Taking the pulse of snowmelt: in situ sensors reveal seasonal, event and diurnal patterns of nitrate and dissolved organic matter variability in an upland forest stream

Abstract

Highly resolved time series data are useful to accurately identify the timing, rate, and magnitude of solute transport in streams during hydrologically dynamic periods such as snowmelt. We used in situ optical sensors for nitrate (NO 3 -) and chromophoric dissolved organic matter fluorescence (FDOM) to measure surface water concentrations at 30 min intervals over the snowmelt period (March 21-May 13, 2009) at a 40.5 hectare forested watershed at Sleepers River, Vermont. We also collected discrete samples for laboratory absorbance and fluorescence as well as d 18 O-NO 3 -isotopes to help interpret the drivers of variable NO 3 -and FDOM concentrations measured in situ. In situ data revealed seasonal, event and diurnal patterns associated with hydrological and biogeochemical processes regulating stream NO 3 -and FDOM concentrations. An observed decrease in NO 3 -concentrations after peak snowmelt runoff and muted response to spring rainfall was consistent with the flushing of a limited supply of NO 3 -(mainly from nitrification) from source areas in surficial soils. Stream FDOM concentrations were coupled with flow throughout the study period, suggesting a strong hydrologic control on DOM concentrations in the stream. However, higher FDOM concentrations per unit streamflow after snowmelt likely reflected a greater hydraulic connectivity of the stream to leachable DOM sources in upland soils. We also observed diurnal NO 3 -variability of 1-2 lmol l -1 after snowpack ablation, presumably due to in-stream uptake prior to leafout. A comparison of NO 3 -and dissolved organic carbon yields (DOC, measured by FDOM proxy) calculated from weekly discrete samples and in situ data sub-sampled daily resulted in small to moderate differences over the entire study period (-4 to 1% for NO 3 -and -3 to -14% for DOC), but resulted in much larger differences for daily yields (-66 to ?27% for NO 3 -and -88 to ?47% for DOC, respectively). Despite challenges inherent in in situ sensor deployments in harsh seasonal conditions, these data provide important insights into processes controlling NO 3 -and FDOM in streams, and will be Electronic supplementary material The online version of this article (

High‐frequency monitoring of catchment nutrient exports reveals highly variable storm event responses and dynamic source zone activation

“…Moreover, antecedent soil moisture, temperature, and groundwater conditions can alter the potential for transformation (e.g., mineralization) and accumulation of nutrients in shallow subsurface flow paths [Agehara and Warncke, 2005]. In particular, extreme flow conditions caused by episodic storm events and seasonal snowmelt have been shown to exert major influences on nutrient export patterns and dynamics [Pellerin et al, 2012;Saraceno et al, 2009;Basu et al, 2010;Khamis et al, 2017]. During these events, changes in surface and subsurface flow paths can modify riparian connectivity to the river catchment and lead to the activation of distant solute and particulate source zones that would not usually contribute to catchment nutrient export under base flow conditions.…”

“…Previous studies characterizing variability in responses of catchment nutrient exports to storm events have focused mainly on single parameters, such as nitrate [Chen et al, 2012;Carey et al, 2014], fluorescent dissolved organic matter [Saraceno et al, 2009], or phosphate [Bowes et al, 2005], although in recent years a growing number have considered varying responses between nutrient types [Drewry et al, 2009;Pellerin et al, 2012;Outram et al, 2014]. Moreover, very few studies have investigated how changes in hydroclimatology, both during and preceding storm events, control river nutrient export and source zone activation under extreme flow conditions, despite a clear requirement for this information for the effective management of catchment water resources both now and under future climate regimes.…”

Quantifying watershed‐scale groundwater loading and in‐stream fate of nitrate using high‐frequency water quality data

“…While field-based studies [Burns, 1998;Peterson et al, 2001;Duff et al, 2008;Mulholland et al, 2008Mulholland et al, , 2009Tank et al, 2008;Hall et al, 2009;Mulholland and Webster, 2010] and modeling approaches [Jaworski et al, 1992;Boynton et al, 1995;Alexander et al, 2000Alexander et al, , 2009Seitzinger et al, 2002;Boyer et al, 2006;Runkel, 2007;Ator and Denver, 2012] have provided much needed information on reach and watershed-scale nitrate dynamics, the limited spatial extent and/or low temporal resolution of discrete data collection continues to be a challenge for quantifying loads and interpreting drivers of change in watersheds. Recent studies have demonstrated that the collection and interpretation of high-frequency nitrate data collected using water quality sensors can be used to better quantify nitrate loads to sensitive stream and coastal environments [Ferrant et al, 2013;Bieroza et al, 2014;Pellerin et al, 2014], and provide insights into temporal nitrate dynamics that would otherwise be difficult to obtain using traditional field-based mass balance, solute injection, and/or isotopic tracer studies [Pellerin et al, 2009[Pellerin et al, , 2012Heffernan and Cohen, 2010;Sandford et al, 2013;Carey et al, 2014;Hensley et al, 2014Hensley et al, , 2015Outram et al, 2014;Crawford et al, 2015]. Coupling these measurements with techniques for quantifying water sources and/or flow paths [Gilbert et al, 2013;Bowes et al, 2015;Duncan et al, 2015] provides further opportunity for understanding and managing the drivers of coastal eutrophication.…”

Modelling landscape controls on dissolved organic carbon sources and fluxes to streams

“…It is well known that such sampling frequencies, which are often dictated by technical or financial constraints, have limitations in terms of missing crucial short-term information and consequent uncertainties in load estimates. However, high resolution data at daily or sub-daily resolution is becoming increasingly feasible, both technically and financially (Pellerin et al 2012;Neal et al 2013). Such data provide a richer resource, which gives more exacting criteria, to challenge biogeochemical models that simulate water quality.…”