Uncertainty in nutrient loads from tile-drained landscapes: Effect of sampling frequency, calculation algorithm, and compositing strategy

Accurate estimates of annual nutrient loads are required to evaluate trends in water quality following changes in land use or management and to calibrate and validate water quality models. While much emphasis has been placed on understanding the uncertainty of nutrient load estimates in large, naturally drained watersheds, few studies have focused on tile-drained fields and small tile-drained headwater watersheds. The objective of this study was to quantify uncertainty in annual dissolved reactive phosphorus (DRP) and nitrate-nitrogen (NO3-N) load estimates from four tile-drained fields and two small tile-drained headwater watersheds in Ohio, USA and Ontario, Canada. High temporal resolution datasets of discharge (10-30 min) and nutrient concentration (2 h to 1 d) were collected over a 1-2 year period at each site and used to calculate a reference nutrient load. Monte Carlo simulations were used to subsample the measured data to assess the effects of sample frequency, calculation algorithm, and compositing strategy on the uncertainty of load estimates. Results showed that uncertainty in annual DRP and NO3-N load estimates was influenced by both the sampling interval and the load estimation algorithm. Uncertainty in annual nutrient load estimates increased with increasing sampling interval for all of the load estimation algorithms tested. Continuous discharge measurements and linear interpolation of nutrient concentrations yielded the least amount of uncertainty, but still tended to underestimate the reference load. Compositing strategies generally improved the precision of load estimates compared to discrete grab samples; however, they often reduced the accuracy. Based on the results of this study, we recommended that nutrient concentration be measured every 13-26 h for DRP and every 2.7-17.5 d for NO3-N in tile-drained fields and small tile-drained headwater watersheds to accurately (±10%) estimate annual loads.