Exploring the Performance of Ensemble Smoothers to Calibrate Urban Drainage Models.

Urban drainage models (UDMs) are often used to manage urban flooding. However, these models generally involve many parameters to represent the underlying complex hydrodynamic processes. This results in significant challenges to achieving effective and robust model calibration especially with frequently limited observations, leading to unreliable model predictions. This paper makes the first attempt at UDM calibration using the Bayesian‐based Ensemble Smoother (ES) method. Three ES variants are considered, that is, the primary ES, the versions with multiple data assimilation (ES‐MDA) and iterative local update (ES‐ILU). Two synthetic cases and one real‐world application with up to 5,236 calibration parameters are tested. Results obtained show that: (a) both ES‐MDA and ES‐ILU can produce effective model calibration with ES‐ILU outperforming ES‐MDA in terms of both accuracy and uncertainty while ES exhibits limited performance; (b) for the real‐world case, both the ES‐MDA and ES‐ILU methods provide better calibration results than the best‐known solution manually obtained, (c) a minimum number of observations are required to enable an overall accurate model calibration (e.g., four and ten more monitoring sites are needed in the two synthetic cases); and (d) the model calibrated using an intense rainfall event is generally robust to make reliable predictions across different rainfall events while the model calibrated using less intense rainfall event does not perform well for more intense rainfall events. It was also found that ubiquitous parameter equifinality significantly hinders unique parameter identification even when overall accurate state estimates are obtained. This should be clearly understood in practical applications. Plain Language Summary: Urban floods have been a serious disaster worldwide. Urban drainage models (UDMs) have been widely used to facilitate flooding prevention and mitigation. However, a challenge associated with the UDMs is that a large number of parameters need to be specified and calibrated. While some optimization‐based or manual calibration methods are available, their efficiency or accuracy are often unsatisfactory. This can lead to unreliable predictions of the rainfall‐runoff process. To this end, this paper proposes the ensemble smoother (ES) methods to calibrate the UDMs. Benefits of these ES methods include the great efficiency, high accuracy and the identification of the uncertainty when calibrating model parameters. These conclusions are based on results of two synthetic cases and one real‐world UDM. Key Points: Ensemble smoothers are promisingly effective and robust methods to calibrate urban drainage modelsUbiquitous parameter equifinality hinders unique parameter identification, which is a concern for practical applicationsEnsemble smoothers are validated on a real‐world case, providing insights into how improved model performance can be achieved [ABSTRACT FROM AUTHOR]