- H31P: Advancing Water Quality Monitoring, Process Understanding, and Forecasting for Sustaining Terrestrial and Aquatic Ecosystem Health IV Poster
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NOLA CC
Primary Convener:Generic 'disconnected' Message
Jared Smith, U.S. Geological Survey
Convener:
Noel Aloysius, University of Missouri
Stefan Krause, University of Birmingham
Zachary Johnson, US Geological Survey
Audrey Sawyer, The Ohio State University
Janet Barclay, United States Geological Survey
Dylan Blaskey, University of South Carolina
Yan Zheng, Southern University of Science and Technology
Neil Fox, University of Missouri Columbia
Early Career Convener:
Tim Stagnitta, USGS New York Water Science Center
Chair:
Jennifer Murphy, USGS Central Midwest Water Science Center
Moussa Yatta, University of Missouri
Joel Blomquist, U.S. Geological Survey
Jared Smith, U.S. Geological Survey
The need for reliable short- and medium-term forecasts of ecologically significant water quality parameters such as nitrates, salinity and dissolved oxygen in aquatic environments has driven advances in observational and monitoring methods, coupled with process-guided modeling techniques. Yet challenges remain to obtain reliable predictions across a range of hydrologic conditions at spatial and temporal scales that are relevant for decision making. Additionally, the mechanisms controlling the activation and transport of pollutants and the impacts of human interventions, land and water management are poorly understood. This is especially true for the groundwater – surface water interfaces, which represent hotspots of environmental pollution. The temporal dynamics of surface water pollutants interacting with heterogeneous subsurface strata is rather complex. This session invites contributions that explore the potential of combining monitoring, observation, process, and modeling studies with a goal of improving water quality forecasts in surface and subsurface hydrological systems.
Index Terms
1830 Groundwater|surface water interaction
1831 Groundwater quality
1871 Surface water quality
1942 Machine learning
Cross-Listed:
IN - Informatics
B - Biogeosciences
GC - Global Environmental Change
Suggested Itineraries:
Machine Learning and AI
Co-Sponsored Sessions:
EGU: European Geosciences Union
Neighborhoods:
3. Earth Covering
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