Time-varying characteristics of saturated hydraulic conductivity in grassed swales based on the ensemble Kalman filter algorithm

Abstract

Saturated hydraulic conductivity (K_s) of the filler layer in grassed swales are varying in the changing environment. In most of the hydrological models, K_s is assumed as constant or decrease with a clogging factor. However, the K_s measured on site cannot be the input of the hydrological model directly. Therefore, in this study, an Ensemble Kalman Filter (EnKF) based approach was carried out to estimate the K_s of the whole systems in two monitored grassed swales at Enschede and Utrecht, the Netherlands. The relationship between K_s and possible influencing factors (antecedent dry period, temperature, rainfall, rainfall duration, total rainfall and seasonal factors) were studied and a Multivariate nonlinear function was established to optimize the hydrological model. The results revealed that the EnKF method was satisfying in the K_s estimation, which showed a notable decrease after long-term operation, but revealed a recovery in summer and winter. After the addition of Multivariate nonlinear function of the K_s into hydrological model, 63.8% of the predicted results were optimized among the validation events, and compared with constant K_s. A sensitivity analysis revealed that the effect of each influencing factors on the K_s varies depending on the type of grassed swale. However, these findings require further investigation and data support.