Graduate Studies


Date of this Version



Reynolds, Kaycee Novak, "Water Quality in Agricultural Watersheds: Exploring Patterns, Fluxes and Uncertainties of Nitrate Using High-Frequency Data" (2015).


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Natural Resource Sciences, Under the Supervision of Professor Amy J. Burgin. Lincoln, Nebraska: May, 2015

Copyright (c) 2015 Kaycee Novak Reynolds


Agricultural intensification in the Midwestern U.S. and the resultant downstream transport of nitrogen has dramatically altered ecosystems on local to regional scales; polluting source water for drinking while inducing harmful algal blooms and hypoxic “dead zones”. The inherently dynamic nature of climate-landscape interactions in agricultural watersheds makes evaluation of nitrate (NO3-) fluxes from these ecosystems complex. The expansion of a high-frequency water quality monitoring network covering ~40% of Iowa, provides direct observations of in situ NO3- concentrations at a 15-minute resolution. Using these high-frequency NO3- data, two types of uncertainty were explored: sampling uncertainty and environmental uncertainty. Understanding NO3- loading to agricultural streams requires optimization of monitoring strategies. In this study, NO3- records were systematically subsampled allowing quantification of sampling uncertainty in annual mean NO3- concentration (C) and total flux (F) estimates for conventional sampling strategies (e.g. equal time-interval and discharge based storm-sampling). The optimal strategy differed for NO3- C and F. Time-interval sampling was optimal for NO3- C, while stage triggered storm-sampling optimally characterized NO3- F at its two coarsest frequencies (i.e. 1x and 2x/event). High-resolution stream monitoring also allows real-time observation of NO3- response to storm events where it exhibits either a ‘concentrating’ or a ‘diluting' pattern. Investigation of seasonal trends in NO3- C response for more than 400 storms across 17 sites in Iowa revealed the predominance of ‘concentrating’ events in the spring and fall, and ‘diluting’ events in the summer. A simple logistic regression model indicated that initial storm NO3- C, cumulative events (within a site-year), and season were significant predictors of binary event response (i.e. ‘concentrating’ or ‘diluting’). As initial storm NO3- C or cumulative events increased, the probability of a ‘concentrating’ event decreased, suggesting that seasonal variation in event response may result from both supply and transport limitations on NO3- loading. As climate becomes more erratic, high-resolution NO3- monitoring will conceivably offer an improvement in our understanding of coupled hydrological and biogeochemical system interactions.

Advisor: Amy J. Burgin