Applications of Machine Learning in Large Scale Hydrology and Water Quality Modeling

Vieira Lucchese, Luisa

H043
Applications of Machine Learning in Large Scale Hydrology and Water Quality Modeling

Session ID#: 250934

Session Description:

Machine Learning (ML) is increasingly used in hydrology and water quality research to process and analyze large datasets and draw new insights. This session will explore recent advancements in ML modeling and their applications in large-scale hydrology, including but not limited to streamflow forecasts, sediment and nutrient load estimation, flood extent mapping, water storage change, or any part of the water cycle. Among the possible data sources for large scale ML models are satellite remote sensing datasets, in-situ observations, and reanalysis data. We welcome contributions using any ML model from tree-based to deep learning, as well as explainable artificial intelligence (XAI) and hybrid modeling techniques. We also invite contributions that present new regional, continental, or global datasets generated by or analyzed with the help of ML.

Co-Sponsor(s):

IN - Informatics

Index Terms:

1855 Remote sensing [HYDROLOGY]
1871 Surface water quality [HYDROLOGY]
1926 Geospatial [INFORMATICS]
1942 Machine learning [INFORMATICS]

Primary Convener: Luisa Vieira Lucchese, University of Pittsburgh, Geology and Environmental Science, Pittsburgh, PA, United States

Conveners: John R. Gardner, University of North Carolina at Chapel Hill, Chapel Hill, United States, Dongmei Feng, University of Cincinnati, Department of Chemical and Environmental Engineering, Cincinnati, United States and Admin Husic, PhD, Virginia Tech, Department of Civil and Environmental Engineering, Manassas, United States

Student/Early Career Convener: Shuyu Y Chang, The Pennsylvania State University, Department of Geography, University Park, United States

Scaling Hyper-resolution Hydrologic Modeling and Applications with AI (Invited) (1850732)

Dr. Noemi Vergopolan¹, Adnan Dehghani¹, Caroline Hashimoto², Onur Güngör Şahin¹, Felipe Saavedra³ and Larisa Tarasova⁴, (1)Rice University, Earth, Environmental and Planetary Sciences, Houston, United States, (2)Rice University, Civil and Environmental Engineering, Houston, United States, (3)Helmholtz Centre for Environmental Research UFZ Halle, Catchment Hydrology, Halle, Germany, (4)Helmholtz Centre for Environmental Research - UFZ, Catchment Hydrology, Halle (Saale), Germany

Application of the LSTM model for streamflow forecasting in the Great-Lakes region (1852630)

Étienne Thomas É. M. Gaborit¹, Gabriel Sasseville², Vincent Fortin¹ and Milena Dimitrijevic³, (1)Environment and Climate Change Canada, Meteorological Research Division, Dorval, QC, Canada, (2)University of Montreal, Montreal, Canada, (3)Environment and Climate Change Canada, Environmental Numerical Prediction Research, Dorval, QC, Canada

Global River Suspended Sediment Flux from Space using Highly Scalable Deep Learning Models (1864225)

Luisa Vieira Lucchese¹, Rangel Daroya², Travis Thomas Simmons³, Punwath Prum⁴, Tamlin Pavelsky⁵, Subhransu Maji⁶, John Gardner⁷ and Colin J Gleason³, (1)University of Pittsburgh, Geology and Environmental Science, Pittsburgh, PA, United States, (2)University of Massachusetts Amherst, College of Information and Computer Sciences, Amherst, MA, United States, (3)University of Massachusetts Amherst, Civil & Environmental Engineering, Amherst, United States, (4)University of Pittsburgh, Geology and Environmental Science, Pittsburgh, United States, (5)University of North Carolina at Chapel Hill, Earth, Marine and Environmental Sciences, Chapel Hill, United States, (6)University of Massachusetts Amherst, College of Information and Computer Sciences, Amherst, United States, (7)University of North Carolina at Chapel Hill, Department of Earth, Marine and Environmental Sciences, Chapel Hill, United States

High-resolution surface and rootzone soil moisture over US cropland: A novel framework assimilating multi-source remote sensing data, machine learning, and the Layered Green and Ampt Infiltration with Redistribution model (1874689)

Shuohao Cai¹, Yijia Xu², Zhengwei Yang³, Wade Crow⁴, Zhou Zhang², Jiali Shang⁵, Jiangui Liu⁵, Peter La Follette⁶, Chris Reberg-Horton⁷, Harry Schomberg⁸, Steven Mirsky⁸, Brian Davis⁹, Sarah Seehaver⁷, Alexis Correira¹⁰, Andrea Basche¹¹, Ashley Waggoner¹, Charles Ellis¹², Dara Park¹³, David Campbell¹⁴, Danielle Treadwell¹⁵, Deann Presley¹⁰, Esleyther Henriquez Inoa¹⁶, Heather Darby¹⁷, Jared Adam¹⁸, Jarrod Miller¹⁹, Joseph Haymaker²⁰, John Wallace²¹, Julia Gaskin²², Kipling Balkcom²³, Lindsey Ruhl²⁴, Mark Reiter²⁰, Matthew Ruark¹, Michael Flessner²⁵, Cynthia Sias²⁵, Payton Davis¹³, Peter Tomlinson²⁶, Richard Smith²⁷, Nicholas Warren²⁷, Ryan Dierking²⁸, Shalamar D Armstrong²⁹, Tauana Almeida³⁰ and Jingyi Huang¹, (1)University of Wisconsin-Madison, Soil and Environmental Sciences, Madison, United States, (2)University of Wisconsin-Madison, Biological Systems Engineering, Madison, United States, (3)USDA, NASS, Washington DC, United States, (4)USDA ARS, Beltsville, United States, (5)Agriculture and Agri-Food Canada, Science and Technology Branch, Ottawa, ON, Canada, (6)Lynker, Boulder, United States, (7)North Carolina State University, Department of Crop and Soil Sciences, Raleigh, United States, (8)USDA ARS, Sustainable Agricultural Systems Laboratory, Beltsville, United States, (9)Organization Not Listed, Washington, DC, United States, (10)Kansas State University, Manhattan, United States, (11)University of Nebraska Lincoln, Department of Agronomy and Horticulture, Lincoln, United States, (12)University of Missouri Extension, Troy, United States, (13)Clemson University, Clemson, United States, (14)University of Florida, Horticultural Sciences Department, Gainesville, United States, (15)University of Florida, Gainesville, United States, (16)North Carolina State University, Raleigh, United States, (17)University of Vermont, Department of Agriculture, Landscape and Environment, Albans, United States, (18)Montana State University, Bozeman, United States, (19)University of Delaware, Plant and Soil Sciences, Newark, United States, (20)Virginia Tech, Eastern Shore Agricultural Research and Extension Center, Painter, United States, (21)Pennsylvania State University, University Park, United States, (22)University of Georgia, Athens, United States, (23)USDA-ARS National Soil Dynamics Laboratory, Auburn, United States, (24)University of Vermont, Albans, United States, (25)Virginia Tech, Blacksburg, United States, (26)Kansas State University, Agronomy, Manhattan, United States, (27)University of New Hampshire, Durham, United States, (28)EarthOptics formally Indigo Ag, Arlington, United States, (29)Purdue University, Agronomy, West Lafayette, United States, (30)University of Nebraska-Lincoln, Lincoln, United States

Dynamic Modeling of Daily Nitrogen Flux Reveals Soil Moisture as a Key Driver and Climate-Induced Risks to Polarized Seasonal Patterns (1883864)

Haiping Ai¹, Kai Zhang¹, Shuyuan Yang², Erika Barcelos³, Roger French³ and Huichun Zhang¹, (1)Case Western Reserve University, Department of Civil and Environmental Engineering, Cleveland, United States, (2)Case Western Reserve University, Department of Electrical, Computer, and Systems Engineering, Cleveland, United States, (3)Case Western Reserve University, Department of Materials Science and Engineering, Cleveland, United States

Machine Learning Prediction of Oxygen-18 in Shallow Lithuanian Groundwater (1890774)

Gintaras Žaržojus, Vytautas Samalavicius, Sonata Gadeikienė, Saulius Gadeikis, Ieva Lekstutytė and Jurga Arustienė, Vilnius University, Faculty of Chemistry and Geosciences, Vilnius, Lithuania

Progress toward the coordinated development of machine learning water quality models at the CONUS extent (1891831)

Galen Gorski¹, Jeremy Diaz¹, Jared David Smith², Alison Appling³, Samantha Oliver⁴, Ken Eng⁵, Lauren Koenig⁶, Wallace Anderson McAliley⁷, Kyle Compare⁸, Janet Barclay⁹, Ellie White¹⁰, Jacob A Zwart¹¹, Christopher T Green¹², Jeffrey Kwang¹³ and Scott Ator¹⁴, (1)U.S. Geological Survey, Water Mission Area, Reston, United States, (2)Cornell University, Civil and Environmental Engineering, Ithaca, United States, (3)University of Wisconsin Madison, Madison, United States, (4)U.S. Geological Survey, Upper Midwest Water Science Center, Madison, United States, (5)U.S. Geological Survey, Water Resources Mission Area, Reston, United States, (6)U.S. Geological Survey, Water Resources Mission Area, Troy, NY, United States, (7)Colorado School of Mines, Golden, United States, (8)U.S. Geological Survey, Water Resources Mission Area, Boise, United States, (9)United States Geological Survey, Water Resources Mission Area, East Hartford, United States, (10)USGS Integrated Information Dissemination Division, Data Science Branch, Reston, United States, (11)USGS Integrated Information Dissemination Division, Data Science Branch, San Francisco, United States, (12)U.S. Geological Survey, Integrated Modeling Prediction Division, Moffett Field, California, United States, (13)U.S. Geological Survey, Water Mission Area-Integrated Information Dissemination Division, St. Paul, United States, (14)U.S. Geological Survey, MD-DE-DC Water Science Center, Baltimore, United States

Event water fraction increases across the conterminous United States under climatic and anthropogenic change (1902788)

Chuqiang Chen, Virginia Tech, Manassas, VA, UNITED STATES and Admin Husic, PhD, Virginia Tech, Department of Civil and Environmental Engineering, Manassas, United States

Performance Comparison of Deep Learning Models Based on Bayesian Optimization for Hydrological Forecasting (1904836)

Jinseo Lee, Hakkwan Kim and Hyunji Lee, Seoul National University, Pyeongchang, South Korea

Uncovering the Impact of Soil Moisture Patterns on Nitrate Transport at the Catchment Scale Using Explainable AI (1909297)

Felipe Saavedra^1,2, Noemi Vergopolan³, Andreas Musolff⁴, Ralf Merz⁵, Carolin Winter⁶, Zhenyu Wang² and Larisa Tarasova⁷, (1)Martin Luther University of Halle-Wittenberg, Halle, Germany, (2)Helmholtz Centre for Environmental Research UFZ Halle, Catchment Hydrology, Halle, Germany, (3)Rice University, Earth, Environmental and Planetary Sciences, Houston, United States, (4)Helmholtz Centre for Environmental Research UFZ Leipzig, Hydrogeology, Leipzig, Germany, (5)Helmholtz Centre for Environmental Research UFZ, Catchment Hydrology, Halle, Germany, (6)University of Freiburg, Chair of Environmental Hydrological Systems, Freiburg, Germany, (7)Helmholtz Centre for Environmental Research - UFZ, Catchment Hydrology, Halle (Saale), Germany

Integrating Deep Learning and MILP for Efficient CPWL Function Fitting in Hydropower Optimization Models (1911698)

Quentin Maximilien Ploussard, PhD¹, Matija Pavicevic¹ and Yu Ao², (1)Argonne National Laboratory, Argonne, IL, United States, (2)Argonne National Laboratory, Argonne, United States

Analysis of Concentration-Discharge Relationships Across CONUS Using Self-Organizing Maps (1912338)

Arlex Marin-Ramirez, University of Louisville, Civil and Environmental Engineering, Louisville, KY, United States and David Tyler Mahoney, University of Louisville, Civil and Environmental Engineering, Louisville, United States

Toward Hyper-Resolution Transient Groundwater Modeling at Continental Scale Using Physics-Guided Machine Learning (1918044)

Yueling Ma^1,2, Danielle Tijerina-Kreuzer³, Amy Defnet^2,4, Georgios Artavanis⁴, Laura E Condon⁵ and Reed M Maxwell^2,6, (1)Princeton University, High Meadows Environmental Institute, Princeton, United States, (2)Princeton University, Integrated GroundWater Modeling Center, Princeton, United States, (3)University of California Berkeley, Energy and Resources Group, Berkeley, United States, (4)Princeton University, Princeton Research Computing, Princeton, United States, (5)University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States, (6)Princeton University, Civil and Environmental Engineering, Princeton, United States

Towards an Integrated Water Quality Forecast in South Florida (Invited) (1918092)

Mauricio Eduardo Arias, University of South Florida Tampa, Civil and Environmental Engineering, Tampa, FL, United States, David Kaplan, University of Florida, Department of Environmental Engineering Science, Gainesville, United States, Qiong Zhang, University of South Florida, Civil and Environmental Engineering, Tampa, United States, Hung Nguyen, University of South Florida Tampa, Civil and Environmental Engineering, Tampa, United States, Sajad Soleymani Hasani, University of South Florida, Tampa, United States, Enrique Orozco Lopez, University of Florida, Engineering School of Sustainable Infrastructure and Environment, Ft Walton Beach, FL, United States, Scotty Lee, University of Florida, Gainesville, United States and Maitane Olabarrieta, University of Florida, Department of Civil and Coastal Engineering, Gainesville, FL, United States

Hybrid Machine Learning Modeling with an Enhanced Preprocessing Framework for Streamflow Forecasting (1919097)

Arvin Samadi Koucheksaraee, North Dakota State University, Civil, Construction and Environmental Engineering (Dept 2470), Fargo, United States and Xuefeng Chu, North Dakota State University, Civil, Construction and Environmental Engineering, Fargo, ND, United States

Leveraging Machine Learning to Uncover Nutrient Trends and Drivers across the Contiguous United States (1920682)

Dr. Qian Zhang, Qian Zhang, University of Maryland Center for Environmental Science / U.S. Environmental Protection Agency Chesapeake Bay Program, Annapolis, United States; Oak Ridge Institute for Science and Education (ORISE) Fellow c/o U.S. Environmental Protection Agency, Washington, D.C., United States and Robert Daniel Sabo, U.S. Environmental Protection Agency, Office of Water, Washington, United States

What Controls River Turbidity? Uncovering the Roles of Climate, Land Use, and Landscape Heterogeneity with a Deep Learning Model (1920811)

Andrew Vierbicher, Penn State, Department of Civil and Environmental Engineering, University Park, United States, Donna M Rizzo, University of Vermont, Department of Civil and Environmental Engineering, Burlington, United States, Kristen Underwood, University of Vermont, Civil & Environmental Engineering; Water Resources Institute, Burlington, United States, Byung Suk Lee, University of Vermont, Computer Science, Burlington, VT, United States, Erin Cedar Seybold, University of Kansas, Kansas Geological Survey, Lawrence, United States, Julia N Perdrial, University of Vermont, Department of Geography & Geoscience, Burlington, VT, United States and Li Li, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, United States

A Scalable Deep Learning Approach for Daily Stream Temperature Prediction Across Unmonitored U.S. River Reaches (1921356)

Md Abu Bakar Bakar Siddik¹, Sean William Donald Turner¹, Jesus Gomez-Velez², Ahad Hasan Tanim¹ and Shih-Chieh Kao³, (1)Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, TN, United States, (2)Oak Ridge National Laboratory, Oak Ridge, United States, (3)Oak Ridge National Laboratory, Environmental Science Division, Oak Ridge, United States

Physics-Guided Hybrid CNN-BiLSTM Model for Streamflow and Water Quality Prediction in River networks using Spatiotemporal Features and Watershed Connectivity. (1922347)

Hadi Mohammed, University of Kansas, Civil, Environmental, and Architectural Engineering, Lawrence, KS, United States and Amy T. Hansen, University of Kansas, Civil, Environmental, and Architectural Engineering

A Hybrid Hydrological Modelling and Machine Learning-Based Framework for Improved Water Quality Predictions (1925382)

Henrique Haas, Columbia University, Center for Climate Systems Research, New York, United States, Azam Khan, Columbia University, Computer Science, New York, United States, Kevin Karl, Columbia University of New York, Center for Climate Systems Research, New York, United States and Alexander C Ruane, NASA Goddard Institute for Space Studies, New York, NY, United States

Evaluating Multi-Sensor Imagery for Flood Segmentation Using Deep Learning (1926326)

Brooke Cox, University of Wisconsin Madison, Madison, WI, United States, Jonathan Sullivan, University of Wisconsin Madison, Center for Sustainability and the Global Environment (SAGE), Nelson Institute for Environmental Studies, Madison, United States and Beth Tellman, University of Wisconsin, Madison, United States

Can Shallow Machine Learning lead to Robust Predictions of Stream Dissolved Oxygen? (1926686)

Omar I. Abdul-Aziz, West Virginia University, Civil and Environmental Engineering, Morgantown, WV, United States

Toward Generalizable and Interpretable Water Quality Modeling with AI-Augmented HSPF (1931996)

Xueting Pu, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, State College, United States and Chaopeng Shen, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, United States

High-Resolution Groundwater Pumping Estimation in New Jersey Using a Three-Stage Machine Learning Ensemble (1934794)

Yousef Kassem^1,2, Yueling Ma^3,4, Lisa Gallagher^4,5 and Reed M Maxwell^4,5, (1)Princeton University, Computer Science, Princeton, United States, (2)High Meadows Environmental Institute, Princeton, United States, (3)Princeton University, High Meadows Environmental Institute, Princeton, United States, (4)Princeton University, Integrated GroundWater Modeling Center, Princeton, United States, (5)Princeton University, Civil and Environmental Engineering, Princeton, United States

Towards Global Estimation of Riverine Suspended Sediment Flux Using Deep Learning (1937616)

Praval Devkota¹, Dongmei Feng², Abhinav Gupta², John Gardner³, Luisa Vieira Lucchese⁴ and Punwath Prum⁵, (1)University of Cincinnati Main Campus, Chemical and Environmental Engineering, Cincinnati, OH, United States, (2)University of Cincinnati Main Campus, Chemical and Environmental Engineering, Cincinnati, United States, (3)University of North Carolina at Chapel Hill, Earth, Marine, and Environmental Sciences, Chapel Hill, United States, (4)University of Pittsburgh, Geology and Environmental Science, Pittsburgh, PA, United States, (5)University of Pittsburgh, Geology and Environmental Science, Pittsburgh, United States

Probabilistic predictions of lake water temperature with mixture density networks (1938415)

Kailong Li¹, Jodi L. Ryder², Mark B Hausner³, Monica M Arienzo³ and Brittany Kruger¹, (1)Desert Research Institute Las Vegas, Las Vegas, NV, United States, (2)US Army Engineer Research and Development Center, Environmental Laboratory, Vicksburg, MS, United States, (3)Desert Research Institute, Division of Hydrologic Sciences, Reno, United States

Do Neural Networks Dream of Electric Hydrographs? Reproducibility and Robustness in Hydrologic Machine Learning (Invited) (1946337)

Konstantinos Andreadis, University of Massachusetts Amherst, Civil & Environmental Engineering, Amherst, United States

Mapping baseflow age across the conterminous United States using process-based modeling and machine learning (1946969)

Wes Zell¹, Paul E. Stackelberg², Ward E Sanford¹ and Kenneth Belitz³, (1)USGS, Integrated Modeling and Prediction Division, Reston, United States, (2)USGS, Earth Systems Processes Division, Troy, United States, (3)USGS, Earth Systems Processes Division, Boston, United States

Towards global scale continuous streamflow prediction in ungauged basins using contextual deep reinforcement learning (1948405)

Ather Abbas and Hylke Beck, King Abdullah University of Science and Technology, Physical Science and Engineering Division, Thuwal, Saudi Arabia

STREAM: a Comprehensive High-Frequency Sensor Database of Water Quality in Rivers in the United States (1951411)

Sai Kiran Kuntla¹, Shannon Speir² and Admin Husic, PhD¹, (1)Virginia Tech, Department of Civil and Environmental Engineering, Manassas, United States, (2)University of Arkansas, Department of Crop, Soil, and Environmental Sciences, Fayetteville, United States

Machine Learning-Based Long Term Future Streamflow Prediction Under CMIP6 Climate Change Scenarios for the Brahmaputra River Basin (1951549)

Mahin Mobarrat and Dr.Md. Mostafa Ali, Bangladesh University of Engineering and Technology, Water Resources Engineering, Dhaka, Bangladesh

Scalable Nested Deep Learning Framework for Real-Time Water Quality Forecasting Across Tributary Networks (1951887)

Shaurya Swami, University of Vermont, Department of Computer Science, Burlington, United States, Kristen Underwood, University of Vermont, Civil & Environmental Engineering; Water Resources Institute, Burlington, United States, Donna M Rizzo, University of Vermont, Department of Civil and Environmental Engineering, Burlington, United States, Scott D Hamshaw, U.S. Geological Survey, Water Mission Area-Integrated Modeling and Prediction Division, Bristol, United States, Patrick J Clemins, University of Vermont, Department of Computer Science, Burlington, VT, United States, Julia N Perdrial, University of Vermont, Department of Geography & Geoscience, Burlington, VT, United States, Andrew W. Schroth, University of Vermont, Geography and Geosciences, Burlington, United States and Harrison Myers, University of Vermont, Burlington, United States

Leveraging Deep Learning and Thermodynamic Models for Projecting Hydro-thermal Dynamics of Lakes on the Tibetan Plateau (1954524)

Mr. Yiming Wang, Di Long and Luoqi Li, Tsinghua University, Department of Hydraulic Engineering, Beijing, China

AI-Driven Climate Impact Assessment with hydrological modeling over a South Asian Transboundary River Basin: LSTM vs. SWAT hydrological modeling for the Upper Meghna. (1954641)

Khandakar M. Anik Rahaman^1,2, AKM Saiful Islam¹, Mohammad Asad Hussain¹, GM Tarekul Islam¹ and Zarin Tasnim¹, (1)Bangladesh University of Engineering and Technology, Institute of Water and Flood Management, Dhaka, Bangladesh, (2)University of Arizona, Hydrology and Atmospheric Sciences, Tucson, United States

Modeling Microbial Contamination in Private Wells Using Satellite Precipitation and Machine Learning (1954919)

Elizabeth Bartuska¹, Edward Beighley², Kyla Drewry³, Kelsey Pieper⁴, Reza Jamshidi², Fatemeh Ghoreishi⁵, Ryan Wang² and C. Nathan Jones⁶, (1)Northeastern University, Department of Civil and Environmental Engineering, Boston, MA, United States, (2)Northeastern University, Department of Civil and Environmental Engineering, Boston, United States, (3)Northeastern University, Department of Civil and Environmental Engineering, Boston, Massachusetts, United States, (4)University of North Carolina at Charlotte, Department of Civil and Environmental Engineering, Charlotte, North Carolina, United States, (5)Northeastern University, Civil and Environmental Engineering, Boston, United States, (6)University of Alabama, Biological Sciences, Tuscaloosa, Alabama, United States

Forecasting Alaskan River Temperature to Assess Salmon Viability (1957213)

Caroline Moy^1,2, Kurt Solander³, Ryan Crumley³, Jonathon P Schwenk⁴, Annalise Khandelwal^2,5, Erik Schoen⁶ and Richard Fiorella³, (1)University of Michigan Ann Arbor, Ann Arbor, MI, United States, (2)Los Alamos National Labratory, Earth and Environmental Sciences Division, Los Alamos, United States, (3)Los Alamos National Laboratory, Earth and Environmental Sciences Division, Los Alamos, United States, (4)Los Alamos National Laboratory, Los Alamos, United States, (5)The Ohio State Univeristy, College of Food, Agricultural, and Environmental Sciences, Columbus, United States, (6)University of Alaska, Fairbanks, Fairbanks, United States

Deep Learning Reveals the Bursty and Seasonal Nature of Continental-Scale Sediment Transport (1957455)

Nishchal Sigdel, Virginia Tech, Blacksburg, VA, UNITED STATES and Admin Husic, PhD, Virginia Tech, Department of Civil and Environmental Engineering, Manassas, United States

Deep Learning Advances Arctic River Water Temperature Predictions (1960085)

Shuyu Y Chang, Franklin & Marshall College, Department of Earth and Environmental Science, and The Chesapeake Watershed Initiative, Lancaster, United States; The Pennsylvania State University, Department of Geography, University Park, United States, Jonathon P Schwenk, Los Alamos National Laboratory, Los Alamos, United States and Kurt Solander, Los Alamos National Laboratory, Earth and Environmental Sciences Division, Los Alamos, United States

Controls on Streamflow Generation and Surface Water Quality Using ML and Streamflow Sensitivity of Fraction of Young Water (1967382)

Timothy Nsubuga¹, Raul Vera², Zelalem Womber³, Vishawjot Sandu⁴, Jimi Oke⁴ and Christian D Guzman¹, (1)University of Massachusetts Amherst, Civil and Environmental Engineering, Amherst, MA, United States, (2)University of Massachusetts Amherst, Civil and Environmental Engineering, Amherst, United States, (3)University of Massachusetts Amherst, Amherst, MA, United States, (4)University of Massachusetts Amherst, Amherst, United States

Spatiotemporal Turbidity Prediction in River Systems using Graph Neural Networks (1984691)

George Myers^1,2, Aidan Hayes³, Shaurya Swami³, Andrew W. Schroth⁴, John Trusal Kemper⁵, Donna M Rizzo^3,6, Scott D Hamshaw⁷ and Kristen Underwood⁸, (1)University of Vermont, Water Resources Institute, Burlington, VT, United States, (2)University of Vermont, Department of Civil and Environmental Engineering, Burlington, VT, United States, (3)University of Vermont, Department of Computer Science, Burlington, United States, (4)University of Vermont, Geography and Geosciences, Burlington, United States, (5)Utah State University, Department of Watershed Sciences, Logan, United States, (6)University of Vermont, Department of Civil and Environmental Engineering, Burlington, United States, (7)U.S. Geological Survey, Water Mission Area-Integrated Modeling and Prediction Division, Bristol, United States, (8)University of Vermont, Civil & Environmental Engineering; Water Resources Institute, Burlington, United States

Near-term Water Quality Forecasting and Uncertainty Analyses in Freshwater Ecosystems using Machine Learning (1987569)

Rohit Shukla¹, Mary E Lofton¹, Adrienne Breef-Pilz¹, Cayelan Carey¹ and Quinn Thomas², (1)Virginia Polytechnic Institute and State University, Biological Sciences, Blacksburg, United States, (2)Virginia Polytechnic Institute and State University, Forest Resources and Environmental Conservation, Blacksburg, VA, United States

Combining climate reanalysis and remote sensing data to predict river discharge using a generalizable, distributed, deep learning model. (1989419)

Theodore Langhorst¹, Colin J Gleason² and Konstantinos Andreadis², (1)University of Massachusetts Amherst, Amherst, MA, United States, (2)University of Massachusetts Amherst, Civil & Environmental Engineering, Amherst, United States

Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans (1992581)

Sheikh Fahim Faysal Faysal Sowrav, Bangladesh Maritime University, Department of Oceanography & Hydrography, Dhaka, Bangladesh and Mohan Kumar Das, National Oceanographic And Maritime Institute (NOAMI), Dhaka, Bangladesh

Groundwater salinity potential in Mexico: insights from unsupervised and supervised learning (2000219)

Christian Felipe Narvaez Montoya, Juan Antonio Torres-Martinez and Jürgen Mahlknecht, Tecnológico de Monterrey, Monterrey, Mexico

When Stream Temperature Meets Streamflow: A Scalable Approach to Modeling Climate-Sensitive Groundwater Recharge (2001987)

Farshid Rahmani¹, Chaopeng Shen², Alison Appling³, Yalan Song², Haoyu Ji², Jiangtao Liu⁴, Kathryn Lawson² and Yoshihide Wada⁵, (1)Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, PA, United States, (2)Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, United States, (3)U.S. Geological Survey, Water Mission Area, Middleton, United States, (4)Pennsylvania State University, Department of Civil and Environmental Engineering, University Park, United States, (5)King Abdullah University of Science and Technology, Biological and Environmental Science and Engineering Division, Thuwal, Saudi Arabia

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H043 Applications of Machine Learning in Large Scale Hydrology and Water Quality Modeling

Session Description:

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H043
Applications of Machine Learning in Large Scale Hydrology and Water Quality Modeling