Uncertainty quantification in reconstruction of sparse water quality time series: Implications for watershed health and risk-based TMDL assessment. (September 2020) - Mallya, Ganeshchandra; Gupta, Abhinav; Hantush, Mohamed M.; Govindaraju, Rao S.
Abstract: Despite the plethora of methods available for uncertainty quantification, their use has been limited in the practice of water quality (WQ) modeling. In this paper, a decision support tool (DST) that yields a continuous time series of WQ loads from sparse data using streamflows as predictor variables is presented. The DST estimates uncertainty by analyzing residual errors using a relevance vector machine. To highlight the importance of uncertainty quantification, two applications enabled within the DST are discussed. The DST computes (i) probability distributions of four measures of WQ risk analysis- reliability, resilience, vulnerability, and watershed health- as opposed to single deterministic values and (ii) concentration/load reduction required in a WQ constituent to meet total maximum daily load (TMDL) targets along with the associated risk of failure. Accounting for uncertainty reveals that a deterministic analysis may mislead about the WQ risk and the level of compliance attained with established TMDLs. Highlights: A web-based tool for uncertainty quantification in water quality applications. Relevance Vector Machine is used to reconstruct sparse water quality data. Software accomplishes watershed health and total maximum daily load analyses. Role of reconstruction uncertainty in water quality time series is elucidated.