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Natural disasters in tourist areas show a trend of rapid increase in recent years and have caused great losses. It has become a main bottleneck restricting the sustainable development of tourism in more than 60% of the tourist areas in China, and has aroused great attention of governments and scientists. This research puts forward the natural disaster risk assessment method in tourist area by the introduction and development of the concept of scenario analysis technique. The method using Participatory Geographic Information System (PGIS) technology, first designs multi- scenarios of hazards in tourist areas, and establishes the curve of relationship between hazard intensity and risk scenarios. Secondly, it establishes the curve of relationship between hazard intensity and vulnerability by making a classification of the exposure including tourism resources and tourist facilities according to the structure in the serving years. Finally, it establishes the comprehensive disaster loss model and calculates the average annual losses in the tourist area by fitting the risk curve. The case takes the example of Jiuzhai Valley (Shuzheng Scenic Spots), a World Natural Heritage site. The research calculates the speed, quantity and impact force of debris flows under 6 kinds of storm scenarios, namely, in 20-year, 50-year, 100-year, 200-year, 500-year and 1,000-year return periods. Then by using Participatory GIS to make survey of tourists and residents in Shuzheng Vallage, it classifies the structure and serving years of different buildings and tourism resource facilities, and sets up vulnerability curves between debris flow and the two Spots under 6 different scenarios; learning the property and building conditions of each household and calculating potential losses that the 6 scenarios may respectively cause to the two villages. Ultimately, carrying out overall analysis of disaster prevention costs and benefits targeted at the acceptable risks and raising measures for natural disaster prevention in Jiuzhai Valley. The research has several features by contrasting with similar studies: 1) The paper provides a systematic method that analyses the hazard, exposure and vulnerability in tourist areas unlike the analysis natural disaster risk assessments in general city communities. Vulnerability in tourist areas is much more complicated compared to the general community as it includes tourism resources, tourism facilities, tourists and residents, et al. 2) The paper adopts Participatory GIS (PGIS) technology, multiscenarios analysis by calculating potential losses despite of the traditional method of making risk assessment by using AHP to establish an index system and weights. 3) The research chooses smallscale Jiuzhai Valley despite of the traditional study area of large and medium scales. By using questionnaire and interview, supported by scenario technology, the research will greatly enhance the exactness and reliability of the natural disaster risk assessment. [ABSTRACT FROM AUTHOR]

In recent years, a number of methods for forecasting the tourism market have been presented. These methods, which are valuable tools for understanding the stationary fluctuation of tourism systems, include the time series method, the econometric method, the artificial neural network and genetic algorithms. Multifractal detrending moving average method (MF-DMA) is a new nonlinear algorithm, which is usually used to analyze a one-dimensional multifractal or higher-dimensional multifractals. In our research, MF-DMA has been successfully used to characterize the temporal fluctuations in the number of daily domestic and foreign tourists in the Jiuzhai Valley in China. The examined data sets correspond to the number of daily Jiuzhai Valley tourists for the time period of January 1, 2007, to December 31, 2010. These data are provided by the Jiuzhai Valley Administration. We disaggregate the data into two time series for domestic and foreign tourists. The lengths of data are all 1460. They show that Jiuzhai Valley tourism markets are characterized by long-term memory and are multifractal in nature. We note that the multifractal spectra extracted from the two time series are slightly different. Multifractality is stronger in the foreign tourists' time series than in the domestics'. This means that the influx of foreign tourists is less linear and predictable than that of domestic tourists. To study the contributions of the long-range correlation on multifractality, we destroy the temporal correlations by randomly shuffling the original time series. After this shuffling procedure, what remains are data with exactly the same fluctuation distributions but without memory or correlation. It is shown that the strength of multifractality in the randomly shuffled time series is much smaller than that of the original time series. This shows that the inherent long-term correlation structures in both time series are the main origins of multifractality. In addition, the major sources of multifractality are studied. Based on the sliding window concept, the time evolutions of the multifractal behavior of domestic and foreign tourists are analyzed and the influence of the Wenchuan earthquake on the Jiuzhai Valley tourism system dynamics are evaluated quantitatively. This study indicates that although the Jiuzhai Valley tourism system was seriously affected by the Wenchuan earthquake, the inherent dynamical mechanism of the tourism system has not fundamentally changed over the long term. By May 12, 2009, the Jiuzhai Valley tourism system' s fractal mechanism had gradually restored to its pre-earthquake state. This means that the Jiuzhai Valley tourism system has the ability to recover in a short time period. Through an in-depth analysis of this phenomenon, we found the critical particle effects on the post-disaster restoration and reconstruction of tourist attractions: a phase transition of quantitative change to qualitative change for tourism market post-disaster recovery, which has fractal characteristics. In the evolution of the tourism system, two key elements play a decisive role; whether to enter a critical state, and the main events that lead to phase transitions. [ABSTRACT FROM AUTHOR]