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Journal of Geographical Sciences >

2018 , Vol. 28 >Issue 2: 152 - 174

DOI: https://doi.org/10.1007/s11442-018-1465-1

Research Articles

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

  • CHEN Jia , 1, 2, * ,
  • YANG Xinjun , 1, 2 ,
  • YIN Sha 1 ,
  • WU Kongsen 1 ,
  • DENG Mengqi 1 ,
  • WEN Xin 1
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  • 1. College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China;
  • 2. Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi'an 710127, China

Author: Chen Jia, PhD Candidate, specialized in social-ecological system and regional sustainable development E-mail:

Corresponding author: Yang Xinjun, Professor, E-mail:

Received date: 2017-06-16

  Accepted date: 2017-07-30

  Online published: 2018-02-10

Supported by

National Natural Science Foundation of China, No.41571163

Northwest University Doctorate Dissertation of Excellence Funds, No.YYB17016

Copyright

Journal of Geographical Sciences, All Rights Reserved
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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

Cite this article

CHEN Jia , YANG Xinjun , YIN Sha , WU Kongsen , DENG Mengqi , WEN Xin . 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 . DOI: 10.1007/s11442-018-1465-1

1 Introduction

Arid and semi-arid regions are sensitive to global environmental change (IPCC, 2014). In the Loess Plateau region of China, drought, soil erosion and other disturbances cause the regional environment to be very fragile. However, with the development of a socio-economy and rapid urbanization, unreasonable land utilization and other disturbances introduced by human activities have made human-environmental conflicts a core problem restricting the sustainable development of the eco-environment and society in semi-arid regions. Therefore, researchers have focused on related issues, such as the ability of ecologically vulnerable areas to adapt to multiple disaster risks and the restoration of the eco-environment, which are mainly related to natural disaster assessment and prevention (Rosa et al., 2013; Wang et al., 2014; Xin et al., 2009), regional eco-environment regulation and sustainable development (Nguyen et al., 2014; Gómez-Ortiz et al., 2013; Li et al., 2003; Zhao et al., 2011), natural resources and land use evaluation (Sannwald et al., 2012), and environmental evolution (Stoetzel et al., 2017; Wang et al., 2002; Lin et al., 2001). Most research results put emphasis on application-oriented single-perspective analysis (Al-Kalbani et al., 2014), which separates the connections in the human-environmental system. Due to the influence of system theory, some scholars began to attach importance to regional system associations, and some studies of the vulnerability of economical-environmental systems and human-environmental relationship evolution have been performed (Lu et al., 2013; Tian et al., 2013; Perry et al., 2013;Liu et al., 2002). However, the studies ignore the inherent connectivity of human and natural environmental factors and the influence of multiple disturbances; thus, the validity of the evaluation results needs to be verified.
Compared to traditional studies of human-environmental relationships, vulnerability research, based on the social-ecological system theory (Holling, 2001), integrates various perspectives and analysis methods to assess risks, sensitivity, adaptation and resilience (Ciftcioglu, 2017; Sannwald et al., 2012; Nelson et al., 2007), and this work provides a new mode of thinking about human-environmental relationships (Speranza et al., 2014; Cumming et al., 2011; Turner et al., 2003). However, two difficulties still exist. On the one hand, the complex evaluation indexes, unsystematic data organization methods and lack of an inductive theoretical model for coordinating the different data, indicators and information are obstacles in the vulnerability assessment. On the other hand, the lack of spatial-temporal revolution of elements in the human-environmental system and the uncertainty of disturbance risk measurements hinder the application of the evaluation outcomes. In recent years, research focus has moved from effects to adaptation and resilience. Additionally, studies about the human-environmental system’s vulnerability emphasizing cross-scale and multi-factor integration are gradually becoming valued by scholars (Yang et al., 2015; Shi et al., 2014; Polsky et al., 2007 Acosta-Michlik et al., 2008; Patterson et al., 2004). Considering theoretical frameworks, Vulnerability Scoping Diagram (VSD) and Agents’ Differential Vulnerability (ADV) have been proposed by Polsky et al. (2007) and Acosta-Michlik et al. (2008). Polsky and Acosta-Michlik, respectively, to provide a clear and comprehensive assessment method of vulnerability through its multi-data organization, explicit vulnerability connotation and index system construction method. Considering methods, Frazier et al. (2014) developed the SERV model to improve the accuracy and spatial availability of vulnerability assessments and to solve uncertain and indistinct problems in the index construction. With a distinct index system as well as the integration of various indexes of the environment’s multiple risks and factors, this model can improve our understanding of the spatial-temporal differences and characteristics of an social-ecological systems (SESs) vulnerability. The American mathematician Yager R.R. constructed the Ordered Weighted Averaging (OWA) algorithm (Yager, 1988; Yager, 1996). The principle of the algorithm is to rearrange the spatial data according to the attribute value. Furthermore, depending on the aggregation of data criterion weight and order weight, the preference of different subjects (decision-making risks) is simulated under the linguistic quantification operator. The OWA assessment method presents decision-making differences owing to subjective weight error and difference values among index factors, and it reflects the decision makers’ attitude with regard to avoiding risks in decision problems, which can reduce the subjective cognition of decision makers impacting evaluation results. This method is an effective means to construct future scenario simulations of a social-ecological system. Therefore, integrating systematic vulnerability based on evaluation and technical methods can be used to effectively understand the interactions and evolution of the coupled human-environmental system.
Yulin City is an ecologically vulnerable area in China and experiences serious problems related to drought, soil desertification and soil erosion. During the period of “the 12th Five-Year Plan” (2011-2015) and “the 11th Five-Year Plan” (2006-2010), the energy industry in Yulin City entered the rapid development stage, and urbanization accelerated markedly, leading to considerable transformations in the regional human-environmental relationships. Therefore, this study has chosen Yulin City as an example to measure its disaster exposure risks and vulnerabilities associated with human activities and data was collected on Yulin City at typical times during 2000-2015. Based on the SES theory and by applying the integration analysis framework of vulnerability and the SERV model, this research expresses the spatial-temporal dynamics of vulnerability from 2000 to 2015 in Yulin City. Moreover, this work simulates the future development scenario of the SES via OWA and can act as a reference to the relevant departments in terms of disaster warning and adaptation management.

2 Study area

The study area is located between 107°28'E-111°15'E and 36°57'N-39°34'N. Yulin City, the northernmost prefecture-level city in Shaanxi Province (Figure 1), is located at the juncture of the Loess Plateau and the southern margin of the Mu Us Sandy Land in a semi-arid region in China. Yulin City has 1 district and 11 counties, with a total population of 3.645 million, and land area of 43,578 km2. In terms of landforms, taking the Great Wall as a boundary, the northern parts are sandy areas, accounting for 42% of the total area, and the southern parts are a hilly-gully region, approximately 58% of the total area. The area features a temperate, semi-arid, continental monsoon climate, with four distinct seasons, short frost-free periods, an annual average temperature of 10°C, an average precipitation of approximately 400 mm and a frost-free period of 150 days. Meteorological disasters, such as drought, hail and frost, are common. Moreover, the combination of hilly landforms composed of loess in the southern areas and the concentrated summer precipitation results in regional soil erosion and other serious ecological problems. Moreover, the regional energy and mineral resources include coal, oil, natural gas, rock salt and others, which have led to large-scale predatory energy exploitation since the late 1980s and caused eco-environmental damage in Yulin City. The advancement of urbanization, irrational land use and industrial economic developments have further aggravated the disturbance of social-ecological systems.
Figure 1 Location of the study area (Yulin City, Shaanxi Province, China)

3 Materials and methods

3.1 Theoretical structures and models

Vulnerability is decomposed into three dimensions - exposure, sensitivity and adaptive capacity -to describe a social-ecological system. The basic idea of the VSD assessment framework is used to guide the whole process from data management to results simulation (Figure 2). Based on the indicators selection principles of SERV, this study constructs multiple factors and an explicit assessment index for quantitative evaluation of vulnerability and further selects proper indexes from the system corresponding to the actual habitat status at the county scale, as referenced from the Shaanxi Province and county almanac. Combining the SERV model and the RS/GIS spatial statistical analysis method, this study evaluates the vulnerability of the SES at the county scale, the spatial-temporal dynamic evolution and the shifting trends. Finally, the OWA algorithm is used to simulate the risks of the future system’s vulnerability for different development scenarios in Yulin City, thus allowing evaluation results to be applied in regional practice.
Figure 2 Ideas and methods of regional vulnerability assessment under the framework of VSD
3.1.1 VSD integration framework
The theoretical structure of the vulnerability assessment is related to the whole study’s cientific quality. By reviewing all the assessment theories and models proposed domestically and internationally, it can be observed that most focus on the causes and mechanisms of vulnerability and explore the inherent relationships among driving factors from different perspectives. However, the assessment framework for multiple factors and multiple risk disturbances at the regional scale is limited, and the VSD framework (Polsky et al., 2007) has made great achievements in many cases. VSD defines vulnerability as three dimensions - exposure, sensitivity and adaptive capacity - and organizes the data using a progressive method of dimension layer-index layer-parameter layer, with eight steps of normative evaluation process (Figure 2). The deconstruction of the vulnerability assessment under this framework is in accord with the trend of integration analysis, and the clear evaluation process can guide the whole process from data processing to results application (Polsky et al., 2007; Huang et al., 2003).
3.1.2 SERV vulnerability model
To overcome limitations of previous vulnerability assessments, Frazier et al. (2014) proposed the SERV model, representing exposure (degree), sensitivity and adaptability through indexes related to the natural environment, social economy, spatial and local characteristics, which are used to evaluate regional vulnerability. This model serves to link theories to disaster risk research more directly. This model also incorporates local specific indicators into the evaluation index system, thereby differing from the previous general ways to organize data. As information provided by specific indicators in each county to reflect the local habitat status, this method solves the problem of the spatial distribution imbalance and dependence index. Simultaneously, due to the inherent connectivity of vulnerability factors, indicators expressing interactions among sensitivity and adaptability are emphasized. Moreover, SERV captures a key but overlooked point: vulnerable regions are not always in exposed regions; thus, the model suggests that limited resources should be allocated to more vulnerable regions, not just highly exposed areas, when developing adaptive strategies for regions (Frazier et al., 2014). The SERV model changes our way of thinking about assessing spatial vulnerability and helps to design targeted disaster reduction strategies, thus guiding the implementation. This model uses three elements for independent calculation, and its static vulnerability calculation equation is as follows:
V= [E+S] - AC(1)
where V represents the vulnerability, E represents the exposure, S represents the sensitivity, and AC represents the adaptive capacity.

3.2 Index system construction

In the field of global environmental change, Adger (2006), Smit (2006), and Turner et al. (2003) generally agree that vulnerability refers to the sensitivity of a social-ecological system exposed to risk or to internal or external disturbances and a state in which the structure and the function of systems may be damaged due to the lack of adaptive capacity. Exposure, sensitivity and adaptive capacity are the three core components of system vulnerability (Turner et al., 2003; Roberts et al., 2003; Adger, 2006; Smit, 2006). Exposure refers to the degree of the external environmental pressure or risk disturbance and stress experienced by a system. Sensitivity is the extent to which exposed units are susceptible to being affected either positively or negatively. Adaptive capacity represents the self-regulating ability of the system in the face of risks and stress as well as the recovery potential in response to external interventions (adaptive management) (Chen et al., 2010). The higher the degrees of exposure and sensitivity, the higher the vulnerability of the social-ecological system, while the higher the adaptive capacity, the lower the vulnerability of the social-ecological system. Therefore, based on the VSD framework and the SERV model, this study links the natural environment, social economy and local specific indicators to disaster risks, thereby building an evaluation index system of social-ecological system vulnerability from the three dimensions of exposure, sensitivity and adaptive capacity (Tables 1 and 2).
3.2.1 Exposure (risk) indicators
The exposure risk in Yulin City is mainly caused by drought, soil erosion and human activities; hence, indicators of drought, soil erosion and human activities are considered with an analytic hierarchy process to determine the weight (Table 1). Drought risk is measured with a comprehensive drought state index, which combines hydrological drought (SRI) and meteorological drought (SPI) (Sun et al., 2014). The universal soil loss equation (USLE) is used for risk assessment of soil erosion (Zhang et al., 2011; Qin et al., 2009). Human activity disturbance is mainly due to the urbanization process and disturbances of the regional social ecosystem related to land use intensity. Finally, the three elements of exposure are processed with the weighted raster operation of ArcGIS to draw the regional exposure risk layer.
Table 1 Index system of exposure risk assessment
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
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):
.
Lx: Comprehensive index of land use degree in the xth sample; Ai: Classification index of land use grade i; Si: Land use area of grade i; S: Total land area of the sample area
Land use intensity (LD) 0.1550

Notes: (1) Drought, soil erosion and human activity indicators are positively related to exposure. (2) SPI and SRI were calculated by standardized precipitation index (SPI) formula from water regime of hydrology and water resources bureau of Yunnan province. (3) Precipitation erosion force factor (R), soil erodibility factor (K), and slope length factor (LS) data were from the National Earth System Science Data Sharing Infrastructure, and the data layer was generated via the spatial matching process. (4) Crop coverage and management factors (C) were estimated via the functional relationship between vegetation coverage N and vegetation coverage factor C: C=0.6508-0.3436lgN. (5) By integrating the results of Hu Wenmin and Cai Chongfa and combining practical land use status in the study area to determine the values for the soil and water conservation measures factor (p), the p values of different land use types were determined.

3.2.2 Sensitivity/adaptive capacity index
Jones and Andrey (2007) and Frazier et al., (2013) stated that there are different key factors of vulnerability in different research fields, locations and specific research scales. For example, the six counties of northern Yulin City are heavily reliant on the energy industry, and the county economic development is relatively better than that of the six agricultural counties of southern Yulin City, which are impoverished mountainous counties. Compared with the six counties in the south, indexes such as energy consumption and the number of employees employed in extractive industries are more likely to be the key factors of the sensitivity of the system in the northern six counties, while indexes such as the output value of major agricultural crops better represent the adaptive capacity (i.e., coping with vulnerability risk) of the six counties in the south. In addition, due to the vulnerability factors related to interaction and internal connectedness, the same factors affect the factors of sensitivity and adaptive capacity in different ways; for example, the forest area for soil conservation not only characterizes the sensitivity of soil erosion but is also an adaptive measure used to limit the risk of soil erosion. Therefore, through the existing literature research and reorganizing the data compiled in County Yearbooks over the years, this article summarizes the elements that have affected the sensitivity and adaptive capacity. In consideration of the actual situation in the study area, using the index selection principle of the SERV model, this research carefully screens the indexes to distinguish general and specific factors for different counties. Using this method, this study preliminarily constructs an index system of sensitivity and adaptive capacity for the counties in the study area. Additionally, this study adopts principal component analysis (PCA) to simplify and compress the relationships among a series of indexes in order to retain the significant variables for which the principal component load coefficient is ≤-0.5 or ≥0.5. After screening, this study constructs the final indicators of sensitivity and adaptive capacity (Table 2), uses the variance contribution rate of the principal component analysis as the weight, and takes the principal components as variables to calculate the index layer of system sensitivity and adaptive capacity.
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%

Notes: (1) Sensitivity and adaptive capacity: these two dimensions have no influence on each other. The detailed results of principal component analysis in each year are not listed here separately. “+” indicates that the indexes and the sensitivity or the adaptive capacity are positively correlated. “-” indicates that the indexes and the sensitivity or the adaptive capacity are negatively correlated. “*” indicates a moderate index, in which a certain value of the index value is the best. The moderate value is calculated from the standard mean value of indexes of each year in the study area. (2) The index system of the dimension of sensitivity and adaptive capacity is composed of universal indexes and specific indexes. Among them, the universal index is suitable for index factors of the whole region and the specific index reflects the index factor satisfying the actual habitat of each county. (3) The minimum eigenvalue means that principal component analysis of the index system in this year was used to extract the principal components with minimum eigenvalues. In Table 2, all the minimum eigenvalues are greater than 1, and the cumulative contribution rate is greater than 85%, indicating that the extraction of the principal components is representative.

3.3 Data sources and processing

The data in this study mainly include remote sensing images and land use type captured in 2000, 2005, 2011, and 2015; meteorologic and hydrologic data (1954-1990 and 2002-2015); and Yulin City social statistics data (2000-2015). The remote sensing images of the study area are the products of the geospatial data cloud produced by the TM instruments aboard the Landsat 4 and 5 satellites, and these images were used to calculate and obtain the vegetation cover data in the study area in typical years. The land use data are from ten years of the Shaanxi provincial ecological database and were used to obtain data on land use intensity and soil conservation measures. The vector data of precipitation, runoff, slope, slope length and soil erosion factors are from the National Earth System Science Data Sharing Infrastructure. Population, environment, society, economic development and other indicators were calculated based on the original data from the Yulin City Statistical Yearbook (2000-2015).
3.3.1 Data spatialization
The spatial processing includes remote sensing data, precipitation/runoff data and statistical data. (1) The remote sensing data are based on Landsat 4-5 TM data, after using ERDAS software to process the original remote sensing images. The Mosaic Tool is adopted to synthesize three periods of remote sensing images for the study area. Modeling the band operation yields the NDVI data covering the study area, and using the mask extraction tool of ArcGIS provides the NDVI data for three typical years in Yulin City, which are used to calculate the vegetation coverage. (2) This research adopts the inverse distance weighted interpolation method for point source data, such as precipitation and runoff, in order to achieve spatial deterministic interpolation and spatialization. (3) For attribute data without X and Y coordinates, such as social statistics, this research uses ArcGIS software to input the attribute data into an attribute list for the administrative division vector in the study area, thereby achieving the spatialization of the data attributes. This study takes a grid as the basic research unit in order to ensure the agreement between the indexes and the spatial locations. We define the spatial data grid size as 30 m×30 m, and the spatial data are fit to the grid using unified Krasovsky ellipsoidal coordinates and Albers projection.
3.3.2 Data standardization
To eliminate the problem of inconsistent dimensions in the index data, this research standardizes the original data of the social statistics. Considering that the positive and negative indicators of vulnerability assessment have different effects on the vulnerability of the system, different standardization methods are adopted. The range standardized method is applied to the positive and negative indicators. For the moderate indexes (that is, the indexes in which moderate values are the best) with a certain ambiguity, this study adopts the method of fuzzy membership function based on the work of the American scholar L.A. Zadeh for non-dimensional processing (Duan, 2005).

3.4 Analytical methods

3.4.1 Variation slope method
Using the variation slope method to calculate the change in the slope of vulnerability for each grid pixel, this research performs a regression simulation between the vulnerability indexes of the social-ecological system of Yulin City during 2000-2015 and time in order to express the variation trend of regional vulnerability over 16 years. The variation slope is positive, which indicates that the vulnerability of the region shows an increasing trend. In contrast, a negative value would indicate that the vulnerability of the region shows a decreasing trend. The specific calculation formula of the slope variation is as follows (Chen et al., 2008):
${{X}_{slope}}=\frac{n\times \sum\limits_{i=1}^{n}{i\times SESsV{{I}_{i}}-}\left( \sum\limits_{i=1}^{n}{i} \right)\left( \sum\limits_{i=1}^{n}{SESsV{{I}_{i}}} \right)}{n\times \sum\limits_{i=1}^{n}{{{i}^{2}}}-{{\left( \sum\limits_{i=1}^{n}{i} \right)}^{2}}}$ (2)
where n represents the year; SESsVI represents the vulnerability index of the ith pixel in the raster data; Xslope represents the variation slope of the grid pixels and the degree of long-term changes in regional vulnerability. The F test is used to test the significance of the variation slope and is calculated as follows:
$F=U/\frac{Q}{n-2}$ (3)
where Q=$\sum\limits_{i=1}^{n}{{{({{Y}_{i}}-\hat{Y})}^{2}}}$ is the error sum of squares; U=$\sum\limits_{i=1}^{n}{(\hat{Y}}-\bar{Y}{{)}^{2}}$ is the regression sum of squares. Yi is the actual value of SESsVI of the ith year, and $\hat{Y}$ is its regression value; $\bar{Y}$ is the average value of each year. According to the calculation of the variation slope and the F value significant test, the results can be divided into three categories: significant increase (Xslope>0, P<0.05), significant decrease (Xslope<0, P<0.05), and no significant change (P>0.05).
3.4.2 OWA analytical method
The Ordered Weighted Averaging (OWA) algorithm is a method based on the ordered weighted mean, and the core of this method involves calculating the criterion weight and order weight of spatial index data. By adjusting the size of the decision risk among the transformation of logical operations, the OWA algorithm obtains results for different risk evaluations. It uses the AHP method to determine the weight of each index, while determining the order weight has various methods. This study adopts the fuzzy quantification model first proposed by Yager, as this model is simple, easy to understand, and widely used (Yager, 1988, 1996). The specific formula is as follows:
${{v}_{j}}={{\left( \sum\limits_{k=1}^{j}{{{w}_{k}}} \right)}^{a}}-{{\left( \sum\limits_{k=1}^{j-1}{{{w}_{k}}} \right)}^{a}}a\in \left( 0-\infty \right),\ \left( j=1,\text{ }2,\text{ }3,\text{ }\ldots ,n \right)$ (4)
where Vj represents order weight; a is the decision risk coefficient, which depends on the degree of optimism of the decision maker facing decision risks; and Wk represents the importance degree of the index, which can be calculated with formula (8):
${{w}_{k}}=\frac{n-{{r}_{k}}+1}{\sum\limits_{I=1}^{k}{\left( n-{{r}_{I}}+1 \right)}}\ \ \left( k=1,\text{ }2,\text{ }3,\text{ }\ldots ,n \right)$ (5)
where n is index number, and rk represents the assignment of the index importance according to the index value, for which the maximum value is 1, the second largest value is 2, and the minimum value is n.

4 Result analysis

4.1 Exposure risk analysis

According to the specific exposure risk type in the study area, this research adopts the ArcGIS raster calculator to perform the weighted operation. The results are shown in Figure 3. The risk exposure from 2000-2015 in the study area formed a pattern approximately characterized by high values in the northwest and low values in the southeast. The pattern after 2005 featured low values in the middle and southern parts and high values in the northern. The area of high exposure risk first increased then decreased from 2000-2015 and is mainly concentrated in Shenmu County, Fugu County and Yuyang District, all of which have dense populations, developed industries, and high-intensity land development. The low exposure risk area is concentrated along the Great Wall (400-450 mm precipitation line) and the southern region.
Figure 3 The spatial framework of exposure risk in Yulin City during 2000-2015
The high-intensity areas of soil erosion are concentrated in the eastern and southwestern regions in Yulin City, while the distribution pattern of drought decreases from the southeast to the northwest. Based on the spatial evolution pattern of the exposure risk in Yulin City over the 16 years (see Figure 3), it is known that the spatial distribution of exposure risk is roughly similar to those of drought and high-intensity human activity. Therefore, the drought and the disturbance due to human activities may be the main influencing factors for the regional exposure risk. However, in recent years, with the implementation of soil conservation measures, such as returning farmland to forest (grass) and comprehensive management of small watersheds, the impact of soil erosion risk has begun to weaken, and the southern ecological condition has clearly gotten better. These trends show that the risk source of exposure is uncertain and complex and includes a reduction in the self-adjustment ability (or sensitivity threshold) of the exposed units, which highlights the potential problems, and constant changes in the type and number of its exposure factors. On the whole, the role of regional topography, climate, hydrology, vegetation and other special natural geographical factors, as well as human activities in ecological system, together determine the spatial distribution characteristics of exposure risk in Yulin City.

4.2 Spatial-temporal evolution of vulnerability of social-ecological system

4.2.1 Spatial-temporal distribution characteristics of vulnerability
On the basis of overlaying the index layers of the exposure, sensitivity and adaptive capacity (derived from the calculation results of the static vulnerability equations of SERV model and the natural breakpoint method), this study divides vulnerability into five categories that represent areas of low vulnerability, low-medium vulnerability, medium vulnerability, medium-high vulnerability and high vulnerability. As shown in Figure 4, the overall vulnerability of the social-ecological system corresponds to the medium level during 2000-2015 in Yulin City. The spatial pattern of the vulnerability can be characterized as “high in northwest and southeast and low along the Great Wall”, and medium- and high-vulnerability areas show a decreasing trend. Meanwhile, there is a significant difference in the spatial-temporal distribution of vulnerability. In 2000, the area of the high-vulnerability region is larger than those of 2005, 2010 and 2015. The high-vulnerability region of the study area has been mainly concentrated in the core area of the northern cities and energy bases for 16 years. The medium-vulnerability area is mostly located near the northwest of the Mu Us Desert, while the low-vulnerability area is located along the Great Wall and the junction of the northern six counties and the southern six counties.
Figure 4 Spatial-temporal distribution of the vulnerability of social-ecological systems (SESs) in Yulin City during 2000-2015
In the time dimension, the overall vulnerability of social-ecological system shows a decreasing trend in the study area during 2000-2015. The proportion of the area with vulnerability levels above medium decreases from 66.587% to 57.103% (see Table 3). In 2000, the area of high vulnerability and medium-high vulnerability is 478.83 km2 and 7023.29 km2, respectively, while the area of medium-high vulnerability region is only 245.344 km2 in 2015. From the point of view of the regionally dominant vulnerability level, the proportions of the area of medium vulnerability are 49.37% and 57.83% in 2000 and 2005, which shows that half of Yulin City experiences some vulnerability. In 2011 and 2015, the dominant level of vulnerability is the low-medium level, accounting for 45.86% and 39.80% of the regional land area, respectively. This pattern indicates that the regional social-ecological system is developing towards a good situation.
Table 3 Classification of social-ecological system vulnerability in the study area during 2000-2015
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
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
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
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
Area/km2 0.414 245.344 24682.578 17344.043 1306.033 24928.336
In the spatial dimension, the spatial variation of vulnerability in the study area shows a southward trend in terms of low vulnerability region over the 16 years. The distribution of the medium-vulnerability area has a relatively large fluctuation, and the northwest-southeast distribution pattern evolves into a primary concentration in the northwest. The high-vunearility region gradually decreases, and it is mainly concentrated in the northern Shenmu-Fugu counties in 2000-2011 and is absent in 2015. Comparing the data of land use types in the study area during 2000-2011, it is found that there is a consistency between the high-vulnerability area and the spatial distribution of the mining area. Shenmu and Fugu counties are an important energy base in China. Mines are densely distributed in this area, resulting in unrecoverable damage to the ecological environment. Excessive exploitation of energy and extensive economic development further exacerbates the vulnerability of a social-ecological system.
4.2.2 Analysis of the trend of vulnerability evolution
According to the variation slope method and the results of the significance test (Figure 5), in the period of 2000-2015 in Yulin City, the slope of the overall vulnerability change is negative accounted for more than 96% of the pixels, and the area of reduced vulnerability is dominant, which further explains the gradual improvement in the regional social-ecological system over the past 16 years.
From the left and right graphs in Figure 5, we can see that the pixels with significantly reduced vulnerability trends (p<0.05) are concentrated in areas of high and medium vulner- ability. These regions are sparsely populated areas in five counties in the northwest and south. The areas where the slope of the change is significantly higher than 0 are distributed along the low-vulnerability Great Wall region. The main reason is that the medium- and high-vulnerability areas are related to soil erosion, northwestern sandstorms and intensive energy development. Due to the effects of soil conservation, returning farmland to forest, shelter forest development and other policy measures, coupled with the decline of the traditional energy economy in recent years, the transformation of the economic structure (driven by the energy resource depletion crisis) has promoted the gradual restoration of the regional eco-environmental system. Thus, the degree of vulnerability in such areas has been reduced. However, this pattern does not show that the vulnerability of the social-ecological system in such areas is lower than other low-vulnerability areas; it only shows that the trend of changes has improved. According to the comparative analysis in Figures 4 and 5, the trend of vulnerability change in medium- and high-vulnerability areas fluctuates greatly but that a significant reduction occurred over the 16 years (Figure 5b). In essence, the high-risk areas of the social-ecological system are still concentrated in the northern (or northwestern) counties that are densely populated and exhibit good economic conditions. However, to a certain extent, the evolution trend over 16 years shows that the previous high-vulnerability area of the social-ecological system in Yulin City has decreased and that the overall system at this stage has a good development trend.
Figure 5 SES vulnerability variation trend (a) and F test (b) in Yulin City during 2000-2015

4.3 Spatial-temporal heterogeneity between the vulnerability and exposure risk

There are spatial distribution differences between the areas with higher vulnerability and the areas with higher exposure risk, but this spatial heterogeneity is often overlooked in most studies. The evaluation of the SERV model advocates allocating limited resources to more vulnerable areas rather than just highly exposed areas, as less exposed but vulnerable areas are often more sensitive to disaster risks. Therefore, a comprehensive vulnerability assessment should focus on the spatial heterogeneity between exposure risk and vulnerability. In this study, vectorization of the grid data of the exposure risk assessment and the results of the spatial-temporal vulnerability evolution and the intersection tool of the overlay analysis were used to obtain a spatial heterogeneity maps of exposure risk and vulnerability levels in 2000, 2005, 2011 and 2015 (Figure 6).
Figure 6 The spatial heterogeneity of SESs vulnerability and exposure risk in Yulin City during 2000-2015
As shown in Figure 6, from 2000-2015, the low exposed-vulnerable areas in Yulin City are mainly distributed in the less developed areas in the south, and the area of spatial het-erogeneity gradually decreases over time but is concentrated in the hilly and gully areas of the southeastern part. Under the condition of low exposure risk, the situation of medium-high vulnerability is bound to reflect the state of high sensitivity and low adaptive capacity in terms of risks. There are five counties to the south of the Great Wall, where the development of traditional agriculture is limited by natural terrain, water resources and limited arable land area, coupled with lagging infrastructure construction and the lack of energy resources. In recent years, the socio-economic development of these regions has lagged far behind that of the northern energy-rich counties, resulting in a low adaptive capacity in the system. Secondly, the regional population density of Yulin City is basically high in the southeast and low in the northwest. The southern counties contain large farmlands and convenient valley roads, resulting in a higher population density. Therefore, the social sensitivity of such areas in the event of a disaster is higher.
Over the past ten years, the population of the southern counties of Yulin City has moved north or settled outside of rural settlements, resulting in the hollowing out of rural settlements and the abandonment of cultivated land, thereby reducing the impact of human activities on the local areas. This phenomenon is beneficial to the restoration of the ecosystem and the reduction of the disaster exposure, but the development of social systems is unbalanced, and the potential vulnerability increases. Therefore, to reduce the vulnerability in southern counties, it is important to increase the investment of resources in low exposure-vulnerability areas, to promote the transformation to a modern agricultural economy, and to enhance the development of capacity with social infrastructure, such as education and technology, disaster prevention and mitigation.

4.4 Vulnerability scenario simulation

The principle of the OWA algorithm, based on the calculation of order weight and criterion weight, is used to obtain a comprehensive evaluation chart of the regional social-ecological system vulnerability by incorporating different factors affecting the vulnerability. Through the setting of different decision risk coefficients, the regional system vulnerability is simulated, generating multiple scenes. The decision-making risk coefficient (a) reflects the optimism of the decision maker on issues, the value of the coefficient a ranges from 0 to infinity, corresponding to a range from optimism to pessimism. When a=1, the attitude of the decision maker is neutral, and the order weights are equal. The OWA algorithm then overlays the traditional criterion weight layer, and the regional system vulnerability corresponds to the status quo. In the case of a>1, the decision maker is pessimistic about the vulnerability of the regional system and believes that there is a high exposure risk in the region and that the significantly increasing vulnerability is harmful to the sustainable development of regional social-ecological system. On the contrary, if a<1, the decision maker is optimistic and believes that the risk of regional vulnerability is manageable or in control and that it does not affect the stable development of the regional social-ecological system.
4.4.1 Scenario index formulation and analysis
The design of the vulnerability scenario index is based on the existing evaluation index system (Tables 1 and 2) and involves integrating and selecting the indexes of exposure risk, sensitivity and adaptive capacity and removing the factors that are complex and that have small loading coefficients. The exposure risk index layer remains unchanged, whereas the top 3 indexes are selected for the indicators of sensitivity and adaptive capacity based on the closeness of the relationship to each principal component. There are 11 scenarios in total, and the weight was determined based on the analytic hierarchy process. Sensitivity indexes include the layers of population, land, ecology and economy; adaptive capacity indexes include the layers of environment and technology, social resources, economic development, disaster prevention and mitigation resource.
To obtain the final results, the order weight of the scene index layer was calculated according to the formulas (4) and (5) (Table 4). The grid index layer was standardized and assigned in ArcGIS 10.0, and the criteria weight and order weight of different decision risk coefficients1(1 The decision risk coefficient is chosen according to the typical parameters in the OWA operator - a corresponds to 0.001, 0.1, 0.5, 1, 2, 10, and 1000 - to express decision-making attitudes of decision makers from optimistic to pessimistic.) were input to calculate the OWA module of multi-criteria evaluation (MCE) in IDRISI 17.0 (Figure 7).
Figure 7 The simulation results of the vulnerability of social-ecological systems (SESs) in decision-making risk
Table 4 Results of the ordered weight
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
According to the natural breakpoint method, the regional vulnerability grid layers of different decision risk assessments are divided into five categories, which represent low, low-medium, medium, medium-high and high vulnerability. As shown in Figure 7, when the decision maker is extremely optimistic to just optimistic (a=0.001 or 0.1), the whole area is basically in a low-vulnerability situation, but the decision-making risk is the highest and is not in line with the actual situation. When the decision maker is relatively optimistic (a=0.5), the majority of the area exhibits low vulnerability, and the northern part has a trend in which the vulnerability increases to a certain extent. When the decision maker is relatively pessi- mistic, the majority of the area exhibits a high degree of vulnerability, and some economically developed areas in the north exhibit high vulnerability. For the decision maker to decrease the risk, the influence of the order weight on the final evaluation result of the evaluation unit increases, which leads an increase in the maximum vulnerability. Therefore, when the decision maker is pessimistic to extremely pessimistic (a=10 or 1000), the social-ecological system of the study area is in a highly vulnerable state, and the decision risk is minimal but is divorced from rational reality. The analysis of the results for different decision-making risk levels can dynamically show the spatial evolution process of the vulnerability level of the social-ecological system in the study area. Moreover, it can simulate the influences of risk-cognitive behaviors of decision makers on system vulnerability changes, which provides decision references for the scenario prediction of system vulnerability.
4.4.2 System vulnerability scenario settings
Based on the dynamic simulation of different decision-making risk levels and considering the rational reality, decision makers often need to assess scientific arguments and trade-offs for the regional development planning and policy formulation. The results of extreme decision-making are not in line with the actual situation. The economic development of the energy industry has been a “double-edged sword” in Yulin City since “the 10th Five-Year Plan” (2001-2005). There is a trade-off relationship between socio-economic development and ecological environment protection in the formulation of development policies. After “the 12th Five-Year Plan” (2011-2015), the energy industry faces the “resources trap”, and the focus of the policy set has turned to the transformation of the economic development structure and the implementation of sustainable development. Therefore, based on the investigation of the socio-economic development strategy and the present situation of Yulin City, three types of possible policy behaviors2(2 Maintaining the status quo type means the regional development policy on the risk (disaster, eco-environmental problems, etc.) control is not strong, and the speed of socio-economic development remains normal. The economic priority type refers to the government’s regional development policy formulation, which is dominated by the speed of economic development and reduces considerations of the eco-environmental problems and natural disaster risk to a certain extent. The sustainability-oriented type refers to regional policy development that emphasizes investment in eco-environment management, increasing disaster risk control, prioritizing sustainable development, reducing the dependence on the energy economy, and changing the model of socio-economic development.) are proposed: the status quo type, the economic priority type, and the sustainability-oriented type. The social-ecological system vulnerability under these three scenarios was simulated and evaluated.
Considering the complexity of the influencing factors of vulnerability in the social-ecological system, the relationships among factors is uncertain, and the decision-making risk range (0.5<a<2) satisfying most index factors represents a more realistic scenario simulation. Therefore, based on the range of the normal decision risk coefficient and the rational thinking of decision makers, a=1 corresponds to maintaining the status quo type, which represents the normal weighting operation results under the existing disaster risk, regional sensitivity and adaptive capacity. Similarly, the decision risk coefficient a= [0.5, 2] interval is further refined and simulated. Considering the small fluctuations in decision makers’ subjective rational ranges and based on previous research results, the decision risk coefficients a=0.8 and a=1.2 are taken as the credible fluctuation interval nodes that are established under the current risk decision coefficient. Among them, a=0.8 indicates that the exposure risk is controlled, and the system vulnerability is low, which is defined as an optimistic decision-making scenario of sustainable development orientation. The value a=1.2 means the disaster risk is difficult to control, which is a pessimistic decision-making scenario of economic development priority.
In Figure 8, it can be observed that the spatial distributions of the “the status quo type” scenario and the vulnerability of the social-ecological system of Yulin City in 2011 are similar, indicating that a=1 (i.e., the criterion weight superposition method) is a special case in the OWA algorithm. This finding also validates the scientific robustness of the scenario index selection. As this scenario type and the vulnerability of Yulin City in 2011 correspond to the status quo, we do not go into detail here. From the sustainability-oriented type to the economic priority type, the vulnerability of the social-ecological system in the three scenarios increases, and the spatial distribution is regular. The medium-high and high vulnerability areas are still distributed in the economically developed areas in the north, and the spatial area of high vulnerability obviously expands outward.
Figure 8 The SESs vulnerability spatial distribution of different scenarios simulation in Yulin city
In the economic priority type policy scenario (a=1.2), shown in Figure 8, the medium-vulnerability area is distributed in the sandstorm arid region in the northwest and in regions prone to soil erosion in the southwest. The high-vulnerability region extends from the intensive Shenmu-Fugu mining area outward, and the whole regional system of Yulin City is characterized by medium-high vulnerability. According to Table 4, the order weight of risk indicators in the a>1 scenario is large, and the weight of adaptive capacity factors is small, which indicates that economic development ignores environmental stress and lacks emphasis on adaptive capacity, which will lead to an increase in system vulnerability.
In the sustainability-oriented type scenario (a=0.8), the southern counties and areas along the Great Wall in Yulin City are basically low-vulnerability areas, and the vulnerability degree in the northern counties is significantly lower than in other scenarios. As shown in Table 4, the order weights of drought, soil erosion and other risk exposure factor are small in the a<1 scenario; that is, risk control and governance measures have been taken, and the emphasis is placed on the index unit with large order weight. This scenario focuses on the environment, technology, social resources, economic development, disaster prevention and mitigation resources as the main factors for building regional adaptive capacity. Therefore, the system vulnerability is significantly decreased, almost the whole region exhibits low-vulnerability, and the high-vulnerability region distribution is very small.
In the above scenario simulations, we can see that different decision risk coefficients result in different vulnerability prediction results for policy-making. Using the multi-scene combination, the OWA algorithm identifies the uncertainty range of the threshold variation of the indexes and predicts the spatial distribution of vulnerability in the study area through the scenario policy, thereby weighing the relationship between “economic development” and “risk control”. In the practical policy formulation, the scenario analysis is not a single optimization of the scheme but takes the floating range of vulnerability of the three scenarios as a reference, which can be adjusted according to the regional development strategy in different stages. In particular, based on the assessment results of the system vulnerability in 2000-2015, during this stage, the regional social-ecological system of Yulin City should carry out vulnerability level zoning. On the one hand, the high-vulnerability region should be strictly controlled to reduce the exposure risk stress of coal and other energy bases, and the landscape reconstruction should be carried out in the areas of high soil erosion and the sandstorm arid area in the northwest. On the other hand, attention should be paid to the high exposure risk source in low vulnerability counties. Guiding and supporting the modern agriculture and industrial economy in southern counties are important to maintaining a regional development balance and narrowing the north-south system vulnerability gap. For future policy formulation, the risk coefficient selection of the critical threshold of the system vulnerability depends on the decision makers' awareness of regional development. Based on the influence on the evaluation results of the criteria weight and order weight of scenario indexes, decision makers can identify the key scenario index of the critical point of the system vulnerability to adapt decision-making ideas to the regional development and to achieve rational and scientific policy-making.

5 Discussion

In this study, county-level data and the VSD evaluation framework were used as practical guidance for the evaluation of vulnerability. However, our study found that there were limitations in the multi-scale vulnerability integration analysis, and it was easy to ignore the small-scale factors, such as the human vulnerability, during a comprehensive analysis. Thus, the VSD framework is not a general vulnerability assessment framework. Using the VSD integrated analysis framework can provide specific steps for the vulnerability evaluation of a regional-scale system in this study. The purpose of this research is to realize the operability of the VSD analysis framework via the integration of the SERV model and the OWA algorithm to ensure the reliability and effectiveness of the research. Therefore, the comprehensive application of different methods of vulnerability integration analysis can provide a realistic reference for future construction of a general vulnerability assessment framework (model).
The SERV model not only provides a screening index system and the index selection of specific vulnerability factors at the county level but also advocates evaluation analysis focusing on resource input for disaster reduction in high-vulnerability areas instead of exposure areas. This promotes the evolution of vulnerability evaluation thinking and avoids the defects of traditional vulnerability research. However, the operability of the specific implementation of this model remains to be optimized; for example, the SERV model is limited to a static vulnerability equation, which fails to consider the contribution differences of exposure, sensitivity, and adaptive capacity to vulnerability. Secondly, the selection and construction of an index system by using the SERV model is still in the exploratory stage and needs to be further perfected and verified. In addition, we found that the improved model could determine the specific vulnerability impacts of indicators in specific locations, thus guiding the mitigation and adaptive strategies for specific locations.
Scenario analysis, a common means of assessing geography-ecological processes, has two different ways of induction: one relies on choosing the optimal value in the assumed several scenarios as the final result, and the other relies on the mutual confirmation of several scenarios to achieve a comprehensive conclusion of the pros and cons (Liu et al., 2014). In this paper, for future vulnerability prediction, the OWA algorithm provides a flexible multi-scenario reference for decision makers and a policy-making method that can be adjusted according to the actual situation indicators (elements) of a given area. Simple system dynamics, artificial neural networks and artificial intelligence simulations provide a fixed optimal prediction scheme for the future but cannot simulate subjective uncertainty indicators and are difficult to use in actual policy-making by decision makers. Comparatively speaking, the flexibility of these methods is also weak. In the vulnerability evaluation with the OWA algorithm, the calculation and application of criterion weight and order weight of indexes is the key to its evaluation and are also the key to the existence of rationality and flexibility.

6 Conclusions

From the perspective of integration analysis of human-environment relationship, taking the semi-arid SESs in Yulin city as the object of vulnerability evaluation, this paper uses the VSD evaluation framework and integrates multiple methods and multi-index elements to achieve innovative vulnerability assessments and practice methods at the regional scale. This work expands the vulnerability assessment research from a single vulnerability index to a system integration research level that includes vulnerability assessment, evolution, and simulation. Through the index organization method of the SERV model and the VSD framework, the method of index selection and construction is improved, and the integration of multiple risks, the multi-factor natural environment and socio-economic indexes is described to better understand the spatial characteristics and evolution of the social-ecological system vulnerability. The OWA scenario simulation provides technical references for policy-making by decision makers.
The main conclusions are as follows: (1) the social-ecological vulnerability of the study area during the period 2000-2015 shows the spatial pattern of “high in the northwest and the southeast and low along the Great Wall”; the medium-vulnerability level is dominant; the high-vulnerability region is concentrated in the northern energy economy-dependent counties and the compact mining area. Over the 16 years, the evolution of the social-ecological system vulnerability of Yulin City has gradually improved; the regional area of high and medium vulnerability has decreased, and the spatial distribution of the low-vulnerability region has shifted southward. (2) The spatial heterogeneity of system vulnerability and exposure risk is significant. The system vulnerability of the northern counties dominated by high exposure-vulnerability is related to natural disaster disturbances and high-intensity human activities that are destroying ecosystems, while the low exposure-vulnerability of the southeastern counties, which have unbalanced development of the society system, is related to high social sensitivity and low economic adaptive capacity. (3) Based on the process simulation of the dynamic evolution of vulnerability via the OWA algorithm and the vulnerability spatial pattern prediction of the three scenarios, it can meet the decision-making needs of regional sustainable development under different policy goals.

The authors have declared that no competing interests exist.

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[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.Indicators of natural disaster resilience are factors that impact the ability to cope with and adapt to a natural disaster and climate change events. They can either contribute to or detract from resilience. Existing research has emphasized the importance of quantifying resilience in order to estimate baseline resilience and measure progress toward resilience enhancement. Previous attempts at quantification of resilience have not incorporated place-specific indicators or differential weighting of indicators for prioritization of resilience enhancement actions. Previous research efforts have also not incorporated spatial and temporal contexts when attempting to quantify resilience indicators. This research demonstrates the importance for quantifying resilience place-specific indicators, differential weighting of indicators, and the spatial and temporal contexts of indicators for resilience estimation and quantification through a case study of Sarasota County, Florida. This case study was conducted in four phases: preliminary interviews, plan review, focus group, and spatial analysis. Preliminary interviews were intended to contribute to development of research goals. The plan review process served to identify Sarasota County's planning priorities to determine possible indicators of resilience unique to Sarasota County as well as existing and planned county hazard mitigation strategies. The focus group was concerned with identifying resilience indicators through a workshop with officials from Sarasota County. The spatial analysis portion used findings from all three previous phases to demonstrate spatial patterns of resilience. This research demonstrates that although national resilience quantification metrics are useful, local scale resilience estimates appear more useful if community hazard mitigation and climate change adaptation is the primary goal.

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[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.Sierra Nevada is a protected mountain in the Iberian Peninsula classified as a Biosphere Reserve (1986), Natural Park (1989) and National Park (1999). All these environmental protection programmers are a consequence of its unique landscape in the context of the mid-latitude semiarid mountains, with enclaves of exceptional scientific and cultural value. Thanks to its high altitude, Sierra Nevada held the southernmost Quaternary glaciers in Europe, as well as it happened during the Little Ice Age. In turn, Sierra Nevada is also singular thanks to its vast cultural heritage, since very early societies settled on its slopes and valleys and accommodate their lifestyles and economy to the characteristics of this mountain environment. Currently, Sierra Nevada has become an important tourist centre and receives a large amount of visitors. This process of change has conditioned the implementation of a different economic model: it brings benefits to the populations but it involves changes in the landscape as well, sometimes questionable. From this perspective, a critical revision of the legislation is required balancing the sustainable economic development of the population and the preservation and safeguarding of the heritage values of the landscape. With this goal, we suggest creating and implementing the Sites of Geomorphological Interest.

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[12]
Holling C S, 2001. Understanding the complexity of economic, ecological, and social systems.Ecosystems, 4: 390-405.

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[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)Currently,the vulnerability study has been widely applied in many research fields as a new paradigm,and achieved many useful results.However,some scholars have found that there are some important problems of the contradiction of the conclusion of different disciplines and duplication of work,as the result of the difference of the academic background and research perspective.So there is an urgent need for a viable theoretical model and assessment framework to integrate the theory and practice of vulnerability research.Based on the analysis of the evolution of vulnerability concept and research content,this paper makes a review of the theoretical model and assessment framework of foreign vulnerability research.The results show that the different explanation of the concept of vulnerability is the main factor which constrains the construction of the general vulnerability theoretical model and assessment framework.Considering the fact that it is unrealistic to achieve the unity of the concept of vulnerability in a short time and the new trends in the vulnerability research,the paper brought some requirements which should be taken seriously in the construction of general theoretical model and assessment framework for future vulnerability research,namely,the multi-scale of spatio-temporal,multi-perturbation,coupled system,and human cultural identity.

[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.Indices are increasingly important for emergency planning at the community level, particularly with respect to identifying vulnerable neighbourhoods and mapping disaster potential. This paper provides both a critical literature review and an empirical case study that highlight the importance of different types of decisions in the construction of vulnerability indices. The case study focuses on the flooding risk in Vancouver, Canada, from both an evacuation and rebuilding perspective. Results of a sensitivity analysis suggest that spatial outcomes of vulnerability are highly sensitive to decisions regarding variable selection and representation, moderately sensitive to decisions about variable weighting and minimally affected by decisions about variable scaling.

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[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)The natural regional vegetation and soil quality in the semi arid Loess Plateau of China have been degraded extremely due to over grazing and frequent reclamation of natural grassland. Functions of the ecosystems and regional sustainable development were seriously threatened. The analysis of the current ecological environmental situation suggests that transformation of natural vegetation to farmlands in the process of frequent reclamation of natural grassland results in water loss, soil erosion and land degradation. Repeated reclamation of wasteland due to the great pressure of food demand resulted from population growth is the key driving force to the degradation of these ecosystems. To restore natural vegetation and soil quality, we have to find a way to meet the requirement of food for the local farmers in a small portion of the land to reduce the pressure of food production for the rest of the land of a region. In semi arid areas, many studies have shown that the key step for increasing grain yield per unit area is to improve field environmental conditions, including soil moisture supply, topsoil temperature and soil nutrient level. This can be accomplished through the combination of water harvesting technology with plastic film mulching and fertilizer application, which can generally increase the unit grain yield twice or more. Based on these technologies, we propose an approach of water harvesting ecological agriculture (WHEA) and associated landscape configuration in the paper. Unit yield of cash and grain crops can be increased greatly through limited irrigation, and the irrigated cropland can be interspersed with improved pastures and restored natural vegetation in a continuous landscape (a typical hill) in WHEA. Further research and dissemination of WHEA can help supply local farmers with sufficient food and higher income. Various types of grasslands will replace cropland and cover a large proportion of the landscape; animal feeding will be mainly dependent upon pen feeding in order to decrease grazing pressure. These strategies closely follow the ecological patterns of natural vegetation and landscape, as well as the planning pattern of regional industrial arrangement. The coexistence of multiple ecological and economic systems in a landscape helps to improve both biodiversity and industrial diversity, and enhance the flexibility and stability of these systems. Therefore, WHEA, an innovative approach for regional development, can lead to significantly improvement in both the restoration of degraded ecosystems and regional sustainable development simultaneously in the semi arid Loess Plateau.

[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)In recent 40 years, the developing rate of desertification is 0 81%-1 64% per year in the north of China The researchers emphasize particularly climate and man made factors of present time, but over look the effect of environmental evolution on the study of desertification The authors analyze evolution process of climate, sand blown by wind, loess and environment of Quaternary since 20000 years, and have the study of quantitative and contrast, using GIS on desertification of three periods of time including dry cold, warm humid and dry cold The result indicates that the important factor to make drought and desertification in north is the uplifting of Qinghai Xizang Plateau in 10 000 years The time scale on which natural factors cause desertification is ten thousand years or thousand years, while that of human efforts is only hundred years or decade Quaternary environment is the foundation of desertification formation and the desertification of vegetation and soil covering is direct cause leading to the present desertifacation In recent 50 years, climate in the north of China evolves to dry and heat Thus, desertification protection is an important task for China in the 21st century

[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)Abstract The fundament of system coupling is heterogeneity, and the basic prerequisite is that there are connective corridors in the same type between ecosystems. The landscape ecological mechanism of system coupling is the spatial difference of non-biotic environment and the heterogeneity caused by disturbances. The force or energy of system coupling is disturbances. From in the view of landscape ecology, system coupling is the merging process of different landscape elements between different landscape ecosystems followed by the process of character changing of landscape elements with scale changing. Based on the essence of disturbances, system coupling can be divided into two types as natural system coupling and artificial system coupling. Natural system coupling is the base of artificial system coupling, and hence, the enhance of eco-productivity of coupling system is based on the optimization of artificial system coupling.

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[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)Abstract Objective: Under the multiple requirements of urbanization, food security and ecosystem management, it is necessary to explore the feasibilities and limitations on how to develop building land on gentle hillside in mountain cities. In the process of mountain development, the ecological sensitive area should be prevented from human interferences and human health from disaster threats. In this study, 12 spatial indices were selected which can represent the landscape and disaster risks in order to find the suitable degree of building land development on gentle hillside. Ordered weighted averaging (OWA) operators are usually used to model a family of parameterized decision strategies, which can be applied to route planning to provide more realistic results. In this method the optimistic degree can be quantified by the ordered weight. In an optimistic preference, the function of the relatively important indices is magnified; while in a pessimistic preference, the importance is relatively ignored. OWA algorithm can be used to conduct the scenario simulations of the feasibility of various construction land under different preferences,which is apparently able to reduce the impact of a single result of a subjective perception by decision-makers, and thus can well reflect the changes of evaluation result led by the slight adjustment of regional policies. In this study, Dali Bai Nationality Autonomous Prefecture was chosen as the study area, and OWA operators were adopted to measure the suitability of building land construction on gentle hillside. By setting up different ordered weights, scenarios can be set with the quantified optimistic or pessimistic preference of the decision makers. The result showed that inthe optimistic scenario, the characteristics of high risk indices like farm land, urban, fault zone, rivers can be obviously detected spatially; in the pessimistic scenario where the degree of reliability for risk indices was decreased, the spatial homogeneity was enhanced on the result layer. Then, three scenarios were built up by the OWA operators, which support three different strategy orientations-urban construction, keeping the present policies and risk control. After defining the suitability sub region, it was found that priority development of small towns should be encouraged in urban construction scenario, while ensuring the land supply for big cities in the risk control scenario. With the assessments, we can describe how to build up the spatial pattern of urbanization after a policy trade-off among different stages of city development, and the results can satisfy the different decisions from the government and various requirements for the layout of construction land from different stakeholders. This study tended to draw a comprehensive conclusion through a combination of inter-scene. Although in a mathematical level, the three policy-perspectives provided by this paper are described as the preference of evaluator's, they are actually a reflection of decision makers'different strategic tendencies and thus are no clear good or bad. This study didn't attempt to select an optimal solution, but tends to describe how to build a spatial pattern of urbanization with policy trade-offs in different stages of urban development, so as to meet the needs of construction land's development under different decisions or layout ideas, with relatively high practical significance.

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[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)The paper built a dynamic simulation model of human-natural evolution by using dynamic model to have an empirical study on the law of human-natural system in Gannan based on the human-natural relationship, which described the potential situation of the evolution of human-natural relationship in Gannan and provided a theoretical basis for coordinating the human-natural relationship to change direction to sustainable development. The results indicate that the human-natural relationship of Gannan was in the maladjusted state and the human-natural system presents an unsustainable evolutionary trend in the future,economic development can only be lasted about 35 years. From the evolvement of the human-natural relation, according to the conditions of Gannan area, some ideas of optimizing the human-natural relationship are put forward. Accelerating the transformation of economic development is the inevitable choice to coordinate the Gannan’s human-natural relationship to move towards the sustainable development, reduce the dependence of economic growth on natural resources and the pressures on the environment can effectively delay the speed of exhaustion of resources, which has the positive impetus function to the human-natural system moving towards the sustainable development. Speeding up technical progress and increased environmental protection investment has a significant role to the coordination of the Gannan’s human-natural relationship. Gannan’s efforts to develop the economy must be accompanied by equally vigorous efforts to control the population, and improve its quality, which ultimately to coordinate the human-natural relationship of Gannan.

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[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.

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[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.Eco-environmental vulnerability assessment is crucial for environmental and resource management. However, evaluation of eco-environmental vulnerability over large areas is a difficult and complex process because it is affected by many variables including hydro-meteorology, topography, land resources, and human activities. The Thua Thien Hue Province and its largest river system, the Perfume River, are vital to the social-economic development of the north central coastal region of Vietnam, but there is no zoning system for environmental protection in this region. An assessment framework is proposed to evaluate the vulnerable eco-environment in association with 16 variables with 6 of them constructed from Landsat 8 satellite image products. The remaining variables were extracted from digital maps. Each variable was evaluated and spatially mapped with the aid of an analytical hierarchy process (AHP) and geographical information system (GIS). An eco-environmental vulnerability map is assorted into six vulnerability levels consisting of potential , slight , light , medium , heavy , and very heavy vulnerabilities, representing 14%, 27%, 17%, 26%, 13%, 3% of the study area, respectively. It is found that heavy and very heavy vulnerable areas appear mainly in the low and medium lands where social-economic activities have been developing rapidly. Tiny percentages of medium and heavy vulnerable levels occur in high land areas probably caused by agricultural practices in highlands, slash and burn cultivation and removal of natural forests with new plantation forests. Based on our results, three ecological zones requiring different development and protection solutions are proposed to restore local eco-environment toward sustainable development. The proposed integrated method of remote sensing (RS), GIS, and AHP to evaluate the eco-environmental vulnerability is useful for environmental protection and proper planning for land use and construction in the future.

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[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.This article describes an integrated dynamic model of The Commonwealth of Dominica, a small Caribbean island state. The modeling approach emphasizes whole-systems assessment and trans-disciplinary analysis, providing a framework to conceptualize the impacts of different tourism development strategies, accounting for interactions between ecology, economy and society. Our use of dynamic modeling differs from established techniques such as simulation, predictive, or mediated modeling; we use the modeling environment primarily as an accounting tool to track the interaction of a large set of heterogeneous data and assumptions. We believe that a model such as ours can provide a valuable tool for the synthesis of data and theories about development alternatives. New data can be added as it becomes available, structural elements can be included as deemed important within a given milieu, and the largely explicit assumptions of the model can be changed to examine alternative views.

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[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.A broad marine policy goal is to maintain healthy marine social–ecological systems that sustain desirable ecosystem services and support human livelihoods. Marine social–ecological systems are already stressed by a number of environmental factors and the impacts of globalisation. Climate change is an additional stress that may push marine social–ecological systems beyond the ranges of past variability to which they have become adapted. Human social systems have well-developed strategies for dealing with variability within their normal ranges of experience, although these capacities are not distributed homogeneously around the globe. This paper addresses the additional impacts of climate change on marine social–ecological systems that are focussed around fishing. For example, human social fishing systems dealing with high variability upwelling systems with rapidly reproducing fish species may have greater capacities to adjust to the additional stress of climate change than human social fishing systems focussed on longer-lived and generally less variable species. The details of local impacts of climate change and its interactions with existing stresses on marine social–ecological systems are difficult to predict but will lead to more extreme events and increased uncertainty. Management must strive to enhance the adaptive capacities of these systems to uncertainty and change. Primary challenges are to address non-climate change stresses such as overfishing and how they may interact with climate change to produce surprises, and to recognise that multiple interacting time, space and organisational scales make identification and resolution of impacts difficult. Additional challenges are to develop integrated observing and modelling systems for the full social–ecological system so as to quickly recognise changes, to enhance communications with stakeholders, and to develop flexible institutions that can adjust rapidly to new circumstances.

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[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.Advancing vulnerability science depends in part on identifying common themes from multiple, independent vulnerability assessments. Such insights are difficult to produce when the assessments use dissimilar, often qualitative, measures. The Vulnerability Scoping Diagram is presented to facilitate the comparison of assessments with dissimilar measures. The diagram is illustrated with recent research on drought vulnerabilities, showing that common insights into vulnerability may emerge if independent research teams use a common structure for organizing information about exposure, sensitivity and adaptive capacity—even if the underlying measures differ between assessments. Broadly adopting this technique, which is grounded in the “Eight Steps” methodological protocol [Schr02ter, D., Polsky, C., Patt, A., 2005. Assessing vulnerabilities to the effects of global change: an eight step approach. Mitigation and Adaptation Strategies for Global Change 10(4), 573–595], will enable a vulnerability meta-analysis, the lessons from which may permit places to identify helpful adaptation or mitigation options without first having to conduct their own vulnerability assessments.

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[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)The algorithm of slope length factor based on up-slop runoff area was mended and new algorithm of slope length factor considering the effect of land use/cover for up-slop runoff was produced.By using geographic information system(GIS) and the revised universal soil loss equation(RUSLE),the soil erosion intensity and its relationship with environmental factors in the Simianyaogou watershed,located at Loess Plateau were studied.The results showed that the average annual soil erosion intensity in the watershed was 4 399.79 t/(km2 a),which was in the category of moderate degree erosion.Both soil erosion intensity and amount increased significantly with the increasing of the slop gradient.80.59% of the total soil loss occurred in the region with a gradient more than 25 degree,of which the area was 59.06% of the total watershed area.Soil erosion intensity varied with slope aspects in a trend of sunny slopehalf-sunny slopehalf-shady slopeshady slope.The area of sunny slope occupyed 45.07% of the total watershed area,but the erosion amount of which occupyed 56.50% of the total erosion amount.In different land use types,the grassland occupyed 57.07% of the total watershede area,but the erosion amount of which occupyed 96.37% of the total erosion amount.So,grassland had became the major erosion and sediment source in the watershed.The study provides technical basis for applying RUSLE to assess soil erosion on Loess Plateau and offers useful references for water and soil resource utilization in the region.

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[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.Since The Brundtland Commission formally put forward the concept of sustainable development in their famous book Our Common Future, the global community has paid great attention to the principles of sustainable developmentHowever, the implementation of sustainable development is still difficult even today, because of its essential complexity Therefore, many research tools for sustainable development have emerged in the past two decadesAmong them are the Sustainable Livelihoods (SL) Approach and Vulnerability Analysis Approach, two analytical tools that can inform sustainable development proposalsThe first focuses on poverty alleviation and the second on mitigation of risks to shocks and stressesThis paper explores the role of these two frameworks in development planning and describes where they intersect and where they differThe first section describes and compares the conceptual and theoretical underpinnings of each approachThe second section describes how each framework operates in implementation, and how the two can reinforce each other in guiding analysis of constraints to and entry points for sustainable development projects

[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.Abstract Landscape protection planning is a complex task that requires an integrated assessment and involves heterogeneous issues. These issues include not only the management of a considerable amount of data to describe landscape features but also the choice of appropriate tools to evaluate the hazards and risks. The landscape assessment phase can provide fundamental information for the definition of a Landscape Protection Plan, in which the selection of norms for protection or rehabilitation is strictly related to hazards, values and risks that are found. This paper describes a landscape assessment methodology conducted by using GIS, concerning landscape hazards, values and risk. Four hazard categories are introduced and assessed concerning urban sprawl and erosion: landscape transformations by new planned developments, intensification of urban sprawl patterns, loss of agriculture land and erosion. Landscape value is evaluated by using different thematic layers overlaid with GIS geoprocessing. The risk of loss of landscape value is evaluated, with reference to the potential occurrence of the previously assessed hazards. The case study is the Province of Enna (Sicily), where landscape protection is a relevant issue because of the importance of cultural and natural heritage. Results show that high value landscape features have a low risk of loss of landscape value. For this reason, landscape protection policies assume a relevant role in landscapes with low-medium values and they should be addressed to control the urban sprawl processes that are beginning in the area. Copyright 2012 Elsevier Ltd. All rights reserved.

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[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.Abstract Drylands are one of the most diverse yet highly vulnerable social-ecological systems on Earth. Water scarcity has contributed to high levels of heterogeneity, variability and unpredictability, which together have shaped the long coadaptative process of coupling humans and nature. Land degradation and desertification in drylands are some of the largest and most far-reaching global environmental and social change problems, and thus are a daunting challenge for science and society. In this study, we merged the Drylands Development Paradigm, Holling's adaptive cycle metaphor and resilience theory to assess the challenges and opportunities for livelihood development in the Amapola dryland social-ecological system (DSES), a small isolated village in the semi-arid region of Mexico. After 450 years of local social-ecological evolution, external drivers (neoliberal policies, change in land reform legislation) have become the most dominant force in livelihood development, at the cost of loss of natural and cultural capital and an increasingly dysfunctional landscape. Local DSESs have become increasingly coupled to dynamic larger-scale drivers. Hence, cross-scale connectedness feeds back on and transforms local self-sustaining subsistence farming conditions, causing loss of livelihood resilience and diversification in a globally changing world. Effective efforts to combat desertification and improve livelihood security in DSESs need to consider their cyclical rhythms. Hence, we advocate novel dryland stewardship strategies, which foster adaptive capacity, and continuous evaluation and social learning at all levels. Finally, we call for an effective, flexible and viable policy framework that enhances local biotic and cultural diversity of drylands to transform global drylands into a resilient biome in the context of global environmental and social change.

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[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)Based on the concept of 'consilience' in integrated risk governance, this paper aims to develop scientific meanings of consilience in a systemic manner from the perspectives of fundamental principles, synergistic efficacy, operational means, and optimization process to improve a system's robustness to resist external disturbs. This paper proposed a new consilience mode for the purpose of complementing the existing theoretical system of integrated risk governance. The results presented in this paper show that the four proposed synergistic principles(tolerance, constraint, amplification and diversification) can well describe the characteristics of consilience in integrated risk governance of a socioecological system. The principles set four optimization goals in terms of 'consenting in minds' and 'gathering in force' in the consilience theory. The consilience mode demonstrates how the synergistic principles and their optimization goals are converted into a series of tasks including the popularization of social perception, the rationalization of cost allocation, the systemization of optimization and the maximization of cost benefit. With implementation of all these tasks, the consensus and social welfare can be maximized while the cost and risk can be minimized in the integrated risk governance of the socio- ecological system. The modeling and simulation results show that a complex network system's robustness can be improved with increased system consilience when facing local or global disturbs. Moreover,this kind of improvement can be achieved by optimizing the structure and function of nodes in a socio-ecological system. The consilience mode also complements current disaster system theory in which the concepts of vulnerability, resilience and adaptation may face limitation of addressing integrated risk governance problems in a socio-ecological system.

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[31]
Smit B, Wandel J, 2006. Adaptation, adaptive capacity and vulnerability.Global Environmental Change, 16(3): 282-292.This paper reviews the concept of adaptation of human communities to global changes, especially climate change, in the context of adaptive capacity and vulnerability. It focuses on scholarship that contributes to practical implementation of adaptations at the community scale. In numerous social science fields, adaptations are considered as responses to risks associated with the interaction of environmental hazards and human vulnerability or adaptive capacity. In the climate change field, adaptation analyses have been undertaken for several distinct purposes. Impact assessments assume adaptations to estimate damages to longer term climate scenarios with and without adjustments. Evaluations of specified adaptation options aim to identify preferred measures. Vulnerability indices seek to provide relative vulnerability scores for countries, regions or communities. The main purpose of participatory vulnerability assessments is to identify adaptation strategies that are feasible and practical in communities. The distinctive features of adaptation analyses with this purpose are outlined, and common elements of this approach are described. Practical adaptation initiatives tend to focus on risks that are already problematic, climate is considered together with other environmental and social stresses, and adaptations are mostly integrated or mainstreamed into other resource management, disaster preparedness and sustainable development programs.

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[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.Livelihood resilience draws attention to the factors and processes that keep livelihoods functioning despite change and thus enriches the livelihood approach which puts people, their differential capabilities to cope with shocks and how to reduce poverty and improve adaptive capacity at the centre of analysis. However, the few studies addressing resilience from a livelihood perspective take different approaches and focus only on some dimensions of livelihoods. This paper presents a framework that can be used for a comprehensive empirical analysis of livelihood resilience. We use a concept of resilience that considers agency as well as structure. A review of both theoretical and empirical literature related to livelihoods and resilience served as the basis to integrate the perspectives. The paper identifies the attributes and indicators of the three dimensions of resilience, namely, buffer capacity, self-organisation and capacity for learning. The framework has not yet been systematically tested; however, potentials and limitations of the components of the framework are explored and discussed by drawing on empirical examples from literature on farming systems. Besides providing a basis for applying the resilience concept in livelihood-oriented research, the framework offers a way to communicate with practitioners on identifying and improving the factors that build resilience. It can thus serve as a tool for monitoring the effectiveness of policies and practices aimed at building livelihood resilience.

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[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.

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[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)Bivariate SPI-SRI drought index was proposed to describe drought behaviors of the Tarim River Basin. Five meteor-hydrological drought conditions were identified with three drought hazard: meteorological drought, both meteorological and hydrological drought, and hydrological drought conditions. Drought hazard was investigated in terms of formation process with the atmospheric and hydrological phases. Stochastic analysis of the developed indicator can be used to assess the dynamics of the transition between drought phases. Time series of the meteor-hydrological drought conditions were investigated as a discrete state, discrete-time homogenous Markov chain. Analysis of the properties of Markov chain aimed to evaluate probably of transition between different conditions, frequency of each conditions, residence time in each condition, time required to move from one condition to another, and predict drought hazard in the next month. The results indicate that:(1) The droughts have the significant impacts during its development stage in the Kaidu and Hotan river basins. The negative influences of droughts are evident during its evolution periods in the Aksu river basin and during its successive periods in the Kaidu and Yarkant river basins. The Kaidu and Yarkant river basins are dominated by meteor-hydrological droughts, and hydrological droughts in the Hotan and Aksu river basins;(2) Occurrence probability is the largest for consecutive wetness or drought conditions in the Kaidu river basin and the probability of condition shifts between conditions 2, 4 and 5 in the Kaidu river basin. No drought condition shifts can be expected between conditions 4 and 2in the Hotan and Kaidu river basins;(3) Forecasting practice of drought conditions, the occurrence probability of drought condition shifts between conditions 2 to 4 and 2 to 5 is the lowest.Largest occurrence probability can be expected for drought condition shifts between non-hydrological drought conditions and condition 4 and the smallest probability could be expected for drought condition shifts between non-hydrological drought and condition 5.

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[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)Unclear concept and scale positioning as well as imperfect evaluation system are the main obstacles of vulnerability evaluation.Starting from the concept,factor and scale of the coupled human-natural systems vulnerability,this article made it clear that the evaluation objective is in regional scale and the analysis framework is composed by climate change and system structural elements.Based on expounding the features of the coupled human-natural systems vulnerability,the authors established firstly an evaluation system of the coupled human-natural systems vulnerability,including the three series factor indexes of sensitivity,exposure and the adaptability,and three evaluation levels of background vulnerability,potential vulnerability and realistic vulnerability according to system relations of the three series factor indexes.Secondly,taking the hilly areas in southern China as an example,the authors established an evaluation index system of coupled human-environment interaction systems vulnerability in the erosion-prone region,which includes regional natural disasters such as debris flow,landslide,drought and flood,and which relies on the principles including the scientific principle,dominant factors principle,pertinence principle,applied principle and operability principle.Finally,the authors concluded that:(1) the regional coupled human-environment interaction systems vulnerability concerns with natural disasters under global climate change disturbance;(2) the sensitivity and damageability are the essential attribute of the vulnerability,while the sensitivity,exposure and adaptability are system factors of the vulnerability;(3) the natural disaster frequency can be seen as the location exposure indexes to reflect the space concentricity of disaster occurences,while the real disaster degree,as a reference value,can be provided to verify the results of the vulnerability evaluation indexes screening,index weights determinating,model validation and threshold value analysis.The research has some references for improving the theory and method of the research on regional coupled human-environment interaction systems vulnerability in different regions under the consistent framework.

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[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)To approach the spatial heterogeneity of human disturbance is of significance in researching the dynamics of land cover change,especially the characteristics of its directional structure.Jinjiang City is a "hot" region of land use change in Fujian Province,and the land has experienced intense human disturbance.This paper studied the spatial heterogeneity of land use intensity and human disturbance in this city in 1989 2001,with systematic grid sampling method and geostatistics in application.The results revealed that there was an obvious spatial heterogeneity of human disturbance in the study area,especially the directional structure of NE-SW caused by the traffic line from Qingyang-Anhai.Human disturbance was grown in the whole area,and the administrative centers served as the growth poles.Because of the associated influence of traffic lines and administrative centers,human disturbance was of a pole-axis structure.

PMID

[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)A series of changes have taken place in the last50years in regional hydrology and e-co-environment in Hexi Corridor region due to dual driving force of intense human activities and regional climate changes.(a)Regional runoff processes flowing out from mountains showed that mean annual outflow of the Shiyang River from mountains at the east section of the corridor tended to decrease significantly;annual outflow of the Heihe River and the Shule River at the middle and west sections of the corridor tended to increase,but the downstream discharge sharply decreased and displayed obvious anthropogenic hydrological features.Water salinization and pollution tended to be exacerbated,of which the ｏ rades of polluted river course reached208km.(b)Forest area in the south Qilian Mountain region decreased by16.5%but it has gradually increased since the1990s.However,natural desert forest in the northern part of the corridor continuously degraded and rapidly disappeared,only in Minqin and Ejin counties about 34.31 10 4 hm 2 of woodland have disappeared.Grassland ecology showed a continuous degradation tendency,grassland area decreased,desertification exacerbated,carrying capacity re-duced and total area of degraded grassland in Hexi corridor region occupied46.86%.(c)Land de-sertification in Hexi corridor region developed rapidly in the last50years and reached a highest an nual rate of2.15%in the early1980s,but from the late1980s to the1990s its develop ment rate significantly reduced.The essential ways to improve regional sustainable development capa-bility are identified as to take river basin as an unit,make overall plans of water use and man-agement by taking water demand for economic development and eco-environmental construction of different regions in to consideration;to take healthy development of ecosystem as the principle,follow ecological laws to exploit and utilize land resources;and to take river basin as an unit,conduct systematic work to protect the ecological function.

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[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)Drought disaster is one of the most hazardous natural disasters on earth.Through the in-depth analysis of causes of drought risk,combined with the principles of natural disaster system,a drought disaster risk assessment model was constructed according to the dangerousness of disaster-inducing factors,vulnerability of disaster-breeding environment,exposure of disaster-bearing body,and disaster prevention and mitigation capabilities.Then,an assessment and regionalization of drought risk was conducted in Hedong area of Gansu Province with ArcGIS platform by integrated consideration of the natural and social characteristics of drought disaster,based on local meteorological,ecological and socio-economic data.The results showed that:(1)The dangerousness of disaster-inducing factors decreases gradually from the middle to both west and east sides of Hedong area,and from large to small is Tianshui,Pingliang,Longnan,Dingxi,Linxia,Gannan and Qingyang.(2)The vulnerability of disaster-breeding environment decreases gradually from north to sourth,and from large to small is Qingyang,Linxia,Dingxi,Pingliang,Tianshui,Gannan and Longnan.(3)The exposure of disaster-bearing body is the highest in Tianshui,followed by Pingliang,Linxia,Dingxi,Longnan,Qingyang and Gannan.(4)The disaster prevention and mitigation capabilities from large to small is Linxia,Tianshui,Pingliang,Dingxi,Longnan,Qingyang and Gannan.(5)The meteorological drought disaster risk decreases from north to south and the highest in Dingxi,followed by Tianshui,Qingyang,Pingliang,Linxia,Gannan and Longnan.

[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)In the past 50 years,the sediment yields of the Loess Plateau have changed significantly,due to both natural and human factors.This study analyzes the temporal and spatial variability of sediment yields of the Loess Plateau,and estimates the effect of precipitation variability on sediment yield using the annual sediment yield.Data were collected from 115 hydrological and 276 meteorological stations.The 10 main tributaries of Middle Yellow River show that the annual sediment discharge has decrease over the past 50 years,especially since 1980s.Two periods were compared:one prior to the implementation of soil conservation methods(1956-1969),and one post-implementation(1970-1989).The sediment yield decreased by 44.3%;there are also significant spatial differences between the periods.The most significant decrease was observed the Middle and Lower Wudinghe River,and in the north central Shanxi Province,where sediment yield decreased by more than 40%.A similar pattern of decline was evident for precipitation records;therefore,precipitation variation might play an important role in the decrease in the sediment mobilization from the landscape,and hence,the decrease in sediment yields.However,human activities such as soil conservation and water diversion contributed significantly more to this decline in the sediment yield than the precipitation.It is estimated that 61%-90% of the decrease in sediment yield can be attributed to human activities.

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[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.We are primarily concerned with the problem of aggregating multicriteria to form an overall decision function. We introduce a new type of operator for aggregation called an ordered weighted aggregation (OWA) operator. We investigate the properties of this operator. We particularly see that it has the property of lying between the “and,” requiring all the criteria to be satisfied, and the “or,” requiring at least one of the criteria to be satisfied. We see these new OWA operators as some new family of mean operators.

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[42]
Yager R R, 1996. Quantifier guided aggregation using OWA operators.International Journal of Intelligent Systems, 11(1): 49-73.

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[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)Social-ecological systems (SESs) are characterized by unpredictability, self-organization and regime shift. Road construction usually exhibits strong influences on the local SESs across multiple scales. By placing the research under the framework of the SESs and resilience theory, we conducted a comprehensive study on the influences of road paving in Shangluo, Shaanxi Province, from two scales oth the local and community scales. Specifically, the local social-ecological system in Shangluo was divided into three dimensions including economic growth, ecological landscape pattern and rural development. In order to understand the impacts of road construction on the economic development, landscape fragmentation, and resilience of rural area, we integrated and analyzed the statistical data, satellite images and questionnaires. The results indicate that, (1) Although road paving is an important factor in the process of poverty-relief, it is not a driving force in economic development. Economic development, in fact, has advanced the development of road paving. (2) Road paving has not only fragmented the local landscape, but also increased the social connectivity. Landscape fragmentation and social connectivity are positively correlated. (3) At the rural community level, the relationship between road paving and social resilience are complex, where the measurement of rural community resilience can be built from collective memory, livelihood diversity, and adaptive capacity. The impact factors of the resilience of rural community have been identified. Finally, based on the results, we highlighted the future work, in particular, the community resilience in the less-developed rural areas.

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[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)Land desertification in arid areas of northwest China could be classified as two types: desert desertification and oasis desertification.Desert desertification presents mostly aeolian desertification,which occurs mainly in terrene desert and sandy desert.As final result of desertification,terrene desert turns into sandy desert,gravel desert or aeolian land.Desertification of sandy desert is a process that fixed and semi-fixed desert turn into mobile or semi-mobile desert,which results finally in formation of mobile desert.Oasis desertification includes main three types: aridilization,aeolian desertification and salinization.Oasis aridilization results normally in degeneration and death of oasis vegetation,which causes oasis lessening.Aridilization is commonly piled by aeolian desertification in marginal part of oasis,which results in an incursion of mobile sand to oasis.If water source is cut entirely,oasis will disappear and become finally sandy desert or aeolian land.Oasis salinization results from excess irrigation in farmland,which causes hoist of ground water level and congregation of salt in surface soil layer from underlying layer,resulting finally in formation of salt oasis or salt desert.In northwest China,desertification of terrene desert exists mostly in east part of Alax area,Inner Mongolia,alluvium fan in south piedmont of Tianshan Mountains,desert steppe area of north Junggar Basin.Desertification of sand desert occurs mainly in Gurbantunggut Desert of Junggar Basin.Oasis aridilization and aeolian desertification are typical in Minqin Oasis of Gansu,and oasis salinization distributes mainly in Akesu oasis and Kashi oasis of Xinjiang.

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[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)Quantitative research on the pattern of landscape changes and soil erosion of Malian River Basin in Loess Plateau was carried out by means of Landsat ETM+(2000) and Landsat TM(2007) satellite images,and supported by GIS and RS technology and methods in landscape ecology.Landscape pattern analysis software(Fragstats 3.3) was used to calculate all kinds of landscape index.Universal Soil Loss Equation(USLE) was employed to analyze soil erosion.The results are as follows: important changes took place in the past eight years in landscape patterns and soil erosion of the Malian River Basin.On the one hand,for landscape pattern change: 1) we analyzed the patch types from the level of classification.Construction land,water body,high covered grassland and forest area were increasing,especially in high covered grassland,which was increased from 593108.80 hm2(2000) to 940098.90 hm2(2007).Rivers,forests,construction land and reservoirs did not significantly increase and their area increases were 21278.82 hm2,12561.13 hm2,4863.72 hm2 and 52.11 hm2,respectively.Instead of low covered grassland,farmland areas were decreased,and the low covered grassland decreased greatly.The low covered grassland area was 542020.78 hm2 in 2000,while it was reduced to 219987.30 hm2 in 2007.The arable land,came the next,reduced by 63712.40 hm2.It can be easily found that over 5% of the low-cover grassland before 2000 was added into the high covered one in the course of the entire landscape pattern change.2) The landscape level analysis shows that: the total number of landscape patches were 1375428 in Malian River Basin in 2000,but it decreased to 805769 seven years later;patch density decreased from 71.98 patch/hm2 in 2000 to 42.21 patch/hm2 in 2007,indicating the reduction of plaque fragmentation;diversity of the index decreased from 1.31 in 2000 to 1.23 in 2007,reflecting the gradual reduction of the number of landscape elements;patch numbers,patch density and diversity index were decreasing.That indicated a reduction for plaque fragmentation,tending to stabilization for ecosystem and gradual increasing of ecological functions.On the other hand,for the quantitative research in the soil erosion,1) from 2000 to 2007,the landscape soil erosion modulus was decreased except reservoir.The reducing of forest land was the most significant,whose average erosion modulus decrease was 348.38 kg/(hm2 a) from 2000 to 2007.The construction land came the next,the average amount of erosion modulus decreased by 144.25 kg/(hm2 a) during this period of time,other landscape types of soil reducing was not obvious.2) Total soil erosion decreased by 12506.76 t,which showed a significant reduction of soil erosion.Most area of Malian River Basin fell into the category of slight and mild erosion,especially the forest in the downstream areas of Ziwuling,where soil erosion was clearly changed from the moderate erosion to the ligh erosion.The river erosion in the deposition zone was more serious,and individual regions were severe erosion.The reasons for the change caused by the restoration of the local vegetation,influence of human activities,urbanization,increasing of construction land and other factors,particularly in the World Bank projects,the state policy of returning farmland to forest and grassland and the effective implementation on it.

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