Vulnerability Assessment of the Mountain-Basin System in the Northern Tianshan Mountains

Home , Changji, Hutubi County, Jinghe County, Kuytun, Manas County, Qitai County

Vulnerability Assessment of the Mountain-basin System in the Northern Tianshan Mountains

Bo Li, Jianzhai Wu, Jie Chong, Rui Hong, Xinshi Zhang State Key Laboratory of Earth Surface Processes and Resource Ecology College of Resource Science and Technology Beijing Normal University Beijing 100875, China

e-mail: [email protected] T

Abstract—mountain-basin system (MBS) is the basic pattern of with, adverse effects of climate change, including climate natural heterogeneity and regionalization in North-western variability and extremesP [6]P P P. The theory of vulnerability China, and its big extent, multi-layer and regular complex of assessment proceeded in many scientific fields, such as

terrestrial ecosystems in Northern Tianshan Mountains, Xinjiang, PP P ecosystem change [16], [20],P eco-environment studyP [10], [26] P China, is the basin and frame to form this kind of pattern. To and disaster studyP [12]P ,P under the background of climate support regional sustainable development both on theory and changing. The Lack of multidisciplinary assessment closely practice, more and more vulnerability assessments became related to policy decision is the shortage of traditional comprehensive studies by combining the ecosystem with socio- assessmentP [15]P .P The vulnerability assessment, which links the economic system. Based on the vulnerability meaning that was regional or interregional environment change with socio- composed of impact and adaptive ability, a vulnerability economic development, would be more significant and assessment on fifteen counties in the northern Tianshan Mountains was presented in this paper. The ecosystem services valuable in social practice and theory studyP [1]P .P Vulnerability changing to land use/cover change (LUCC) was regarded as assessment should combine social science and natural science, impact. Based on fourteen indexes from resource holding, society and focus on integrating the information across scales and and economy development data, the adaptive ability was disciplines including various human activities, and regard evaluated by using various methods and means such as AHP (the social-economic-natural complex ecosystem as study objectP [1],P analytic hierarchy process), fuzzy comprehensive judging model, [9], [25]. LUCC data and GIS. The impact was divided into four grades The northern slope of the Tianshan Mountains is a typical, and the adaptive capacity three grades. And the fifteen counties wide-range, multi-hierarchy, regular mountain-basin system were divided into five grades under the certain assessment principles, and the higher grade meant more vulnerability. (MBS)P P [23]P P in arid areas of northwestern China, and also is the Results showed that: The first grade included Usu City and politics, economy, culture center of Xinjiang, and the key area Changji City. The second included Hutubi County, Miquan of China Western Development. Resent years environmental County, Fukang City, Jimsar County, Qitai County and Mori problems, resulted from population increase, industry and Kazak Autonomous County. Karamay City and Urumqi City agriculture development, unreasonable land use, have affected were in the third grade, the fourth grade was composed of eco-economic and social development. This paper was aiming Kuytun City and Shawan County, and Jinghe County, Shihezi at discussing on the vulnerability of regional complex City and Manas County fell into the fifth grade. Vulnerability ecosystem to support the sustainable development of this kind assessment results reflected both eco-environment change and of typical area. socio-economic development level, which were the key references to ecological restoration and industry structural adjustment. It is II. STUDY AREA necessary to make great efforts to carry out relevant countermeasures and decrease vulnerability, which would benefit The study area located in the north of Xinjiang Uigur the sustainable development of this kind of typical terrestrial ecosystem complex of MBS in North-western China, and additionally other similar areas.

Keywords-Mountain-basin system; vulnerability; the northern Tianshan Mountains

I. INTRODUCTION The vulnerability assessment, which has been given special attention in many global environment and development questionsP [8]P ,P was defined in third Assessment Report (TAR) of the Intergovernmental Panel on Climate Change (IPCC): the degree to which a system is susceptible to, or unable to cope Figure 1. Administrative divisions of the MBS in northern Tianshan Mountains, Xinjiang, China

Authorized licensed use limited to: NATIONAL LIBRARY OF CHINA. Downloaded on April 14, 2009 at 02:51 from IEEE Xplore. Restrictions apply. Autonomous Region of Province and the south of Zhungarian B. Methods of vulnerability assessment desert, including Urumqi City, Karamay City, Shihezi City, The template is used to format your paper and style the text. Changji City, Miquan City, Fukang City, Hutubi County, All margins, column widths, line spaces, and text fonts are Manas County, Shawan County, Usu City, Kuytun City, Qitai prescribed; please do not alter them. You may note County, Jimsar County, Mori Kazak Autonomous County, peculiarities. For example, the head margin in this template Jinghe County (Fig. 1)P [2].P The elevation in study area varies measures proportionately more than is customary. This from about 5000m in mountain to 200m in desert margin, and measurement and others are deliberate, using specifications the annual precipitation changes approximately from 500mm in that anticipate your paper as one part of the entire proceedings, mid-lower mountain to 100mm in northern desert, meantime and not as an independent document. Please do not revise any the annual average temperature changes from less than 2℃ in of the current designations. In this paper the relative change of mountain to 16~18℃ in plain, which show its typical physical ecosystem services represented potential impact and was geography characters of verticalTT belts TPT [3].PTPTP InTT 2005 its total 4 determined as follows: population was 516.62×10PP ,PP and accounts for 26.32% of whole 8 Xinjiang, but its GDP was 1129.37×10PP yuan,PP 51.34% of whole ESrc= ESr()j − ESr ()i (2) Xinjiang [14]P .PPP

ESp III. DATA RESOURCE ESr = (3) The data used in this study, included land use/cover change ESpb (LUCC) data, and the natural data and socio-economic data in study area. The LUCC data was in the form of coverage at the Where ESrc is the relative change of ecosystem service scale of 1:100 000, and included six land use classifications and 25 sub-classifications, which was derived from the analysis value, ESr is the relative value of ecosystem service, ESp is on Landsat TM remote image data of two terms, 1989 and the ecosystem service value per area, ESpb is the background 2000. The nature and socio-economic data came from relevant value, which equal to the average value per area of ecosystem researches and statistics yearbooks in study area. service in study area. i, j are the begin and end of study period. Impact was gotten by the standardization of ESrc . IV. METHODOLOGIES Adaptive capacity (AC) was quantified by fuzzy model A. Connotation of vulnerability based on the indexes of the three aspects of economy, resource Vulnerability assessment included both the traditional and social development. After different elements of the contents of impact assessment, and the assessment of regional vulnerability function have been quantified by (2) and (3), the adaptive capacity to cope with potential impacts on global combination of the potential impact and the adaptive capacity should be taken into account mainly according to the meaning change [15].P Impact was the influence of global change to region. Adaptive capacity was regional potential capacity for of vulnerability elements and specialty of detail data. Both influenced-system to reduce potential loss by adjustment, impact and adaptive capacity were relative value of every which was usually determined by knowledge and technology assessment cell, so the simple calculation was unsuitable. In the paper, the vulnerability degree was determined by a series of level, economic power, and so on [11].P Vulnerability was the steps of classification assessment according to concerned function of impact and adaptive capacityP [9]P :P principles. VfIAC= (, ) (1) ()j ()jj () V. IMPACT ASSESSMENT A. Calculation of impact Where V is the vulnerability at the time of j, I is the ()j ()j In this paper the impact of ecosystem to regional impact at he time of j, AC is the adaptive capacity level at socioeconomic system was defined as the relative change of ()j ecosystem service value caused by LUCC, because of the he time of j, j is the end of study period. serious contradiction of human activity with land in study area.

TABLE I. BIOME EQUIVALENTS FOR THE EIGHT LAND-USE CATEGORIES AND CORRESPONDING ECOLOGICAL VALUE COEFFICIENTS Built-up Cropland Forest area Grass land Water area Unused land area Land High- Middle- Lower- Gobi & use/cover Farm Wood covered covered Permanent Construction else covered else Marshes Uncovered else land land grass grass glacier&snows land grass land land land Rock Middle- Lower- Corresponding Farm Wood Sparse High- covered covered Permanent Water Construction covered Wetland Bare land Desert ecosystem land land woodland grass grass glacier&snows body land grass land land land Ecological value coefficients 7528 31352 16254 10253 7887 5521 1271 50079 6190 68315 1385 458 2 (yuan/(hmP .a))P

Authorized licensed use limited to: NATIONAL LIBRARY OF CHINA. Downloaded on April 14, 2009 at 02:51 from IEEE Xplore. Restrictions apply. Firstly the ecological value coefficient should be TABLE II. INDEX VALUE AND CLASS OF REGIONAL IMPACT determined before estimating the ecosystem service change. Impact class District (index value)

According the related research resultsP [7],P [18],PPP the quantified First grade Usu City (1.00), Changji City (0.82), Qitai County (0.68) Jimsar County (0.38), Mori County (0.27), Miquan County value coefficients of various kinds of ecosystems were to be Second grade gained. By expert consulting, the amended coefficient of high- (0.23), Fukang County (0.16), Hutubi County (0.06) Karamay City (-0.02), Urumqi City (-0.04), Kuytun City (- covered grassland, middle-coved grassland and lower-coved Third grade 0.06), Shawan County (-0.08), Jinghe County (-0.09) grassland, respectively were 1.2, 1.0, and 0.7. The ecological Forth grade Manas County (-0.19), Shihezi City (-1.00) value coefficients of permanent glacier&snows and bare land, referenced to the estimate results by remote sensingP [5]P .P The From 1989 to 2000, the eco-environment change in the east ecological value coefficient was listed in Table 1. part of study area was better than the west. The districts with positive impact mostly locate in the east part of study except The ecological value coefficient of every ecosystem was in for Usu City. And district with negative impact, only Urumqi the reasonable bound of single ecosystem according to the City is in the east part. The worst districts including Manas relevant research results [5], [17], [19], [21].PPP The area change County, Shihezi City, located in west part. Of course, the of 12 ecosystem classifications was gained by LUCC data differences of natural conditions in different regions were the (Table 1). The relative change of ecological value of each main causes of spatial heterogeneity of impact, but the assessment cell was calculated by (2) and (3), and then by difference of land use change resulted from regional socio- standardization, the impact index was gotten and divided into economic activities might be more critical or important, which four grades, in Table 2. And the higher the grade was, the resulted in the difference of ecosystem service function change. bigger the negative force was, and the more degenerative its eco-environment was. VI. ADAPTIVE CAPACITY ASSESSMENT

B. Results and analysis A. Assessment system The impact resulted from region ecosystem services value Constructing the index system of adaptive capacity change due to the LUCC, reflected the influence of eco- assessment should accord to not only the general principles environment change to social-economic system. Assessment including objectivity, scientism and validity, but also the cells in study area were of significant differences. Usu City was special principles as the following: a) to embody the essence of the best and the worst district was Shihezi City. As showed in adaptive capacity, which is the potential adaptive of regional Table 2, the first grade districts include Usu City, Changji City, socioeconomic system to natural ecosystem, therefore majority Qitai County and the second grade cells were Jimsar County, should come from socioeconomic aspect; b) principles of Mori Kazak Autonomous County, Miquan County, Fukang integrity and being identifiable; c) principles to be comparable, County, Hutubi County. The districts belonged to those two accountable, feasible and sensible; d) adaptability principle of grade had the positive impact index value, reflecting that eco- scale to assessment cells. Reference to the relevant researchesP environment protection in these districts had been paid more [4]P ,PPP [22], [24], [27], in Table 3, the index system of adaptive attention to, and correspondingly their eco-environmental capacity assessment with four layers was founded by the quality was better. analytic hierarchy process (AHP). The first layer is object, namely regional adaptive capacity. The second is item Karamay City, Urumqi City, Kuytun City, Shawan County, including resources, society and economy. The third layer is Jinghe County were fall in the third grade and the fourth grade factor, which is to analyze item in detail. The fourth is index, composed of Manas County and Shihezi City. Impact index which is the further analysis on factor. value of those districts was negative. In these districts more attention had been paid to economic development, and more resources were derived from their ecosystems, but more negative influences were forced to their ecosystems.

TABLE III. THE ASSESSMENT INDEX SYSTEM AND WEIGHT VALUE Weight Weight Weight Object Item Factor Index value value value Land Crop land area per capita 0.576 0.476 Resources resource Land desertification rate 0.424 0.184 level Human Population density 0.386 0.524 resource Nonagricultural population rate 0.614 Science Students per 10 000 population 0.424 Education 0.436 level Expenditures for scientific and technological activities per capita 0.576 Adaptive Society Infrastructure Numbers of telephone subscribers per hundred families 0.389 Capacity development 0.494 0.389 level Basic installation investment unit area 0.611 index level Social Numbers of hospital beds per 10 000 population 0.667 Insurance 0.175 level Beds of welfare institute per 10 000 population 0.333 Economy Per capita GDP 0.635 Economy 0.524 quantities Added values of tertiary industry per capita 0.365 development 0.322 Economy Percentage of primary industry 0.586 level 0.476 efficiency Self-supporting ratio of finance 0.414

Authorized licensed use limited to: NATIONAL LIBRARY OF CHINA. Downloaded on April 14, 2009 at 02:51 from IEEE Xplore. Restrictions apply. TABLE IV. INDEX VALUE AND CLASS OF REGIONAL ADAPTIVE CAPACITY B. Assessment methods Adaptive District (index value) In Table 3, The Weight value of index system was capacity class gotten by AHP based on construction of the judgment First grade Karamay City (0.8179), Urumqi City (0.6178) matrix, hierarchy total array, consistency test, and expert Shihezi City (0.4878), Miquan County (0.3689), Changji consultation. Taking into account that index system is Second grade City (0.3537), Fukang County (0.3521), Shawan County (0.3369), Kuytun City (0.3337), Usu City (0.3086) multiplayer, a fuzzy and comprehensive judging model [19] PPP Manas County (0.2724), Jinghe County (0.2545), Hutubi was adopted as the follows: Third grade County (0.2491), Jimsar County (0.2113), Qitai County Suppose that the first assessment model has p objections (0.1882), Mori County (0.1871) Jimsar County, Hutubi County, Manas County, Jinghe County with the value of uijk (i＝1,2,3; j=1,2…n; k=1,2…m; n=2,3; and Mori Kazak Autonomous County were in the third grade. m=2), u s (s=1,2,…p) is the value of s objections. ijk As for the items of adaptive capacity assessment, Shawan (max) (s ) (min) (s ) County had the highest resources level and the lowest was Let uuijk= max ijk , uuijk= min ijk , then s s Jinghe County. As for the Society development level and Economy development level, the highest was Karamay City, ss⎡⎤⎡⎤(min) (max) (min) , if is a positive ruuijk=−⎣⎦⎣⎦ ijk ijk/ u ijk − u ijk uijk and the corresponding lowest districts were Jimsar County, Qitai County. index. And russ=−⎡⎤⎡⎤(max) u/ u (max) − u (min) , if u is ijk⎣⎦⎣⎦ ijk ijk ijk ijk ijk Region adaptive capacity is closely related to its economy a negative index. If index has Mark-value, means showed development level. Karamay City, Urumqi City, Shihezi City above was carried independent at the two side of Mark-value. and Changji City were the key areas and increasing poles of s economic development belt in the northern slope of the rijk is subordinate degree of the s assessment cell and the Tianshan Mountains. Higher economic development level can Standardized matrix was gotten: promote the level of other aspects such as science, education and basic establishments. With high adaptive capacity (1) (2) (p ) ⎡⎤rrij11 ijK r ij 1 Karamay City and Urumqi City was in the first grade, but Qitai ⎢⎥County, Jimsar County and Mori Kazak Autonomous County rr(1) (2) r (p ) R = ⎢⎥ij22 ijK ij 2 with low adaptive capacity were backward regions grade and in ij (4) third grade. ⎢⎥MMMM ⎢⎥ rr(1) (2) r (p ) Adaptive capacity of districts distributed evenly in the ⎣⎦⎢⎥ijm ijmK ijm study area, which was different from the impact. One hand, Karamay City and Urumqi City with negative impact were of If Wij (Wij ＝Wij1 , Wij2 ,…, Wijm ) is the weight value of highest adaptive capacity. On the other side, Qitai County, Jimsar County, Mori Kazak Autonomous County with the index, then the first assessment Vij is the follows: positive impact had lower adaptive capacity. This reflected the VWR=×; If W (W ＝W , W ,…, W ) is the weight realistic contradiction of the eco-environment protection and ij ij ij i i i1 i2 im economic development in the study area. value of factor, then the second assessment is the follows: ＝ VWRiii=×; If W (W W1 , W2 ,W3 ) is the weight value VII. VULNERABILITY ASSESSMENT of item, then the final assessment result is the follows: A. Regional vulnerability classification VWRiii=×. The vulnerability of assessment cells was determined by

Based on statistic dataP [13]P ,P the final result V is subordinate their impact and adaptive capacity, and it was necessary to degree of the assessment cell to adaptive capacity as showed in Preparation for assessment Steps and principle of assessment Table 4. The region adaptive capacities were divided into three grades, and higher grade meant lower regional adaptive capacity. ① ② ③ ④ ⑤ ⑥ ⑦ 1 2 1 1 1 1 1 2 3 1 1 1 1 C. Results analysis 3 4 2 2 2 2 1 3 1 2 2 2 Adaptive capacity reflects the level of regional economy, 2 2 4 2 2 2 2 science and education, basic establishment and so on. The 3 5 2 2 2 2 1 4 2 2 3 3 higher adaptive capacity means the better power for region to 3 2 5 2 2 3 4 buffer and reduce the negative influence of ecosystem change. 3 6 3 3 4 5 The highest was Karamay City and the lowest was Mori Kazak 1 5 2 2 3 4 4 Autonomous County. 2 6 3 3 4 5 3 7 3 3 4 5 As for the adaptive capacity, in Table 4, the first grade Figure 2. Steps and law of region vulnerability assessment included Karamay City, Urumqi City. The second included In Fig. 2: ①② the grade of impact; the grade of adaptive capacity; ③ Sum of impact grade and adaptive capacity grade; ④ The firstly classify (base on the Sum of impact grade and adaptive Usu City, Changji City, Miquan County, Fukang County, capacity grade); ⑤ The secondly classify (the key action of positive impact); ⑥ The thirdly classify (the essence difference between positive impact and negative impact); ⑦ The fourthly classify Kuytun City, Shawan County and Shihezi City. Qitai County, (adaptive capacity becoming more important to vulnerability assessment under negative impact).

Authorized licensed use limited to: NATIONAL LIBRARY OF CHINA. Downloaded on April 14, 2009 at 02:51 from IEEE Xplore. Restrictions apply. combine the two aspects. The impact index and adaptive Urumqi City were in the third grade, because of the highest capacity index in the above had been calculated in relevant adaptive capacity; Kuytun City and Shawan County were in the value. So the direct calculation using them to determine the fourth grade and were of higher vulnerability because of their vulnerability would not be right. Based on the grade of impact negative impact and lower adaptive capacity; The fifth grade and adaptive capacity, Qualitative and quantitative methods districts, most vulnerable areas, were composed of Jinghe and their combination were used to determine the vulnerability County, Shihezi City and Manas County. Shihezi City and under the direction of assessment principles showed in Fig. 2. Manas County were of the serious negative impact from the change of ecosystem service, especially in Shihezi City. Jinghe The principles of vulnerability assessment were: a) Firstly, County had the third grade impact and lower adaptive capacity. The higher grade of impact meant the more intensive negative impact, and the higher grade of adaptive capacity meant the The above assessment results would support the decision- weaker adaptive capacity, so the sum of their grade number making for regional sustainable development. The reflected the difference of vulnerability, and the bigger sum countermeasures, two ways to decrease the vulnerability should value meant the higher vulnerability. b) When the impact was be the key directions for the coordinate development of positive, it was the critical factor to the vulnerability. The ecosystem and eco-social system. The first was to make the adaptive capacity was more important to the vulnerability when impact be positive, which the essence was to improve the eco- the impact was positive, than the time impact was negative. c) environment protection. The second was to increase the The positive or negative impact represented the better or worse adaptive capacity, which meant to improve the economy eco-environment, which was the fact that already happened. development. The harmony development of eco-economy And meanwhile the adaptive capacity was the incepting and demanded the combination of structure and function of social- buffer capacity to impact and couldn’t change regional impact economic-natural complex ecosystem. The district belong to nature. The final result was vulnerability assessment grade. different grade of vulnerability should keep the balance of eco- environment protection and economy development mainly The theoretical possible cases in the vulnerability considering its vulnerability factors. assessment were listed in Fig. 2 (⑦, the fourthly classify, was the final result). Based on the grade of impact and adaptive In Manas County and Shihezi City the eco-environment capacity listed in Table 2 and Table 4, the grade of region protection should be improved, which was the critical question vulnerability was determined under the principles listed in Fig. for their sustainable development. In industrial development 2. The final result was showed in Fig. 3, which illustrated and structure adjustment, more attention should be paid to the vulnerability distribution. optimization of land pattern. Their development greatly was on the cost of the eco-environment deteriorative. And it is B. Vulnerability analysis impossible to decrease vulnerability only by simple and single The vulnerability in study area was divided into five grades, economy development. and the higher the grade was, the more vulnerable the district Because of the advantages of the resource and location, was. The result showed that the western part of study area was Karamay City and Urumqi City were the most powerful more vulnerable than eastern, and districts with fifth grade economic districts in study area and even in whole Xinjiang. were located in the middle and western part. But they were in the third vulnerability grade. And the higher The first grade included Usu City and Changji city, where the grade was in districts with the third, fourth and fifth the ecosystem services were of the maximum increase due to vulnerability grade, and the more necessary to enhance eco- land use change and at the same time, their adaptive capacity environment protection. was the second grade, therefore the two cities had lowest Increasing the adaptive capacity was effective measure to vulnerability; Hutubi County, Miquan County, Fukang city, decrease the vulnerability. Because of the lower economic Jimsar County, Qitai County and Mori Kazak Autonomous power, Qitai County and Mori Kazak Autonomous County had County fell in the second grade. Although Qitai County was in the lowest adaptive capacity index values, 0.094 and 0.003. the first grade in impact, its vulnerability grade was second The aim of sustainable development was not the slow-speed because of its lowest adaptive capacity; Karamay City and economy development, but was the balance of eco- environment protection and economy development. The districts in the first vulnerability grade or second should emphasize their economy development to improve the people’s living standard as long as not at the cost of eco-environment deterioration. Most of Cities and Counties as assessment cells in this paper spanned the whole structure of MBS, mountain- oasis/ecotone-desert basin system, from the south to the north. Its inner natural landscape complex matches green bridge

system (GBS) [23],P theoretical model of sustainable development of MBS. And accordingly the development Figure 3. Sketch of vulnerability assessment in study area strategy to decrease vulnerability should contribute to the realization of GBS.

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