Changing the water permeability ratio of urban underlying surface helps alleviate urban flood. This paper designs the swale identification experiment to modify the flood-submerging simulation experiment based on the SCS-CN model and proves that the results generated by the modified experiment better reflect the realities. The modified flood-submerging simulation experiment is then applied to downtown Wuhan to obtain the quantitative data. The data are used to quantify the catchment capacities of the lots. Based on the rainfall collection capacities, the maximum surface rainfall runoff volume that would not cause flood is arrived at using the rainfall runoff formula. The maximum runoff volume represents the rainwater storage capacities of the lot based on the proportion of the green space that is identified within the study area. The results suggest that this rainwater storage capacity evaluation model works efficiently to identify the urban areas with flood risks and provides the rainwater runoff thresholds for different areas. Adjustments in the spatial patterns and proportions of the green space help ensure that the rainwater runoff volume is below the thresholds, thus contributing to the prevention and control of the urban flood risks.

In this paper, green-channel waveforms obtained with Mapper5000 which is a new airborne LiDAR bathymetry (ALB) system are analyzed. A waveform comparison method is proposed to extract the characteristics of the waveforms. By deducing the theoretical distribution of the surface return intensity, the causes of the errors of the surface signal detection are determined and classified into two types. Then the main influence factors of the errors are statistically discussed. The results show that the detection errors from the extremely shallow water depend on the depth, while the errors caused by the weak surface return are mainly determined by scan angle, sensor altitude and clarity of the water.