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Deterministic and Stochastic Principles to Convert Discrete Water Quality Data into Continuous Time Series

Author

Listed:
  • Danieli Mara Ferreira

    (Federal University of Parana)

  • Marcelo Coelho

    (Federal University of Parana)

  • Cristovao Vicente Scapulatempo Fernandes

    (Federal University of Parana)

  • Eloy Kaviski

    (Federal University of Parana)

  • Daniel Henrique Marco Detzel

    (Federal University of Parana)

Abstract

Limited water quality data is often responsible for incorrect model descriptions and misleading interpretations in terms of water resources planning and management scenarios. This study compares two hybrid strategies to convert discrete concentration data into continuous daily values for one year in distinct river sections. Model A is based on an autoregressive process, accounting for serial correlation, water quality historical characteristics (mean and standard deviation), and random variability. The second approach (model B) is a regression model based on the relationship between flow and concentrations, and an error term. The generated time series, here referred to as synthetic series, are propagated in time and space by a deterministic model (SihQual) that solves the Saint-Venant and advection-dispersion-reaction equations. The results reveal that both approaches are appropriate to reproduce the variability of biochemical oxygen demand and organic nitrogen concentrations, leading to the conclusion that the combination of deterministic/empirical and stochastic components are compatible. A second outcome arises from comparing the results for distinct time scales, supporting the need for further assessment of statistical characteristics of water quality data - which relies on monitoring strategies development. Nonetheless, the proposed methods are suitable to estimate multiple scenarios of interest for water resources planning and management. Graphical Abstract

Suggested Citation

  • Danieli Mara Ferreira & Marcelo Coelho & Cristovao Vicente Scapulatempo Fernandes & Eloy Kaviski & Daniel Henrique Marco Detzel, 2021. "Deterministic and Stochastic Principles to Convert Discrete Water Quality Data into Continuous Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3633-3647, September.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:11:d:10.1007_s11269-021-02908-1
    DOI: 10.1007/s11269-021-02908-1
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    References listed on IDEAS

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    1. Ankita P. Dadhich & Rohit Goyal & Pran N. Dadhich, 2021. "Assessment and Prediction of Groundwater using Geospatial and ANN Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2879-2893, July.
    2. Jiping Yao & Guoqiang Wang & Weina Xue & Zhipeng Yao & Baolin Xue, 2019. "Assessing the Adaptability of Water Resources System in Shandong Province, China, Using a Novel Comprehensive Co-evolution Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 657-675, January.
    3. Hamed Taherdoost, 2018. "A review of technology acceptance and adoption models and theories," Post-Print hal-03741843, HAL.
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