Quantifying rain-driven NO3-N dynamics in headwater: value of applying SISO system identification to multiple variables monitored at the same high frequency

The nitrate–nitrogen (NO 3 -N) concentration is a key variable affecting the ecosystem services supported by headwater streams. The availability of such data monitored continuously at a high frequency (in parallel to hydrometric and other water quality data) potentially permits a greater insight into the dynamics of this key variable. This study demonstrates how single-input single-output (SISO) system identification tools can make better use of these high-frequency data to identify a reduced number of numerical characteristics that support new explanatory hypotheses of rain-driven NO 3 -N dynamics. A second-order watershed managed for commercial forestry in upland Wales (United Kingdom) provided the illustrative data. Fifteen-minute rainfall time series were used to simulate NO 3 -N concentration dynamics and the potentially associated dynamics in dissolved organic carbon (DOC) and runoff, monitored at the same high resolution for two 30-day periods with a differing temperature regime. The approach identified robust, high-efficiency models needing few parameters. Comparison of only three derived dynamic response characteristics (DRCs) of δ , TC , and SSG for the three variables for the two different periods led to new hypotheses of rain-driven NO 3 -N dynamics for further exploratory field investigation.