Uncertainty quantification between simulated and observed water quality simulations needs to be improved. This study generated and evaluated probabilistic hydrologic and water quality predictions in 18 locations across the U.S. using residual-based modeling. A Box-Cox transformation scheme group provided the best predictive uncertainties for all case studies. The tradeoffs in the performance metrics for a single variable predictive uncertainty in a single study watershed were more obvious than those for all hydrologic or water quality cases. Compared to a single realization of simulations, the ensemble average of hydrologic and water quality simulations better represented the predictive uncertainty, especially for large watersheds. This study recommends various opportunities via residual error scheme selection, data monitoring improvement, and hydrologic model enhancement to robust hydrologic and water quality predictive uncertainties. The results could improve the quantification of the predictive uncertainty of hydrologic and water quality simulations and guide probabilistic prediction enhancement.

模拟和观察到的水质模拟之间的不确定性量化需要改进。本研究使用基于残差的模型生成并评估了美国 18 个地点的概率水文和水质预测。Box-Cox 变换方案组为所有案例研究提供了最佳的预测不确定性。与所有水文或水质案例相比，单个研究流域中单个变量预测不确定性的性能指标权衡更为明显。与模拟的单一实现相比，水文和水质模拟的整体平均值更好地代表了预测的不确定性，特别是对于大流域。本研究通过残差方案选择、数据监控改进、和水文模型增强，以增强强大的水文和水质预测不确定性。结果可以提高水文和水质模拟的预测不确定性的量化，并指导概率预测的增强。