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Monitoring the riverine pulse: Applying high‐frequency nitrate data to advance integrative understanding of biogeochemical and hydrological processes

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Abstract 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 study 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 multiyear 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. This article is categorized under: Science of Water > Water Quality Science of Water > Methods Water and Life > Nature of Freshwater Ecosystems
Simple conceptual model illustrating some of the key transport and transformation processes affecting the transport and fate of NO3 in watersheds that are discussed in this study
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Illustration of four processes that may affect the downstream fate of NO3 in rivers: Tributary and point discharge mixing, groundwater transport, evapotranspiration effects on groundwater inflow rates, and denitrification in the hyporheic zone
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Two examples of artificial drainage systems commonly present in watersheds, which affect NO3 in runoff and resulting hysteresis, flushing and cQ relations. (a) Tile drains used in agricultural land and (b) sewer pipes which can leak outward to the subsurface but can also be infiltrated under wet conditions
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Hysteresis index (HI) (modified from Fig. 3 in Lloyd et al. ()) for an example of clockwise hysteresis with increasing concentrations during rising discharge. Normalized concentration is calculated as: cnorm = cicmin/cmaxcmin, where cnorm is the normalized concentration, ci the concentration at given time during the event, and cmin and cmax, the minimum and maximum concentrations, respectively. The HI is calculated as the difference in normalized concentrations on the rising and falling hydrograph limbs: HI = (cnorm rising) – (cnorm falling). In this illustration, HI is evaluated at 0.1 intervals of maximum event discharge yielding values from +1 to −1, positive values indicate clockwise hysteresis and negative values indicate anticlockwise hysteresis. Normalized values can be evaluated incrementally or broadly as the mean event response
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Idealized representation of (a) clockwise and (b) anticlockwise hysteresis illustrating that each pattern can occur with either concentration or dilution during rising discharge. Other hysteresis metrics are shown such as rising and falling hydrograph slopes in panel a, and loop width and area in panel b
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Generalized solute–discharge relations for a 24‐hr hydrologic event. (a) Solute peak concentration precedes discharge peak; (b) solute peak concentration lags discharge peak; (c) solute dilutes, minimum precedes discharge peak; and (d) solute shows small peak during rising hydrograph, then dilutes, minimum lags discharge peak
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Water and Life > Nature of Freshwater Ecosystems
Science of Water > Methods
Science of Water > Water Quality

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