Residual-Oriented Optimization of Antecedent Precipitation Index and Its Impact on Flood Prediction Uncertainty

Antecedent moisture conditions are essential in explaining differences in the translation of flood-producing precipitation to floods. This study proposes an empirical residual-oriented antecedent precipitation index (RAPI) to estimate and further link antecedent moisture conditions with flood predictive uncertainty. By developing a fully kernel-based residual error model without functional presumptions, the proposed RAPI is calibrated as the regressor of the deterministic model residual. Furthermore, the MI-LXPM algorithm is applied to search for optimal parameters in mixed-integer constraints. The rationality of the proposed framework is demonstrated by its application to a case study in South-East China. The quality of probabilistic streamflow predictions is then quantified using reliability, precision, and the NSE of the prediction mean. The results show that the RAPI closely connects to the uncertainty of hourly flood prediction as a proxy of antecedent soil moisture. The influence of RAPI is mainly on the precision and unbiasedness of flood prediction. Compared with the deterministic model output, the RAPI provides a better flood prediction of small to median flood events as a regressor. Along with the proposed date-driven residual error model, the framework can be applied to any pre-calibrated hydrological model and help modelers achieve high-quality probability flood prediction.