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

Abstract

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 (NO) 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 NO concentrations during storm events in a 100-km agricultural catchment in Germany. Two years (Jan 2002 to Dec 2002 and Oct 2005 to Sep 2006) of high-frequency measurements of NO concentrations, discharge and precipitation were used. An Event Response Reconstruction (ERR) model was developed using NO 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, NO 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 NO concentrations (rdN), time to maximum change in NO concentration (TdN) and time to 50% recovery of NO concentration (TN ). The ERR consisted of building linear models of discharge and precipitation to predict these three descriptors. The ERR approach greatly improved NO 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 NO data.

Improved Representation of Flow and Water Quality in a North-Eastern German Lowland Catchment by Combining Low-Frequency Monitored Data with Hydrological Modelling

“…The difference can be a rough estimate of NO 3 -N transformations and plant uptake rates. The results of this study demonstrate, in line with similar studies [63,64], that using the explanatory strength of quantitative hydrological data can significantly improve load estimates.…”

Droughts can reduce the nitrogen retention capacity of catchments

“…Daily long-term temperature and precipitation data were provided by the German Meteorological Service (Deutscher Wetterdienst, DWD) and gridded via external drift kriging following Zink et al (2017). N input data (i.e., fertilizer, manure, and plant residues), land use management (i.e., crop rotation), and atmospheric deposition were based on agricultural authority data obtained from X. and Jomaa et al (2018). Accordingly, N input to agricultural fields in mHM-SAS follows a 2-or 3year crop rotation, as is typical in this area and as implemented in X. and Nguyen et al (2022).…”

Variabilidade da resposta hidroquímica a eventos de precipitação utilizando monitoramento de alta frequência em bacias hidrográficas subtropicais, sul do Brasil

“…Bowes et al, 2009;Carey et al, 2014;House and Warwick, 1998;Lawler et al, 2006). These loops depend on chemical and hydrological processes of each catchment (House and Warwick, 1998;Dupas et al, 2015;Fovet et al, 2018;Jomaa et al, 2018;Vongvixay et al, 2018). In these cases, a clockwise hysteresis, have been explained by transport of readily available elements during the rising stage of the hydrograph (Gao et al, 2007;Smith and Dragovich, 2009), whereas delayed response in the hysteresis indicates a distant source leading to anticlockwise hysteresis patterns (López-Tarazón et al, 2009;Mano et al, 2009;Zhao et al, 2017).…”