Improving probabilistic monthly water quantity and quality predictions using a simplified residual-based modeling approach. (October 2022) - Guo, Tian; Liu, Yaoze; Shao, Gang; Engel, Bernard A.; Sharma, Ashish; Marshall, Lucy A.; Flanagan, Dennis C.; Cibin, Raj; Wallace, Carlington W.; Zhao, Kaiguang; Ren, Dongyang; Vera Mercado, Johann; et al.
Abstract: 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. Graphical abstract: Image 1 Highlights: Improving probabilistic monthly water quantity and quality predictions by modeling. Monthly water quality predictive uncertainty was evaluated in 18 diverse locations. Ensemble averages of nutrient simulations better represent predictive uncertainty. Transformation schemes with the best predictive performance metrics were identified. The determined best transformation schemes were transferable for various cases.