The vulnerability evolution and simulation of social-ecological systems in a semi-arid area: A case study of Yulin City, China

doi:10.1007/s11442-018-1465-1

Abstract

Abstract:

Taking the semi-arid area of Yulin City as an example, this study improves the vulnerability assessment methods and techniques at the county scale using the VSD (Vulnerability Scoping Diagram) assessment framework, integrates the VSD framework and the SERV (Spatially Explicit Resilience-Vulnerability) model, and decomposes the system vulnerability into three dimensions, i.e., exposure, sensitivity and adaptive capacity. Firstly, with the full understanding of the background and exposure risk source of the research area, the vulnerability indexes were screened by the SERV model, and the index system was constructed to assess the characteristics of the local eco-environment. Secondly, with the aid of RS and GIS, this study measured the spatial differentiation and evolution of the social-ecological systems in Yulin City during 2000-2015 and explored intrinsic reasons for the spatial-temporal evolution of vulnerability. The results are as follows: (1) The spatial pattern of Yulin City’s SESs vulnerability is “high in northwest and southeast and low along the Great Wall”. Although the degree of system vulnerability decreased significantly during the study period and the system development trend improved, there is a sharp spatial difference between the system vulnerability and exposure risk. (2) The evolution of system vulnerability is influenced by the risk factors of exposure, and the regional vulnerability and the spatial heterogeneity of exposure risk are affected by the social sensitivity, economic adaptive capacity and other factors. Finally, according to the uncertainty of decision makers, the future scenarios of regional vulnerability are simulated under different decision risks by taking advantage of the OWA multi-criteria algorithm, and the vulnerability of the regional system under different development directions was predicted based on the decision makers' rational risk interval.

Key words: social-ecological systems (SESs), VSD assessment framework, vulnerability, Yulin City

Jia CHEN, Xinjun YANG, Sha YIN, Kongsen WU, Mengqi DENG, Xin WEN. The vulnerability evolution and simulation of social-ecological systems in a semi-arid area: A case study of Yulin City, China[J].Journal of Geographical Sciences, 2018, 28(2): 152-174.

Figures/Tables 12

Figure 1

Figure 2

Table 1

Table 2

Screening results of the sensitivity/adaptive capacity index"

Dimension layer	Principal component factor		Universal index						Index properties		Specific index				Index pro- perties
Sensi- tivity	Basic population principal component		The proportion of female population (%) The proportion of agricultural population (%) The proportion of employed population of agriculture, forestry, animal husbandry and fishery (%) Natural population growth rate (‰) The average education level of labor (years) Population density (people/km²)						+ * - + - +		The number of employees employed in extractive industry (people)				*
	Agriculture and land principal component		The proportion of cultivated land area (%) The proportion of paddy field/irrigated land area (%) The growth rate of housing construction area (%) The proportion of abandoned cultivated land area due to disasters (%) Grain yield per unit area (ha/kg)						* * + + -		Aquaculture area (ha) Main cash crop yield (Chinese jujube etc.) (tons)				* -
	Ecology and environment principal component		Forest coverage (%) The proportion of effective irrigation area (%) Area of forest for water and soil conservation (10³ ha) Total energy consumption (tons of standard coal)						- - - *		Total emission of industrial waste/waste water (10⁴ tons)				+
	Economic development principal component		Per capita gross domestic product (yuan) The proportion of total value of output of agriculture, forestry, animal husbandry and fishery (%) Industrial structure dependence index Engel’s coefficient Comprehensive energy consumption per unit GDP (10,000 yuan/ tons of standard coal)						- - + + +		Industrial water consumption per unit GDP (tons/10⁴ yuan)				+
Adaptive capacity	Education and technology principal component		The number of teachers of per ten thousand people possessed (people/10,000 people) The proportion of students on campus (%) The number of communications equipment per 100 households (telephone/100 households) Comprehensive utilization of product output value of “three wastes” (10⁴ yuan) The proportion of fiscal expenditure on education (%)						+ + + + +		The attainment rate of the industrial wastewater (%) Water saving irrigation machinery (suits)/drainage and irrigation power machinery (kw)				+ +
	Social infrastructure principal component		The number of medical beds of per ten thousand people possessed (people/10,000 people) The number of health service employees (people) Fiscal expenditure on water affairs of agriculture and forestry (10⁴ yuan) Social security expenditure per capita (10⁴ yuan/10,000 people) The number of employees of transportation, storage and postal service (people)						+ + + + +		/
	Population and economy principal component		Per capita net income of farmers (yuan) The density of social fixed assets investment (10⁴ yuan/ km²) The total output value of agriculture, forestry, animal husbandry and fishery (10⁴ yuan) The third industrial added value accounted for the proportion of GDP (%) Local fiscal expenditure (10⁴ yuan) Industrial structure diversification index						+ + + + + +		/
Dimension layer		Principal component factor		Universal index				Index properties		Specific index			Index properties
Adaptive capacity		Disaster prevention and mitigation facilities principal component		Reservoir capacity (10⁴ m³) The density of drought resistant infrastructure (number/km²) Afforestation area in the year (ha) The area of forest for water and soil conservation (1000 ha)				+ + + +		Dike length (km)			+
Results of sensitivity principal component							Results of adaptive capacity principal component
Years				2000	2005	2011	Years			2000		2005		2011
Minimum eigenvalue				1.070	1.139	1.330	Minimum eigenvalue			1.202		1.062		1.351
Cumulative contribution rate				91.94%	90.75%	89.99%	Cumulative contribution rate			88.30%		89.53%		88.75%

Table 2

Figure 3

Figure 4

Table 3

Figure 5

Figure 6

Figure 7

Table 4

Figure 8

References 45

[43]	Yang X J, Shi Y Z, Wang Z Q, 2015. Exploring the impacts of road construction on a local social-ecological system in Qinling mountainous area: A resilience perspective.Acta Geographica Sinica, 70(8): 1313-1326. (in Chinese) doi: 10.11821/dlxb201508010
[44]	Zhao H L, Zhao X Y, Zhang T Het al., 2011. Desertification process and its spatial differentiation in arid areas of Northwest China.Journal of Desert Research, 31(1): 1-8. (in Chinese) doi: 10.3724/SP.J.1011.2011.00197
[1]	Acosta-Michlik L, Espaldon V, 2008. Assessing vulnerability of selected farming communities in the Philippines based on a behavioural model of agent’s adaptation to global environmental change.Global Environmental Change, 18(4): 554-563. doi: 10.1016/j.gloenvcha.2008.08.006
[2]	Adger W N, 2006. Vulnerability.Global Environmental Change, 16: 268-281. doi: 10.1016/j.gloenvcha.2006.02.006
[3]	Al-Kalbani M S, Price M F, Abahussain Aet al., 2014. Vulnerability assessment of environmental and climate change impacts on water resources in Al Jabal Al Akhdar, Sultanate of Oman.Water, 6: 3118-3135 doi: 10.3390/w6103118
[4]	Chen C C, Xie G D, Zhen Let al., 2008. Analysis of Jing he watershed vegetation dynamics and evaluation of its relation to precipitation.Acta Ecologica Sinica, 28(3): 925-938. (in Chinese) doi: 10.1016/S1872-2032(08)60032-3
[5]	Chen P, Chen X L, 2010. Summary on research of coupled human-environment system vulnerability under global environmental change. Progress in Geography, 29(4): 454-462. (in Chinese) doi: 10.11820/dlkxjz.2010.04.010
[6]	Ciftcioglu G C, 2017. Assessment of the resilience of socio-ecological production landscapes and seascapes: A case study from Lefke Region of North Cyprus.Ecological Indicators, 73: 128-138. doi: 10.1016/j.ecolind.2016.09.036
[7]	Cumming G S, 2011. Spatial Resilience in Social-Ecological System. University of Cape Town: Springer Press.
[8]	Duan C, 2005. Regional sustainable development evaluation index system and comprehensive evaluation.Technoeconomics & Management Research, (3): 27-28. (in Chinese)
[9]	Frazier T G, Thompson C M, Dezzani R Jet al., 2014. A framework for the development of the SERV model: A spatially explicit resilience-vulnerability model.Applied Geography, 51: 158-172. doi: 10.1016/j.apgeog.2014.04.004
[10]	Frazier T G, Thompson C M, Dezzani R Jet al., 2013. Spatial and temporal quantification of resilience at the community scale.Applied Geography, 42: 95-107. doi: 10.1016/j.apgeog.2013.05.004
[11]	Gómez-Ortiz A Oliva M., Salvà-Catarineu M et al., 2013. The environmental protection of landscapes in the high semiarid Mediterranean mountain of Sierra Nevada National Park (Spain): Historical evolution and future perspectives.Applied Geography, 42: 227-239. doi: 10.1016/j.apgeog.2013.02.006
[12]	Holling C S, 2001. Understanding the complexity of economic, ecological, and social systems.Ecosystems, 4: 390-405. doi: 10.1007/s10021-001-0101-5
[13]	Huang J Y, Liu Y, Ma Let al., 2012. Review on the theoretical model and assessment framework of foreign vulnerability research.Areal Research and Development, 31(5): 1-15. (in Chinese)
[14]	IPCC, 2014. Climate Change 2014. Impacts, adaptation, and vulnerability Part B: Regional aspects. In: Working Group II Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.
[15]	Jones B, Andrey J, 2007. Vulnerability index construction: Methodological choices and their influence on identifying vulnerable neighborhoods.International Journal of Emergency Management, 4(2): 269-295. doi: 10.1504/IJEM.2007.013994
[16]	Li F M, Xu J Z, Sun G J, 2003. Restoration of degraded ecosystems and development of water-harvesting ecological agriculture in the semi-arid Loess Plateau of China.Acta Ecologica Sinica, 23(9): 1901-1909. (in Chinese)
[17]	Lin N F, Tang J, 2001. Study on the environmental evolution and the causes of desertification in arid and semiarid regions in China.Scientia Geographica Sincia, 21(1): 24-29. (in Chinese)
[18]	Liu X L, Ren J Z, 2002. Landscape ecological mechanism on system coupling of the meta-ecosystem consisted of mountain, desert and oasis in Hexi corridor,Gansu,China.Chinese Journal of Applied Ecology, 13(8): 979-984. (in Chinese) doi: 10.1007/s11769-002-0041-9 pmid: 12418260
[19]	Liu Y X, Peng J, Han Y Net al., 2014. Suitability assessment for building land consolidation on gentle hillside based on OWA operator: A case in Dali Bai Nationality Borough in Yunnan, China.Acta Ecologica Sinica, 34(12): 3188-3197. (in Chinese) doi: 10.5846/stxb201312233004
[20]	Lu C P, Chen X P, Wang H Jet al., 2013. A study on dynamic simulation of human-natural relationship evolution in Northwest ethnic minority: Case of Gannan.Journal of Natural Resources, 28(7): 1255-1263. (in Chinese) doi: 10.11849/zrzyxb.2013.07.017
[21]	Nelson D R, Adger W N, Brown K, 2007. Adaptation to environmental change: Contributions of a resilience framework.Annual Review of Environment and Resources, 32: 395-419. doi: 10.1146/annurev.energy.32.051807.090348
[22]	Nguyen A K, Liou Y A, Li M Het al., 2016. Zoning eco-environmental vulnerability for environmental management and protection.Ecological Indicators, 69: 100-117. doi: 10.1016/j.ecolind.2016.03.026
[23]	Patterson T, Guldenb T, Cousins Ket al., 2004. Integrating environmental, social and economic systems: A dynamic model of tourism in Dominica.Ecological Modelling, 175: 121-136. doi: 10.1016/j.ecolmodel.2003.09.033
[24]	Perry R I, Ommer R E, Barange Met al., 2010. The challenge of adapting marine social-ecological systems to the additional stress of climate change.Current Opinion in Environmental Sustainability, 2: 356-363. doi: 10.1016/j.cosust.2010.10.004
[25]	Polsky C, Neff R, Yarnal B, 2007. Building comparable global change vulnerability assessments: The vulnerability Sco-ping Diagram .Global Environmental Change, 17(34): 472-485. doi: 10.1016/j.gloenvcha.2007.01.005
[26]	Qin W, Zhu Q K, Zhang Y, 2009. Soil erosion assessment of small watershed in Loess Plateau based on GIS and RUSLE.Transactions of the CSAE, 25(8): 157-163. (in Chinese) doi: 10.3969/j.issn.1002-6819.2009.08.029
[27]	Roberts M G, Yang G A, 2003. The international progress of sustainable development research: A comparison of vulnerability analysis and the sustainable livelihoods approach.Progress in Geography, 22(1): 11-21.
[28]	Rosa D L, Martinico F, 2013. Assessment of hazards and risks for landscape protection planning in Sicily.Journal of Environmental Management, 127: S155-S167. doi: 10.1016/j.jenvman.2012.05.030 pmid: 22766043
[29]	Sannwald E H, Palacios M R, Arredondo Moreno J Tet al., 2012. Navigating challenges and opportunities of land degradation and sustainable livelihood development in dryland social-ecological systems: A case study from Mexico.Philosophical Transactions of the Royal Society B, 367: 3158-3177. doi: 10.1098/rstb.2011.0349 pmid: 23045713
[30]	Shi P J, Wang M, Hu X Bet al., 2014. Integrated risk governance consilience mode of social-ecological systems.Acta Geographica Sinica, 69(6): 863-876. (in Chinese) doi: 10.11821/dlxb201406012
[31]	Smit B, Wandel J, 2006. Adaptation, adaptive capacity and vulnerability.Global Environmental Change, 16(3): 282-292. doi: 10.1016/j.gloenvcha.2006.03.008
[32]	Speranza C I, Wiesmann U, Rist S, 2014. An indicator framework for assessing livelihood resilience in the context of social-ecological dynamics.Global Environmental Change, 28: 109-119. doi: 10.1016/j.gloenvcha.2014.06.005
[33]	Stoetzel E, Cornette R, Lalis Aet al., 2017. Systematics and evolution of the Meriones shawii/grandis complex (Rodentia, Gerbillinae) during the Late Quaternary in northwestern Africa: Exploring the role of environmental and anthropogenic changes.Quaternary Science Reviews, 164: 199-216. doi: 10.1016/j.quascirev.2017.04.002
[34]	Sun P, Zhang Q, Bai Y Get al., 2014. Transitional behaviors of hydrometeorological droughts in Xinjiang using the Markov chain model.Geographical Research, 33(9): 1647-1657. (in Chinese) doi: 10.11821/dlyj201409006
[35]	Tian Y P, Xiang Q C, Wang P, 2013. Regional coupled human-natural systems vulnerability and its evaluation indexes.Geographical Research, 32(1): 55-63. (in Chinese) doi: 10.11821/yj2013010006
[36]	Turner B L, Kasperson R E, Matson P A, 2003. A framework for vulnerability analysis in sustainability science. In: Proceedings of the National Academy of Sciences of the United States of America, 8074-8079.
[37]	Wang G J, Liao S G, 2006. Spatial heterogeneity of land use intensity.Chinese Journal of Applied Ecology, 17(4): 611-614. (in Chinese) pmid: 16836088
[38]	Wang G X, Chen G D, Shen Y P, 2002. Features of eco-environmental changes in Hexi Corridor Region the last 50 years and comprehensive control strategies.Journal of Natural Resources, 17(1): 78-86. (in Chinese) doi: 10.1007/s11769-002-0026-8
[39]	Wang Y, Wang J S, Yao Y B, 2014. Assessment and regionalization on meteorological drought disaster risk in the Hedong area of Gansu province, China. Journal of Desert Research, 34(4): 1115-1124. (in Chinese)
[40]	Xin Z B, Xu J X, Yu X X, 2009. Temporal and spatial variability of sediment yield on the Loess Plateau in the past 50 years.Acta Ecologica Sinica, 29(3): 1129-1139. (in Chinese) doi: 10.1360/972009-1551
[41]	Yager R R, 1988. On ordered weighted averaging aggregation operators in multi-criteria decision making.IEEE Transactions on Systems, Man and Cybernetics, 18(1): 183-190. doi: 10.1109/21.87068
[42]	Yager R R, 1996. Quantifier guided aggregation using OWA operators.International Journal of Intelligent Systems, 11(1): 49-73. doi: 10.1002/(SICI)1098-111X(199601)11:1<>1.0.CO;2-3
[45]	Zhang J X, Zhang B, Zhang Het al., 2011. Landscape pattern change and soil erosion research: Take Malian River Basin in Loess Plateau as an example.Journal of Natural Resources, 26(9): 1513-1525. (in Chinese) doi: 10.1007/s12583-011-0162-0

Dimension layer	Element type	Index layer	Weight	Indicator description and calculation
Exposure	Drought (0.4126)	Standardized precipitation index (SPI)	0.2751	SPI-SRI drought state model, combining the meteorological and horological drought indexes, adopting 12 months of data to reflect periodic changes in the river water level and reservoir
	Drought (0.4126)	Standardized runoff index (SRI)	0.1375
	Soil erosion (0.3275)	Rainfall erosion (R)	0.0345	Universal soil loss equation (USLE): A=R×K×LS×C×P A: Soil loss volume; R: Rainfall erosion; K: Soil erodibility; LS: Length of slope; C: Crop cover and management; P: Soil and water conservation measures
		Soil erodibility (K)	0.0629
		Length of slope (LS)	0.0307
		Crop cover and management (C)	0.0900
		Soil and water conservation measures (P)	0.1095
	Human activities (0.2599)	Urbanization rate (UB)	0.1049	Calculation formula of comprehensive land use intensity (Wang et al., 2006): . L_x: Comprehensive index of land use degree in the xth sample; A_i: Classification index of land use grade i; S_i: Land use area of grade i; S: Total land area of the sample area
	Human activities (0.2599)	Land use intensity (LD)	0.1550

Year / Type		High vulnerability	Medium-high vulnerability	Medium vulnerability	Low-medium vulnerability	Low vulnerability	Above medium vulnerability
2000	Proportion/%	1.098	16.116	49.373	21.459	11.951	66.587
2000	Area/km²	478.836	7023.390	21515.901	9351.638	5208.232	29018.127
2005	Proportion/%	1.791	2.679	57.833	25.471	12.224	62.303
2005	Area/km²	780.551	1167.519	25202.816	11099.949	5327.163	27150.886
2011	Proportion/%	1.460	5.644	44.871	45.863	2.160	51.975
2011	Area/km²	636.319	2459.650	19553.933	19986.428	941.6683	22649.902
2015	Proportion/%	0.0009	0.563	56.540	39.800	2.997	57.103
2015	Area/km²	0.414	245.344	24682.578	17344.043	1306.033	24928.336

Decision risk coefficient Decision-maker's risk attitude		a=0.0001 Extremely optimistic	a=0.1 Optimistic	a=0.5 Relatively optimistic	a=1 Unbiased representation	a=2 Relatively pessimistic	a=10 Pessimistic	a=1000 Extremely pessimistic
OWA Order weight	Environment and technology	1.000	0.786	0.301	0.090	0.008	0.000	0.000
	Social resources	0.000	0.056	0.124	0.090	0.024	0.000	0.000
	Economic development	0.000	0.034	0.095	0.090	0.041	0.000	0.000
	Disaster prevention and mitigation resources	0.000	0.025	0.080	0.090	0.057	0.000	0.000
	Basic population	0.000	0.020	0.071	0.090	0.074	0.000	0.000
	Land and agriculture	0.000	0.017	0.064	0.090	0.090	0.002	0.000
	Ecosystem	0.000	0.014	0.059	0.090	0.107	0.008	0.000
	Economic resources	0.000	0.012	0.055	0.090	0.124	0.030	0.000
	Drought	0.000	0.011	0.051	0.090	0.140	0.093	0.000
	Soil erosion	0.000	0.010	0.048	0.090	0.157	0.251	0.000
	Human activity	0.000	0.009	0.046	0.090	0.173	0.614	1.000