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

Low-frequency grab sampling cannot capture fine dynamics of stream solute concentrations, which results in large uncertainties in load estimates. The recent development of high-frequency sensors has enabled monitoring solute concentrations at sub-hourly time scales. This study aimed to improve nitrate (NO₃) load estimates using high-resolution records (15-min time interval) from optical sensors to capture the typical concentration response to storm events. An empirical model was developed to reconstruct NO₃ concentrations during storm events in a 100-km² agricultural catchment in Germany. Two years (Jan 2002 to Dec 2002 and Oct 2005 to Sep 2006) of high-frequency measurements of NO₃ concentrations, discharge and precipitation were used. An Event Response Reconstruction (ERR) model was developed using NO₃ concentration descriptor variables and predictor variables calculated from discharge and precipitation records. Fourteen events were used for calibration, and 27 events from four periods of continuous records of high-frequency measurement were used for validation. During all selected storm events, NO₃ concentration decreased during flow rise and increased during the recession phase of the hydrograph. Three storm descriptor variables were used to describe these dynamics: relative change in concentration between initial and minimum NO₃ concentrations (rdN), time to maximum change in NO₃ concentration (TdN) and time to 50% recovery of NO₃ concentration (TN _rec ). The ERR consisted of building linear models of discharge and precipitation to predict these three descriptors. The ERR approach greatly improved NO₃ load estimates compared to linear interpolation of grab sampling data (error decreased from 10 to 1%) or flow-weighted estimation of load (error is 7%). This study demonstrated that ERR based on a few months of high-resolution data enables accurate load estimates from low-frequency NO₃ data.