Improving nitrate load estimates in an agricultural catchment using Event Response Reconstruction.

Area of Science:

• Environmental Science
• Hydrology
• Water Quality Monitoring

Background:

• Traditional low-frequency sampling methods yield uncertain stream solute load estimates.
• High-frequency sensors offer sub-hourly solute concentration data, capturing dynamic changes.
• Nitrate (NO3) load estimation accuracy is crucial for agricultural catchment management.

Purpose of the Study:

• To improve nitrate (NO3) load estimates using high-resolution sensor data.
• To develop and validate an empirical model for reconstructing NO3 concentrations during storm events.
• To assess the accuracy of the developed model against traditional estimation techniques.

Main Methods:

• Utilized high-frequency (15-min) NO3 concentration, discharge, and precipitation data from a German agricultural catchment.
• Developed an Event Response Reconstruction (ERR) model predicting NO3 concentration dynamics using discharge and precipitation.
• Calibrated the ERR model with 14 storm events and validated with 27 events from continuous high-resolution records.

Main Results:

• NO3 concentrations typically decreased during storm flow rise and increased during recession.
• The ERR model accurately predicted three key storm event descriptors: rdN, TdN, and TNrec.
• ERR significantly reduced NO3 load estimation errors from 10% to 1% compared to linear interpolation.

Conclusions:

• High-resolution data and the ERR model enable accurate NO3 load estimation even with low-frequency data.
• The ERR approach offers a substantial improvement over traditional grab sampling and flow-weighted methods.
• This methodology enhances the reliability of water quality monitoring in agricultural catchments.