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

Abstract

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.

Parameter Optimization of SWMM Model Using Integrated Morris and GLUE Methods

“…Table 1 summarizes the main parameters of the model under study. It is a hydrological hydrodynamics-based urban storm water model primarily used to simulate both water quantity and quality during a single rainfall event or continuous rainfall process in cities [29][30][31][32]. The model parameters of SWMM can be divided into two categories: nonempirical parameters and empirical parameters.…”

“…2.2.1. SWMM Model The USEPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) is an urban hydrological model developed in 1971 by the United States Environmental Protection Agency to handle the expanding urban drainage problem.It is a hydrological hydrodynamics-based urban storm water model primarily used to simulate both water quantity and quality during a single rainfall event or continuous rainfall process in cities[29][30][31][32]. The model parameters of SWMM can be divided into two categories: nonempirical parameters and empirical parameters.…”