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Volume 9
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10.3390/w9110895
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Article

Effect of Water Quality Sampling Approaches on Nitrate Load Predictions of a Prominent Regression-Based Model

by
Amirreza Sharifi
1,
Haw Yen
2,
Carlington W. Wallace
3,4,*,
Gregory McCarty
4,*,
Wade Crow
4,
Bahram Momen
5,
Megan W. Lang
6,
Ali Sadeghi
4,
Sangchul Lee
5,
Judith Denver
7 and
Martin C. Rabenhorst
5
1
Department of Energy and Environment, Government of the District of Columbia, Washington, DC 20002, USA
2
Blackland Research and Extension Center, Texas A&M University, Temple, TX 76502, USA
3
Department of Geography, The Pennsylvania State University, University Park, PA 16802, USA
4
Hydrology and Remote Sensing Laboratory, USDA-ARS, Beltsville, MD 20705, USA
5
Department of Environmental Science and Technology, University of Maryland, College park, MD 20740, USA
6
Branch of Geospatial Mapping and Technical Support, National Wetland Inventory, Falls Church, VA 22041, USA
7
U.S. Geological Survey, Dover, DE 19901, USA
*
Authors to whom correspondence should be addressed.
Water 2017, 9(11), 895; https://doi.org/10.3390/w9110895
Submission received: 5 October 2017 / Revised: 27 October 2017 / Accepted: 6 November 2017 / Published: 16 November 2017
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Abstract

High frequency in-situ measurements of nitrate can greatly reduce uncertainty in nitrate flux estimates. Water quality databases maintained by various federal and state agencies often consist of pollutant concentration data obtained from periodic grab samples collected from gauged reaches of a stream. Regression models, such as the LOAD ESTimator (LOADEST), are frequently used to model variations in concentrations associated with changes in water discharge to provide integrated solute flux measurements. However, uncertainty in the relationships between nutrient concentration and flow may lead to errors in the corresponding flux estimates. In this study, a high frequency, in-situ measurement of nitrate concentration was implemented to ascertain uncertainty in the concentration/discharge relationship caused by nitrate hysteresis. It was found that observed nitrate hysteresis, as influenced by complex storm/watershed interactions, was not readily predictable. Therefore, it can lead to substantial nitrate flux uncertainty, based on periodic grab sample monitoring approaches. Scientists and engineers should take advantage of the proposed findings in future studies to enhance the quality of the associated decision making processes.
Keywords: hysteresis; in-situ measurements; LOADEST; Nitrogen; water quality sampling hysteresis; in-situ measurements; LOADEST; Nitrogen; water quality sampling

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MDPI and ACS Style

Sharifi, A.; Yen, H.; Wallace, C.W.; McCarty, G.; Crow, W.; Momen, B.; Lang, M.W.; Sadeghi, A.; Lee, S.; Denver, J.; et al. Effect of Water Quality Sampling Approaches on Nitrate Load Predictions of a Prominent Regression-Based Model. Water 2017, 9, 895. https://doi.org/10.3390/w9110895

AMA Style

Sharifi A, Yen H, Wallace CW, McCarty G, Crow W, Momen B, Lang MW, Sadeghi A, Lee S, Denver J, et al. Effect of Water Quality Sampling Approaches on Nitrate Load Predictions of a Prominent Regression-Based Model. Water. 2017; 9(11):895. https://doi.org/10.3390/w9110895

Chicago/Turabian Style

Sharifi, Amirreza, Haw Yen, Carlington W. Wallace, Gregory McCarty, Wade Crow, Bahram Momen, Megan W. Lang, Ali Sadeghi, Sangchul Lee, Judith Denver, and et al. 2017. "Effect of Water Quality Sampling Approaches on Nitrate Load Predictions of a Prominent Regression-Based Model" Water 9, no. 11: 895. https://doi.org/10.3390/w9110895

APA Style

Sharifi, A., Yen, H., Wallace, C. W., McCarty, G., Crow, W., Momen, B., Lang, M. W., Sadeghi, A., Lee, S., Denver, J., & Rabenhorst, M. C. (2017). Effect of Water Quality Sampling Approaches on Nitrate Load Predictions of a Prominent Regression-Based Model. Water, 9(11), 895. https://doi.org/10.3390/w9110895

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