Assessing the Runoff Reduction Potential of Highway Swales and WinSLAMM as a Predictive Tool

Time-varying characteristics of saturated hydraulic conductivity in grassed swales based on the ensemble Kalman filter algorithm -A case study of two long-running swales in Netherlands

Saturated hydraulic conductivity (Ks) of the filler layer in grassed swales are varying in the changing environment. In most of the hydrological models, Ks is assumed as constant or decrease with a clogging factor. However, the Ks 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 Ks of the whole systems in two monitored grassed swales at Enschede and Utrecht, the Netherlands. The relationship between Ks 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 Ks 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 Ks into hydrological model, 63.8% of the predicted results were optimized among the validation events, and compared with constant Ks. A sensitivity analysis revealed that the effect of each influencing factors on the Ks varies depending on the type of grassed swale. However, these findings require further investigation and data support.

Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds

Machine Learning (ML) algorithms provide an alternative for the prediction of pollutant concentration. We compared eight ML algorithms (Linear Regression (LR), uniform weighting k-Nearest Neighbor (UW-kNN), variable weighting k-Nearest Neighbor (VW-kNN), Support Vector Regression (SVR), Artificial Neural Network (ANN), Regression Tree (RT), Random Forest (RF), and Adaptive Boosting (AdB)) to evaluate the feasibility of ML approaches for estimation of Total Suspended Solids (TSS) using the national stormwater quality database. Six factors were used as features to train the algorithms with TSS concentration as the target parameter: Drainage area, land use, percent of imperviousness, rainfall depth, runoff volume, and antecedent dry days. Comparisons among the ML methods demonstrated a higher degree of variability in model performance, with the coefficient of determination (R2) and Nash–Sutcliffe (NSE) values ranging from 0.15 to 0.77. The Root Mean Square (RMSE) values ranged from 110 mg/L to 220 mg/L. The best fit was obtained using the AdB and RF models, with R2 values of 0.77 and 0.74 in the training step and 0.67 and 0.64 in the prediction step. The NSE values were 0.76 and 0.72 in the training step and 0.67 and 0.62 in the prediction step. The predictions from AdB were sensitive to all six factors. However, the sensitivity level was variable.

Descriptive Analysis of the Performance of a Vegetated Swale through Long-Term Hydrological Monitoring: A Case Study from Coventry, UK

Vegetated swales are a popular sustainable drainage system (SuDS) used in a wide range of environments from urban areas and transport infrastructure, to rural environments, sub-urban and natural catchments. Despite the fact that vegetated swales, also known as grassed swales, have received scientific attention over recent years, especially from a hydrological perspective, there is a need for further research in the field, with long-term monitoring. In addition, vegetated swales introduce further difficulties, such as the biological growth occurring in their surface layer, as well as the biological evolution taking place in them. New developments, such as the implementation of thermal devices within the cross-section of green SuDS for energy saving purposes, require a better understanding of the long-term performance of the surface temperature of swales. This research aims to contribute to a better understanding of these knowledge gaps through a descriptive analysis of a vegetated swale in Ryton, Coventry, UK, under a Cfb Köppen climatic classification and a mixed rural and peri-urban scenario. Precipitation and temperature patterns associated with seasonality effects were identified. Furthermore, a level of biological evolution was described due to the lack of periodical and planned maintenance activities, reporting the presence of both plant species and pollinators. Only one event of flooding was identified during the three hydrological years monitored in this research study, showing a robust performance.