H055
Frontiers in Water Quality: Insights from large-scale, long-term, and high-resolution datasets
Frontiers in Water Quality: Insights from large-scale, long-term, and high-resolution datasets
Session ID#: 158920
Session Description:
Water quality is temporally and spatially dynamic in response to a multitude of overlapping drivers. Developing our understanding of water quality variation, and thus enabling reliable and robust predictors, requires data sets that span the gamut of spatiotemporal variation. This session seeks to coalesce emerging insights about water quality variation from novel data sets, including where investments have been made in long-term data collection, or where sensors and satellites have transformed the scales at which observations are possible. Widespread evidence of declining water quality in aquifers, coastal systems, streams, lakes and wetlands creates an urgent need to define how to accurately document declines, assess efforts to remedy these declines, and learn the underlying mechanisms that contribute to the magnitude and trajectories of decline and recovery. We further welcome contributions that use emerging water quality tools and data to better understand social inequities with respect to pollutant exposure and access to restoration efforts.
Index Terms:
1831 Groundwater quality [HYDROLOGY]
1834 Human impacts [HYDROLOGY]
1871 Surface water quality [HYDROLOGY]
Primary Convener: Admin Husic, University of Kansas, Civil, Environmental and Architectural Engineering, Lawrence, KS, United States
Convener: Matthew J Cohen, Univ Florida-SFRC, Gainesville, FL, United States
See more of: Hydrology
