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 knowledg

SUBMITTER: Aubert AH 

PROVIDER: S-EPMC5004126 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature