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Open AccessArticle

Quantifying the Role of Large Floods in Riverine Nutrient Loadings Using Linear Regression and Analysis of Covariance

1
Department of Agricultural and Biological Engineering, University of Illinois, 1304 W. Pennsylvania Avenue, Urbana, IL 61801, USA
2
Department of Research and Development, Swedish Meteorological and Hydrological Institute, Norrköping SE-601 76, Sweden
3
Illinois State Water Survey, Prairie Research Institute, University of Illinois, 2204 Griffith Dr., Champaign, IL 61820-7463, USA
4
Department of Agricultural and Biological Engineering, University of Illinois, 1304 W. Pennsylvania Avenue, Urbana, IL 61801, USA
5
Department of Civil and Environmental Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul 03722, Korea
6
Department of Civil and Environmental Engineering, Konkuk University, 120 Neungdong-Ro, Gwanjin-Gu, Seoul 05029, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2018, 10(8), 2876; https://doi.org/10.3390/su10082876
Received: 19 July 2018 / Revised: 3 August 2018 / Accepted: 5 August 2018 / Published: 13 August 2018
(This article belongs to the Special Issue Conflict Analysis and Sustainable Management of Water Resources)
This study analyzes the role of large river flow events in annual loads, for three constituents and for up to 32 years of daily data at multiple watersheds with different land-uses. Prior studies were mainly based on simple descriptive statistics, such as the percentage of nutrient loadings transported during several of the largest river flows, while this study uses log-regression and analysis of covariance (ANCOVA) to describe and quantify the relationships between large flow events and nutrient loadings. Regression relationships were developed to predict total annual loads based on loads exported by the largest events in a year for nitrate plus nitrite nitrogen (NO3-N + NO2-N, indicated as total oxidized nitrogen; TON), total phosphorus (TP), and suspended solids (SS) for eight watersheds in the Lake Erie and Ohio River basins. The median prediction errors for annual TON, TP, and SS loads from the top five load events for spatially aggregated watersheds were 13.2%, 18.6%, and 13.4%, respectively, which improve further on refining the spatial scales. ANCOVA suggests that the relationships between annual loads and large load events are regionally consistent. The findings outline the dominant role of large hydroclimatic events, and can help to improve the design of pollutant monitoring and agricultural conservation programs. View Full-Text
Keywords: ANCOVA; annual load; Lake Erie; Ohio River; log-regression; suspended solids; total oxidized nitrogen; total phosphorus ANCOVA; annual load; Lake Erie; Ohio River; log-regression; suspended solids; total oxidized nitrogen; total phosphorus
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MDPI and ACS Style

Verma, S.; Bartosova, A.; Markus, M.; Cooke, R.; Um, M.-J.; Park, D. Quantifying the Role of Large Floods in Riverine Nutrient Loadings Using Linear Regression and Analysis of Covariance. Sustainability 2018, 10, 2876.

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