3.3.1. Temporal Variation Characteristics of Extreme Precipitation under Different Scenarios
As shown in the figures (
Figure 7 and
Figure 8), the extreme precipitation indexes of each sub-regions in the Lancang-Mekong Basin show an upward trend in the future. In region I, except for the RX5day under the SSP1-2.6 scenario and the R10, R20, RX5day, SDII under the SSP5-8.5 scenario, the upward trend is not significant, the change rates of other indexes are significantly increased under each scenario. In the SSP1-2.6 and SSP2-4.5 scenarios of region II, the change rates of most indexes significantly increased, while in the SSP3-7.0 and SSP5-8.5 scenarios, the upward trend of all indexes is not significant. In region III, under the SSP1-2.6 and SSP5-8.5 scenarios, except for R10 under the SSP1-2.6 scenario and PRCPTOT, R10 under the SSP5-8.5 scenario, other indexes show a significant upward trend. However, under the SSP2-4.5 and SSP3-7.0 scenarios, only the R95p and SDII showed a significant upward trend under the SSP3-7.0 scenario, and no significant increase is observed in other indexes. In the SSP5-8.5 scenario of reigon IV, only the upward trend of SDII is not significant, while other indexes increased significantly. In other scenarios, except for the significant upward trend of R20 under the SSP1-2.6 scenario, the upward trend of other indexes is not significant.
Comparing the annual average of extreme precipitation indexes in the future period with the historical period (
Table 6), it can be concluded that: (1) Most indices showed an increasing trend in the future period; only R10 under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios in region II and the SSP3-7.0 scenario in the region III have a small decrease. (2) From the perspective of each region, in the future, the growth rates of R10 and R20 in region I will be relatively large, increasing by more than 16% and 20%, respectively, compared to the historical period. However, the growth rates of R20 and RX5day in regions II, III, and IV will be relatively large, with the growth rates of the two indexes concentrated between 4–11% and 18–31%, respectively. (3) The magnitude of index changes varies among different regions under four scenarios, with most indexes in regions I, III, and IV showing the largest increase under the SSP5-8.5 scenario and most indexes in region II showing the largest increase under the SSP1-2.6 scenario. The indexes of region I show the smallest increase under the SSP2-4.5 scenario, while the indexes of regions II, III, and IV show a relatively flat increase under the SSP2-4.5 and SSP3-7.0 scenarios. Overall, in any scenario in the future, the Lancang-Mekong Basin will experience stronger extreme precipitation events. Therefore, each region should choose to develop under the scenario of relatively small changes in extreme precipitation index within its own situation to reduce the risk of extreme weather disasters.
3.3.2. Spatial Variation Characteristics of Extreme Precipitation under Different Scenarios
Figure 9,
Figure 10,
Figure 11 and
Figure 12 show the spatial distribution of extreme precipitation indexes change rates in the Lancang-Mekong Basin under different scenarios in the future period. In the region I, the indexes change rates show obvious zonal differentiation. Under the scenarios SSP1-2.6 and SSP3-7.0, from north to south, the change rates of each index show a trend of decreasing–increasing–decreasing, and the maximum value is concentrated in the south-central region, where all indexes show an upward trend. Under the scenario of SSP2-4.5, the change rates of each index show an increasing–decreasing–increasing–decreasing trend from north to south, and the maximum change rates are distributed in the south-central region. Except that the change rates of R10 and SDII tend to be 0 in some parts of the northern and central regions, the change rates of other indexes show an increasing trend in the whole region. In the SSP5-8.5 scenario, R95p and SDII show a decreasing trend from north to south, and the rate of change of R95p in the southern region slightly increases, and the maximum values of the two indexes are in the northern region, while the change regularity of other indexes is the same as that in the SSP1-2.6 scenario.
In region II, under the scenario of SSP1-2.6, the indexes change rates gradually increase from north to south. Except for R10, the maximum of the index change rate is in the south-central region, the maximum of the other indexes’ change rates are concentrated in the southern region. Under the scenario of SSP2-4.5, all the indexes change rates increase first and then decrease from north to south, and the maximum change rates are concentrated in the south-central region. Under SSP3-7.0, the change rate of PRCPTOT increases gradually from north to south, and the change rate of RX5day decreases first and then increases from north to south. In most areas, the change rate of RX5day is concentrated in the range of 0~10 mm/10a, and only in a small part of the central area, the change rate decreases slightly. The change rates of other indexes show a trend of increase–decrease–increase from north to south, and the maximum change rates of all indexes are concentrated in the southern region. Under the scenario of SSP5-8.5, the change trend of each index change rate is mostly the same as that of SSP1-2.6, and the maximum value of each index change rate is concentrated in the southern region.
In region III, the maximum value of the indexes change rates under the four scenarios is concentrated in the central region. The distribution of the maximum value of the indexes change rates under different scenarios is slightly different. In the SSP1-2.6 scenario, except for a small part of the southern region, the indexes’ change rates in other regions show an increasing trend, and the maximum value is distributed in the central eastern region. Under the SSP2-4.5 scenario, the maximum distribution of indexes’ change rates is the same as SSP1-2.6, with most indexes showing a decrease in change rate in the western region. Under the SSP3-7.0 and SSP5-8.5 scenarios, the indexes’ change rates show an increasing trend throughout the entire region. The maximum value of the indexes’ change rates under the SSP3-7.0 scenario is distributed on the east and west sides of the central region, while the maximum value of the indexes’ change rates under the SSP5-8.5 scenario is distributed in the central region. In all four scenarios, the rates of change of the indexes gradually decreases from the maximum to both ends.
In region IV, under the SSP1-2.6 scenario, the change rate of each index gradually increases from northwest to southeast, and the maximum value is distributed in the central eastern region. Under the SSP2-4.5 scenario, the change rate of the RX5day shows an increasing trend in most regions, with the increase concentrated in 0~10 mm/10a, only slightly decreasing in the central eastern region. The maximum change rate of RX5day is distributed in the northern and southern regions, while the distribution regularities of other indexes is generally the same as under the SSP1-2.6 scenario. In the SSP3-7.0 and SSP5-8.5 scenarios, except for R10 and R20, the maximum value of the other indexes change rates are distributed in the central eastern region, and the change rates in the northern eastern region also increase significantly. The change rates gradually decrease from the central eastern region to the east and west sides, while the maximum value of the R10 and R20 change rate is distributed in the southern region, and their change rates first decrease and then increase from north to south.
Overall, in the future, the central southern area of region I, the central southern area of region II, the central area of region III, and the southeastern area of region IV in the Lancang-Mekong Basin will face stronger extreme precipitation events. Especially in areas I and IV, the increase in extreme precipitation events will lead to more serious consequences. The southern area of region I is the narrowest part of the entire watershed, and extreme precipitation events are more likely to cause disasters such as flash floods and mudslides, causing serious damage to the local ecosystem. The southeast of region IV is a large area of farmland, and a large increase in its index change rate can easily cause crop root hypoxia and death, leading to huge economic losses. Compared with the historical period, except for region III, the maximum distribution of indexes change rates of each region will move southward in the future. In addition, the maximum value of indexes changes rates in the future and the minimum value of indexes change rates in the historical period in regions I, II, and III are all distributed in the same region. However, regions with relatively low index change rates during historical periods often face problems such as insufficient flood control facilities, incomplete warning systems, and weak flood control awareness among residents, which can easily lead to larger disasters when facing sudden extreme precipitation events. Therefore, it is necessary for countries in the Lancang-Mekong Basin to strengthen their prediction and protection against disasters such as heavy rainfall and floods in the future.
3.3.3. Kernel Density Estimation of Extreme Precipitation
Figure 13,
Figure 14,
Figure 15 and
Figure 16 show the Kernel Density Estimation of extreme precipitation indexes in each region of the Lancang-Mekong Basin. In region I (
Figure 13), the six indexes’ kernel density curves all move to the right, indicating that the mean extreme precipitation indexes in the region will increase from 2021 to 2050; the amount of precipitation and the number of precipitation days will increase in the future. In the kernel density of extreme precipitation indexes of region II (
Figure 14), the position of the kernel density curves of most indexes is not much different from that of the historical period, but the tail end of the kernel density curves mostly extend to the right, which indicates that the mean value of extreme precipitation indexes in the future period are not much different from that of the historical period. However, the increasing probability of medium and high values of each index indicates that the possibility of extreme precipitation events in this region will increase in the future. In the kernel density of extreme precipitation indexes of region III (
Figure 15), the kernel density curves of all indexes in the future period move to the right compared with the historical period, it indicates that the mean extreme precipitation indexes in this region will increase in the future period. The tail end of the kernel density curves of R20, R95p, RX5day, and SDII all extend to the right under the four scenarios, indicating that the probability of high values in each index increases. Therefore, the possibility of extreme precipitation events in this region in the future also increase. In the kernel density of extreme precipitation indexes of region IV (
Figure 16), the kernel density curves also move to the right, and the amount of precipitation and the number of precipitation days in the future will increase. Under the scenarios of SSP1-2.6, SSP3-7.0, and SSP5-8.5, the tail end of most of the kernel density curves extend to the right. This indicates that the probability of extreme precipitation events will increase in the future. Under the scenarios SSP2-4.5, the tail of kernel density curves of R95p, RX5day, and SDII extend to the right, while the PRCPTOT, R10, and R20 does not. It indicates that under the SSP2-4.5 scenario, the possibility of maximum rainfall in this region is increasing, while the possibility of maximum rainfall days is not increasing. Therefore, in the future, rainfall in this region will be more concentrated, and extreme precipitation events will be more likely to occur.