Knowledge discovery from high-frequency stream nitrate concentrations: hydrology and biology contributions

Abstract

High-frequency, in-situ monitoring provides large environmental datasets. These datasets will likely bring new insights in landscape functioning and process scale understanding. However, tailoring data analysis methods is necessary. Here, we detach our analysis from the usual temporal analysis performed in hydrology to determine if it is possible to infer general rules regarding hydrochemistry from available large datasets. We combined a 2-year in-stream nitrate concentration time series (time resolution of 15 min) with concurrent hydrological, meteorological and soil moisture data. We removed the low-frequency variations through low-pass filtering, which suppressed seasonality. We then analyzed the high-frequency variability component using Pareto Density Estimation, which to our knowledge has not been applied to hydrology. The resulting distribution of nitrate concentrations revealed three normally distributed modes: low, medium and high. Studying the environmental conditions for each mode revealed the main control of nitrate concentration: the saturation state of the riparian zone. We found low nitrate concentrations under conditions of hydrological connectivity and dominant denitrifying biological processes, and we found high nitrate concentrations under hydrological recession conditions and dominant nitrifying biological processes. These results generalize our understanding of hydro-biogeochemical nitrate flux controls and bring useful information to the development of nitrogen process-based models at the landscape scale.

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

“…With frequencies greater than 1 per day, the spectral slope steepened suggesting that either in‐stream or riparian processes were damping NO 3 − concentrations as well as those of DOC. More recently, Aubert, Thrun, Breuer, and Ultsch () removed the temporal NO 3 − signal component by applying Pareto Density Estimation in a low‐order agricultural catchment. This analysis identified a low NO 3 − mode associated with high hydrologic connectivity and dominance of denitrification, and a high NO 3 − mode associated with hydrograph recession and dominance of nitrification.…”

Using High‐Resolution Data to Assess Land Use Impact on Nitrate Dynamics in East African Tropical Montane Catchments

“…In the case of NO 3 ‐N, such high‐frequency data have been used in temperate regions to improve load estimates (Ferrant et al, ; Fogle et al, ; Pellerin et al, ; Rozemeijer et al, ), to identify NO 3 ‐N sources (Bowes et al, ), to assess responses to rainfall events and hysteresis (Dupas et al, ; Lloyd et al, ; Sherson et al, ), diurnal nutrient cycling patterns (Aubert & Breuer, ; Aubert et al, ; Halliday et al, ; Pellerin et al, ), responses to disturbances (Sherson et al, ), and nutrient dynamics in general (Bende‐Michl et al, ; Halliday et al, ; Neal et al, ). Furthermore, in situ instruments can also be used to identify “hot moments” in nutrient concentrations and export.…”

High‐Frequency Sensor Data Reveal Across‐Scale Nitrate Dynamics in Response to Hydrology and Biogeochemistry in Intensively Managed Agricultural Basins

“…To date, these data have been used largely to explore nitrate load timing, rate, and magnitude and thus prediction capabilities for downstream environmental consequences (Jones et al, ; Pellerin et al, , ). To our knowledge, across‐scale nitrate process dynamics have not been systematically investigated in heavily agriculturally managed landscapes, although some work has occurred in less impacted watersheds (Aubert et al, ; Downing et al, ). This is, in part, because the high‐frequency nitrate data generated from these sensors are only recently long enough to use frequency analysis tools on, such as spectral analysis among others, to extract information about the signature of different processes on change in variability in NO 3 − across a range of scales.…”