Effect of Water Quality Sampling Approaches on Nitrate Load Predictions of a Prominent Regression-Based Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Historic Discharge and Water Quality Sampling Data
2.3. Real-Time Nitrate Data Processing
2.4. USGS LOAD ESTimator
2.5. Prediction Intervals for Regression Relationships
3. Results
3.1. Analysis of Nitrate Grab Samples
3.2. Analysis of Real-Time Nitrate Data
3.3. LOADEST Regression Analysis
3.4. Validation of Regression Analysis
4. Discussion
4.1. Effect of Sampling Time on Storm Load Prediction
4.2. Effect of Hysteresis on Nitrate Load Estimation
4.3. Potential Impact on Correlated Applications
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Calibration Dataset | ||||||
---|---|---|---|---|---|---|
All | 455 | 6.1043 | 0.8492 | −0.0459 | 0.95 | 0.0766 |
Base + Rising | 391 | 5.8298 | 0.9125 | −0.0295 | 0.95 | 0.0669 |
Base + Falling | 354 | 6.0002 | 0.8357 | −0.0524 | 0.95 | 0.0684 |
Baseflow | 291 | 5.0926 | 0.9765 | −0.0006 | 0.94 | 0.0568 |
Rising | 100 | 7.1893 | 0.8060 | −0.0240 ₦ | 0.82 | 0.0929 |
Falling | 63 | 7.0563 | 0.7218 | −0.0313 ₦ | 0.85 | 0.0880 |
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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
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 StyleSharifi, 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