Evaluating rainstorm hazard prevention and mitigation capability in mountainous ecological scenic areas: A case study of the Qinling Mountains, China

Abstract

Mountainous areas have become tourism hotspots in recent years, at the same time, some tourism safety issues have been revealed. To study rainstorm hazard prevention and mitigation capabilities, four representative scenic areas in the Qinling Mountains of China were selected as research foci for this study. The similar weight method and the unascertained measure model were used to establish an evaluation index, and the weights of indices at all levels were determined. Rainstorm hazard prevention and mitigation capabilities in the four scenic areas and on defferent slopes were then evaluated quantitatively. The results found that : according to the rainstorm hazard prevention and mitigation capabilities, the rankings of these scenic areas from high to low were Taibai Mountain National Forest Park, Cuihua Mountain National Geopark, Huayang Scenic Area, and Jinsixia National Forest Park. The rainstorm hazard prevention and mitigation capability on the north of the Qinling Mountains were higher than on the south slope, and the difference was relatively large. Rainstorm hazard prevention and mitigation capabilities on the eastern and western Qinling Mountains were more similar. These conclusions could provide a reference for regional rainstorm hazard prevention and mitigation measures, as well as sustainable development.

Keywords:

• mountainous ecological scenic area
• rainstorm hazard prevention and mitigation capability
• unascertained measure model
• similar weight method
• Qinling Mountains
• China

Acknowledgement

The authors would like to thank the Management Committees from Taibai Mountain National Forest Park, Cuihua Mountain National Geopark, Huayang Scenic Area, and Jinsixia National Forest Park for assistance with data preparation. We are grateful to Yanze Wang for his efforts during the survey. We specially thank the China Meteorological Administration, the China Meteorological Science Data Sharing Service Network, the Science Data Center of Chinese Academy of Science, and the Meteorological Bureau of Shaanxi Province for providing meteorological and image data.

Additional information

Funding

This research work was finacially supported by the National Nature Science Foundation Project of China (No.41371497).