Evaluating global climate model (GCM) outputs is essential for accurately simulating future hydrological cycles using hydrological models. The GCM multi-model ensemble (MME) precipitation simulations of the Climate Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6, respectively) were spatially and temporally downscaled according to a multi-site statistical downscaling method for the Hanjiang River Basin (HRB), China. Downscaled precipitation accuracy was assessed using data collected from 14 meteorological stations in the HRB. The spatial performances, temporal performances, and seasonal variations of the downscaled CMIP5-MME and CMIP6-MME were evaluated and compared with observed data from 1970–2005. We found that the multi-site downscaling method accurately downscaled the CMIP5-MME and CMIP6-MME precipitation simulations. The downscaled precipitation of CMIP5-MME and CMIP6-MME captured the spatial pattern, temporal pattern, and seasonal variations; however, precipitation was slightly overestimated in the western and central HRB and precipitation was underestimated in the eastern HRB. The precipitation simulation ability of the downscaled CMIP6-MME relative to the downscaled CMIP5-MME improved because of reduced biases. The downscaled CMIP6-MME better simulated precipitation for most stations compared to the downscaled CMIP5-MME in all seasons except for summer. Both the downscaled CMIP5-MME and CMIP6-MME exhibit poor performance in simulating rainy days in the HRB.

In several cities, permeable brick pavement (PBP) plays a key role in stormwater management. Although various hydrological models can be used to analyze the mitigation efficiency of PBP on rainfall runoff, the majority do not consider the effect of multi-layered pavement on infiltration in urban areas. Therefore, we developed a coupled model to evaluate the potential effect of PBP in reducing stormwater runoff at a watershed scale. Specifically, we compared the hydrological responses (outflow and overflow) of three different PBP scenarios. The potential effects of PBP on peak flow (PF), total volume (TV), and overflow volume (OV) were investigated for 20 design rainstorms with different return periods and durations. Our results indicate that an increase in PBP ratio reduces both PF (4.2–13.5%) and TV (4.2–10.5%) at the outfall as well as the OV (15.4–30.6%) across networks. The mitigation effect of PBP on OV is linearly correlated to storm return period and duration, but the effects on PF and TV are inversely correlated to storm duration. These results provide insight on the effects of infiltration-based infrastructure on urban flooding.

The frequency of urban storms has increased, influenced by the climate changing and urbanization, and the process of urban rainfall runoff has also changed, leading to severe urban waterlogging problems. Against this background, the risk of urban waterlogging was analyzed and assessed accurately, using an urban stormwater model as necessary. Most studies have used urban hydrological models to assess flood risk; however, due to limited flow pipeline data, the calibration and the validation of the models are difficult. This study applied the MIKE URBAN model to build a drainage system model in the Beijing Future Science City of China, where the discharge of pipelines was absent. Three methods, of empirical calibration, formula validation, and validation based on field investigation, were used to calibrate and validate the parameters of the model. After the empirical calibration, the relative error range between the simulated value and the measured value was verified by the formula as within 25%. The simulated runoff depth was consistent with a field survey verified by the method of validation based on field investigation, showing the model has good applicability in the study area. Then, the rainfall scenarios of different return periods were designed and simulated. Simulation results showed that, for the 10-year return period, there are overflow pipe sections in northern and southern regions, and the number of overflow pipe sections in the northern region is more than that in the southern region. For the 20-year return period and 50-year return period, the number of overflow pipe sections and nodes in the northern region increased, while for the 100-year return period, the number of overflow nodes both increased. With the increase in the rainfall return period, the pipe network load increased, the points and sections prone to accumulation and waterlogging increased, and the regional waterlogging risk increased. The southern region is prone to waterlogging because the pipeline network density is higher than that in the northern region and the terrain is low-lying. This study provides a reference for the establishment of rainwater drainage models in regions with similar database limitations and provides a technical reference for the calibration and validation of stormwater models that lack rainfall runoff data.

Green infrastructure (GI) has been implemented globally to mitigate the negative effects of urbanization. GI also regulates the urban runoff process and reduces non-point source pollution by intercepting initial runoff pollution and stormwater storage. In this paper, the impacts on GI were quantified and analyzed, considering eight designed storms with a 24 h duration and eight others with a 2 h duration with the combination of two characteristics (return period and peak time). The runoff process and reduction effect of pollutants were simulated for GI combinations (green roofs, vegetative swale, bio-retention units, and permeable pavement) using the Storm Water Management Model, taking the Dongshan campus of Shanxi University as an example case study. The results show that the GI combination can reduce runoff, suspended solids (SS), and chemical oxygen demand (COD). For short- and long-duration rainstorms, the average reduction rates of runoff, SS, and COD were 39.7%, 38.8%, and 39.6%, and 36.5%, 31.7%, and 32%, respectively, indicating its better effectiveness for short-duration storms. The GI’s effect was more sensitive during the short-duration storms owing to the greater absolute value of the 2 h elastic coefficients versus that of the 24 h, and the best reduction effect was observed with a rainfall peak coefficient of 0.1. These results provide a scientific reference for GI planning and implementation under a changing climate in the future.

Urban flooding is increasing due to climate change and the expansion of impervious land surfaces. Green roofs have recently been identified as effective solutions for mitigating urban stormwater. However, discharge routes that involve receiving catchments of stormwater runoff from roofs to mitigate high flows have been limited. Thus, a hydrological model was constructed to investigate the effects of changing discharge routes on stormwater flow. Three hypothetical scenarios were assessed using various combinations of discharge routes and roof types. The reduction effects on outflow and overflow were identified and evaluated across six return periods of designed rainstorms in the Tai Hung Tulip House community in Beijing. The results showed that green roofs, together with the discharge routes connecting to pervious catchments, were effective in reducing peak flow (13.9–17.3%), outflow volume (16.3–27.3%), drainage overflow frequency, and flood duration. Although mitigation can be improved by considering discharge routes, it is limited compared to that achieved by the effects of green roofs. However, integrating green roofs and discharge routes can improve community resilience to rainstorms with longer return periods. These results provide useful information for effective design of future stormwater mitigation and management strategies in small-scale urban areas.

The hydrological cycle is altered by climate change and human activities, amplifying extreme precipitation and heightening the flood risk regionally and globally. It is imperative to explore the future possible alterations in flood risk at the regional scale. Focusing on the Hanjiang river basin (HRB), this study develops a framework for establishing a scientific assessment of spatio-temporal dynamics of future flood risks under multiple future scenarios. In this framework, a GCMs statistical downscaling method based on machine learning is used to project future precipitation, the PLUS model is used to project future land use, the digitwining watershed model (DWM) is used to project future runoff, and the entropy weight method is used to calculate risk. Six extreme precipitation indices are calculated to project the spatio-temporal patterns of future precipitation extremes in the HRB. The results of this study show that the intensity (Rx1day, Rx5day, PRCPTOT, SDII), frequency (R20m), and duration (CWD) of future precipitation extremes will be consistently increasing over the HRB during the 21st century. The high values of extreme precipitation indices in the HRB are primarily located in the southeast and southwest. The future annual average runoff in the upper HRB during the near-term (2023–2042) and mid-term (2043–2062) is projected to decrease in comparison to the baseline period (1995–2014), with the exception of that during the mid-term under the SSP5-8.5 scenario. The high flood risk center in the future will be distributed in the southwestern region of the upper HRB. The proportions of areas with high and medium–high flood risk in the upper HRB will increase significantly. Under the SSP5-8.5 scenario, the area percentage with high flood risk during the future mid-term will reach 24.02%. The findings of this study will facilitate local governments in formulating effective strategic plans for future flood control management.