Spatiotemporal assessment of rainstorm hazard risk in Qinling mountains of China

ABSTRACT

Rainstorms are natural hazards, and they create hurdles in many parts of the world. They often cause damages to the life, properties, and human activities. The Qinling Mountains of China are among the few areas that are extremely hit by the rainstorms, but their hazard risks have not fully been assessed. This article takes an attempt to fill this gap by providing a comprehensive spatiotemporal risk assessment for the area. By using the geographic information system-enhanced Analytical Hierarchy Process assessment framework and the comprehensive index model, the rainstorm hazard risks can be evaluated from four different aspects. At last, the comprehensive rainstorm hazard risks have been assessed and zoned. Results indicate that with regional climate change, the frequencies of rainstorm have slightly increased over the past period of 46 years, and rainstorm hazard risks become higher on the whole. As a strong seasonal variation in the rainstorm days, the highest rainstorm hazard risks occur in July annually. The comprehensive rainstorm hazard risks in the southern regions of the Qinling Mountains are obviously higher than those in the northern regions. The Hanzhong and Ankang basins along the Hanjiang River are under the highest risk of rainstorms. The big cities of Xi'an and Baoji, piedmont plain and regions with good transportation, are under the lowest risk. The spatial distribution of rainstorm hazard risk shows that it has certain regulations along the rivers, ridges, and valleys, but it may be affected by the factors of rainfall and human activities. This article is significant for strategic environmental planning and hazard emergency management of the study area as well as in similar climatic regions of the world.

KEYWORDS:

• rainstorm hazards
• spatiotemporal assessment
• comprehensive risk index model
• assessment framework
• the Qinling Mountains of China

Acknowledgments

The authors are thankful to Hui Zhang, who provided much of the basic information regarding the land use and helped them during the data processing. They are very grateful to the China Meteorological Administration, the China Meteorological Science Data Sharing Service Network, the Sciences Data Center of Chinese Academy of Sciences, the Meteorological Bureau of Shaanxi Province, and all other stakeholders for their unconditional support and data sharing (i.e., meteorological data, land use data, and image data).

Funding

This research work was funded by the Natural Science Foundation Project of China (Vide No. 41371497) and the Scientific Research Plan Project of Provincial Key Laboratory of Education Department of Shaanxi Province (Vide No. 11JS014).