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• W4254334518 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." @default.
• W4254334518 created "2022-05-12" @default.
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• W4254334518 date "2021-07-20" @default.
• W4254334518 modified "2023-09-25" @default.
• W4254334518 title "Deterministic and Stochastic Principles to Convert Discrete Water Quality Data into Continuous Time Series" @default.
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• W4254334518 doi "https://doi.org/10.1007/s11269-021-02908-1" @default.
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