**4. Discussion**

**Figure 8** shows the BGSA test results of RX1day in the five sub-regions of the MLRYRB. The thin blue line represents the RX1day sequence, the thick line represents the average RX1day sequence, and the red line represents the split year. Since the BGSA method is based on the non-stationary detection of the mean value, the variation of RX1day will affect the non-stationarity of the extreme rainfall series to a certain extent. It can be considered that the non-stationarity for extreme rainfall series at a given threshold should be similar to the non-stationarity for the maximum rainfall series. After comparing the non-stationarity of the extreme rainfall series screened by different thresholds and RX1day series, the extreme rainfall threshold obtained would have a certain reference. Comparing the non-stationary split point years of extreme rainfall series under different thresholds, the extreme rainfall thresholds in the MLRYRB should be between 40 and 60 mm.

After selecting a more reasonable threshold range, this study compared the nonstationarity and the p-value of the extreme rainfall series under different thresholds. When the extreme rainfall series tends to be relatively stable, the corresponding p-value will also respond higher. Combined with the threshold uncertainty under different return periods, when the threshold was selected below 40 mm, the extreme rainfall series were in a strong non-stationary state, the fitting results were relatively poor, and the calculated design return years could not be distinguished very well. Therefore, below 40 mm was not a suitable threshold of extreme rainfall to screen the rainfall sequence, which is also consistent with the conclusion drawn by the BGSA method. When the rainfall threshold was greater than 60 mm, the p value in Sub-regions II, IV, and V begins to decline. For Sub-regions I and III, although the fitting effect was the best when the threshold was greater than 60 mm, the corresponding return period was not within a reasonable range. Therefore, based on the above findings, the reasonable range of the extreme rainfall threshold of each sub-region in the MLRYRB should be between 40 and 60 mm with the largest p value. Therefore, the extreme rainfall thresholds of the five sub-basins were 45, 60, 50, 45, and 60 mm, respectively. It is found that the

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

**Figure 8.** *BGSA test results of RX1day in the five sub-regions of the MLRYRB.*

extreme rainfall thresholds of the sub-regions close to the coast were higher than those far from the coast, which might be due to that the sub-regions close to the coast are more susceptible to typhoons. Regarding the whole MLRYRB, the thresholds of 40 and 60 mm corresponds to the 95% quantile and 99% quantile of the daily rainfall. So, the range of 40–60 mm is reasonable when screening and calculating extreme rainfall and different thresholds can be selected for the screening of extreme rainfall sequences for different computing requirements. Due to climate change, the definition of regional extreme rainfall is of great significance for precipitation forecasting and flood protection. The method presented in this study can judge the extreme rainfall thresholds of regions under changing environments and obtain the most suitable thresholds. When the threshold of extreme rainfall is low, it often does not work; when the threshold of extreme rainfall is high, rainfall forecasting tends to increase the risk of local flood disasters. Therefore, the extreme rainfall threshold with regional characteristics is also more reasonable. Moreover, this study adopts a generalized method and has no regional limitations, which means that it will be applicable to other regions and future climate model simulations. For different regions and different environmental backgrounds, the choice of extreme rainfall needs to consider the local specific conditions.
