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

Author

Listed:
  • Siddhartha Verma

    (Department of Agricultural and Biological Engineering, University of Illinois, 1304 W. Pennsylvania Avenue, Urbana, IL 61801, USA)

  • Alena Bartosova

    (Department of Research and Development, Swedish Meteorological and Hydrological Institute, Norrköping SE-601 76, Sweden)

  • Momcilo Markus

    (Illinois State Water Survey, Prairie Research Institute, University of Illinois, 2204 Griffith Dr., Champaign, IL 61820-7463, USA
    Department of Agricultural and Biological Engineering, University of Illinois, 1304 W. Pennsylvania Avenue, Urbana, IL 61801, USA)

  • Richard Cooke

    (Department of Agricultural and Biological Engineering, University of Illinois, 1304 W. Pennsylvania Avenue, Urbana, IL 61801, USA)

  • Myoung-Jin Um

    (Department of Civil and Environmental Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul 03722, Korea)

  • Daeryong Park

    (Department of Civil and Environmental Engineering, Konkuk University, 120 Neungdong-Ro, Gwanjin-Gu, Seoul 05029, Korea)

Abstract

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 (NO 3 -N + NO 2 -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.

Suggested Citation

  • Siddhartha Verma & Alena Bartosova & Momcilo Markus & Richard Cooke & Myoung-Jin Um & Daeryong Park, 2018. "Quantifying the Role of Large Floods in Riverine Nutrient Loadings Using Linear Regression and Analysis of Covariance," Sustainability, MDPI, vol. 10(8), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2876-:d:163572
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    Citations

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    Cited by:

    1. Kichul Jung & Myoung-Jin Um & Momcilo Markus & Daeryong Park, 2020. "Comparison of Long Short-Term Memory and Weighted Regressions on Time, Discharge, and Season Models for Nitrate-N Load Estimation," Sustainability, MDPI, vol. 12(15), pages 1-24, July.
    2. Nun Pitalúa-Díaz & Fernando Arellano-Valmaña & Jose A. Ruz-Hernandez & Yasuhiro Matsumoto & Hussain Alazki & Enrique J. Herrera-López & Jesús Fernando Hinojosa-Palafox & A. García-Juárez & Ricardo Art, 2019. "An ANFIS-Based Modeling Comparison Study for Photovoltaic Power at Different Geographical Places in Mexico," Energies, MDPI, vol. 12(14), pages 1-16, July.
    3. Daeryong Park & Myoung-Jin Um & Momcilo Markus & Kichul Jung & Laura Keefer & Siddhartha Verma, 2021. "Insights from an Evaluation of Nitrate Load Estimation Methods in the Midwestern United States," Sustainability, MDPI, vol. 13(13), pages 1-23, July.

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