Water Quality in Agricultural Watersheds: Exploring Patterns, Fluxes and Uncertainties of Nitrate Using High-Frequency Data

Authors

• Kaycee N. Reynolds, University of Nebraska-LincolnFollow

Date of this Version

4-24-2015

Document Type

Article

Citation

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

Comments

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

Abstract

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