Risk Analysis of Snow Disaster in the Pastoral Areas of the Qinghai-Tibet Plateau

Risk analysis of snow disaster in the pastoral areas of the Qinghai-Tibet Plateau

LIU Fenggui1,2, MAO Xufeng1, *ZHANG Yili2, CHEN Qiong1, LIU Pei1, ZHAO Zhilong1,2

1. School of Life and Geographic Science, Qinghai Normal University, Xining 810008, China; 2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Abstract: Snow disaster is one of the top ten natural disasters worldwide, and the most se- vere natural disaster to affect the pastoral areas of the Qinghai-Tibet Plateau. Based on the hazard harmfulness data collected from historical records and data collected from entities affected by this hazard in 2010, a comprehensive analysis of the 18 indexes of snow disaster on the Qinghai-Tibet Plateau was conducted, encompassing the hazard harmfulness, the amount of physical exposure the hazard-bearing entities face, the sensitivity to the hazard, and the capacity to respond to the disaster. The analysis indicates that: (1) areas at high-risk of snow disaster on the Qinghai-Tibet Plateau are located in certain areas of the counties of Yecheng and Pishan in the Xinjiang region; (2) areas at medium-risk of snow disaster are found between the Gangdise Mountains and the Himalayas in the central-western part of the Qinghai-Tibet Plateau, and the southeastern part of the southern Qinghai Plateau; (3) the risk of snow disaster is generally low throughout the large area to the south of 30°N and the region on the border of the eastern Qinghai-Tibet Plateau. Overall, the risk of snow disaster in high-altitude areas of the central Qinghai-Tibet Plateau is higher than that at the edge of the plateau.

Keywords: Qinghai-Tibet Plateau; snow disaster; risk; pastoral area

1 Introduction In the context of global climatic change, natural disasters are increasingly more frequent and intense, meaning that humanity faces an increased risk from natural disasters (Stocker et al., 2013). On the Qinghai-Tibet Plateau, with its high elevation, cold climate and widely dis- tributed livestock farming, the frequency of snow disaster is typically the highest from Oc- tober to May. Over the past 40 years, the area of the plateau that is snow-covered has mark- edly increased (Gao et al., 2003), making it the most important and critical region in the Northern Hemisphere with regard to interannual variation in snow cover (Yang et al., 2001). Snow cover in this area has a significant influence not only on regional human activities, but

Received: 2013-10-11 Accepted: 2013-12-06 Foundation: National Basic Research Program of China, No.2010CB951704; National Natural Science Foundation of China, No.40761003, No.41271123 Author: Liu Fenggui (1966–), PhD and Professor, specialized in regional geography related to the Tibetan Plateau. E-mail: [email protected] *Corresponding author: Zhang Yili, Professor, E-mail: [email protected]

www.geogsci.com www.springerlink.com/content/1009-637x 412 Journal of Geographical Sciences

also on the climate of the neighboring areas, including East Asia, and perhaps even the entire Northern Hemisphere (Chen et al., 1978; Wei et al., 1995; 1998; 2001; 2002; 2005; Chen et al., 2000). On the Qinghai-Tibet Plateau, the recent expansion in the period of snow cover during spring and winter, combined with the increased grazing capacity of pastures, has led to snow disasters having a drastic negative impact on human activities and stock farming to the point where it is now one of the most common natural disasters in this region.

Table 1 Basic framework and index system for China’s snow disaster risk research Indicators of Surveyed Indicators of the degree susceptibility Indicators of the Indictors of the of hazard risk due to entities affected ability to cope with Literature areas to snow disas- snow disaster ter by snow disaster snow disaster Inner Snow Water Equivalent Normalized Tachiiri et Mongolia (SWE), snow depth Difference al., 2008 Vegetation Index (NDVI) Xilingol Average annual income The number of of herdsmen; feed re- livestock; and serve; livestock pens; ratio of the num- annual snowfall; duration ber of cattle to of snow cover; rainfall sheep by the end during growing season of June Wu et al., (April–September); the 2008 number of days when wind speed >6 m/s; mean daily temperature <-5C; or temperature drop >12C compared to 5 days previously Northern Depth and coverage of Grazing capacity; Availability and Xinjiang snow cover; duration of livestock struc- yield of pastures; Liu et al., low temperature ture and body height of grass 2008 condition Sanjian- Mean temperature; snow- Pasture type; Construction of Emergency capa- gyuan fall; duration of snow yield of grass; disaster preven- bility of the gov- Region cover; wind speed grass height; tion base; net ernment; emer- (Three grazing capac- income per gency forage grass; Zhang et Rivers’ ity herdsman; animal overgrazing rate; al., 2009 Source products market- and preparation of Region) ing situation; herdsmen for over- health of animals wintering

Qinghai Scale, frequency, and Gradient and Population den- density of historical snow aspect of slope; sity; farming, disasters vegetation type husbandry, and industrial output Fu et al., per unit area; 2010 traffic density; abundance of pasture resources China Number of snow days; Population Total number of GDP per capita; days with snow cover; below 15 years inhabitants; live- gross industrial wind speed in winter and old; population stock numbers; output/number of spring; area and depth of density; agri- pasture area; industrial enter- snow; multi-aspect mean cultural acreage precipitation prises; average temperature in winter and during June to level of education; Chen et spring; snowfall; eleva- August; incomes density of road al., 2010 tion; and slope aspect from farming, network; the num- forestry, hus- ber of hospital beds bandry, side-line production, and fishery Qinghai Assessment of GDP per capita; livestock numbers; area and yield of pasture; frequency He et al., of snow disaster 2010

Qinghai Depth and duration of Livestock Li et al., snow cover (1978–2005) mortality 2013 LIU Fenggui et al.: Risk analysis of snow disaster in the pastoral areas of the Qinghai-Tibet Plateau 413

Snow disasters are classified into snow cover, snowdrift and avalanche, separated according to their mechanistic origins. The occurrences of snow cover are largely concentrated in the northeastern Qinghai-Tibet Plateau in western China, the central and eastern highlands of Inner Mongolia and regions in the north of the Tianshan Mountains of Xinjiang (Hao et al., 2002). In particular, on the northern Qinghai-Tibet Plateau (north of 30°N), snow cover disaster is characterized by large areas of snow-coverage, a high frequency of snow-coverage events, a high degree of harmfulness, etc., making this region a key area for studying snow accumulation and disaster risk. At present, due to different understandings of concepts such as “natural hazard risk” and “vulnerability”, the evaluation systems used to quantify the snow disaster risk differing from one region to another (Table 1). This paper presents a comprehensive assessment of snow cover disaster risk of the Qinghai-Tibet Pla- teau, based on the multi-risk assessment method (Geriving, 2006a, 2006b) in combination with snow disaster data recorded over the past 60 years from 210 county-level administrative units and their geographical conditions, along with the profile data of hazard-affected entities from 201 counties on the plateau during 2010. This work aims to provide a reference for reducing the risk of snow cover disasters in the Qinghai-Tibet Plateau region.

2 Data sources

(1) Historical snow disaster data was collated from various sources including: China Mete- orological Disaster Encyclopedia (Volume of Qinghai, Volume of Tibet, Volume of Xinjiang, Volume of Yunnan, Volume of Sichuan, and Volume of Gansu) (Wang et al., 2007; Liu et al., 2008; Shi et al., 2006; Liu et al., 2006; Zhan et al., 2006; Dong et al., 2005); Natural Disas- ters in Qinghai (Natural Disasters in Qinghai Compilation Committee, 2003); Meteorologi- cal Bulletins (1995–2010) of Tibet Autonomous Region1, Qinghai (Qinghai Province Cli- matic Data Center, 2006), Sichuan (Sichuan Province Meteorological Observatory, 2004), Gansu (Lanzhou Central Meteorological Observatory, 2003), Xinjiang Autonomous Region (Xinjiang Autonomous Region Climate Center, 2011), and Yunnan (Yunnan Province Cli- mate Center, 2010) by their respective meteorological bureau; China’s County Statistical Yearbook (2011) (China’s County (city) Socio-economic Statistical Yearbook Editorial Committee, 2010); Qinghai Statistical Yearbook (2011) (Qinghai Statistical Yearbook Edito- rial Committee, 2011); Tibet Statistical Yearbook (2011) (Tibet Statistical Yearbook Editorial Committee, 2011); Yunnan Statistical Yearbook (2011) (Yunnan Statistical Yearbook Edito- rial Committee, 2011); Sichuan Statistical Yearbook (2011) (Sichuan Statistical Yearbook Editorial Committee, 2011); Gansu Statistical Yearbook (2011) (Gansu Development Year- book Editorial Committee, 2011); Xinjiang Statistical Yearbook (2011) (Xinjiang Statistical Yearbook Editorial Committee, 2011). (2) Meteorological data, observed by 105 weather stations within the Qinghai-Tibet Pla- teau region, was taken from the Surface Meteorological Data of China. (3) Map data from specific administrative regions was taken from the 1:4 million basic map database of China; data on road traffic was taken from the 1:1 million basic map database of China; DEM (Digital Elevation Model) data was taken from the 1 km DEM pro-

1 Tibet Meteorological Bureau. Tibet Meteorological Bulletin. Tibet Meteorological Bureau, 2009: 1-14.

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duced by the United States Geological Survey.

3 Research methods

3.1 Theoretical basis

In the 1990s, scholars recognized that natural disasters had both natural and social attributes (Ma et al., 1992; Niu, 1990). By the 2000s, with more in-depth studies of disasters and risks, a framework for natural disaster risk assessment was established (Greiving, 2006; Birkmann et al., 2006; Figure 1), which focused on the identification of risk factors as well as analyses of the physical exposure and vulnerability of hazard-affected entities. Both the theory and methodology of the identification of risk factors has become matured, Figure 1 Risk factor chart illustrating the but the fact that a certain hazard-affected group’s European multiple risk assessment (Birkmann et vulnerability correlates with several different fac- al., 2006) tors such as the social group’s susceptibility, the degree of physical exposure to hazards, the social, economic and cultural context, and its ability to cope with disasters (Birkmann et al., 2006. This has led to large differences in the understanding of these related concepts. The definition of “vulnerability” occupies a spec- trum, at one end considering only the intrinsic vulnerability of the natural system, to a more generalized and comprehensive view encompassing both natural and social systems at the other end. With the changing focus from environmental factors to human ones, the definition of vulnerability now more closely considers the effect that people have on the formation and alleviation of vulnerability and considers the active adaptation of people as the key issue in vulnerability evaluation, reflecting the expanding and changing definition of this term (Birkmann et al., 2006) (Figure 2). Based on the above theory, this paper divides the snow disaster hazard risk into, a) historical disaster hazard risk, and b) the potential hazard risk associated with environmental factors. The former category refers to the hazard risk of past natural disasters, as recorded in various documents, including hazards associated with disaster and vulnerability of hazard-affected entities. The frequency and intensity of the historical hazard risk is characterized by a specific regularity, which is of practical significance for understanding the hazard risk and, thus, the risk of disaster associated with that hazard. The hazard risk associated with historical disasters is an important component of overall hazard risks, and is referred to as ‘historical hazard risk’ for short. The second category, the potential hazard risk, refers to the risk associated with specific environmental factors, such as those induced by geographical conditions, which contribute to the hazards associated with different natural disasters. This form of risk can be comprehensively analyzed and determined based on geographical parameters. This form of hazard risk is usually independent of the vulnerability of hazard-affected entities and differs from the historical hazard risk. When the potential hazard risk associated with environmental factors coincides with an increased vulnerability of hazard-affected entities, there is the possibility of a natural disaster, i.e., a LIU Fenggui et al.: Risk analysis of snow disaster in the pastoral areas of the Qinghai-Tibet Plateau 415

potential hazard risk. In this paper, the vulnerability of hazard-affected entities encompasses three aspects: physical exposure, susceptibility, and responsiveness to disaster. The degree of vulnerability varies with the development of social and economic structures, science and technology. The degree of risk may also correspondingly vary with the degree of vulnerability, even in a stable geographical environment where the potential hazard risk associated with environmental factors is uniform. The vulnerability assessment for hazard-affected entities in this paper is solely based on the profile data of 2010.

Figure 2 Chart illustrating the evolving definition of the vulnerability concept (Birkmann et al., 2006)

3.2 Analysis methods 3.2.1 Hazard risk analysis As described above, the hazard risk associated with natural disasters represents the combination of the historical hazard risk and the potential hazard risk associated with geographical factors. In formula (1) (Ge et al., 2008), Hl represents the historical hazard risk, as reflected by the frequency and intensity of historical disasters. Ai represents whether a snow disaster occurred or not in year i (if a disaster did occur, i will be designated as 1, or if not as 0); Dij refers to the number of times a snow disaster occurred during the No. j statistical unit in the year of i; n is the statistical time period; i is the specific year (i=1, 2, 3, …, n); j is the statistical unit (j=1, 2, 3, …, k). All values are dimensionless in this study. 10nnk 1 HAli  D ij (1) nniij1 The potential risk of disaster associated with the geographical environment encompasses only the natural risk factors. This form of risk, referred to as potential hazard risk (formula 2)

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in this paper, is usually composed of multiple factors; these factors are relatively complex, are unrelated to hazard-affected entities, and some are difficult to quantify. Hq represents the potential hazard risk; Qq is the natural risk factor numbered q among geographical environmental factors; m is the number of natural risk factors (q = 1, 2, 3, …, m). m HQqq  (2) q1

The comprehensive hazard risk of a certain natural disaster can be obtained by adding Hl and Hq. However, the different mechanisms and processes associated with natural disasters may result in different degrees of historical hazard risk and potential hazard risk. For exam- ple, regions that have previously been affected by flooding are usually still subject to a relatively high risk of this type of disaster, while a smaller risk of flooding should be expected in an area which has never flooded previously (Wohl et al., 2000). By contrast, the future risk of a landslide hazard is greatly reduced in an area once this disaster has occurred. In this regard, the normal practice is to utilize Analytical Hierarchy Process, Delphi Method, Neighborhood Analogy and other methods to give different weight to the two categories of hazard risk for a more accurate assessment. 3.2.2 Vulnerability analysis The current assessment of vulnerability is based on three main indexes: the physical exposure of hazard-affected groups, the susceptibility, and the ability of the social group to cope with the disaster. Physical exposure, a comprehensive index, reflects the population density, GDP, infrastructure, and stock of crops, livestock and other material objects of a social system in a region threatened by natural disaster. Susceptibility reflects the endurance of an intrinsic element of physical exposure in the case of natural disaster. The responsiveness of a social group to disaster indicates the capability of the group to forecast, prevent, adapt to, and recover from a natural disaster. The specific formula for vulnerability is expressed below, VES(1 D ) (3) where V is the vulnerability of a hazard-affected entity to a natural disaster; E is the exposure index of the hazard-affected entity; S is the susceptibility index of the hazard-affected body; D is the index of responsiveness to disaster.

3.3 Index system and weight coefficient of snow cover risk assessment

3.3.1 Index system of hazard risk assessment On the Qinghai-Tibet Plateau, snow days are most common in the period from October to May (Li, 1993; Wang et al., 2007). Snow accumulation does not always constitute a disaster because a snow disaster is the result of both natural and social process. The natural processes associated with snow disaster, caused by accumulation, include three factors: the snow fall period, the snow accumulation period, and the possibility of reoccurrence. During the first period, snowfall is the most important factor in the formation of a hazard, while the duration of snow cover is the second most important factor. Snow depth and number of days with snow cover are two indicators of snowfall and duration of snow cover, respectively. Data to quantify snow cover are available from different sources, such as visible light data LIU Fenggui et al.: Risk analysis of snow disaster in the pastoral areas of the Qinghai-Tibet Plateau 417

observed by optical remote sensor, microwave data obtained through microwave remote sensors, as well as data showing the surface area of the snow cover (or data on the snow cap extent) based on satellite observations gathered by the National Oceanic and Atmospheric Administration (NOAA). Comparing these data with those gathered by meteorological ob- servatories, researchers have found that the surface snow cover of the Qinghai-Tibet Plateau are most reliable and, therefore, most widely used data (Wei et al., 2002). In this paper, the average maximum snow depth in a year and the average number of days with surface snow cover per year have been adopted as the most important potential risk factors in a hazard-inducing environment. The likelihood of snow disaster in an area is usually judged by the trend. The frequency and magnitude of a historical hazard can determine its intensity and is, thus, regarded as the key criteria for judgment of the trend. The frequency and intensity is usually measured by the area of snow coverage and the depth of the snow, respectively, however, in this paper we consider only the former parameter (because of the difficulty in accessing snow depth data associated with historical disasters) attributed to snow disasters during the past 60 years. Generally, the regions with a high frequency of snow cover are more likely to be adversely affected again. In addition, other factors like wind speed (Wu et al., 2008; Zhang et al., 2009; Chen et al., 2010), temperature (Wu et al., 2008; Liu et al., 2008; Zhang et al., 2009; Chen et al., 2010) and topographic features (Chen et al., 2010) of a snow-covered area complicate the determination of snow cover risk on a macro scale, and are therefore not considered in this macro-scale regional study, but must be considered when conducting a relatively small-scale assessment or snowdrift hazard risk assessment. 3.3.2 Index system of vulnerability assessment The social processes associated with snow disaster are reflected in the vulnerability of a social system to disaster, including the vulnerability of the population directly affected by the disaster, as well as the vulnerability of the livestock, houses and tents of herdsmen in the snow-covered area. The integrated vulnerability of all these hazard-affected entities is referred to as “physical exposure”, of which the amount will influence the degree of hazard. Generally, physical exposure is quantified through the amount of death and injury incurred by people and the number of livestock killed during a snow disaster, as the damage to tents and houses is not usually significant. For this reason, in this paper the population density and an inventory of livestock are adopted as the assessment indexes for physical exposure. Herdsmen engaged in livestock production and small livestock are more vulnerable to snow hazards than non-agricultural workers and larger livestock, therefore, the greater the number of herdsmen and small livestock, the larger the susceptibility to disaster. The quantity of hazard-affected entities are regarded as indexes of susceptibility. In the case of a snow disaster, the more capable a social system is of responding to disaster, the less vulnerable it will be. This capability depends not only on the local basic medical provision (the number of medical personnel and hospital beds etc.), the infrastructure (e.g., the communications capacity, availability of road vehicles, etc.), education and income levels of affected groups, and the capability of the government to manage the emergency, but also on regional special countermeasures (e.g., the forecasting ability and disaster-prevention engineering) and environmental conditions (e.g., road provision, location and topographic conditions) for disaster relief. With regard to the issue of whether the pasture itself should be included as a haz-

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ard-affected entity, the snow accumulation process can be beneficial to pasture without causing any damage. However, in the alpine meadows of the Qinghai-Tibet Plateau, it is unlikely that the pasture will be mowed and stored for forage, and so the forage reserve is also a form of engineering needed to prevent disaster. Thus, forage reserve is considered as an engineering measure for disaster prevention in this paper. Giving consideration to both the natural and social factors that affect the Qinghai-Tibet Plateau, and under the principles of scientific research, comprehensiveness, dominance, hierarchy and feasibility, this paper adopts the Analytic Hierarchy Process (or “AHP method” for short) to analyze the contributions of 18 selected risk assessment indexes (Table 2) to the hazard risk and to determine the weight coefficient of each index, so as to measure their rate of contribution to the overall snow cover hazard risk (Table 2).

Table 2 The index system and weight coefficient of the snowstorm risk assessment on the Qinghai-Tibet Pla- teau

First-level index Second-level index Third-level index

hp Frequency of historical snow disasters (0.50) Hl Historical disaster hazard (0.7) ha Intensity of historical snow disasters (0.50) H Hazard hr Average maximum snow depth per year (0.50) Ha Potential disaster hazard (0.3) hs Average duration of snow cover per year (0.50)

E1 Livestock inventory at the end of the year (0.50) E Physical exposure (0.53) E2 Population density (0.50)

S1 Proportion of non-agricultural working population S Susceptibility (0.17) (0.25)

S2 Proportion of small livestock (0.75)

Bs Number of hospital beds per 1000 residents (0.07)

Ws Medical personnel per 1000 residents (0.07)

Di Basic ability to Ps Average level of education (0.11) V Vulnerability cope with disasters T Communications capacity (0.08) (0.4) s D Ability to C Balance of savings per capita (0.45) cope with s disasters Zs Capability of the government to manage emergencies (0.3) (0.22)

Ys Ability to forecast disaster (0.17) Dz Special ability (0.4) Xs Capability for disaster prevention engineering (0.83)

Dh Environmental Qs Relief amplitude (0.33) conditions during disaster relief (0.2) Js Density of road network (0.67)

4 Research scope With a total land area of 257.24×104 km2 (Zhang et al., 2002), the whole region of the Qinghai-Tibet Plateau encompasses 210 county-level units spanning Tibet, Qinghai, Sichuan, Gansu, Xinjiang and Yunnan provinces. Based on China’s Land Utilization and Land Cover Vector Diagram (250 m*250 m), an analysis found that approximately 163.05×104 km2 of land in this region are used for farming and raising livestock, of which alpine grasslands LIU Fenggui et al.: Risk analysis of snow disaster in the pastoral areas of the Qinghai-Tibet Plateau 419

covers an area of 157.67×104 km2, marshlands cover 2.23×104 km2, bottomland cover 1.28×104 km2, rural residential areas cover 0.06×104 km2, dry lands cover 1.78×104 km2, and paddy fields cover 0.03×104 km2 (Zhao et al., 2013) (Figure 3).

Figure 3 Agricultural and pastoral areas of the Qinghai-Tibet Plateau (Zhao et al., 2013)

5 Snow cover risk analysis of the Qinghai-Tibet Plateau

5.1 Hazard risk analysis of snow cover

In accordance with the hazard risk analysis model (1) detailed in Section 2.2.1, data on historical snow coverage during the period spanning 1951–2010 from 210 county-level administrative units (units for short) of the Qinghai-Tibet Plateau were analyzed using the law of large numbers and central limit theory. In this analysis, any county is marked as “1” if a snow disaster occurred at least once over the last 60 years. Ai is the frequency (occurrences/year) of i-level snow disaster (i.e., the number of occurrences of i-level disasters). Dij is the area of these units where snow disasters occurred; j is the number of the units affected at least once by an i-level snow disaster. Dividing Ai and Dij separately by the number of statistical years allows the frequency (hp) and intensity (ha) of the historical hazard risk to be derived, respectively. The two results added together is the hazard risk of historical snow cover (Hl). The potential hazard risk (Ha) can be obtained by adding together the number of days with snow cover (hr) and the average maximum snow depth (hs) from 1950 to 2010, both of which are obtained from the surface meteorological data over this period. Next, the results of the historical hazard risk and potential hazard risk are compared and analyzed. We find that the units once affected by historical snow disasters do not entirely coincide with those deemed to be a potential hazard risk now. The primary cause for this discrepancy is the difference in hazard-affected entities, because the historical hazard risk has implied some- thing about the state of hazard-affected entities in the past. For this reason, the paper gives a weight coefficient of 0.7 to the hazard risk associated with historical snow disaster and 0.3 to the hazard risk associated with potential snow disaster. Combining the index weight of each level (Table 2), the assessment model for snow disaster risk for the Qinghai-Tibet Pla-

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teau is as follows:

HH0.7la 0.3 H 0.7(0.5 h pa  0.5 h ) 0.3(0.5 hh rs  0.5 ) (4) where H represents the comprehensive hazard risk associated with snow disaster; hp the frequency of historical disasters; ha the historical intensity; hr the average number of days with snow cover per year; hs the average maximum snow depth per year. Given the large difference in units of measurement and physical dimensions for the indexes, all figures were standardized in calculation with the threshold value method. Finally, the processed data is presented as a map showing the distribution of comprehensive snow cover risk on the Qing- hai-Tibet Plateau (Figure 4).

Figure 4 Distribution of snow hazards in the agricultural and pastoral areas of the Qinghai-Tibet Plateau

As shown in Figure 4, the areas threatened by the highest snow cover hazard risk are located in the three counties of Tashkurghan, Karghalik, and Pishan in the northern section of the Karakoram Mountains, which together cover an area of 2.69×104 km2. In these areas, the number of days with snow cover every year can exceed 300, with the longest snow fall period, beginning earlier and ending later than in other regions (Wei et al., 2002). During the period from October to May, the areas covered with permanent snow are more susceptible to snow disasters (Wang et al., 2012). Areas at relatively high hazard risk of snow cover include the following 25 counties: Tianjun, Gangcha, Dulan, Qumalai, Zhiduo, Zaduo, Chengduo, Maqin, Maduo, Dari, Henan, Yushu, Nangqian, Banma, Cuona, Nielamu, Angren, Dingri, Dege, Ganzi, Seda, Shiqu, Litang, Aba, and Ruoergai, which are scattered across the southern Qinghai Plateau, southern Tibet, and northwestern Sichuan province. In these areas, which cover a vast expanse, water vapor flows from the southwest and the southeast and converges at low elevations during winter and spring above the regions around 30°N latitude, and then spreads out. The southwestern water vapor flow moves across the Hengduan Mountains and then rises against the high Bayan Har Mountains. As a result, snow disasters are likely to occur at the southern edge and eastern foothills of the Bayan Har Mountains. In LIU Fenggui et al.: Risk analysis of snow disaster in the pastoral areas of the Qinghai-Tibet Plateau 421

winter, the variation in the location of the subtropical high may be a main factor influencing snow disasters in these areas (Sa et al., 2013). Therefore, both the frequency and the intensity of historical snow disasters are relatively high in this region. The areas affected by the smallest risk of snow disaster include: Pangkog, Nyima, Gol- mud, Lenghu, Mangya, and Da Qaidam, which cover an area of 33.51×104 km2. Located in the hinterlands of the Chang Tang Plateau and the Qaidam Basin, these areas are supplied with little water vapor due to the surrounding high mountains, leading to low snow coverage (Dong et al., 2001; Wang et al., 2007).

5.2 Vulnerability analysis of snow cover

According to Table 1 and the vulnerability analysis method outlined in Section 2.2.2, and based on the profile data of 2010 from the Qinghai-Tibet Plateau, the vulnerability analysis of snow cover on the Qinghai-Tibet Plateau was carried out from the three perspectives of physical exposure, susceptibility, and ability of hazard-affected entities to cope with disaster. 5.2.1 Physical exposure of hazard-affected entities Physical exposure in a grazing district mainly considers the number of threatened people, houses, livestock, and other material objects necessary for economic production. In this paper, population density and livestock inventory at the end of the year are adopted as indicators of the physical exposure associated with snow cover. Combining the weight of each indicator (Table 2), the physical exposure can be expressed as,

EE0.512 0.5 E (5) Using the dimensionless calculation method, the distribution of physical exposure of snow disaster-affected entities on the Qinghai-Tibet Plateau can be determined, as shown in the distribution map (Figure 5a). 5.2.2 Susceptibility of hazard-affected entities On the Qinghai-Tibet Plateau, people are not engaged in agricultural work, but rather engaged in business, public service or other trades, mainly inhabit cities and towns, where they are less threatened by snow disasters. Compared to these people, those working in livestock farming are more susceptible to snow disasters. Furthermore, examining the details of livestock killed by snow disasters reveals that small livestock (e.g., sheep) are more susceptible to snow disasters than big livestock. Therefore, the proportion of workers engaged in primary industry and the proportion of small livestock are adopted as indicators of the susceptibility of hazard-affected entities to snow disasters on the Qinghai-Tibet Plateau (Table 2). Finally, susceptibility can be expressed as,

SS0.2512 0.75 S (6) the distribution of which on the Qinghai-Tibet Plateau can be determined using the dimensionless calculation method, as shown in the map (Figure 5b). 5.2.3 Ability to cope with disasters The overall ability to cope with disasters is a combination of the basic ability to cope along with other special coping abilities, as well as the specific conditions during disaster relief.

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Figure 5 Across agricultural and pastoral areas of the Qinghai-Tibet Plateau: distribution of physical exposure to snow disaster (a); sensitivity to snow disaster (b); ability to respond to snow disaster (c); vulnerability to snow disaster (d)

The basic ability to respond to disasters depends on the social infrastructure, while special abilities include the ability to forecast and prevent disasters. Conditions during disaster relief are the specific natural environmental conditions and social conditions present during disaster relief. By combining the indexes and their weight coefficients (Tables 1 and 2), the comprehensive abilities to cope with snow cover on the Qinghai-Tibet Plateau can be expressed as,

D 0.4DDizh 0.4 0.2 D (7)

where the basic abilities (Di) can be expressed as,

Dis0.07BWPTCZ 0.07 sssss 0.11 0.08 0.45 0.22 (8)

the special abilities (Dz) can be expressed as,

Dzs0.17YX 0.83 s (9) and conditions during disaster relief can be expressed as,

Dhss0.67JQ 0.33 (10) With the dimensionless calculation method, the spatial distribution of the ability to cope with snow cover disasters on the Qinghai-Tibet Plateau can be mapped (Figure 5c). 5.2.4 Vulnerability of hazard-affected entities By combining the coefficients of indexes in Table 1 and the analysis model in Section 2.2.2, integrating physical exposure, susceptibility and the ability to respond to the hazard, the overall vulnerability of hazard-affected entities to snow cover disasters on the Qinghai-Tibet Plateau can be expressed as, VES 0.53 0.17 0.3(1 D ) (11) LIU Fenggui et al.: Risk analysis of snow disaster in the pastoral areas of the Qinghai-Tibet Plateau 423

Through dimensionless calculation based on this formula, the distribution of the vulnerability to snow cover on the Qinghai-Tibet Plateau can be mapped (see Figure 5d). From the results of this analysis, the hazard-affected entities most vulnerable to snow cover are predominantly located in the three Tibetan counties of Gerze, Pangkog and Nyima, and some parts of Karghalik County in the Xinjiang region, covering an area of 38.32×104 km2. The high vulnerability in these regions can be attributed to the harsh natural environmental conditions, the poor social and economic situations of the population, a large proportion of small livestock relative to larger livestock, and poor transport connections. By contrast, the hazard-affected entities in the eastern and southern parts of the Plateau (largely to the south of 30°N) are least vulnerable to snow cover disasters due to a better ability to cope with disasters, less physical exposure (e.g., a smaller livestock inventory at the end of each year) and lower vulnerability (e.g., a smaller proportion of small livestock relative to big livestock).

5.3 Comprehensive analysis of snow cover risk

The comprehensive snow cover hazard risk (Figure 6) on the Qinghai-Tibet Plateau can be obtained by analyzing the results in sections 3.1 and 3.2 based on the basic risk analysis model (UNDHA, 1991): RHV  (12) where R is the snow cover risk index; H is the hazard risk index of snow cover; V is the overall vulnerability of hazard-affected entities to snow disaster. In terms of risk levels of snow cover disasters on the Qinghai-Tibet Plateau, regions within the counties of Karghalik, Tashkurghan and Pishan in the Xinjiang region, which cover a total area of 1.95×104 km2, are threatened by the highest level of risk. Areas with a medium level of risk from snow disaster are found in central-western Tibet between the Gangdise Mountains and the Himalayas, and the southeastern part of the southern Qinghai

Figure 6 Distribution of snow risk in agricultural and pastoral areas of the Qinghai-Tibet Plateau

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Plateau. The risk level associated with snow cover is generally low in the vast regions to the south of 30°N latitude and at the eastern edge of the Qinghai-Tibet Plateau. Furthermore, the regions subjected to the lowest level of snow cover risk are scattered in four Tibetan counties (i.e., Gerze, Nyima, Pangkog and Namling), 5 counties or cities in Qinghai province (i.e., Golmud, Zhiduo, Lenghu, Mangya and Da Qaidam), 2 counties in Xinjiang (i.e., Akto and Yarkand), and certain regions of Gongshan in Yunnan province, covering a total area of 25.34×104 km2. From the perspective of the whole plateau, high-altitude areas in the hinterland are subject to a much higher risk than the marginal regions. Regions at mid–high risk levels are all located to the south of the Kunlun Mountains and the southern Qinghai Plateau to the north of the Tanggula Mountains, where the physical exposure of hazard-affected entities to snow cover hazards and other risk factors may coincide with each other.

6 Summary

(1) The Qinghai-Tibet Plateau is a region of China that very frequently experiences snow cover disasters. An accurate and objective assessment of snow cover risk is important if the hazard of snow disaster is to be mitigated. With the help of risk assessment theories formu- lated in China and abroad, and based on historical snow disaster data, this paper has calculated the historical hazard risk and the potential hazard risk from snow disasters. An index system of vulnerability analysis, including physical exposure, susceptibility and the ability to cope with disaster, is established based on data from 2010 collated from 210 counties on the Qinghai-Tibet Plateau, which reflects the overall risk assessment of snow disaster on the Qinghai-Tibet Plateau. (2) Analysis reveals that the highest hazard risks of snow cover disaster are found in the southern part of the Qinghai Plateau, southern Tibet and southwestern Sichuan, while risks in the Chang Tang Plateau and the Qaidam Basin are the lowest. The highest vulnerability of hazard-affected entities to snow cover disasters on the plateau is found in the Tibetan counties of Gerze, Pangkog and Nyima, and certain units of Karghalik county in Xinjiang, while the lowest vulnerability to snow cover disaster is found in the eastern and southern parts of the plateau, especially the units to the south of 30°N. (3) On the Qinghai-Tibet Plateau, the areas at highest risk of snow disaster are located in the counties of Karghalik, Tashkurghan and Pishan, while the areas at mid-level risk are found in central-western Tibet between the Gangdise Mountains and the Himalayas, and in the southeastern part of the southern Qinghai Plateau. The snow cover risk level is generally low in the vast regions to the south of 30°N and on the eastern edges of the Qinghai-Tibet Plateau. From the perspective of the whole plateau, high-altitude areas in the hinterland are subjected to a much higher risk than the marginal areas. From the results above, it can be concluded that with the expansion of hazard-affected populations the risk of snow disasters on the Qinghai-Tibet Plateau is likely to grow, and that the hazard risk calculated from historical snow disasters and the hazard risk associated with potential snow disasters will converge. On the Qinghai-Tibet Plateau, the 30°N latitude line appears to be significant in the context of snow disaster, suggesting that the physical and geographical significance of this feature should be further studied. LIU Fenggui et al.: Risk analysis of snow disaster in the pastoral areas of the Qinghai-Tibet Plateau 425

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