Uncertainty-based parameter estimation for urban pollutant buildup and washoff simulation using a multiple pattern inverse modeling approach

Reliable model parameter estimation for stormwater runoff water quality modeling has always been a challenge, especially in ungauged areas. Insufficient local observations can increase parameter uncertainty, leading to modeling outputs of limited credibility. This paper proposes an event-based multiple pattern inverse modeling approach to estimate regional-scale parameters characterizing the buildup and washoff processes in urban watersheds. The approach explicitly accounts for system uncertainties by simultaneously using a wide range of storm events. A genetic algorithm was employed to solve the inverse models. The solution populations that minimize the error between the observed and predicted values were then filtered into distinct parameter groups using a K-means clustering algorithm. Each identified parameter group may be used to represent the buildup and washoff processes.