Collapsing gullies involve considerable erosion and extreme landform changes and frequently occur in South China. The amount of rainfall infiltration has a direct influence on the occurrence of collapsing gullies. However, the mechanistic effect of infiltration or rainfall on the development of collapsing gullies still needs to be explored, as it is difficult to spatially describe infiltration amounts of event rainfall using existing models (e.g., SWAT and WEPP). A model based on cellular automata (CA) was developed for the spatial simulation of infiltration amounts after event rainfall by analyzing water allocation within and between the cells of the model. The spatial distributions of infiltration amounts after event rainfalls with differing intensities and durations were then predicted and spatially compared with the distribution of collapsing gullies. The results demonstrate that the model performs with good accuracy, as indicated by the Nash coefficient of 0.63. The variations among different rainfall infiltration amounts demonstrate that both rainfall intensity and duration promote spatial variability in infiltration amounts and reduce regional infiltration efficiency. The spatial comparison between rainfall infiltration amounts and collapsing gullies shows that rainfall infiltration promotes the occurrence of collapsing gullies, but steep topographical conditions are also required to collapse the hillside soil. The comparison also reveals that a lower-intensity rainfall event contributes more to the development of collapsing gullies than a higher-intensity rainfall event, mainly due to the soil absorbing more rainfall under lower intensity, increasing in soil weight to induce collapse. This CA-based model for spatially simulating infiltration amounts provides an improved and effective method for understanding the spatial and temporal variations in rainfall infiltration and to better capture the trends in the occurrence of collapsing gullies from hydrological effects. The model can also be customized for convenient and practical application in other watersheds.

崩塌的沟壑涉及大量的侵蚀和极端的地貌变化，在华南地区经常发生。降雨入渗量直接影响崩塌的沟壑的发生。但是，仍需探索入渗或降雨对塌陷的沟壑发展的机械作用，因为很难使用现有模型（例如，SWAT和WEPP）在空间上描述事件降雨的入渗量。通过分析模型单元内和单元之间的水分配，开发了基于元胞自动机（CA）的模型，用于事件降雨后的入渗量空间模拟。然后，预测了不同强度和持续时间的事件降雨后入渗量的空间分布，并与塌陷的沟壑的分布进行了空间比较。结果表明，该模型具有良好的精度，如Nash系数0.63所示。不同降雨入渗量之间的差异表明降雨强度和持续时间都促进了入渗量的空间变化并降低了区域入渗效率。降雨入渗量与塌陷沟壑之间的空间比较表明，降雨入渗促进塌陷沟壑的发生，但还需要陡峭的地形条件来塌陷山坡土壤。比较还表明，强度较低的降雨事件比强度较高的降雨事件对塌陷沟壑的发展贡献更大，这主要是由于土壤在强度较低的情况下吸收了更多的降雨，增加了土壤的重量导致塌陷。这种基于CA的用于空间模拟入渗量的模型提供了一种改进的有效方法，可用于了解降雨入渗的时空变化并更好地从水文影响中捕获崩塌的沟壑的发生趋势。该模型还可以定制，以便在其他流域中方便实用。这种基于CA的用于空间模拟入渗量的模型提供了一种改进的有效方法，可用于了解降雨入渗的时空变化并更好地从水文影响中捕获崩塌的沟壑的发生趋势。该模型还可以定制，以便在其他流域中方便实用。这种基于CA的用于空间模拟入渗量的模型提供了一种改进的有效方法，可用于了解降雨入渗的时空变化并更好地从水文影响中捕获崩塌的沟壑的发生趋势。该模型还可以定制，以便在其他流域中方便实用。