Widespread deployment of sensors that measure river nitrate (NO3-) concentrations has led to many recent publications in water resources journals including review papers focused on data quality assurance, improved load calculations, and better nutrient management. The principal objective of this paper is to review and synthesize studies of high-frequency NO3- data that have aimed to improve understanding of the hydrologic and biogeochemical processes underlying episodic, diel, and long-term stream NO3- dynamics. Investigations have provided unprecedented detail on hysteresis and flushing patterns during high flow, seasonal variation during baseflow, and responses to multi-year climate variation. Analyses of high-frequency data have led to notable advances in understanding how climate variation affects spatial and temporal NO3- patterns, especially dry-wet cycles and antecedent moisture. Further advances have been limited by few investigations that include high-frequency measurements outside the channel and the short duration of many records. High-frequency data for multiple constituents have provided new insight to the relative roles of hydrology and biogeochemistry as highlighted by studies of the roles of autotrophic uptake, denitrification, riparian evapotranspiration, and temperature-driven changes in viscosity as drivers of diel patterns. Comparisons of short-duration high-frequency data with long-duration low frequency data have described similarities and differences in concentration – discharge patterns and highlighted the role of legacy stores. Investigators have applied innovative analysis approaches not previously possible with low-frequency or temporally-irregular data. Future availability of long-duration high-frequency data will provide new insight to processes, resulting in improved conceptual models and a deeper understanding of the role of climate variation.
|Title||Monitoring the Riverine Pulse: Applying high-frequency nitrate data to advance integrative understanding of biogeochemical and hydrological processes|
|Authors||Douglas A. Burns, Brian A. Pellerin, Matthew P. Miller, Paul Capel, Anthony J. Tesoriero, Jonathan M. Duncan|
|Publication Subtype||Journal Article|
|Series Title||WIREs Water|
|Record Source||USGS Publications Warehouse|
|USGS Organization||WMA - Integrated Modeling and Prediction Division|