Evaluation of the hydrological performance of grassed swales usually needs long-term monitoring data. At present, suitable techniques for simulating the hydrological performance using limited monitoring data are not available. Therefore, current study aims to investigate the relationship between saturated hydraulic conductivity (K_s) fitting results and rainfall characteristics of various events series length. Data from a full-scale grassed swale (Enschede, the Netherlands) were utilized as long-term rainfall event series length (95 rainfall events) on the fitting outcomes. Short-term rainfall event series were extracted from these long-term series and used as input in fitting into a multivariate nonlinear model between K_s and its influencing rainfall indicators (antecedent dry days, temperature, rainfall, rainfall duration, total rainfall, and seasonal factor (spring, summer, autumn, and winter, herein refer as 1, 2, 3, and 4). Comparison of short-term and long-term rainfall event series fitting results allowed to obtain a representative short-term series that leads to similar results with those using long-term series. A cluster analysis was conducted based on the fitting results of the representative rainfall event series with their rainfall event characteristics using average values of influencing rainfall indicators. The seasonal index (average value of seasonal factors) was found to be the most representative short rainfall event series indicator. Furthermore, a Bayesian network was proposed in the current study to predict if a given short-term rainfall event series is representative. It was validated by a data series (58 rainfall events) from another full-scale grassed swale located in Utrecht, the Netherlands. Results revealed that it is quite promising and useful to evaluate the representativeness of short-term rainfall event series used for long-term hydrological performance evaluation of grassed swales. Graphical Abstract: (Figure presented.).

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.

As a result of the fast growth of remote sensing and data assimilation technology, many global land use land cover (LULC) and climate reanalysis data sets have been used to advance our understanding of climate and environmental change. This paper investigates the precipitation variations of the Yangtze Delta Megalopolis by using precipitation reanalysis data under conditions of dynamic urban sprawl. Compared with current precipitation characteristic analyses, which are often based on a limited number of ground rainfall stations, the approach followed in this study comprises a grid-based statistical method using large sets of samples with a uniform distribution and a same representative grid area. This novel approach of dynamic sampling is applied in this study to overcome the temporal and spatial inconsistency of stationary sampling. This approach allows to examine the impact of urbanization on regional precipitation characteristics. The Yangtze Delta Megalopolis (YDM) region, one of the most developed regions in China, was selected as a case study to evaluate the impact of urbanization on subsequent precipitation features. The results reveal that the annual total precipitation (TP) and the maximum daily precipitation (MDP) in both urban and non-urban areas of the YDM region generally have increased during the past 30 years. Hence, the region has become increasingly humid. Extrema of annual MDP and TP show obvious spatial characteristics, in which most maxima are located in the southern part of YDM while minima are more concentrated in the northern part. This newly developed approach has potentials for application in studies where underlying surface features exhibit rapid alterations. The findings of this case study provide relevant information for planning and design of regional water resources management, flood risk management, and planning of the urban drainage system of the YDM region.

Stormwater biofiltration systems (SBS) are a popular technology for mitigating the negative effects of urbanization on the hydrological processes and water quality in urban areas. However, little is known about SBS's long-term performance in actual field conditions. The findings of a review of the scientific literature on the long-term performance of SBS are presented in this paper. The findings show that only a few studies have investigated the performance of SBS and its change over time, and that the results of laboratory and field experiments differed due to the presence of plants, regular maintenance, and some uncertain environmental factors. Based on the existing knowledge gaps in this field, the main challenges observed was the lack of long-term field data series, and the existing mathematical models are not able to accurately forecast the long-term performance of SBS. This could be owing to the difficulties in monitoring activities, the high costs involved and the unpredictability around the operational timeframe. Future study should concentrate on the implementation of simulation and modeling-based research in pilot and full-scale SBS, and the inclusion of new performance indicators should be considered as a priority.

As a result of high-density urbanization and climate change, both the frequency and intensity of extreme urban rainfall are increasing. Drainage systems are not designed to cope with this increase, and as a result, floods are becoming more common in cities, particularly in the rapidly growing cities of China. To better cope with more frequent and severe urban flooding and to improve the water quality of stormwater runoff, the Chinese government launched the national Sponge City Construction (SCC) program in 2014. The current SCC design standards and guidelines are based on static values (e.g., return periods, rainfall intensities, and volume capture ratio (VCR)). They do not fully acknowledge the large differences in climate conditions across the country and assume that the hydraulic conditions will not change over time. This stationary approach stems from the traditional engineering approach designed for grey infrastructure (following a “one size fits all” approach). The purpose of this study was to develop a methodology to assess the VCR baseline (before construction in the pre-development stage) and changes in VCR (difference between the VCR of the pre-and post-development stage). The VCR of the post-development stage is one of the required indicators of the Assessment Standard for Sponge Cities Effects to evaluate SCC projects. In this study, the VCR was derived from remote-sensing-based land use land cover (LULC) change analysis, applying an unsupervised classification algorithm on different Landsat images from 1985 to 2015. A visualization method (based upon Sankey chart, which depicts the flows and their proportions of components) and a novel and practical partitioning method for built-up regions were developed to visualize and quantify the states and change flows of LULC. On the basis of these findings, we proposed a new indicator, referred to as VCRa − L, in order to assess the changes in urban hydrology after SCC construction. This study employed the city of Nanjing as a case study and analyzed detailed information on how LULC changes over time of built-up areas. The surface area of the urban and built-up areas of Nanjing quadrupled from 11% in 1985 to 44% in 2015. In the same period, neither the entire city nor its subregions reached the VCR target of 80%. The proposed new methodology aims to support national, regional, and city governments to identify and prioritize where to invest and implement SCC measures more effectively in cities across China.