Spatial distribution of rainstorm hazard risk based on EW-AHP in mountainous scenic area of China

ABSTRACT

Rainstorm hazards seriously affect the lives of residents and activities of tourists in rainstorm harder-hit scenic areas. The assessment of rainstorm hazard risk has a great significance in the area. Based on the disciplines of geography and tourism, an assessment system has been constructed by using the Entropy Weight-Analytic Hierarchy Process and exponential model. Finally, the Huayang Ancient Town Scenic Area in China has been taken as study area, and its rainstorm hazard risk and spatial distribution have been evaluated. The results indicate the following: The rainstorm hazard risk areas are mainly concentrated along rivers, valleys, or roads; The specific spatial distributions are: rainstorm hazard risk of the Huayang Old Streets is high, that of the Red Cliff Valley and both sides of the Mandarin Duck River are higher, that of the Golden Monkey Valley and the Macaque Park are lower, that of the Tangluo Ancient Road is low, and the risks of other areas are medium; The main influencing factors of rainstorm hazard risk are elevation, terrain, gap and width of river, distance to river, building density, tourist flow and tourism activity; People's awareness, system of monitoring and early warning, emergency plan, facilities and equipment for rainstorm hazard prevention and mitigation need to be continuously improved.

KEYWORDS:

• rainstorm hazard risk
• assessment system
• EW-AHP
• spatial distribution
• the Huayang Ancient Town Scenic Area in the Qinling Mountains of China

Acknowledgments

The authors would like to thank the Management Committee of Huayang Ancient Town Scenic Area for providing information and data about national economy and social development, tourism industry, and disaster prevention and mitigation. They are grateful to Yanze Wang for his help during the data survey and progressing in the scenic area. They specially thank the China Meteorological Administration, the China Meteorological Science Data Sharing Service Network, the Sciences Data Center of Chinese Academy of Sciences, and the Meteorological Bureau of Shaanxi Province for providing meteorological data and image data.

Funding

This research work was financially supported by the Natural Science Foundation Project of China (No. 41371497).