High frequency data provide new insights into evaluating and modeling nitrogen retention in reservoirs

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.

Spatial and Temporal Variability in Concentration‐Discharge Relationships at the Event Scale

“…This study mainly relies on previously published concentration and discharge data (Kong et al., 2019; Rode, Halbedel, et al., 2016; Werner et al., 2019; Winter et al., 2020). Water quality data and discharge were measured every 15 min.…”

Explaining the Variability in High‐Frequency Nitrate Export Patterns Using Long‐Term Hydrological Event Classification

“…Nitrate concentration data were collected between 2013 and 2017 via TRIOS ProPS-UV sensors at 15 min intervals (Kong et al, 2019;Rode, Angelstein, et al, 2016), which we aggregated to hourly averages. Data from the WB catchment were previously published by Kong et al (2019) and Musolff et al (2021), data from the three Selke catchments (SH, MD, and HD) were previously published by Musolff et al (2021), Rode, Angelstein, et al (2016), Winter et al (2021), and X. Yang, Jomaa, et al (2018).…”

Smart Nutrient Retention Networks: a novel approach for nutrient conservation through water quality management

“…At the waterbody level, nutrients are either retained or flow freely with the water in dissolved or particulate forms such as detrital matter or phytoplankton (Teurlincx et al 2019). Nutrient retention processes include (1) natural internal retention (e.g., long-term storage by sedimentation, burial of biomass, and P bound to mineral particles; Uhlmajnn and Horn 1992, Smolders et al 2006, Finlay et al 2013, Kong et al 2019, (2) natural losses from the waterbody (e.g., denitrification and consumption by migrating waterfowl; Saunders and Kalff 2001, Doughty et al 2016, Kong et al 2019, or (3) harvesting by humans (e.g., in the form of macrophytes, sediment, or fish). Water management can influence nutrient retention pathways directly (e.g., harvesting by humans), and indirectly through ecosystem state management (e.g., increased denitrification by bank reshaping).…”

“…Nutrient retention models simulating individual waterbodies are often process-based (i.e., employing process rates and mechanistic insights to estimate nutrient retention; Van Gerven et al 2009). For example, PCLake(+) (Janse 2005, Janssen et al 2019a, PCDitch (Janse andVan Puijenbroek 1997, Janse 2005), and the GLOBIO-Wetlands model (under development; Janse et al 2019) are process-based models from which nutrient retention processes and balances can be derived (Kong et al 2019). These models include feedback loops between ecological states and nutrient retention and have been used to explore water quality management options (Janssen et al 2019b).…”