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Insights from an Evaluation of Nitrate Load Estimation Methods in the Midwestern United States

Author

Listed:
  • Daeryong Park

    (Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Korea)

  • Myoung-Jin Um

    (Department of Civil Engineering, Kyonggi University, Suwon 16227, Korea)

  • Momcilo Markus

    (Prairie Research Institute, University of Illinois, Champaign, IL 61820, USA)

  • Kichul Jung

    (Division for Integrated Water Management, Korea Environment Institute, Sejong 30147, Korea)

  • Laura Keefer

    (Prairie Research Institute, University of Illinois, Champaign, IL 61820, USA)

  • Siddhartha Verma

    (Department of Agricultural and Biological Engineering, University of Illinois, Urbana, IL 61801, USA)

Abstract

This study investigated the accuracy and suitability of several methods commonly used to estimate riverine nitrate loads at eight watersheds located southwest of Lake Erie in the Midwestern United States. This study applied various regression methods, including a regression estimator with five, six, and seven parameters, an estimator enhanced by composite, triangular, and rectangular error corrections with residual and proportional adjustment methods, the weighted regressions on time, discharge, and season (WRTDS) method, and a simple linear interpolation (SLI) method. Daily discharge and nitrate concentration data were collected by the National Center for Water Quality Research. The methods were compared with subsampling frequencies of 6, 12, and 24 times per year for daily concentrations, daily loads, and annual loads. The results indicate that combinations of the seven-parameter regression method with composite residual and rectangular residual adjustments provided the best estimates under most of the watershed and sampling frequency conditions. On average, WRTDS was more accurate than the regression models alone, but less accurate than those models enhanced by residual adjustments, except for the most urbanized watershed, Cuyahoga. SLI was the most accurate in the Vermilion and Maumee watersheds. The results also provide some information about the effects of rating curve shape and slope, land use, and record length on model performance.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7508-:d:589012
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    References listed on IDEAS

    as
    1. Rafael Rodríguez & Marcos Pastorini & Lorena Etcheverry & Christian Chreties & Mónica Fossati & Alberto Castro & Angela Gorgoglione, 2021. "Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach," Sustainability, MDPI, vol. 13(11), pages 1-17, June.
    2. Jaehak Jeong & Narayanan Kannan & Jeff Arnold & Roger Glick & Leila Gosselink & Raghavan Srinivasan, 2010. "Development and Integration of Sub-hourly Rainfall–Runoff Modeling Capability Within a Watershed Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(15), pages 4505-4527, December.
    3. Lavaire, Tito & Gentry, Lowell E. & David, Mark B. & Cooke, Richard A., 2017. "Fate of water and nitrate using drainage water management on tile systems in east-central Illinois," Agricultural Water Management, Elsevier, vol. 191(C), pages 218-228.
    4. 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.
    5. 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.
    6. Kichul Jung & Deg-Hyo Bae & Myoung-Jin Um & Siyeon Kim & Seol Jeon & Daeryong Park, 2020. "Evaluation of Nitrate Load Estimations Using Neural Networks and Canonical Correlation Analysis with K-Fold Cross-Validation," Sustainability, MDPI, vol. 12(1), pages 1-17, January.
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