Monitoring the riverine pulse: Applying high‐frequency nitrate data to advance integrative understanding of biogeochemical and hydrological processes

Burns, Douglas A.; Pellerin, Brian A.; Miller, Matthew P.; Capel, Paul D.; Tesoriero, Anthony J.; Duncan, J. M.

doi:10.1002/wat2.1348

Cited by 87 publications

(86 citation statements)

References 173 publications

(482 reference statements)

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“…The fine temporal scale observations of in-stream concentrations provided by high-temporal nutrient loggers have transformed our understanding of in-stream processes, especially for nitrogen (Burns et al, 2019). The high frequency sampling provided by data loggers has shown that diel patterns in N signals can be out of phase with the timing of metabolic signals indicated by dissolved oxygen diel cycles (Hensley & Cohen, 2016;Nimick, Gammons, & Parker, 2011).…”

Section: High-temporal Sensors and The Impact On Understanding Nutrmentioning

confidence: 99%

Section: High-temporal Sensors and The Impact On Understanding Nutrmentioning

confidence: 99%

“…Nutrient sensors have the potential to unravel some of the complexities of signals in urban streams and other disturbed systems (Burns et al, 2019;Pellerin et al, 2016). High temporal data have allowed for detailed storm analysis of nutrient dynamics, with concentration-discharge relationships being used to evaluate sources of nutrients to streams (Burns et al, 2019). Bowes et al (2015) used distinct patterns in hysteresis plots of nutrients and flow to characterize different sources of nutrients including remobilized bed sediments, WWTPs, and diffuse inputs.…”

Section: High-temporal Sensors and The Impact On Understanding Nutrmentioning

confidence: 99%

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Downstream evolution of wastewater treatment plant nutrient signals using high‐temporal monitoring

Ledford

Toran

2019

Hydrological Processes

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Wastewater treatment plants are major point-sources of nutrients to streams globally, but the impact on receiving streams is not always clear. Previous research has shown mixed responses in receiving streams, with some showing no net retention through instream processing for large distances below plants and some showing high rates of processing and retention. This study focuses on Sandy Run, a small, suburban stream in Montgomery County, PA, that receives effluent from two plants, where effluent makes up an estimated 50% of outlet discharge at baseflow. Two sites were monitored in late summer baseflow using high-temporal loggers to evaluate nitrate and phosphate retention with distance below the plants. Effluent quantity was monitored immediately below the effluent outfalls using specific conductivity as a conservative signal of solute fluctuations throughout the day. A site 1 km downstream showed diel nitrate changes, but despite moderate gross primary productivity and ecosystem respiration rates, there was little net retention of nutrients and the diel nitrate signal can be attributed to advection and dispersion of variable upstream effluent. A site 5.4 km below the plant showed a diel nitrate signal as well, but baseflow daily hysteresis plots of nitrate and specific conductivity showed the effluent and nitrate peaks did not coincide. Instead, the effluent input signal was seen overnight, but there was in-stream removal and release processes during the day. Over the distance to this site, the stream was metabolizing some of the high nutrient loads, although gross primary productivity and ecosystem respiration rates were lower. It is important to understand subdaily changes in nutrient processing to fully quantify the impacts of effluent on small streams at different scales. Furthermore, looking at the diel signal without considering conservative transport would overestimate in-stream processing. K E Y W O R D Sdaily hysteresis, ecosystem respiration (ER), gross primary productivity (GPP), high-temporal sensor, in-stream metabolism, nitrogen cycling, urban stream, wastewater treatment plant

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Section: High-temporal Sensors and The Impact On Understanding Nutrmentioning

confidence: 99%

Section: High-temporal Sensors and The Impact On Understanding Nutrmentioning

confidence: 99%

Section: High-temporal Sensors and The Impact On Understanding Nutrmentioning

confidence: 99%

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Downstream evolution of wastewater treatment plant nutrient signals using high‐temporal monitoring

Ledford

Toran

2019

Hydrological Processes

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“…For example, while a lumped hydrologic and nitrogen model that provides continuous estimate of pathway loading is robust (Hartmann et al, 2016; Husic, Fox, Adams, Ford, et al, 2019; Sullivan et al, 2019), there are less model‐intensive alternatives such as loadograph separation (Fenton et al, 2017; Mellander et al, 2012; Miller et al, 2017). Loadograph separation only requires nitrate concentration and discharge, which are commonly becoming ubiquitous, particularly with the widespread use of aquatic nitrate sensors that provide continuous estimates of nitrate concentration (Burns et al, 2019). Further, with regard to nitrate sourcing, stable isotopes are commonly employed (Lutz et al, 2019; H. Zhang, Kang, et al, 2020; Wang et al, 2020; Z. Zhang, Chen, Cheng, Li, et al, 2020; Z. Zhang, Chen, Cheng, & Soulsby, 2020) but sources have also been unmixed with elemental concentration data alone (Katz et al, 2011; Yue et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

Optimal Transport for Assessing Nitrate Source‐Pathway Connectivity

Husic

Fox

Mahoney

et al. 2020

Water Resources Research

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Excessive nitrate threatens a wide range of water resources, aquatic habitats, and sensitive infrastructure. Despite this problem, tracing a nutrient from its eventual fate back to its origin remains an elusive challenge due to heterogeneity in how nutrient sources and hydrologic pathways are connected. Typically, this problem is underdetermined (i.e., too many unknowns, not enough equations) and cannot be solved with existing methodologies. The theory of optimal transport allows for the solution of underdetermined systems, and here we construct a novel formulation for its use in water quality modeling. Our objective was to develop an optimal transport modeling framework-coupled to Bayesian source unmixing, loadograph pathway separation, and geospatial connectivity analysis-to apportion nitrate loading from three sources (soil, fertilizer, and manure) across three pathways (quick, intermediate, and slow), resulting in nine possible source-pathway couplings (soil-quick, soil-intermediate, …, manure-slow). We apply this model to a 30 month elemental (NO 3 −) and isotopic (δ 15 N and δ 18 O) nitrate data set from a karst watershed in Kentucky, USA. Modeling results indicate that-of the nine possible source-pathway couplings-nearly 60% of nitrate export is facilitated by just three: fertilizer-quick (16.4%), manure-intermediate (15.4%), and soil-slow (27.2%). Further, we reinforce the need to explicitly consider heterogeneity in source-pathway connectivity as homogeneous assumptions lead to erroneous inferences. The applicability of the model, its input requirements, and transferability to other sites is discussed. Lastly, we simulated two land management scenarios (field buffers and septic repair) and demonstrate how optimal transport can be used to test nutrient reduction strategies.

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“…To summarize, high-resolution sensing of nitrate data has shown utility in quantifying diel, event, and longer-term N loading dynamics (Heffernan and Cohen, 2010;Pellerin et al, 2012Pellerin et al, , 2014Carey et al, 2014;Burns et al, 2016;Rode et al, 2016) and improved assessments of nitrate biological processing, including continuous estimates of biotic processing rates in streams (Heffernan and Cohen, 2010;Rode et al, 2016) and the ability to partition and better understand sources and pathways of nitrate loading at the watershed scale (Koenig et al, 2017;Kraus et al, 2017;Miller et al, 2017;Wollheim et al, 2017). Despite this, few studies have assessed the utility of these data to improve numerical water quality models (Burns et al, 2019;Jiang et al, 2019). Based on the utility of high-resolution nitrate data to quantify in situ biochemical processes and improve the characterization of nutrient loadings at reach and watershed scales (table A2), we investigated the utility of integrating high-resolution data streams with models in order to quantify the nitrate budget of confluence floodplain wetlands.…”

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confidence: 99%

Quantifying Nitrate Dynamics of a Confluence Floodplain Wetland in a Disturbed Appalachian Watershed: High-Resolution Sensing and Modeling

Jensen¹,

Ford²

2019

Transactions of the ASABE

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Abstract. Floodplain wetlands often form at confluences of main river channels and small, low-order tributary streams, yet their impact on nutrient removal and downstream loading is poorly understood. We coupled high-resolution water quality data with deterministic numerical modeling of hydrologic, hydraulic, and biochemical processes impacting nitrate fate and transport in a confluence floodplain wetland along the Ohio River from June 2017 to May 2018. The modeling results were used to quantify loading from a disturbed forested watershed (Fourpole Creek watershed) and the nitrate removal capacity of the wetland. Loading from the Fourpole Creek watershed to the wetland was on the same order of magnitude of other disturbed forested systems on an annual basis (3.6 kg N ha-1 year-1); however, the timing of peak nitrate loading and concentration from the tributary was associated with high flow conditions, contrasting with the timing of loadings typical of disturbed forested landscapes. Our results show that coupling the model with high-resolution data allowed us to estimate removal rates in the wetland, suggesting that 2.6% to 58.5% (optimally 12.7%) of nitrate was removed on an annual basis, despite the wetland comprising only 0.42% of the overall drainage area. We found that increasing inundation of the wetland confluence promoted enhanced residence times of stormflows and was the most important driver of nitrate removal rate and loading, with peak wetland inundation periods representing 26% of the removal load but occurring only 9% of the time. The findings of this study highlight the importance of using high-resolution data-model integration to quantify nutrient dynamics in complex landscapes, identifies the significance of connectivity of watershed nitrate loadings to floodplain wetland soils, and highlights the importance that these features will have in the future, given the enhanced potential for harmful algal blooms in the Ohio River. Keywords: Confluence floodplain wetlands, High-resolution water quality data, Nitrate fate and transport modeling, Nitrate loading, Wetland nitrate removal.

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Monitoring the riverine pulse: Applying high‐frequency nitrate data to advance integrative understanding of biogeochemical and hydrological processes

Abstract: Widespread deployment of sensors that measure river nitrate (NO 3 −

Cited by 87 publications

References 173 publications

Downstream evolution of wastewater treatment plant nutrient signals using high‐temporal monitoring

Downstream evolution of wastewater treatment plant nutrient signals using high‐temporal monitoring

Optimal Transport for Assessing Nitrate Source‐Pathway Connectivity

Quantifying Nitrate Dynamics of a Confluence Floodplain Wetland in a Disturbed Appalachian Watershed: High-Resolution Sensing and Modeling

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