H017
Advances in Water Quality Prediction
Session ID#: 160512
Session Description:
With increasing data availability, process understanding, and computational resources, predicting water quality has never been more achievable. Yet challenges remain to ensure that predictions are reliable across a range of hydrologic conditions and human impacts at the spatial and temporal scales that are relevant to water and land management decisions. We welcome presentations that provide data-driven approaches to water quality prediction for temperature, salinity, dissolved oxygen, sediment, nitrogen, phosphorous, or other constituents. Approaches may include:
1) prediction under different hydrologic conditions and extremes,
2) multi-constituent prediction (e.g., streamflow and other constituents),
3) evaluating how water quality models scale in space, time, or with varying degrees of hydrologic alteration or land surface management,
4) developing forecast-ready water quality models in managed and unmanaged reaches,
5) integrating groundwater and surface water quality predictions, or
6) integrating process-based and machine learning approaches to guide predictions.
Index Terms:
1805 Computational hydrology [HYDROLOGY]
1871 Surface water quality [HYDROLOGY]
1910 Data assimilation, integration and fusion [INFORMATICS]
1942 Machine learning [INFORMATICS]
Primary Convener: Jared David Smith, U.S. Geological Survey, Integrated Modeling and Prediction Division, Reston, VA, United States
Conveners: Jeffrey Michael Sadler, USGS, Integrated Information Dissemination Division, Data Science Branch, Middleton, WI, United States, Alice M Carter, University of Montana, Flathead Lake Biological Station, Polson, MT, United States and Jared Willard, University of Minnesota, Minneapolis, MN, United States
Student/Early Career Convener: Jared Willard, University of Minnesota, Minneapolis, MN, United States
A Quantitative Assessment of the Influence of Physicochemical Factors on the Variations of Groundwater Salinity in the Subsurface Natural Environment: A Case Study of Shariatpur District, Bangladesh (1050424)
Riaz Hossain Khan, BRAC University, James P Grant School of Public Health, Dhaka, Bangladesh and Farhana Zaman, Former MS student, University of Development Alternative, Department of Biotechnology and Genetic Engineering, Dhaka, Bangladesh
Application of Reinforcement Learning to Calibrate Cyanobacteria using Daily Water Quality Parameters in EFDC Model (1057726)
Seokmin Hong1, Kyung Hwa Cho2, Ather Abbas3, Soobin KIM1, DoHyuck Kwon1, Nakyung Yoon3, Daeun Yun3 and Sanguk Lee4, (1)Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of (South), (2)School of Urban and Environmental Engineering, Ulsan National Institute Science and Technology (UNIST), Ulsan, South Korea, (3)Ulsan National Institute of Science and Technology, Ulsan, South Korea, (4)Korea Water Resources Corporation, Daejeon, South Korea
Deep Neural Network for Nowcasting Chemical Accident in Freshwater (1065321)
Soobin KIM, Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of (South), Ather Abbas, Ulsan National Institute of Science and Technology, Ulsan, South Korea, JongChoel Pyo, Korea Environment Institute, Sejong, South Korea, Hyein Kim, Ulsan National Institute Science and Technology (UNIST), Ulsan, South Korea, Sang-Soo Baek, Yeungnam University, Gyeongsan, South Korea and Kyung Hwa Cho, School of Urban and Environmental Engineering, Ulsan National Institute Science and Technology (UNIST), Ulsan, South Korea
How do Water Quality Models Respond to Growing vs. Non-growing Season Landuse Inputs? An Exploration with Google Earth Engine and Dynamic World (1075253)
Daniel T Myers1, Diana Oviedo-Vargas2, David Jones3, Darren L Ficklin4 and John Paul Schmit3, (1)Stroud Water Research Center, Avondale, PA, United States, (2)Stroud Water Research Center, Avondale, United States, (3)National Park Service, National Capital Region Network, Washington, United States, (4)Indiana University Bloomington, Bloomington, IN, United States
Using Sentinel-2 Images to Map Dissolved Oxygen in the Little Miami River in Machine Learning Environment (1089285)
Eric Ariel L Salas1, Sakthi Kumaran1, Eric B Partee2, Leeoria Willis3 and Kayla Mitchell3, (1)Central State University, Wilberforce, OH, United States, (2)Little Miami Conservancy, Loveland, United States, (3)Central State University, Wilberforce, United States
Towards Entity-Aware Amortized Variational Inference: An application in Streamflow modeling (Invited) (1094621)
Rahul Ghosh1, Wallace Anderson McAliley2, Arvind Renganathan3, Kshitij Tayal3, Xiaowei Jia4 and Vipin Kumar5, (1)University of Minnesota-Twin Cities, Department of Computer Science/Engineering, Minneapolis, United States, (2)USGS Colorado Water Science Center Golden, Golden, United States, (3)University of Minnesota Twin Cities, Department of Computer Science/Engineering, Minneapolis, United States, (4)University of Pittsburgh, Department of Computer Science, Pittsburgh, PA, United States, (5)University of Minnesota Twin Cities, Department of Computer Science/Engineering, Minneapolis, MN, United States
Prediction modelling framework comparative analysis of dissolved oxygen concentration variations using support vector regression coupled with multiple feature engineering and optimization methods in the South-to-North Water Diversion Project of China (1098657)
Xizhi Nong1, Cheng Lai1, Lihua Chen1, Dongguo Shao2 and Chi Zhang2, (1)Guangxi University, College of Civil Engineering and Architecture, Nanning, China, (2)Wuhan University, State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan, China
Random forest modeling and Shapley values reveal controls of sediment transport and hysteresis patterns in a low-gradient watershed (1123433)
Arlex Marin-Ramirez1, David Tyler Mahoney1, Leonie Bettel2, Brenden Riddle3 and James Fox2, (1)University of Louisville, Civil and Environmental Engineering, Louisville, KY, United States, (2)University of Kentucky, Department of Civil Engineering, Lexington, KY, United States, (3)University of Kentucky, Department of Civil Engineering, Lexington, United States
Evaluating water-quality monitoring protocols for obtaining accurate model estimates of the frequency, magnitude, and duration of river water-quality exceedances (1136570)
Jory S Hecht, U.S. Geological Survey, Integrated Modeling and Prediction Division, Water Resources Mission Area, Northborough, United States, Mikaela L Cherry, U.S. Geological Survey, Nebraska Water Science Center, Lincoln, United States, Jeffrey G Chanat, U.S. Geological Survey, Virginia Water Science Center, Richmond, United States, Sarah M Stackpoole, U.S. Geological Survey, Earth Systems Processes Division, Water Resources Mission Area, Denver, United States and Robert M Hirsch, U.S. Geological Survey, Integrated Modeling and Prediction Division, Water Resources Mission Area, Reston, United States
Evaluating Spatiotemporal Trends of Water Quality in a Coupled Terrestrial-Coastal Basin: The Case of St. Lucie Estuary and River Basin (1151886)
Yuan Zou, Florida State University, Tallahassee, FL, United States, Ebrahim Ahmadisharaf, FAMU-FSU College of Engineering, Department of Civil and Environmental Engineering, Tallahassee, United States and Ming Ye, Florida State University, Earth, Ocean, and Atmospheric Science, Tallahassee, FL, United States
Reconstructing Ion Concentrations from River Discharge with Deep Learning (Transformer) and High Frequency Data. (1158122)
Antoine Dolant1, Jinyu WANG2, Paul Floury1, Praveen Kumar3 and Jennifer L Druhan4, (1)EXTRALAB, Paris, France, (2)University of Illinois at Urbana-Champaign, Champaign, IL, UNITED STATES, (3)University of Illinois at Urbana Champaign, Department of Civil and Environmental Engineering, Urbana, United States, (4)University of Illinois, Department of Geology, Urbana Champaign, United States
Classical Machine Learning for Widespread Stream Temperature Predictions: Demonstrations in the Pacific Northwest and Mid Atlantic Regions (1159818)
Helen Weierbach1, Jared Willard2, Aranildo Lima3, Valerie C Hendrix4, Danielle S Christianson4, Michaelle Lubich4 and Charuleka Varadharajan4, (1)Lawrence Berkeley National Laboratory, Earth and Environmental Sciences Area, Berkeley, United States, (2)University of Minnesota, Minneapolis, MN, United States, (3)Aquatic Informatics, Vancouver, Canada, (4)Lawrence Berkeley National Laboratory, Berkeley, CA, United States
Using Deep Learning and Site Clustering to Develop Predictions of In-stream Nitrate in Intensively Managed Watersheds (Invited) (1164974)
Galen Gorski, US Geological Survey, Reston, VA, United States, Laurel Larsen, University of California, Department of Geography, Department of Civil and Environmental Engineering, Berkeley, United States, Jordan Wingenroth, University of California Berkeley, Geography, Berkeley, CA, United States, Liang Zhang, University of California, Berkeley, Department of Civil and Environmental Engineering, Berkeley, CA, United States and Dino G. Bellugi, University of California, Berkeley, Department of Geography, Berkeley, CA, United States
Reconstruction of Sparse Stream Flow and Concentration Time-series Through Compressed Sensing (1166584)
Wasif Bin Mamoon1, E Schwartz2, Kun Zhang3 and Anthony Parolari1, (1)Marquette University, Department of Civil, Construction and Environmental Engineering, Milwaukee, United States, (2)University of Michigan- Ann Arbor, Ann Arbor, United States, (3)Marquette University, Department of Civil, Construction and Environmental Engineering, Milwaukee, WI, United States
Coupling High-frequency Sensing and 2-d Numerical Modelling to Analyze Nitrate Dynamics in a Large River (1167372)
Amirreza Zarnaghsh, University of Kansas, Civil, Environmental, and Architectural Engineering, Lawrence, KS, United States, Admin Husic, University of Kansas, Civil, Environmental and Architectural Engineering, Lawrence, KS, United States, Michelle C Kelly, Michigan Technological University, Houghton, United States and Amy J Burgin, University of Nebraska Lincoln, Lincoln, NE, United States
The Impacts of Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Prediction (1167890)
Farshid Rahmani, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, PA, United States, Alison Appling, USGS Integrated Information Dissemination Division, Data Science Branch, Middleton, WI, United States, Lauren Koenig, U.S. Geological Survey, Albany, NY, United States and Chaopeng Shen, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, United States
Twenty years of seasonal predictions reveal when, where, and why nutrient concentration standards may be exceeded across the Illinois River Basin (1173665)
Noah M Schmadel1, Olivia Miller2, David A Saad3, Gregory E. Schwarz4, Dale M Robertson3, Scott Ator5, Andrew Sekellick6, Kenneth Skinner7 and Richard A Smith8, (1)USGS Oregon Water Science Center, Portland, OR, United States, (2)U.S. Geological Survey, Utah Water Science Center, Salt Lake City, United States, (3)USGS Upper Midwest Water Science Center, Middleton, WI, United States, (4)U.S. Geological Survey, Reston, VA, United States, (5)USGS Baltimore, Baltimore, United States, (6)USGS Maryland/Delaware/District of Columbia Water Science Center, Baltimore, MD, United States, (7)U.S. Geological Survey Idaho Water Science Center, Boise, United States, (8)United States Geological Survey (USGS), Reston, United States
Application of HEC-RAS for Modelling Water Quality Parameters in a Canal Flowing Near a Semi-Arid Urban Area (1180590)
Bidhan Kumar Sahu, Indian Institute of Technology Gandhinagar, Ahmedabad, 382, India, Yash Duggad, Indian Institute of Technology Gandhinagar, Ahmedabad, India and Pranab Mohapatra, Indian Institute of Technology Gandhinagar, Discipline of Civil Engineering, Gandhinagar, India
Estimating Chlorophyll-a in Small Lakes in Northeastern US using Landsat (1185553)
Elijah Hall1, John Gardner2 and Emily M. Elliott1, (1)University of Pittsburgh, Pittsburgh, PA, United States, (2)University of Pittsburgh, Department of Geology and Environmental Science, Pittsburgh, PA, United States
Modeling continental US stream water quality using Long-Short Term Memory (LSTM) and Weighted Regressions on Time, Discharge, and Season (WRTDS) (1187853)
Kuai Fang, Stanford University, Earth System Sciences, Stanford, CA, United States and Kate Maher, Stanford University, Earth Systems Science, Stanford, United States
Impacts of an Acid-Sulfate Volcanic-Hosted Geothermal System on Downstream Water Quality: Sulphur Creek, Valles Caldera, NM (1188642)
Daniel Lavery1, Laura J Crossey2, Abdulmehdi Ali2 and Jon K Golla3, (1)University of New Mexico Main Campus, Albuquerque, United States, (2)University of New Mexico Main Campus, Albuquerque, NM, United States, (3)University of Illinois at Urbana-Champaign, Department of Geology, Urbana, United States
