*3.4. Sensitivity Analysis between the Flash Floods and Economy Development*

By combining the data of the flash floods and GDP and using Formula (6), we calculated the value of sensitivity (*S*) of the flash floods to the economic development of each city in Yunnan Province, as shown in Figure 7. From an overall perspective, fewer regions experienced medium and high sensitivity during the study period, and the quantity of regions without sensitivity first decreased and then remained stable. During 1995–2000, all regions were non-sensitive. In comparison, the quantity of low-sensitivity regions during 2000–2005 and 2005–2010 increased to six (Nujiang, Baoshan, Lijiang, Yuxi, Honghe, and Wenshan) and nine (Zhaotong, Qujing, Kunming, Honghe, Xishuangbanna, Puer, Lincang, Baoshan, and Diqing), respectively. Simultaneously, Kunming was the highest sensitivity region (*S* = 21.86) and low-sensitivity (*S* = 0.98) during these two periods. In the 2010–2015 time period, the flash floods in half of the regions were not sensitive to GDP in Yunnan Province, whereas Diqing was the mid-sensitivity region (*S* = 10.67). *Sustainability* **2019**, *11*, x FOR PEER REVIEW 12 of 18 six (Nujiang, Baoshan, Lijiang, Yuxi, Honghe, and Wenshan) and nine (Zhaotong, Qujing, Kunming, Honghe, Xishuangbanna, Puer, Lincang, Baoshan, and Diqing), respectively. Simultaneously, Kunming was the highest sensitivity region (*S* = 21.86) and low-sensitivity (*S* = 0.98) during these two periods. In the 2010–2015 time period, the flash floods in half of the regions were not sensitive to GDP in Yunnan Province, whereas Diqing was the mid-sensitivity region (*S* = 10.67).

**Figure 7.** The spatial−temporal variation of sensitivity from 1995 to 2015: (**a**) during 1995–2000, (**b**) during 2000–2005, (**c**) during 2005–2010, and (**d**) during 2010–2015. **Figure 7.** The spatial–temporal variation of sensitivity from 1995 to 2015: (**a**) during 1995–2000, (**b**) during 2000–2005, (**c**) during 2005–2010, and (**d**) during 2010–2015.

### **4. Discussion**

[51].

#### **4. Discussion**  *4.1. Temporal and Spatial Distribution of the Flash Floods*

*4.1. Temporal and Spatial Distribution of the Flash Floods*  In this study, a significant variation occurred in the annual and monthly quantity of the flash floods on a provincial scale (Figure 2). The annual quantity gradually increased from 1949 to 2015. The probable causes included human activity and the heavy precipitation that has become more frequent over these years [49,50]. However, the possible reason why flash floods were rare in the 1950s is that many flash floods were not recorded in the early years of the founding of the People's Republic of China. In addition, our finding revealed that the most frequent quantity of flash floods occurred during 1996–2015, and the average quantity of flash floods was 102 per year. This result In this study, a significant variation occurred in the annual and monthly quantity of the flash floods on a provincial scale (Figure 2). The annual quantity gradually increased from 1949 to 2015. Theprobable causes included human activity and the heavy precipitation that has become more frequent over these years [49,50]. However, the possible reason why flash floods were rare in the 1950s is thatmany flash floods were not recorded in the early years of the founding of the People's Republic of China. In addition, our finding revealed that the most frequent quantity of flash floods occurred during1996–2015, and the average quantity of flash floods was 102 per year. This result showed that, in general, a similar trend occurred in China, which has been supported by existing studies [25,29]. In the

precipitation on flash floods, because heavy precipitation mainly occurred in these three months

The kernel density estimation, unbalanced regions, standard deviational ellipse, and gravity centers showed that flash floods were mainly concentrated in Baoshan, Dali, Chuxiong, Kunming, Yuxi, Qujing, and Zhaotong (Figures 3 and 4). This high concentration of flash floods may have

showed that, in general, a similar trend occurred in China, which has been supported by existing studies [25,29]. In the meantime, Yangtze River Basin floods have triggered multiple flash floods, meantime, Yangtze River Basin floods have triggered multiple flash floods, which can be explained by the fact that economic development achieved through reform and opening-up has damaged the environment [21]. Regarding monthly variations, flash floods have been concentrated in June, July, and August. The finding was further evidence of the effects of precipitation on flash floods, because heavy precipitation mainly occurred in these three months [51].

The kernel density estimation, unbalanced regions, standard deviational ellipse, and gravity centers showed that flash floods were mainly concentrated in Baoshan, Dali, Chuxiong, Kunming, Yuxi, Qujing, and Zhaotong (Figures 3 and 4). This high concentration of flash floods may have been caused by the elevated levels of precipitation in the central and western regions of Yunnan Province, which have played an important role as external factors [52]. The formation of flash floods was affected by precipitation, environmental background conditions, and human activities, etc. Of the above-mentioned regions, environmental background condition and human activities were other important influencing factors, with the exception of precipitation. In Yunnan, most of the regions are predominately mountainous landscapes [53], which provided ideal geographical conditions for the formation of flash floods. Additionally, the urban agglomerations and major economic area are located in the central regions of Yunnan [54], which leads to the proliferation of environmental destruction, which was another important reason for the formation of flash floods in these regions. Furthermore, previous researchers have investigated the species distribution in Yunnan, their study have revealed low-species areas that were consistent with the high-incidence regions of flash floods [55]; these areas have a high density of human activity, which leads to the destruction of hydrological processes, thus providing conditions for the occurrence of flash floods. According to the results of the change trajectories of gravity centers (Figure 5), the trajectories of flash floods were somewhat similar to those for precipitation but not for population. This finding can be explained by the existing research. Liu et al., for example, found that the influence of precipitation on the flash floods was greater than that of human activities over the short term (i.e., during the 15-year period from 2001 to 2015) [25].

### *4.2. Driving Factors Influencing the Flash Floods in Yunnan Province*

Our findings showed that precipitation and ELE were the main factors affecting flash floods from 2001 to 2015 (Table 5 and Figure 6). This result indicated that flash floods might be easily induced in high-altitude areas by heavy precipitation, and vegetation cover was negatively correlated with flash floods in grassland (*r* = −0.241, *P* = 0.016), farmland (*r* = −0.222, *P* = 0.029), and forest (*r* = −0.234, *P* = 0.02), which is consistent with existing studies [5,21,29]. Regarding the different subregions of the landcover, the driving factors revealed different effects on flash floods. In the grassland, the H24\_20 had the maximum correlation with the flash floods, but the ELE did not exceed the average contribution rate for the interaction of driving factors. This is because the topographic relief of the grassland was relatively small. Additionally, the economic activities were not obvious [56], and the short periods of heavy precipitation did not produce strong scour. In this surrounding, the TR, SLP, PD, and GDP had less influence on flash floods (Table 5). In comparison, the contribution rate of ELE and PD exceeded the average value for the interaction of driving factors in settlement. As the population increased, the economic activities expanded to the high altitude area and the ecological system was damaged, which determined the elevation and human activities had a certain boost on flash floods [57]. In contrast, the TR, SLP, and NDVI showed a pretty low correlation to flash floods, which likely occurred because the topographic relief, slope, and vegetation coverage held to a relatively low level in the settlement [58]. This subregion was dominated by small basins and presented the most relevant relationship between flash floods and M10\_20. Such a relationship was consistent with the short duration precipitation, which could cause flooding in small basins that have a low time of concentration. For the farmland, the TR and SLP had a greater effect on flash floods than on other subregions (Table 5). This topographic relief and slope mainly included the lower hilly areas of the farmland, which had a higher population density and greater influence on the formation of the flash floods [29,59]. Regarding the forest, GDP had a higher correlation to flash floods than other subregions (Table 5). A possible reason for this

correlation was that economic development led to the destruction of forests [60]; we can draw a similar conclusion from the effects of NDVI on flash floods.

### *4.3. Sensitivity Analysis of the Flash Floods to Economic Development*

The sensitivity analysis of the flash floods to GDP is important for sustainable socioeconomic development. The phenomenon of sensitivity rising can influence many development issues, including an imbalance in regional structures and tourism damage [61,62]. The results of our sensitivity analysis showed that all regions were non-sensitive during 1995–2000 (Figure 7a). The early stage of the reform and opening-up of China's western region was relatively hysteretic for economic development; the intensity of human activity was maintained at a low level and the ecosystem structure was relatively complete [63], which determined this non-sensitivity pattern. During 2000–2005, however, the regions with low-sensitivity increased significantly, and Kunming was the most sensitive area (Figure 7b). In this period, the Development of the West Region, a significant plan for western development launched in 2000, saw economic development in the western city, which was promoted through a massive investment of national capital [64]. Consequently, the quantity of regions with low-sensitivity increased gradually. As the economic and cultural center in Yunnan, Kunming had the strongest human activity [65], which led to the highest sensitivity occurring in this region. With the management of mineral resources in Yunnan Province and regulations to restore farmland to forest, the sensitivity of flash floods to economic development obviously decreased in Kunming during 2005–2010. The quantity of regions with low-sensitivity increased gradually in this period, which can be explained primarily by the macroscopic development of the area [66]. Subsequently, more attention was paid to ecological protection and harmonious economic development, including primary functional area planning (2011) and national new urbanization planning (2014) [60], which determined that the sensitivity of flash floods to economic development decreased gradually during 2010–2015.

To prevent and control flash floods, we need to not only control human activities, such as excessive deforestation and mining, but also implement harmonious economic development and protect the integrity of ecosystem. Regarding the high-incidence areas of flash floods, the rational use of land and flexible barriers should be utilized in these regions. In addition, the government should strengthen its ability to predict flash floods during heavy periods of precipitation.

Our study shows that precipitation and elevation in settlement have a significant impact on flash floods than in other subregions. This result reveals the potential impact of human activities on the environment. Similarly, partition research should be considered when studying the relationship between flash floods and driving forces in other parts of the world, as the flash floods differ in background conditions in different subregions of landcover. However, the study has some drawbacks, including, for example, the time series of driving factors did not begin in 1949. In the future, a longer time series of data can be performed. Precipitation data interpolation should consider mountain terrain in order to reflect the change of climate with elevation. In addition, the spatial analysis methods used in this paper included the kernel density estimation, spatial mismatch analysis, standard deviational ellipse, and spatial gravity center model, which can highlight the spatial distribution characteristics of flash floods on a large regional scale, but have a weak application effect on small regions (e.g., flood protection works, excessive human exposure within the floodplain, basin shape, local geomorphology, and others). As to the principal component analysis, the method to determine the number of principal components has not been unified, which may lead to the loss of local information.

#### **5. Conclusions**

In this study, we examined the temporal and spatial distribution of flash floods; the driving factors for the spatial distribution of the flash floods in different subregions of the landcover; and the sensitivity (*S*) of flash floods to economic development using the changepoint, kernel density estimation, spatial mismatch analysis, standard deviational ellipse, spatial gravity center model, Pearson's correlation coefficient, multiple linear regression, principal component analysis, and sensitivity analysis. Yunnan

Province, in southwestern China, was selected as the study region because of the high incidence area of flash floods and its representative major mountainous landform. The temporal variation of the flash floods showed that the annual quantity of flash floods gradually increased from 1949 to 2015, and the months with the greatest quantity of flash floods were June, July, and August. As to spatial patterns, the regions with a high quantity of flash floods included Zhaotong, Qujing, Kunming, Yuxi, Chuxiong, Dali, and Baoshan. Furthermore, an analysis of the driving factors for flash floods in different subregions of the landcover showed that precipitation and ELE were the main factors from 2001 to 2015. In the settlement, precipitation and ELE had a greater effect on flash floods than in other subregions. Finally, the spatial–temporal variation of sensitivity revealed that the quantity of regions with non-sensitivity first decreased and then remained stable (from 1995 to 2015). During 2000–2005, Kunming had the highest sensitivity; then, this sensitivity gradually began to weaken.

On the basis of the results of this study, the most effective measure is early warning preferential to heavy precipitation events in the short term. In the long term, we should control human activities and reduce the ecosystem vulnerability caused by damage to species and to vegetation and by population growth to effectively prevent flash floods. According to the sensitivity of flash floods to economic development, Yunnan Province should take note of the previous lessons of imbalance and continue to introduce a balanced development policy.

**Author Contributions:** Conceptualization, J.X. and C.Y.; formal analysis, C.Y.; data and resources, W.C. and C.Z.; writing—original draft preparation, J.X. and C.Y.; writing—review and editing, L.G. and X.Z.; supervision, W.C.; funding acquisition, W.C. and C.Z.

**Funding:** This research was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20030302), China Geological Survey Project (DD20190637), Open Subject of Big Data Institute of Digital Natural Disaster Monitoring in Fujian (NDMBD2018003), Southwest Petroleum University of Science and Technology Innovation Team Projects (2017CXTD09), and National Flash Flood Investigation and Evaluation Project (SHZH-IWHR-57).

**Conflicts of Interest:** The authors declare no conflict of interest.

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