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Similarity Metrics-Based Uncertainty Analysis of River Water Quality Models

Author

Listed:
  • Shirin Karimi

    (University of Tehran)

  • Bahman Jabbarian Amiri

    (University of Tehran)

  • Arash Malekian

    (University of Tehran)

Abstract

Uncertainty analysis (UA) is essential to reinforce the decisions made by water resource engineers and managers. In this study, the stepwise multiple linear regression procedure assessed the relationship between water quality parameters and physical characteristics of 48 catchments in the southwestern basin of the Caspian Sea, Iran. The results of the modeling showed that the coefficient of determination ranged between 0.47 and 0.68 and indicated a positive relationship between the area (%) of agricultural lands and the sodium adsorption ratio (SAR), potassium (K) and total dissolved solids (TDS). A negative relationship was also found between bicarbonate (HCO3−) and the area (%) of the intermediate-density forest. In contrast to previous studies focusing on analyzing the uncertainty of the model parameters, we addressed the uncertainty of the model variables. The results of the GLUE-based uncertainty analysis (UA) performed on the model’s variables indicated that the measures of the R-factor for all models were between 0.13 and 0.98. The lowest R-factor was obtained for the HCO3− model (0.13) suggesting it performed well when predicting HCO3−. To increase the degree of objectivity in the GLUE-UA method, a set of similarity metrics, including Czekanowski, Motyka, Ruzicka, Cosine, Kumar-Hassebrook, Jaccard and Dice was applied to determine the degree of proximity and or similarity between the probability density functions of the measured and simulated water quality parameters. The measures of the similarity metrics for the HCO3− model were generally close to 1, indicating good performance and low uncertainty, while it showed higher uncertainty (between 0.2487 and 0.897) for the other three models (SAR, K, and TDS).

Suggested Citation

  • Shirin Karimi & Bahman Jabbarian Amiri & Arash Malekian, 2019. "Similarity Metrics-Based Uncertainty Analysis of River Water Quality Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 1927-1945, April.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:6:d:10.1007_s11269-019-02205-y
    DOI: 10.1007/s11269-019-02205-y
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    1. Francesca Palomba & Giorgio Cesari & Remo Pelillo & Andrea Petroselli, 2018. "An Empirical Model for River Ecological Management with Uncertainty Evaluation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 897-912, February.
    2. He, Jianqiang & Jones, James W. & Graham, Wendy D. & Dukes, Michael D., 2010. "Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method," Agricultural Systems, Elsevier, vol. 103(5), pages 256-264, June.
    3. Mansour Talebizadeh & Saeid Morid & Seyyed Ayyoubzadeh & Mehdi Ghasemzadeh, 2010. "Uncertainty Analysis in Sediment Load Modeling Using ANN and SWAT Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(9), pages 1747-1761, July.
    4. Emanuele Borgonovo & William Castaings & Stefano Tarantola, 2011. "Moment Independent Importance Measures: New Results and Analytical Test Cases," Risk Analysis, John Wiley & Sons, vol. 31(3), pages 404-428, March.
    5. Francesca Palomba & Giorgio Cesari & Remo Pelillo & Andrea Petroselli, 2018. "Correction to: An Empirical Model for River Ecological Management with Uncertainty Evaluation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1931-1931, March.
    6. A. Sharafati & H. M. Azamathulla, 2018. "Assessment of Dam Overtopping Reliability using SUFI Based Overtopping Threshold Curve," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2369-2383, May.
    7. Zhengli Zhu & Chunxia Zhao & Yingkun Hou, 2012. "Research on Similarity Measurement for Texture Image Retrieval," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-14, September.
    8. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
    9. Azam Haidary & Bahman Amiri & Jan Adamowski & Nicola Fohrer & Kaneyuki Nakane, 2013. "Assessing the Impacts of Four Land Use Types on the Water Quality of Wetlands in Japan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2217-2229, May.
    10. A. Kay & H. Davies & V. Bell & R. Jones, 2009. "Comparison of uncertainty sources for climate change impacts: flood frequency in England," Climatic Change, Springer, vol. 92(1), pages 41-63, January.
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