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.

尽管有多种方法可用于不确定性量化，但它们在水质 (WQ) 建模实践中的使用受到限制。在本文中，提出了一种决策支持工具 (DST)，该工具使用流作为预测变量从稀疏数据生成连续时间序列的 WQ 负载。DST 通过使用相关向量机分析残差来估计不确定性。为了强调不确定性量化的重要性，讨论了 DST 中启用的两个应用程序。DST 计算 (i) WQ 风险分析的四个度量的概率分布 - 可靠性、弹性、脆弱性、和流域健康 - 与单一确定性值和 (ii) WQ 成分中的浓度/负载减少，以满足总最大日负载 (TMDL) 目标以及相关的故障风险。考虑到不确定性，确定性分析可能会误导 WQ 风险和已建立的 TMDL 所达到的合规水平。