Improving probabilistic monthly water quantity and quality predictions using a simplified residual-based modeling approach

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.

Automated summary

This study generated and evaluated probabilistic hydrologic and water quality predictions at 18 locations in the U.S., and found the best predictive uncertainties using a residual-based modeling approach. The ensemble average of hydrologic and water quality simulations better represented the predictive uncertainty compared to a single realization of simulations, especially for large watersheds. The study recommends various methods to improve the robustness and uncertainty of hydrologic and water quality predictions.