Optimizing Sampling Strategies for Riverine Nitrate Using High-Frequency Data in Agricultural Watersheds.

Area of Science:

• Environmental Science
• Hydrology
• Biogeochemistry

Background:

• Agricultural streams face significant nitrate loading, impacting water quality.
• Effective monitoring strategies are crucial for understanding hydrologic and biogeochemical processes.
• High-frequency monitoring networks provide valuable data for assessing water quality parameters.

Purpose of the Study:

• To quantify the bias and precision of different monitoring strategies for nitrate parameters.
• To evaluate the effectiveness of various sampling regimes in agricultural settings.
• To inform the design of optimized nitrate monitoring strategies in the Midwest.

Main Methods:

• Utilized a high-frequency nitrate monitoring network in Iowa with 15-minute resolution data.
• Performed systematic subsampling of continuous nitrate records to simulate common sampling regimes.
• Quantified uncertainties (bias and precision) for nitrate concentration, exceedance of drinking water standards, peak concentrations, and flux.

Main Results:

• Time-interval sampling efficiently characterized most nitrate parameters, including mean concentration and exceedances.
• Stage-triggered storm sampling precisely captured nitrate flux with minimal sampling effort (0.19% of observations).
• Time-interval sampling demonstrated the highest return on investment for accurately quantifying nitrate parameters per sampling effort.

Conclusions:

• Time-interval sampling is generally the most effective strategy for comprehensive nitrate monitoring in agricultural watersheds.
• Specific strategies like stage-triggered sampling can be highly efficient for estimating nitrate flux.
• Uncertainty estimates derived from this study aid in designing cost-effective and accurate water quality monitoring programs.