Factors Affecting Nitrate Concentrations in Stream Base Flow

Wherry, Susan A.; Tesoriero, Anthony J.; Terziotti, Silvia

doi:10.1021/acs.est.0c02495

Cited by 41 publications

(22 citation statements)

References 62 publications

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“…During spring precipitation events, water infiltrates rapidly through relatively bare soils, encountering accumulated nitrogen stocks in the shallow subsurface from previous years or early season fertilizer application, and is routed off the landscape through tile drains (Van Meter et al, 2020;Royer et al, 2006). High-flow periods can also reduce the ability of biological processes to alter NO − 3 concentrations (Rodríguez-Blanco et al, 2015;Royer et al, 2006;Wollheim et al, 2018). This seasonality of the NO − 3 concentration has been previously observed in the Raccoon River watershed (Schilling and Zhang, 2004) and other agricultural catchments in the Midwest (Dupas et al, 2017;Van Meter et al, 2020;Pellerin et al, 2014).…”

Section: During Stormflow Periods No −mentioning

confidence: 63%

Hydrologic regimes drive nitrate export behavior in human-impacted watersheds

Gorski

Zimmer

2021

Hydrol. Earth Syst. Sci.

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Abstract. Agricultural watersheds are significant contributors to downstream nutrient excess issues. The timing and magnitude of nutrient mobilization in these watersheds are driven by a combination of anthropogenic, hydrologic, and biogeochemical factors that operate across a range of spatial and temporal scales. However, how, when, and where these complex factors drive nutrient mobilization has previously been difficult to capture with low-frequency or spatially limited data sets. To address this knowledge gap, we analyzed daily nitrate concentration (c) and discharge (Q) data for a 4-year period (2016–2019) from five nested, agricultural watersheds in the midwestern United States that contribute nutrient loads to the Gulf of Mexico. These records allow us to investigate nutrient mobilization patterns at a temporal and spatial resolution not previously possible. The watersheds span two distinct landforms shaped by differences in glacial history, resulting in natural soil properties that necessitated different drainage infrastructure across the study area. To investigate nutrient export patterns under different hydrologic conditions, we partitioned the hydrograph into stormflow and baseflow periods and examined those periods separately through the analysis of their concentration–discharge (c–Q) relationships on annual, seasonal, and event timescales. Stormflow showed consistent chemostatic patterns across all seasons, while baseflow showed seasonally dynamic c–Q patterns. Baseflow exhibited chemodynamic conditions in the summer and fall and more chemostatic conditions in the winter and spring, suggesting that water source contributions during baseflow were nonstationary. Baseflow chemodynamic behavior was driven by low-flow, low-NO3- conditions during which in-stream and near-stream biological processing likely moderated in-stream NO3- concentrations. Additionally, inputs from deeper groundwater with longer residence times and lower-NO3- concentration likely contributed to low-NO3- conditions in stream, particularly in the larger watersheds. Stormflow c–Q behavior was consistent across watersheds, but baseflow c–Q behavior was linked to the intensity of agriculture and the density of built drainage infrastructure, with more drainage infrastructure associated with higher loads and more chemostatic export patterns across the watersheds. This suggests that the way humans replumb the subsurface in response to geologic conditions has implications for hydrologic connectivity, homogenization of source areas, and, subsequently, nutrient export during both baseflow and stormflow. Our analysis also showed that anomalous flow periods greatly influenced overall c–Q patterns, suggesting that the analysis of high-resolution records at multiple scales is critical when interpreting seasonal or annual patterns.

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Section: During Stormflow Periods No −mentioning

confidence: 63%

Hydrologic regimes drive nitrate export behavior in human-impacted watersheds

Gorski

Zimmer

2021

Hydrol. Earth Syst. Sci.

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“…Simultaneously, the difficulty of data acquisition must be considered [ 39 , 51 ]. For the simplicity and feasibility of the model, the input indicators must be at a sufficiently small scale to make estimations [ 52 ]. For the convenience of data acquisition, we only selected five water-quality parameters that can be measured easily in situ .…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based estimation of riverine nutrient concentrations and associated uncertainties caused by sampling frequencies

Chen

Zhang

Lin³

et al. 2022

PLoS ONE

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Accurate and sufficient water quality data is essential for watershed management and sustainability. Machine learning models have shown great potentials for estimating water quality with the development of online sensors. However, accurate estimation is challenging because of uncertainties related to models used and data input. In this study, random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) models are developed with three sampling frequency datasets (i.e., 4-hourly, daily, and weekly) and five conventional indicators (i.e., water temperature (WT), hydrogen ion concentration (pH), electrical conductivity (EC), dissolved oxygen (DO), and turbidity (TUR)) as surrogates to individually estimate riverine total phosphorus (TP), total nitrogen (TN), and ammonia nitrogen (NH4+-N) in a small-scale coastal watershed. The results show that the RF model outperforms the SVM and BPNN machine learning models in terms of estimative performance, which explains much of the variation in TP (79 ± 1.3%), TN (84 ± 0.9%), and NH4+-N (75 ± 1.3%), when using the 4-hourly sampling frequency dataset. The higher sampling frequency would help the RF obtain a significantly better performance for the three nutrient estimation measures (4-hourly > daily > weekly) for R2 and NSE values. WT, EC, and TUR were the three key input indicators for nutrient estimations in RF. Our study highlights the importance of high-frequency data as input to machine learning model development. The RF model is shown to be viable for riverine nutrient estimation in small-scale watersheds of important local water security.

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“…It is interesting to note that while temporal patterns in chloride concentrations in POBR were strong and signi cant, there were no signi cant trends in loads. Trends in concentrations are likely more indicative of internal watershed processes as most of our samples were taken under base ow conditions (Table 1) and therefore are less affected by hydrologic conditions than load estimates (Wherry et al 2021).…”

Section: Chloridementioning

confidence: 99%

“…Interestingly, there was no correlation between deposition and nitrate loads in either stream. As for chloride, analysis of base ow nitrate concentrations may be more useful for assessing the effects of environmental change on internal watershed processes than analysis of loads, which are dominated by hydrologic factors (Wherry et al 2021).…”

Section: Nitratementioning

confidence: 99%

Long-term trends in nitrate and chloride in streams in an exurban watershed

et al. 2023

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The lines between natural areas and human habitats have blurred as urbanization continues, creating a need for the study of ecosystems at all levels of development. This need is particularly acute for exurban environments, which have low population density but are rapidly changing and have a dynamic mix of natural and human-dominated features. We examined long-term (1998 -2018) trends in nitrate and chloride concentrations and uxes in forested and exurban streams in Baltimore County, Md USA. Concentrations and uxes of nitrate and chloride were an order of magnitude higher in the exurban stream than the forested stream and were increasing even though snowfall and road salt use did not increase over the study period. In the forested stream, concentrations of chloride increased from 1998 to 2008, but decreased from 2008 to 2018 due to unquanti ed factors. Concentrations of nitrate decreased in the forested stream, likely due to decreases in atmospheric deposition. These decreases in atmospheric deposition, and efforts to reduce fertilizer use by county governments, do not appear to have affected nitrate concentrations in the exurban watershed. Any efforts to reduce the concentrations and uxes of chloride and nitrate in exurban streams will likely bene t substantially from further understanding of the mechanisms underlying the temporal patterns. SynopsisLong-term monitoring of chloride and nitrate in exurban streams suggests that more aggressive efforts are needed to reduce these solutes.

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Factors Affecting Nitrate Concentrations in Stream Base Flow

Cited by 41 publications

References 62 publications

Hydrologic regimes drive nitrate export behavior in human-impacted watersheds

Hydrologic regimes drive nitrate export behavior in human-impacted watersheds

Machine learning-based estimation of riverine nutrient concentrations and associated uncertainties caused by sampling frequencies

Long-term trends in nitrate and chloride in streams in an exurban watershed

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