High frequency data provide new insights into evaluating and modeling nitrogen retention in reservoirs. (1st December 2019)

• Record Type: Journal Article
• Authors: Kong, Xiangzhen; Zhan, Qing; Boehrer, Bertram; Rinke, Karsten
• Is Part Of: Water research. Volume 166(2019)
• Publication Date: 2019-12-01
• Subjects: Nitrogen removal -- Reservoir -- High frequency monitoring -- Statistical modeling -- Ecosystem modeling; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2019.115017
• Languages: English
• ISSNs: 0043-1354

Abstract

Abstract: Freshwater ecosystems including lakes and reservoirs are hot spots for retention of excess nitrogen (N) from anthropogenic sources, providing valuable ecological services for downstream and coastal ecosystems. Despite previous investigations, current quantitative understanding on the influential factors and underlying mechanisms of N retention in lentic freshwater systems is insufficient due to data paucity and limitation of modeling techniques. Our ability to reliably predict N retention for these systems therefore remains uncertain. Emerging high frequency monitoring techniques and well-developed ecosystem modeling shed light on this issue. In the present study, we explored the retention of NO3 –N during a five-year period (2013–2017) in both annual and weekly scales in a highly flushed reservoir in Germany. We found that annual-averaged NO3 –N retention efficiency could be up to 17% with an overall retention efficiency of ∼4% in such a system characterized by a water residence time (WRT) of ∼4 days. On the weekly scale, the reservoir displayed negative retention in winter (i.e. a source of NO3 –N) and high positive retention in summer (i.e. a sink for NO3 –N). We further identified the critical role of Chl-a concentration together with the well-recognized effects from WRT in dictating NO3 –N retention efficiency, implying the significance of biological processes including phytoplankton dynamics in driving NO3 –N retention. Furthermore, our modeling approach showed that an established process-based ecosystem model (PCLake) accounted for 58.0% of the variance in NO3 –N retention efficiency, whereas statistical models obtained a lower value (40.5%). This finding exemplified the superior predictive power of process-based models over statistical models whenever ecological processes were at play. Overall, our study highlights the importance of high frequency data in providing new insights into evaluating and modeling N retention in reservoirs. Graphical abstract: Image 1 Highlights: High frequency data are used to quantify N retention in a highly flushed reservoir. Inter-annual N retention efficiency can be variable up to 17%. Intra-annual N retention efficiency exhibits strong seasonal patterns. Seasonal patterns are attributed to water residence time and algal biomass. Process-based model is superior than statistical model in N retention prediction.