In recent decades, low impact development (LID) has become an increasingly important concern as a state-of-the-art stormwater management mode to treat urban flood, preferable to conventional urban drainage systems. However, the effects of the combined use of different LID facilities on urban flooding have not been fully investigated under different rainfall characteristics. In this study, a residential, neighborhood-scale catchment in Shenzhen City, southern China was selected as a case study, where the effects of four LID techniques (bio-retention, bio-swale, rain garden and pervious pavement) with different connection patterns (cascaded, semi-cascaded and paralleled) on runoff reduction efficiency were analyzed by the storm water management model (SWMM), promoted by the U.S. EPA. Three kinds of designed storm events with different return periods, durations and time-to-peak ratios were forced to simulate the flood for holistic assessment of the LID connection patterns. The effects were measured by the runoff coefficient of the whole storm–runoff process and the peak runoff volume. The results obtained indicate that the cascaded connect LID chain can more effectively reduce the runoff than that in the paralleled connect LID chain under different storms. The performances of the LID chains in modeling flood process in SWMM indicate that the runoff coefficient and the peak runoff volume increase with the increase in the rain return periods and the decrease in rain duration. Additionally, the move backward of the peak rain intensity to the end of the storm event slightly affects the peak runoff volume obviously while gives slight influence on the total runoff volume. This study provides an insight into the performance of LID chain designs under different rainfall characteristics, which is essential for effective urban flood management.

This study assesses spatial and temporal variation in environmental variables in relation to phytoplankton community size and composition in a typically eutrophic river reservoir (Hai River, northern China). The aim is to identify environmental parameters governing spatial and temporal differences in phytoplankton density and composition. Physicochemical parameters, including nutrient concentrations, were determined in monthly surface water samples from 2015. The average concentration of key eutrophication indexes (i.e., total phosphorous (TP: 0.24 ± 0.11 mg·L⁻¹), total nitrogen (TN: 2.96 ± 1.60 mg·L⁻¹), and Chlorophyll a (Chl a: 38.5 ± 11.5 mg·m⁻³)) substantially exceeded threshold values for eutrophic streams. Moreover, the eutrophication increased significantly downstream along the river reservoir as a consequence of an increasing fraction of agricultural and industrial land-use in the watershed. 103 phytoplankton species were identified, of which Chlorophyta was the dominated phylum (47 species), followed by Bacillariophyta (23 species) and Cyanophyta (18 species). No spatial difference in species distribution (ANOVA, p > 0.05) were found, while the temporal differences in species composition exhibited significant heterogeneity (ANOVA, p < 0.001). Phytoplankton abundance was highest in early summer (June and July), with maximum values increasing from 1.78 × 10⁸ and 2.80 × 10⁸ cells·L⁻¹ in upstream and middle reaches, respectively, to 4.18 × 10⁸ cells·L⁻¹ furthest downstream. Cyanophyta, also known as Cyanobacteria and commonly referred to as blue-green algal, are known to constitute algae bloom in eutrophic systems. Common species are Microcystis marginata, Microcystis flos-aquae, and Oscillatoria sp. This was the dominant phyla during summer months, especially in the middle and lower reaches of the stream reservoir where it accounted for 88.9% of the phytoplankton community. Shannon weaver index (H’) and Pielous’s evenness index (J’) were extremely low (1.91–2.43 for H’ and 0.39–0.45 for J’) in samples collected from the lower part of the stream during the period of algal bloom, indicating an imbalance in the phytoplankton communities. Canonical correspondence analysis (CCA) indicated that water temperature (WT) and possible pH, along with nitrate (NO₃-N) and nitrite (NO₂-N), were the most important explanatory parameters in regard to phytoplankton composition. This research provides an understanding of the role of physicochemical water quality parameters in governing algal blooms and phytoplankton composition in river reservoirs.