent using Surrogate Models (1185784)
Le Duc and Yohei Sawada, The University of Tokyo, Institute of Engineering Innovation, Graduate School of Engineering, Tokyo, Japan
 
Application of a Polynomial Chaos Expansion (PCE) algorithm in the Uncertainty Assessment of a Semi-Distributed Hydrological Model (1190699)
Tirupati Bolisetti, Rahul Narula and Tejith Pogakula, University of Windsor, Civil and Environmental Engineering, Windsor, ON, Canada
 
A Probabilistic Approach for Implicit Geological Modeling (1199279)
Luiz Gustavo Rasera1, Jeff Boisvert2 and Shaun Barker1, (1)University of British Columbia, Vancouver, BC, Canada, (2)University of Alberta, Edmonton, AB, Canada
 
Machine Learning and Remote sensing method to Determine the Relationship Between Climate and Groundwater Recharge. (1200200)
Adya Aiswarya Dash, Indian Institute of Technology,Kharagpur, Kharagpur, India
 
Data Assimilation with Integrated Terrestrial System Models: Opportunities and Challenges (Invited) (1044720)
Harrie-Jan Hendricks Franssen1, Ching Pui Hung2, Fang Li2 and Haojin Zhao2, (1)Forschungszentrum Jülich GmbH, Julich, Germany, (2)Agrosphere Institute (IBG-3) Forschungszentrum Jülich, Deutschland, Germany
 
Improving the Prediction Capacity of a Hybrid Method of SWAT and LSTM by Reducing Parameter Uncertainty. (1047500)
Hyemin Jeong1, Sangchul Lee2, Dongho KIM1, Yongsung Kwon1 and Byeongwon Lee1, (1)University of Seoul, Seoul, Korea, Republic of (South), (2)USDA-ARS, Beltsville, United States
 
Calibrating a Hydrological Model for Pesticide Transfer while Considering Rain Uncertainty (1066586)
Katarina Radišić1, Claire Lauvernet1 and Arthur Vidard2,3, (1)INRAE, RiverLy, Villeurbanne, France, (2)CNRS, Grenoble, France, (3)Inria, Grenoble, France
 
The sensitivity of objective function choice to model related choices in characterizing operations of a multi-purpose reservoir (1071211)
Manvitha Molakala1 and Riddhi Singh1,2, (1)Indian Institute of Technology Bombay, Department of Civil Engineering, Mumbai, India, (2)Indian Institute of Technology Bombay, Interdisciplinary Programme in Climate Studies, Mumbai, India
 
Adaptive Sequential Monte Carlo in the Context of Hydrogeological Inverse Problems with Complex Spatial Priors (1071370)
Macarena Amaya1, Niklas Linde1 and Eric Laloy2, (1)University of Lausanne, Lausanne, Switzerland, (2)Belgian Nuclear Research Centre (SCK-CEN), Mol, Belgium
 
Uncertainty-based calibration in analyzing the role of waterbodies in flood moderation: an urbanization-hydrological-hydraulic framework (1077509)
Nithila Devi Nallasamy and Soumendra Nath Kuiry, Indian Institute of Technology Madras, Chennai, India
 
Comparison of Univariate Assimilation of Different Remotely Sensed Soil Moisture Products and the Assimilation of a Merged Soil Moisture Product, Estimated from the Individual Products and In Situ Data using LSTM (1085060)
Haojin Zhao, Forschungszentrum Jülich GmbH, Jülich 52428, Germany, Carsten Montzka, Forschungszentrum Jülich, Institute of Bio and Geosciences (Agrosphere, IBG-3), Jülich, Germany, Harry Vereecken, Forschungszentrum Julich, Institute of Bio and Geosciences (Agrosphere, IBG-3), Julich, Germany and Harrie-Jan Hendricks Franssen, Forschungszentrum Jülich GmbH, Julich, Germany
 
Exploring and finding optimal Pump-and-Treat (P&T) strategies at chlorinated solvent-contaminated aquifer (1100813)
Suhho Lee1, In-Woo Park1, Seong-Sun Lee2, Jiyun Kim3 and Kang Kun Lee4, (1)Seoul National University, Seoul, Korea, Republic of (South), (2)Seoul National University, School of Earth and Environmental Sciences, Seoul, South Korea, (3)Seoul National University, Seoul, South Korea, (4)Seoul National University, School of Earth and Environmental Science, Seoul, South Korea
 
A benchmark study on remotely sensed data assimilation for water budget estimation over different hydroclimatic areas (1102021)
Alexandra Gemitzi1, Amir Hossein Ahrari2, Philip Brunner3, Bin Fang4, Kedar Ghag2, Ali Torabi Haghighi2, Bjørn Kløve2, Maria Kofidou1, Venkataraman (Venkat) Lakshmi4, Anandharuban Panchanathan2, Clément Roques3, Stavros Stathopoulos1, Radosław Szostak5, Przemysław Wachniew5, Mirosław Zimnoch5 and Luca Zappa6, (1)Democritus University of Thrace, Environmental Engineering, Xanthi, Greece, (2)University of Oulu, Water, Energy and Environmental Engineering Research Unit, Oulu, Finland, (3)University of Neuchâtel, Centre for Hydrogeology and Geothermics, Neuchâtel, Switzerland, (4)University of Virginia, Engineering Systems and Environment, Charlottesville, United States, (5)AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Kracow, Poland, (6)Vienna University of Technology, Department of Geodesy and Geoinformation, Vienna, Austria
 
Multidimensional stochastic inversion for consistent 3D hydro-geological models (1102309)
Alexis Neven, Ludovic Schorpp and Philippe Renard, University of Neuchâtel, Centre for Hydrogeology and Geothermics, Neuchâtel, Switzerland
 
Information Geometry of Statistical Manifolds and its Use in Data Assimilation (Invited) (1110236)
Daniel M Tartakovsky, Stanford University, Energy Science and Engineering, Stanford, United States and Francesca Boso, Stanford University, Energy Science Engineering, Stanford, United States
 
Surrogate-Model Assisted Calibration and Posterior Distribution Evaluation of a 3-D Subsurface Flow Model (1122796)
Olaf A. Cirpka and Jonas Allgeier, University of Tübingen, Department of Geosciences, Tübingen, Germany
 
Deep learning based optimization under uncertainty for surfactant-enhanced DNAPL remediation in highly heterogeneous aquifers (1140406)
Jianwen Du, University of Arizona, Hydrology and Atmospheric Sciences, Tucson, United States
 
Numerical Modeling for Advanced Pumping Test Interpretation: Influence of Spatiotemporal Discretization on Solution Accuracy and Runtimes (1154227)
Ronny Figueroa1,2 and Etienne Bresciani1,2, (1)Center for Advanced Studies in Arid Zones (CEAZA), La Serena, Chile, (2)Quitai-Anko Water Technological Center, La Serena, Chile
 
Statistical models of depth-to-bedrock across eastern Wisconsin, USA using AEM data (1161200)
David J Hart1, Lisa Haas1, Matthew Rehwald1, Burke J Minsley2 and Cody Calkins3, (1)Wisconsin Geological and Natural History Survey, Madison, United States, (2)USGS, Geology, Geophysics, and Geochemistry Science Center, Denver, United States, (3)Wisconsin Department of Agriculture, Trade, and Consumer Protection, Madison, United States
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