H019
Advances in Remote Sensing, AI, and Modeling for Hydrology and the Terrestrial Water Cycle
Session ID#: 251224
Session Description:
Hydrology is at the forefront of Earth system science, propelled by rapid advancements in satellite remote sensing, physically based modeling, and artificial intelligence. This session explores how the integration of observational data, advanced data assimilation techniques, and state-of-the-art machine learning—including physics-informed and differentiable models—is reshaping our ability to understand the terrestrial water cycle. We welcome contributions focused on: • Physics-informed machine learning, differentiable modeling, and hybrid approaches for simulating hydrologic processes • Integration of remote sensing with land surface and hydrologic models• Uncertainty quantification, spatiotemporal error modeling, and calibration/validation strategies • Hybrid AI–data assimilation (DA) frameworks that leverage deep learning to address the limitations of traditional DA methods, including bias, scalability, and non-Gaussian uncertainty • Detection and forecasting of hydrological extremes, including floods, droughts, and flash droughts This session brings together hydrologists, Earth scientists, and AI practitioners to share advances, address challenges, and foster collaboration toward global water sustainability.
Co-Sponsor(s):
Index Terms:
1833 Hydroclimatology [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1855 Remote sensing [HYDROLOGY]
1876 Water budgets [HYDROLOGY]
Primary Convener: Hyunglok Kim, GIST (Gwangju Institute of Science and Technology), Department of Environment and Energy Engineering, Gwangju, South Korea
Conveners: Venkataraman (Venkat) Lakshmi, University of Virginia, Civil and Environmental Engineering, Charlottesville, United States, Kristen Marie Whitney, NASA Goddard Space Flight Center, Hydrological Sciences Laboratory, Greenbelt, United States and ManhHung Le, NASA Goddard Space Flight Center, Greenbelt, MD, United States
Student/Early Career Convener: Ehsan Jalilvand, PhD, NASA Goddard Space Flight Center, Hydrological Sciences Laboratory, Greenbelt, United States
Operational Hydrological Modeling in Data-Scarce Regions: Assessing the Impact of Fine-Resolution Soil Moisture Assimilation (1851609)
Sebastian Marshall1, Thanh Nhan Duc Tran1, ManhHung Le2, Binh Quang Nguyen3,4, Bin Fang1, Hesham Elhaddad5, John B Eylander6, Sameh Kantosh4, Sydney Crisanti7, John D Bolten8 and Venkataraman (Venkat) Lakshmi9, (1)University of Virginia, Civil and Environmental Engineering, Charlottesville, VA, United States, (2)Hydrological Sciences Laboratory, NASA Goddard Space Flight Centre, Greenbelt MD, United States, (3)The University of Danang-University of Science and Technology, Water Resources Engineering, Danang, Vietnam, (4)Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan, (5)Western Michigan University, Department of Geological and Environmental Sciences, Kalamazoo, United States, (6)USACE/ERDC/CRREL, Hanover, United States, (7)US Army Engineer Research and Development Center, Coastal & Hydraulics Laboratory, Vicksburg, United States, (8)Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt MD, United States, (9)University of Virginia, Civil and Environmental Engineering, Charlottesville, United States
Interpreting Upstream Influence on Downstream River Discharge Forecasting: Insights from ConvLSTM and SHAP Analyses Across Spatial Scales and Temporal Horizons (1857684)
Ziyue Zhu1, Mohammad Saeedi2, Ruicheng Xu3, John B Eylander4, Venkataraman (Venkat) Lakshmi2 and Sydney Crisanti5, (1)University of Virginia, Charlottesville, VA, United States, (2)University of Virginia, Civil and Environmental Engineering, Charlottesville, United States, (3)University of Missouri Columbia, Columbia, United States, (4)USACE/ERDC/CRREL, Hanover, United States, (5)U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, United States
Toward Improving Groundwater Estimation: Assessing Surface Water Storage in the La Plata River Basin (1858532)
Maria Laila Jover, University of Virginia, Civil and Environmental Engineering, Charlottesville, VA, United States, Augusto Getirana, NASA Goddard Space Flight Center, Greenbelt, MD, United States, John B Eylander, USACE/ERDC/CRREL, Hanover, United States, Sydney Crisanti, U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, United States and Venkataraman (Venkat) Lakshmi, University of Virginia, Civil and Environmental Engineering, Charlottesville, United States
Capturing Fleeting Snow: High-Resolution Monitoring of Intermittent Snow Cover Dynamics with Deep Learning and PlanetScope Imagery (Invited) (1861727)
Zhaocheng Wang1, Jaya Baskar2, Maneesh Sarma2, Bohumil Svoma3 and Enrique R Vivoni4, (1)Arizona State University, Center for Hydrologic Innovations, Tempe, AZ, United States, (2)Arizona State University, School of Computing and Augmented Intelligence, Tempe, United States, (3)Salt River Project, Tempe, United States, (4)Arizona State University, School of Sustainable Engineering and the Built Environment, Tempe, AZ, United States
Integrating Temporal and Spatial Strengths: Advancing High-Resolution Global Soil Moisture Gap-Filling through POBI and NSTI Synergy (1863137)
Ziyue Zhu, University of Virginia, Charlottesville, VA, United States, Hyunglok Kim, Gwangju Institute of Science and Technology (GIST), School of Environment and Energy Engineering, Gwangju, South Korea, John B Eylander, USACE/ERDC/CRREL, Hanover, United States, Sydney Crisanti, U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, United States and Venkataraman (Venkat) Lakshmi, University of Virginia, Civil and Environmental Engineering, Charlottesville, United States
Expanding the Observational Record for Multidecadal Land Reanalysis: A Multi-Sensor Data Assimilation Framework (1869235)
Andrew M Fox, Morgan State University, Baltimore, MD, United States, Rolf H Reichle, NASA Goddard Space Flight Center, Greenbelt, United States and Qing Liu, Science Systems and Applications, Inc., Lanham, MD, United States
Hypernetworks for deep learning-based hydrological prediction (1876022)
Mohammad Sina Jahangir, Cornell University, Biological and Environmental Engineering, Ithaca, United States, Shahryar Fazli, Chapman University, Schmid College of Science and Technology, Orange, CA, United States, John Quilty, University of Waterloo, Civil and Environmental Engineering, Waterloo, ON, Canada and Scott Steinschneider, Cornell University, Ithaca, United States
Coupling ParFlow with the Common Land Model through a Hybrid Physical–Surrogate Framework (1876917)
Chen Yang1, Aoqi Sun1, Shupeng Zhang1, Yongjiu Dai1, Stefan J Kollet2 and Reed M Maxwell3, (1)Sun Yat-Sen University, School of Atmospheric Sciences, Guangzhou, China, (2)Research Center Juelich, Jülich, Germany, (3)Princeton University, Civil and Environmental Engineering, Princeton, United States
A Data-Driven Approach for Estimating Groundwater Irrigation for Major Agricultural Basins of the Western United States (1879046)
Md Fahim Hasan1, Ryan Smith1, Frances V Davenport1 and Sayantan Majumdar2, (1)Colorado State University, Civil and Environmental Engineering, Fort Collins, United States, (2)Desert Research Institute, Division of Hydrologic Sciences, Reno, United States
Can terrestrial water storage improve S2S hydrologic forecast skill in data-sparse regions to support early warning systems? (1882042)
Shraddhanand Shukla1, Weston B Anderson2, Benjamin Cook3, Amy McNally4, Kimberly Slinski5, Abheera Hazra4 and Bailing Li6, (1)University of California Santa Barbara, Climate Hazards Center, Geography Department, Santa Barbara, United States, (2)University of Maryland, College Park, Geographical Sciences, College Park, United States, (3)NASA Goddard Institute for Space Studies, New York, United States, (4)NASA Goddard Space Flight Center, Greenbelt, United States, (5)NASA Goddard Space Flight Center, Hydrological Sciences Laboratory, Greenbelt, United States, (6)Earth System Science Interdisciplinary Center, University of Maryland, College Park, United States
AI Improves the Accuracy, Reliability, and Economic Value of Continental-Scale Flood Predictions (1888112)
Vinh Tran1, Taeho Kim2, Donghui Xu3, Hoang Tran4, ManhHung Le5, Thanh Nhan Duc Tran6, Jongho Kim7, Daniel B Wright8, Trung Tran9, Valeriy Yu Ivanov2 and Pedro Restrepo10, (1)University of Michigan, Ann Arbor, United States, (2)University of Michigan, Civil and Environmental Engineering, Ann Arbor, United States, (3)Pacific Northwest National Laboratory, Richland, WA, United States, (4)Pacific Northwest National Laboratory, Richland, United States, (5)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (6)University of Virginia, Civil and Environmental Engineering, Charlottesville, VA, United States, (7)Ulsan University, Seoul, North Korea, (8)University of Wisconsin-Madison, Department of Civil and Environmental Engineering, Madison, United States, (9)University of Ulsan, School of Civil and Environmental Engineering, Ulsan, South Korea, (10)Consultant, Minneapolis, Minneapolis, United States
Spatial patterns of hydroclimatic controls on groundwater storage change in Central and Southern Arizona (1901679)
Behshad Mohajer1, James S Famiglietti1,2, Hrishikesh Arvind Chandanpurkar2 and Fengwei Hung2, (1)Arizona State University, School of Sustainable Engineering and the Built Environment, Tempe, United States, (2)Arizona State University, School of Sustainability, Tempe, United States
Assimilating High-Resolution Snow Water Equivalent Observations into Noah-MP to Improve Seasonal Streamflow Prediction in the Colorado River Basin (1904928)
Xueli Huo1, Yuan Qiu2, Feng-Wei Hung1, Guo-Yue Niu3, Ali Behrangi3 and James S Famiglietti1, (1)Arizona State University, School of Sustainability, Tempe, United States, (2)Arizona State University, School of Sustainability, Tempe, AZ, United States, (3)University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, United States
Assessing Chlorophyll Concentration in Using Geostationary Ocean Color Imager Satellite and Deep Learning approach: Case study of Reclaimed Areas of Saemangeum Watershed (1906652)
Yejin Lee, Jeonbuk National University, Jeonju, Jeonbuk State, SOUTH KOREA, Suhwan Kim, Jeonbuk National University, Jeonju, Korea, South, SOUTH KOREA, Dohee Han, Jeonbuk National University, Jeonju, Republic of Korea, Korea, South, SOUTH KOREA and Jong-Min Yeom, Jeonbuk National University, Jeonsu-si, South Korea
AMSR2SMAP: Converting AMSR2 soil moisture data to L-band format using image-to-image translation (1909582)
Gyeongbin Lee1, Jaese Lee1 and Jungho Im2, (1)Ulsan National Institute of Science and Technology, Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan, South Korea, (2)Ulsan National Institute of Science and Technology, Civil Urban Earth and Environmental Engineering, Ulsan, South Korea
Groundwater Level Imputation Using Machine Learning in the Transboundary Karst Region of Lithuania and Latvia (1913399)
Vytautas Samalavicius1, Jānis Bikše2, Gintaras Žaržojus1, Inga Retike2, Ilya Zaslavsky3, Assemzhan Kunsakova1 and Jurga Arustienė1, (1)Vilnius University, Faculty of Chemistry and Geosciences, Vilnius, Lithuania, (2)University of Latvia, Faculty of Science and Technology, Riga, Latvia, (3)University of California San Diego, San Diego Supercomputer Center, La Jolla, United States
Leveraging Remotely Sensed Soil Moisture for Improved Hydrologic Simulations in Heterogeneous Watersheds (1924737)
Junyu QI, University of Maryland College Park, College Park, MD, United States, Avay Risal, Texas Water Development Board, Austin, United States and Ritesh Karki, Ulteig, Fairfax, United States
Groundwater Level Prediction in the Pra Basin, Ghana: Evaluating the Temporal Generalization of Ensemble and Deep Learning Models (1925475)
Evans Manu, CSIR - Water Research Institute, Groundwater and Geoscience Division, Accra, Ghana, Sayantan Majumdar, Desert Research Institute Reno, Reno, United States, Marco De Lucia, GFZ Helmholtz Centre for Geosciences, Geochemistry Department, Potsdam, Germany, Emmanuel Obuobie, Council for Scientific and Industrial Research, Water Research Institute, Accra, Ghana, Alexandra Lutz, Desert Research Institute, Division of Hydrologic Sciences, Reno, United States and Michael Kühn, GFZ Helmholtz Center for Geosciences, Geochemistry, Potsdam, Germany; University of Potsdam, Institute of Geosciences, Potsdam, Germany
Machine Learning-Based Soil Moisture Downscaling from Integration of SMAP and MODIS Observations and Soil Maps (1929214)
Saman Rabiei, Department of Soil, Water and Ecosystem Sciences, University of Florida, Gainesville, United States, Ebrahim Babaeian, University of Florida, Soil and Water Sciences Department, Gainesville, United States and Sabine Grunwald, University of Florida, Soil, Water and Ecosystem Sciences Department, Gainesville, United States
A two-step iterative data assimilation and calibration approach for improving large-scale hydrological processes in the Ganga basin (1935292)
Supriya Tiwari1, Bhaskar Ramachandra Nikam2, Vaibhav Garg3 and Maike Schumacher1, (1)Aalborg University, Geodesy Group, Department of Sustainability and Planning, Aalborg, Denmark, (2)Indian Space Research Organisation, Earth Observation Applications & Disaster Management Support Programme Office, Bengaluru, India, (3)Indian Institute of Remote Sensing, Indian Space Research Organisation, Water Resources Department, Dehradun, India
Improved Snowmelt Flood Simulation through GRACE/GRACE-FO Data Assimilation in Noah-MP (1937762)
Eunsaem Cho, NASA Goddard Space Flight Center / University of Maryland, Greenbelt, United States, Dr. Eunsang Cho, Ph.D., NASA Goddard Space Flight Center & University of Maryland College Park, Greenbelt, United States, Bailing Li, Earth System Science Interdisciplinary Center, University of Maryland, College Park, United States, Carrie Vuyovich, NASA Goddard Space Flight Center, Greenbelt, MD, United States and Jennifer M Jacobs, University of New Hampshire, Institute for the Study of Earth, Oceans, and Space; Civil and Environmental Engineering, Durham, United States
A Generalised Two-Stage Precipitation Merging Framework Using Signal-to-Noise Ratio Optimisation (SNR-opt) (1941432)
Suraj Shah1, Yi Liu2, Seokhyeon Kim3 and Ashish Sharma1, (1)University of New South Wales, School of Civil and Environmental Engineering, Sydney, NSW, Australia, (2)University of New South Wales, School of Civil and Environmental Engineering, Sydney, Australia, (3)Kyung Hee University, Department of Civil Engineering, Yongin, South Korea
Performance of Artificial Intelligence-Machine Learning Based Hydrological Models under Climate Change: A Review Study (1946363)
Muhammed Ucun, University of Virginia, Charlottesville, VA, United States, Sophia Bakar, RTI International, Research Triangle Park, United States, Venkataraman (Venkat) Lakshmi, University of Virginia, Civil and Environmental Engineering, Charlottesville, United States and Venkataraman Lakshmi
Deep Learning-Based Surrogate Modeling for the Evaluation of Land Data Assimilation Schemes (1948243)
Subin Kim, GIST Gwangju Institute of Science and Technology, Gwangju, Korea, Republic of (South), Yonghwan Kwon, KIAPS (Korea Insititute of Atmospheric Prediction Systems), Seoul, South Korea and Hyunglok Kim, GIST Gwangju Institute of Science and Technology, Gwangju, South Korea
Global Dominance of Seasonality in Shaping Lake-Surface-Extent Dynamics (Invited) (1949045)
Di Long1, Luoqi Li1, Mr. Yiming Wang1 and Iestyn Woolway2, (1)Tsinghua University, Department of Hydraulic Engineering, Beijing, China, (2)Bangor University, Bangor, LL59, United Kingdom
Data‑Driven Framework for Quantifying Dryland Water Storage Patterns and Near‑Future Projections (1958058)
Hadi Karimi1, Mohamed Sultan1, Hesham Elhaddad2, Mustafa Kemal Emil2 and Karem Abdelmohsen3,4, (1)Western Michigan University, Department of Geological and Environmental Sciences, Kalamazoo, MI, United States, (2)Western Michigan University, Department of Geological and Environmental Sciences, Kalamazoo, United States, (3)Western Michigan University, Kalamazoo, United States, (4)Arizona State University, School of Sustainability, Tempe, United States
Turning Streams into Rain Gauges: Leveraging Long-Term Streamflow Data to Recover Historical Precipitation (1962109)
Mohammad Saeedi1, Hyunglok Kim2, John D Bolten3, Matthew Rodell4, John B Eylander5, Sydney Crisanti6 and Venkataraman (Venkat) Lakshmi1, (1)University of Virginia, Civil and Environmental Engineering, Charlottesville, United States, (2)Gwangju Institute of Science and Technology (GIST), School of Environment and Energy Engineering, Gwangju, South Korea, (3)Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt MD, United States, (4)NASA GSFC HSL, Greenbelt, United States, (5)USACE/ERDC/CRREL, Hanover, United States, (6)U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, United States
Validation of Swot Derived Reservoir Water Levels in the Upper Paraná River Basin (1962617)
Eduardo Dose, Entidad Binacional Yacyreta, Ayolas, Paraguay, Augusto Getirana, NASA Goddard Space Flight Center, Greenbelt, MD, United States, John D Bolten, Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt MD, United States, Elyssa Collins, NASA Goddard Space Flight Center, Greenbelt, United States and Venkataraman (Venkat) Lakshmi, University of Virginia, Civil and Environmental Engineering, Charlottesville, United States
Data-Driven Streamflow Prediction Using CYGNSS-Based Surface Water Maps and Neural Networks for Applications in Ungauged Basins (1963141)
Anna Valcarcel, University of California Berkeley, Civil and Environmental Engineering, Berkeley, CA, United States, Tianjiao Pu, Princeton University, Civil and Environmental Engineering & High Meadows Environmental Institute, Princeton, United States and Cynthia Gerlein-Safdi, University of California Berkeley, Civil and Environmental Engineering, Berkeley, United States
Benchmarking Precipitation Forcings and Assessing Incremental Runoff Contributions with the NASA LIS-HyMAP Model in the Yacyreta Catchment (1970889)
Eduardo Dose1,2, Augusto Getirana3, John D Bolten4, Sujay V Kumar5, Lucas Chamorro1, John B Eylander6 and Venkataraman (Venkat) Lakshmi7, (1)Entidad Binacional Yacyreta, Ayolas, Paraguay, (2)Universidad Nacional de Itapua, Encarnacion, Paraguay, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (4)Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt MD, United States, (5)NASA Goddard Space Flight Center, Greenbelt, United States, (6)USACE/ERDC/CRREL, Hanover, United States, (7)University of Virginia, Civil and Environmental Engineering, Charlottesville, United States
Fusion of Sentinel-1 and Sentinel-2 Data for High-Accuracy Mapping of Rice Field Spatial Distribution in Bangladesh (1972853)
Md Asadullahil Galib Fardin, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh; Institute of Water and Flood Management, Bangladesh, Dhaka, Bangladesh and Sara Nowreen, Bangladesh University of Engineering and Technology, Institute of Water and Flood Management, Dhaka, Bangladesh
Deep Learning-Based Prediction of GRACE-Derived Terrestrial Water Storage Anomalies in the Nile Basin (2003–2024) Using a CNN-LSTM Approach (1981818)
Bewuket Tefera, University of Florida, Geography Department, Gainesville, United States, Jane Southworth, University of Florida, Department of Geography, Gainesville, United States, Mohammad Safaei, University of Florida, Gainesville, FL, UNITED STATES and Nafyad Serre Kawo, Moore engineering Inc, Hydrogeophysicist, Lakewood, United States
Distinct Hydrologic Response Patterns and Trends Worldwide Revealed by Physics-embedded Learning (1990383)
Haoyu Ji1, Yalan Song1, Tadd Bindas1, Chaopeng Shen1, Yuan Yang2, Ming Pan2, Jiangtao Liu1, Farshid Rahmani1, Ather Abbas3, Hylke Beck3, Kathryn Lawson1 and Yoshihide Wada4, (1)Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, United States, (2)University of California San Diego, Scripps Institution of Oceanography, Center for Western Weather and Water Extremes, La Jolla, United States, (3)King Abdullah University of Science and Technology, Physical Science and Engineering Division, Thuwal, Saudi Arabia, (4)King Abdullah University of Science and Technology, Biological and Environmental Science and Engineering Division, Thuwal, Saudi Arabia
