*Article* **The Spatiotemporal Distribution of Flash Floods and Analysis of Partition Driving Forces in Yunnan Province**

**Junnan Xiong <sup>1</sup> , Chongchong Ye <sup>1</sup> , Weiming Cheng 2,\* , Liang Guo <sup>3</sup> , Chenghu Zhou <sup>2</sup> and Xiaolei Zhang <sup>3</sup>**


Received: 10 April 2019; Accepted: 20 May 2019; Published: 23 May 2019

**Abstract:** Flash floods are one of the most serious natural disasters, and have a significant impact on economic development. In this study, we employed the spatiotemporal analysis method to measure the spatial–temporal distribution of flash floods and examined the relationship between flash floods and driving factors in different subregions of landcover. Furthermore, we analyzed the response of flash floods on the economic development by sensitivity analysis. The results indicated that the number of flash floods occurring annually increased gradually from 1949 to 2015, and regions with a high quantity of flash floods were concentrated in Zhaotong, Qujing, Kunming, Yuxi, Chuxiong, Dali, and Baoshan. Specifically, precipitation and elevation had a more significant effect on flash floods in the settlement than in other subregions, with a high *r* (Pearson's correlation coefficient) value of 0.675, 0.674, 0.593, 0.519, and 0.395 for the 10 min precipitation in 20-year return period, elevation, 60 min precipitation in 20-year return period, 24 h precipitation in 20-year return period, and 6 h precipitation in 20-year return period, respectively. The sensitivity analysis showed that the Kunming had the highest sensitivity (*S* = 21.86) during 2000–2005. Based on the research results, we should focus on heavy precipitation events for flash flood prevention and forecasting in the short term; but human activities and ecosystem vulnerability should be controlled over the long term.

**Keywords:** flash flood; driving factor; sensitivity analysis; subregion of landcover; Yunnan Province

### **1. Introduction**

Flash floods are one of the most severe natural disasters to try to prevent and deal with in the aftermath. They are responsible for loss of life and serious destruction to property and infrastructure, severely affecting a region's economic development [1–3]. According to an investigation by the World Meteorological Organization, the loss of property resulting from flash floods ranks in the top 10 among a range of natural disasters in 75% of countries [4]. As an economically developed country, flash floods in the United States ranked first in causes of death, with approximately 100 lives lost each year [5]. In addition, a total of 28,826 flash floods occurred between 2015 and 2017, and 10% of these flash floods resulted in property losses exceeding \$100,000 (US dollars) per flash flood disaster [6]. In Europe, 40% of flood-related casualties during 1950–2006 were due to flash floods [7], which is already more than 80% in southern Europe [8]. China is one of the countries that experiences the most flash floods, and about 4.63 million km<sup>2</sup> of land tend to be impacted by flash floods, which have threatened 560 million

people [3]. Yunnan is one of the most important areas of ecological value in the world [9] where flash floods are rapidly increasing, causing serious threat to people's lives and property [10].

In recent years, the vast majority of studies conducted on flash floods have indicated that flash floods are the combined result of various spatiotemporal factors [11–13]. Existing studies on flash floods have focused primarily on the following aspects: (1) the risk assessment [14–16]; (2) the occurrence, development, and influence studied from the perspective of a disaster mechanism [3,17,18]; and (3) the spatial–temporal distribution and influencing factors [19–21]. Further, risk assessment of flash floods has been used to classify a region as low risk, mid risk, or high risk and this is primarily based on the county and province scale [22]. The formation of flash floods is very complicated and has led to many such studies being carried out only in typical watersheds [23]. Regarding the spatiotemporal pattern and driving factors of flash floods, most of the research uses the global spatial autocorrelation, kernel density estimation, hot spot (Getis-Ord Gi\*) analysis, standard deviational ellipse, and spatial gravity center migration to explore the spatial and temporal characteristics of flash floods [24–26]. The driving force analysis of flash floods is primarily based on geographical detectors, and the driving factors selected by the researchers have included precipitation, terrain, and human activities, et al., which were dominated by static indices [27–29]. Such research generally selects the province (or country) as the research object.

Some research has been conducted on the spatiotemporal distribution and driving factors of flash floods, although some problems still remain. First, Yunnan has a complex number of characteristics, including its mountain range, human activity, precipitation, and ecosystems. In such complicated conditions, the characteristics of flash floods there remain unknown. Second, Yunnan is one of the most important ecological areas in the world, and the species diversity has been acknowledged by the International Union and the World Wildlife Fund for the Conservation of Nature [9]. There are differences in the weather, landform, and background conditions in each subregion of landcover. As a result, the nonpartitioned approach no longer applies in such a complex ecosystem region. Additionally, the study area is divided into different subregions of landcover, which reflect well the extent of environmental damage caused by human activities. For example, the human activity in the settlement is significantly greater than in other subregions. Third, previous studies have explored how driving factors interact based on two factors that use geographical detectors [30,31], which cannot reflect the contribution rate of multiple factor interactions on the flash floods. Finally, most studies have revealed the influence of human activity on the occurrence of flash floods [32,33], but the response of flash floods on economic development is not well understood in Yunnan. Hence, it is necessary to explore the spatiotemporal variations and driving factors (in different subregions of the landcover, which include grassland, settlement, farmland, and forest) of flash floods and to conduct a sensitivity analysis of the response of flash floods on economic development in Yunnan Province.

The objectives of this study were to (1) measure the spatial–temporal variation of flash floods using the changepoint, kernel density estimation, spatial mismatch analysis, standard deviational ellipse (SDE), and spatial gravity center model; (2) analyze the driving factors for the spatial pattern of flash floods in different subregions of the landcover using the Pearson correlation coefficient, multiple linear regression, and principal component analysis; and (3) conduct a sensitivity analysis to investigate the response of flash floods on economic development. The results can provide references for the prevention of flash floods.

#### **2. Materials and Methods**

#### *2.1. Study Area*

Yunnan Province (21◦–29◦ N, 97◦–106◦ E) is located in southwestern China and is situated on the eastern edge of the collision zone between the Eurasian and Indian continental plates. This region is characterized by some of the most complicated and neotectonic activity and tectonic movements on the Chinese mainland (Figure 1) [34]. Yunnan follows the spatial extent of the land area for this region and covers roughly 394,100 km<sup>2</sup> ; the population was 47.7 million at the end of 2016 (http://www.yn.gov.cn/yn\_yngk/gsgk/index.html). Economic activities are gradually gaining momentum, and the yearly economy has exceeded 1.63 trillion yuan (RMB; http://www.stats.gov.cn/) since 2017 in Yunnan. Although Yunnan's economy has made significant progress, the level of urbanization remains lows, and the township density is relatively sparse [35]. Yunnan is located in the Yunnan–Guizhou Plateau; and most of the areas are dominated by high mountains, deep valleys, and dense mountain streams where vertical differentiation in heat and water occurs as a result of changes in landform and altitude [36]. In addition, the precipitation in northwestern and central Yunnan has increased in recent years [37]. All of these conditions have contributed to making this study area one of the most geophysical hazard-prone regions in China. movements on the Chinese mainland (Figure 1) [34]. Yunnan follows the spatial extent of the land area for this region and covers roughly 394,100 km2; the population was 47.7 million at the end of 2016 (http://www.yn.gov.cn/yn\_yngk/gsgk/index.html). Economic activities are gradually gaining momentum, and the yearly economy has exceeded 1.63 trillion yuan (RMB; http://www.stats.gov.cn/) since 2017 in Yunnan. Although Yunnan's economy has made significant progress, the level of urbanization remains lows, and the township density is relatively sparse [35]. Yunnan is located in the Yunnan–Guizhou Plateau; and most of the areas are dominated by high mountains, deep valleys, and dense mountain streams where vertical differentiation in heat and water occurs as a result of changes in landform and altitude [36]. In addition, the precipitation in northwestern and central Yunnan has increased in recent years [37]. All of these conditions have contributed to making this study area one of the most geophysical hazard-prone regions in China.

**Figure 1.** The study area: (**a**) the distribution of flash floods in Yunnan Province; (**b**): the geographical position of Yunnan in China; (**c**): the different subregions of landcover in Yunnan **Figure 1.** The study area: (**a**) the distribution of flash floods in Yunnan Province; (**b**): the geographical position of Yunnan in China; (**c**): the different subregions of landcover in Yunnan Province.

Province.

#### *2.2. Selection and Pretreatment of Data*

*2.2. Selection and Pretreatment of Data*  The flash flood data used in this research are from the National Flash Flood Investigation and Evaluation Project, which was launched in 2013 to research the historical flash floods that occurred from 1949 to 2015 on a national scale, and the disaster areas were assumed as a point for collection [29]. In Yunnan, the number of flash floods was 3166, which we derived from the above-mentioned project. Current research has shown that the occurrence of flash flood is closely related to precipitation, topography, and human influence [14,21,29]. To explore the relationship between flash floods and the driving factors of flash floods, we selected the following factors: precipitation factors (unit: mm), elevation (ELE, unit: m), topographic relief (TR, unitless), slope (SLP, unit: °), normalized difference vegetation index (NDVI, unitless), population density (PD, unit: person/km2), and GDP (unit: yuan/km2). The precipitation factors included the 10 min precipitation (M10\_20), 60 min precipitation (M60\_20), 6 h precipitation (H6\_20), and 24 h precipitation (H24\_20) in 20-year return period, which reflect the impact of various precipitation intensities on flash floods. In addition, the station data of annual mean precipitation used to calculate the gravity center in The flash flood data used in this research are from the National Flash Flood Investigation and Evaluation Project, which was launched in 2013 to research the historical flash floods that occurred from 1949 to 2015 on a national scale, and the disaster areas were assumed as a point for collection [29]. In Yunnan, the number of flash floods was 3166, which we derived from the above-mentioned project. Current research has shown that the occurrence of flash flood is closely related to precipitation,topography, and human influence [14,21,29]. To explore the relationship between flash floods and the driving factors of flash floods, we selected the following factors: precipitation factors (unit:mm), elevation (ELE, unit: m), topographic relief (TR, unitless), slope (SLP, unit: ◦ ), normalized difference vegetation index (NDVI, unitless), population density (PD, unit: person/km<sup>2</sup> ), and GDP (unit: yuan/km<sup>2</sup> ). The precipitation factors included the 10 min precipitation (M10\_20), 60 min precipitation (M60\_20), 6 h precipitation (H6\_20), and 24 h precipitation (H24\_20) in 20-year return period, which reflect the impact of various precipitation intensities on flash floods. In addition, the station data of annual mean precipitation used to calculate the gravity center in Yunnan Province were acquired from the National Weather Service of China (http://www.cma.gov.cn/). Table 1 shows the source and resolution of all raw data sets.

Yunnan Province were acquired from the National Weather Service of China

(http://www.cma.gov.cn/). Table 1 shows the source and resolution of all raw data sets.

In this study, we interpolated precipitation raster data by the Inverse Distance Weighted in ArcGIS10.2 (ESRI, Inc., Redlands, CA, USA), which was based on the vector data of precipitation factors, and the final resolution was 1 km × 1 km. We calculated topographic relief using focal statistics in the ArcGIS 10.2 software, which was represented by elevation standard deviation based on the DEM data; the SLP also was generated by this data.


**Table 1.** Source and resolution of the raw data sets.
