*Threshold Recognition Based on Non-Stationarity of Extreme Rainfall in the Middle… DOI: http://dx.doi.org/10.5772/intechopen.109866*

is necessary to determine the areas with the same changing characteristics for division. In this study, it is considered that the variations of extreme rainfall in different regions will be affected by the differences in regional characteristics. Therefore, the K-mean clustering was used to divide sub-regions with the same rainfall characteristics according to rainfall, terrain, longitude, and latitude, and the SC value was used to evaluate the results of clustering. When the SC reaches the maximum, the result is considered to be optimal. The numbers of clusters from 2 to 6 were calculated, and the corresponding SC values are shown in **Table 1**. When the number of clusters was 5, the SC value was the largest (i.e., 0.54). So, the MLRYRB could be divided into five sub-regions (**Figure 2**). Li et al. used Hierarchical Climate Regionalization to study the regionalization of the MLRYRB [39], and their results also showed that the division of five sub-regions was reasonable. After the clustering was completed, the homogeneity test was carried out on the rainfall sequences of the rain gauge stations in each sub-region. Then, the regional attribution of the boundary rain gauge stations was adjusted to ensure that the rainfall sequences of the meteorological stations in each sub-region could pass the homogeneity test.
