EAR-Climate: Continental-Scale Determinants of Stream Solute Spatial and Temporal Patterns

点击翻译按钮获取中文摘要

This project seeks answers to foundational questions about links between landscapes and rivers, with broad societal implications for how water quality is measured and managed, and thus is relevant to environmental stewardship and restoration. Statistical models will be developed to predict spatial and temporal patterns in water quality using continental-scale data across the eastern United States. Strong existing partnerships with management agencies will be leveraged to disseminate findings, synthesized as principles for effective and efficient design of water quality monitoring programs. The approaches will be iteratively developed in collaboration with global partners, with the exchange facilitated by research workshops. Students engaged in this work will be trained in science communication for general audiences. Finally, efforts will be made to recruit and retain student and post-doc participants from underrepresented groups. Stream solute signals encode information about upstream sources, landscape filtering, and network transformations, yet a comprehensive predictive understanding remains elusive. This work seeks to understand the origins of spatial vs temporal variability in water quality patterns, answering the following specific questions: What is the relative importance of stream solute spatial vs. temporal variability? What are the drivers of stream solute spatial and temporal variability? The primary focus is on three contrasting solute groups (geogenic, biogenic, anthropogenic) with varying links to geographical, anthropogenic, hydrological, and climatological drivers. A series of predictions about controls on patterns of space-time variation will be evaluated, using a continental-scale data inventory across the eastern United States, spanning large gradients in latitude and hydroclimate zones, lithology, wetland and forest cover, and anthropogenic land use intensity. The project seeks to explain the substantial spatial variation in mean concentrations observed for all three solute groups, based on the coupled solute-specific influences of ecoregion, network position, and solute source area. Mean concentrations are expected to vary with aggregated landscape properties, such as wetland coverage or agricultural land use intensity, and mean climate conditions, such as aridity and temperature. It is hypothesized that temporal variability around the mean can be understood and explained in terms of key drivers such as flow variation, seasonality, and network position, albeit with solute-specific sensitivities. This spatial vs. temporal variance framework provides a novel template for summarizing, understanding, and predicting variation in water quality.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.