Nitrate sinks and sources as controls of spatio-temporal water quality dynamics in an agricultural headwater catchment

Abstract

Abstract. Several controls are known to affect water quality of stream networks during flow recession periods, such as solute leaching processes, surface water-groundwater interactions as well as biogeochemical in-stream turnover processes. Throughout the stream network, combinations of specific water and solute export rates and local in-stream conditions overlay the biogeochemical signals from upstream sections. Therefore, upstream sections can be considered functional units which could be distinguished and ordered regarding their relative contribution to nutrient dynamics at the catchment outlet. Based on snapshot sampling of flow and nitrate concentrations along the stream in an agricultural headwater during the summer flow recession period, we determined spatial and temporal patterns of water quality for the whole stream. A data-driven, in-stream-mixingand-removal model was developed and applied for analysing the spatio-temporal in-stream retention processes and their effect on the spatio-temporal fluxes of nitrate from subcatchments. Thereby, we have been able to distinguish quantitatively between nitrate sinks, sources per stream reaches, and subcatchments, and thus we could disentangle the overlay of nitrate sink and source signals. For nitrate sources, we determined their permanent and temporal impact on stream water quality and for nitrate sinks, we found increasing nitrate removal efficiencies from upstream to downstream. Our results highlight the importance of distinct nitrate source locations within the watershed for in-stream concentrations and in-stream removal processes, respectively. Thus, our findings contribute to the development of a more dynamic perception of water quality in streams and rivers concerning ecological and sustainable water resource management.

Patterns in the linkage of water quantity and quality during low‐flows

“…The temporal resolution of the water quality data presents a source of uncertainty and limits the interpretation of cause and effect to some degree. For individual small catchments with different land use in Baden-Württemberg, Lange and Haensler (2012; forest) and Schuetz, Gascuel-Odoux, Durand, and Weiler (2016;agriculture) found that, on a monthly scale, nitrate concentrations can change quickly during low-flow periods. Such changes were not captured by our dataset due to low temporal resolution.…”

A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

“…In terms of applications, the Hudson River basin regional scale model may be used to continuously forecast the overall variability of the water resources Pryet et al, 2015;Saraiva Okello et al, 2015), predicting fate and transport of water quality such as nitrate (Schoonover and Lockaby, 2006;Wang et al, 2012;Bastola and Misra, 2015;Schuetz et al, 2016) and climate change scenarios (Ducharne et al, 2007(Ducharne et al, , 2010Graham et al, 2007;Quintana Seguí et al, 2010;Habets et al, 2013). Moreover, socio-economic analysis may be used to weigh how such improved forecasts can prevent loss of life and minimize the damage to property, with the aid of effective communication and social media.…”

“…HEC-HMS, developed by the US Army Corps of Engineers, is a conceptual semi-distributed hydrological model that has been used extensively in rainfall-runoff modeling and other related hydrological studies (Anderson et al, 2002;Neary et al, 2004;Knebl et al, 2005;Amengual et al, 2009;Chu and Steinman, 2009;Halwatura and Najim, 2013;Meenu et al, 2013;Seyoum et al, 2013;Zhang et al, 2013;Yang and Yang, 2014). The model uses a number of adjustable empirically derived parameters that describe the overall structure of the basin including parameters for runoff, baseflow and river routing (Feldman, 2000).…”

“…The simulated flow hydrographs were calibrated against hourly river flow observations to obtain optimal performance in terms of both runoff volume and peak flow. The hydrological parameters were modified to produce a best-fit model using a root mean square error (RMSE) objective function within the HEC-HMS model's Nelder-Mead optimization method (Barati, 2011;Seyoum et al, 2013), aiming at maximizing the fit between simulated streamflow and observations at 15 US Geological Survey (USGS) gauging stations (Fig. 2).…”

Estimation of baseflow nitrate loads by a recursive tracing source algorithm in a rainy agricultural watershed

“…Nitrate (NO 3 − ) has high mobility and can be lost from the vadose zone by leaching to groundwater because of its solubility and electronegativity (Chowdary, Rao, & Sarma, 2005). Therefore, NO 3 − is the most common pollutant found in shallow aquifers and can cause a crises in related to the drinking water supply and eutrophication of water bodies in local river systems (Gutiérrez, Biagioni, Alarcón-Herrera, & Rivas-Lucero, 2018;Schuetz, Gascuel-Odoux, Durand, & Weiler, 2016). Due to the high fertilizer application rate in modern intensive agricultural production, groundwater nitrate contamination has become a global problem (Chen, Wu, Hu, & Li, 2010;Liu, Ming, & Ankumah, 2005).…”