The understanding of large‐scale rainfall microphysical characteristics plays a significant role in meteorology, hydrology and natural hazards managements. Traditional instruments for estimating raindrop size distribution (DSD), including disdrometers and ground dual‐polarization radars, are available only in limited areas. However, the development of space‐based radars and mesoscale numerical weather prediction models would allow for DSD estimation on a large scale. This study investigated the performance of the weather research and forecasting (WRF) model and the global precipitation measurement mission (GPM) dual‐frequency precipitation radar for DSD retrieval under different conditions. The DSD parameters (D_m and N_w), rain rate (R), rainfall kinetic energy (KE) and radar reflectivity (Z) were estimated in Chilbolton, United Kingdom, by using long‐term disdrometer observations for validation. The rainfall kinetic energy–rain rate (KE–R) and radar reflectivity–rain rate (Z–R) relationships were explored using a disdrometer, the WRF model and GPM. It was found that the DSD parameter distribution trends of the three approaches are similar although the WRF model has larger D_m and smaller N_w values. In terms of the rainfall microphysical relationship, GPM performs better when both Ku‐ and Ka‐band precipitation radars (KuPR and KaPR) observe precipitation simultaneously (R > 0.5 mm h⁻¹), while the WRF model shows high accuracy in light rain (R < 0.5 mm h⁻¹). The fusion of GPM and WRF model is recommended for the improved understanding of rainfall microphysical characteristics in ungauged areas.

Amidst escalating urbanization and increasing extreme climatic events, strengthening flood resilience strategies in global cities has become imperative. This study introduces an innovative spatiotemporal urban flood simulation model that seamlessly integrates diverse refined and multi-spatiotemporal scales, ranging from 7.5 to 60 min and 100-2000 m, respectively. The model comprises multi-scale radar rainfall inversion (MRI), fine-grained coupled flood simulation model (FGCFS), and transformer-CNN flood prediction (TCFP) modules. Employing the Nanjing urban area as a case study, the model's efficacy is subjected to rigorous assessment. The advantages derived from integrated refinement coupling and boundary conditions through FGCFS and TCFP are accentuated. Impressively, the results underscore the robust performance of radar rainfall inversion across most scales, revealing a correlation coefficient surpassing 0.8 and a root-mean-square error of under 5.2 mm. FGCFS achieves optimal simulated water depth changes at 7.5 min × 500 m resolution, with the Nash efficiency coefficient exceeding 0.69 (0.94 at YS observation point and 0.89 at SXM observation point), alongside percentage deviations below 12.89 (3.59 at SXM observation point and 2.42 at XJL observation point). TCFP's learning proficiency is showcased through error convergence to 0.002 m after twenty iterations, particularly suitable for resolutions below 4 m. Notably, both FGCFS and TCFP demonstrate efficient utilization of resources, enabling streamlined simulations across varying data resolutions. Consequently, our study propels a sophisticated framework harmonizing multi-scale data integration, refinement coupling, and dynamic allocation. Our work extends beyond practical solutions, offering a glimpse into the future of flood simulation modeling, and reaffirming its pivotal role within the realm of environmental research and management.

For urban watersheds, the storm sewer network provides indispensable data for flood modeling but often needs to be simplified to balance the conflict between the large amount of data and current computing power. The sensitivity of a flood simulation to the data precision of a storm sewer network needs to be explored to develop reasonable generalization strategies. In this study, the impact of using the stroke scaling method to generalize a storm sewer network on a flood simulation was analyzed in terms of the total inflow of the outfalls and flood results. The results of the three study basins showed that different complexities of a sewer network did not have a significant effect on the outfall’s total inflow for an area with a single drainage system but did for an area with multiple drainage systems. In addition, serious flooding was mainly distributed at the backbone pipes, which can be identified with the simplified sewer network. Several effective generalization strategies were developed for sewer networks that consider the distribution characteristics of the drainage system and application requirements. This study is theoretically important for better understanding the data sensitivity of flood modeling and simulation and practically important for improving the modeling efficiency and the accuracy of urban flood simulation.

Reference Evapotranspiration (ETo) and soil moisture deficit (SMD) are vital for understanding the hydrological processes, particularly in the context of sustainable water use efficiency in the globe. Precise estimation of ETo and SMD are required for developing appropriate forecasting systems, in hydrological modeling and also in precision agriculture. In this study, the surface temperature downscaled from Weather Research and Forecasting (WRF) model is used to estimate ETo using the boundary conditions that are provided by the European Center for Medium Range Weather Forecast (ECMWF). In order to understand the performance, the Hamon’s method is employed to estimate the ETo using the temperature from meteorological station and WRF derived variables. After estimating the ETo, a range of linear and non-linear models is utilized to retrieve SMD. The performance statistics such as RMSE, %Bias, and Nash Sutcliffe Efficiency (NSE) indicates that the exponential model (RMSE = 0.226; %Bias = −0.077; NSE = 0.616) is efficient for SMD estimation by using the Observed ETo in comparison to the other linear and non-linear models (RMSE range = 0.019–0.667; %Bias range = 2.821–6.894; NSE = 0.013–0.419) used in this study. On the other hand, in the scenario where SMD is estimated using WRF downscaled meteorological variables based ETo, the linear model is found promising (RMSE = 0.017; %Bias = 5.280; NSE = 0.448) as compared to the non-linear models (RMSE range = 0.022–0.707; %Bias range = −0.207–−6.088; NSE range = 0.013–0.149). Our findings also suggest that all the models are performing better during the growing season (RMSE range = 0.024–0.025; %Bias range = −4.982–−3.431; r = 0.245–0.281) than the non−growing season (RMSE range = 0.011–0.12; %Bias range = 33.073–32.701; r = 0.161–0.244) for SMD estimation.

Herein, the raindrop size distribution (DSD) and rainfall microphysics characteristics are investigated at multiple stations within the Yangtze River Delta (YRD) under two air pollution conditions. Raindrop data from 14 Thies Clima Laser Precipitation Monitor disdrometers spanning approximately 2 years were employed together with corresponding air pollution data. Rain gauge data and several types of meteorological data were also used. The DSD characteristics were found to vary across the YRD; thus, we compiled DSD‐based demarcations between convective rain and stratiform rain at each site. During summer, the average mass‐weighted mean drop diameter parameter value was higher by a mean 20%, and the average normalized intercept parameter value was lower by a mean 12% in polluted atmosphere conditions for both convective rain and stratiform rain, as well as for different rainfall intensity classes. The differences in atmospheric conditions between clear and polluted air conditions partially account for the uncertainties in the DSD characteristics. Air pollution resulted in uncertainty in the shape parameter‐slope parameter relationship and in the rainfall kinetic energy‐rainfall intensity (KE‐R) relationship. The air pollution index can be integrated into the KE‐R relationship to improve the KE simulation accuracy. In addition, air pollution causes some uncertainty in the radar reflectivity‐rainfall intensity (Z‐R) relationship, while the acceptable deviation of the Z‐R relationship did not significantly impact the accuracy of rainfall simulations. This study improves our understanding of DSD and provides a comprehensive overview of the relationship between air pollution and rainfall microphysics for a typical urban agglomeration area.