Mississippi River Nitrate Loads from High Frequency Sensor Measurements and Regression-Based Load Estimation

Abstract

Accurately quantifying nitrate (NO3-) loading from the Mississippi River is important for predicting summer hypoxia in the Gulf of Mexico and targeting nutrient reduction within the basin. Loads have historically been modeled with regression-based techniques, but recent advances with high frequency NO3- sensors allowed us to evaluate model performance relative to measured loads in the lower Mississippi River. Patterns in NO3- concentrations and loads were observed at daily to annual time steps, with considerable variability in concentration-discharge relationships over the two year study. Differences were particularly accentuated during the 2012 drought and 2013 flood, which resulted in anomalously high NO3- concentrations consistent with a large flush of stored NO3- from soil. The comparison between measured loads and modeled loads (LOADEST, Composite Method, WRTDS) showed underestimates of only 3.5% across the entire study period, but much larger differences at shorter time steps. Absolute differences in loads were typically greatest in the spring and early summer critical to Gulf hypoxia formation, with the largest differences (underestimates) for all models during the flood period of 2013. In additional to improving the accuracy and precision of monthly loads, high frequency NO3- measurements offer additional benefits not available with regression-based or other load estimation techniques.

A Lab-On-Chip Phosphate Analyzer for Long-term In Situ Monitoring at Fixed Observatories: Optimization and Performance Evaluation in Estuarine and Oligotrophic Coastal Waters

“…While this approach provides powerful insights into the cycling of phosphate in natural waters, such sampling rates are inadequate to characterize conditions during episodic and transient events, which can have a disproportionate impact on nutrient concentrations and significant ecological implications (Johnson et al, 2010). Without high-frequency data and a greater spatial coverage than can be afforded via discrete sampling, estimates of the nutrient load delivered into water bodies are fraught with uncertainties (Pellerin et al, 2014(Pellerin et al, , 2016, and finescale 3D biogeochemical models cannot be properly validated to inform resource managers (Wild-Allen and Rayner, 2014). An improved understanding of biogeochemical variability is also needed to untangle natural vs. anthropogenic signals in timeseries records.…”

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.…”

Nutrient Dynamics of the Delta: Effects on Primary Producers

“…Collection of nutrient data at frequent intervals in aquatic systems has in almost all cases revealed much higher temporal variability than was evident in less frequent discrete sample collection (Bende-Michl et al 2013;Pellerin et al 2009Pellerin et al , 2011Pellerin et al , 2014Wild-Allen and Rayner 2014). These data also revealed patterns in nutrient dynamics that occur at yearly, seasonal, diurnal, tidal, and individual-event time-scales, which are difficult if not impossible to detect using lowerresolution data (Bende-Michl et al 2013;Bowes et al 2009;Cohen et al 2012Cohen et al , 2013Pellerin et al 2009Pellerin et al , 2011Pellerin et al , 2014Wild-Allen and Rayner 2014).…”

“…Comparison of nutrient fluxes and loads calculated using less-frequent grab sample data to that calculated from high-frequency data has demonstrated that data collection at more frequent intervals improves accuracy, even in large rivers that are assumed to be buffered from short-term nutrient pulses (Carey et al 2014;Cassidy and Jordan 2011;Pellerin et al 2014). Assessments of these types of nutrient data do not yet exist for the Delta.…”