Development of error correction techniques for nitrate-N load estimation methods

SummaryThis study used Monte Carlo sub-sampling and error-corrected statistical methods to estimate annual nitrate-N loads from two watersheds in central Illinois. The study objectives were (1) to evaluate the performance of various statistical load estimation methods for different combinations of monitoring durations and frequencies on nitrate-N load estimation accuracy, and (2) to develop and validate new empirical error correction techniques applied to the selected load estimation methods. We compared three load estimation methods (the 7-parameter regression estimator, the ratio estimator, and the flow-weighted average estimator) applied at 1, 2, 4, 6, and 8-week sampling frequencies and 1, 2, 3, and 6-year monitoring durations. Five error correction techniques; the existing composite method, and four new error correction techniques developed in this study; were applied to each combination of sampling frequency, monitoring duration and load estimation method. The newly proposed error correction techniques resulted in most accurate load estimates in 33 of 38 acceptable sampling combinations for both watersheds. On average, the most accurate error correction technique, (proportional rectangular) resulted in 15% and 30% more accurate load estimates when compared to the most accurate uncorrected load estimation method (ratio estimator) for the two watersheds. Using more accurate load estimation methods it is also possible to design more cost-effective monitoring plans by achieving the same load estimation accuracy with fewer observations.

► We assess performance of load estimation methods for predicting nitrate-N loads.
► We develop four new empirical error correction techniques for load estimation methods.
► Error correction techniques improved load estimates for most sampling scenarios.
► Error correction techniques require fewer observations to achieve similar accuracies.
► These techniques can potentially be used to design cost-effective monitoring plans.