A fuzzy analytic hierarchy process (FAHP) approach to eco-environmental vulnerability assessment for the danjiangkou reservoir area, China

Ecological Modelling 220 (2009) 3439–3447

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Ecological Modelling journal homepage: www.elsevier.com/locate/ecolmodel

A fuzzy analytic hierarchy process (FAHP) approach to eco-environmental vulnerability assessment for the danjiangkou reservoir area, China Lu Li a , Zhi-Hua Shi a,∗ , Wei Yin b , Dun Zhu a , Sai Leung Ng c , Chong-Fa Cai a , A-Lin Lei b a b c

Key Lab of Subtropical Agriculture & Environment of Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, PR China Changjiang Water Resource Protection Institute, Yangtze River Water Resources Commission, Wuhan 430051, PR China Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

a r t i c l e

i n f o

Article history: Received 15 April 2009 Received in revised form 31 July 2009 Accepted 5 September 2009 Available online 6 October 2009 Keywords: Eco-environment Vulnerability assessment Fuzzy AHP Danjiangkou reservoir area

a b s t r a c t The Danjiangkou reservoir lies in the upper Hanjiang basin and is the source of water for the Middle Route Project (MRP) under the South-to-North Water Transfer Scheme (SNWT) in China. The eco-environment of water resource areas plays an important role in water conservation and purification and would have significant implications for the economic prosperity in Hanjiang basin as well as for the SNWT. Focusing on the Danjiangkou Reservoir Area (DRA), this study established an environmental information system database. Based on the database, an eco-environmental vulnerability assessment method using integrated fuzzy AHP (FAHP) and GIS was developed for the DRA. According to eco-environmental conditions and anthropic effects, vulnerability was classified into five levels: potential, light, medium, heavy and very heavy. The eco-environmental vulnerability distribution and major problems of the eco-environment were analysed and discussed. The results indicated that eco-environmental vulnerability in the DRA was moderate overall. Regions with lower eco-environmental vulnerability were located in Qinling Mountain area in the northwest, Daba Mountain area in the south and the area immediately surrounding Danjiangkou Reservoir in the east. Two regions with very high eco-environmental vulnerability were located in the north of Danjiangkou Reservoir in Henan province and in the western part of Shanxi province. On the basis of analysis of distribution of the different factors of eco-environmental vulnerability, different environmental protection measures were proposed for different areas. This study demonstrated that the proposed method was an effective approach for the assessment of environmental vulnerability. The results gained closely reflected the reality of the eco-environmental vulnerability of the DRA. © 2009 Elsevier B.V. All rights reserved.

1. Introduction The current development of China is adversely affected by water scarcity. Whilst China comprises 22% of the world’s population it has only 8% of the earth’s total surface freshwater. Moreover, the distribution of water resources is highly disproportionate across the country. For example, the Yangtze River accounts for more than 80% of the nation’s total runoff, but the three northern rivers (Yellow River, Huai River, and Hai River) have a combined runoff below 6.5%. In response to the water deficiency of Northern China, a scheme called the Middle Route Project (MRP) under the Southto-North Water Transfer Scheme (SNWT) was devised to divert water from Han River, a tributary of Yangtze River, to supply two provinces (Henan and Hebei) and two municipalities (Beijing and

∗ Corresponding author at: College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, PR China. Tel.: +86 27 87288249; fax: +86 27 87288618. E-mail addresses: [email protected], [email protected] (Z.-H. Shi). 0304-3800/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2009.09.005

Tianjin) in the north. The main canal of the MRP has a total length of 1264 km, and is designed to have an annual water diversion capacity of 13–14 billion m3 . This is thus a massive inter-basin water transfer project and one of the country’s most important projects. The Danjiangkou reservoir, which has a drainage area of 95,200 km2 , is the source of water for the MRP. Since guaranteeing the water quality of the Yangtze River is tantamount to safeguarding the lifeline for the future of China, both public and government sectors have been increasingly interested in the water quality of the Danjiangkou reservoir (Chen et al., 2007; Zhu et al., 2008). The ecoenvironment plays an important role in water conservation and purification. Evaluation of the current eco-environmental status of the Danjiangkou Reservoir Area is necessary for proper protection of the eco-environment and alleviation of water pollution. Eco-environmental vulnerability has become a central focus of the global change and sustainability research communities (Adger, 2006; Ford et al., 2006). It has also become a popular topic in the domain of environmental resource research, especially eco-environmental vulnerability assessment (Eakin and Luers, 2006; Villa and MacLeod, 2002). Integrated analysis of

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eco-environmental vulnerability helps to ascertain the key ecoenvironmental characteristics of a study area. Furthermore, results of systemic assessments can help to identify particular problems within the study area so that appropriate measures can be taken to address these. Nevertheless, regional analysis of eco-environmental condition and vulnerability represents a significant assessment challenge. There is no widely accepted theoretical and methodological system for eco-environmental vulnerability assessment. A primary problem of regional vulnerability assessment is the integration of information from many different sources into an overall ranking of relative vulnerability (Wickham et al., 1999). Therefore, it is crucial to select detailed assessment methods. Several methods have previously been employed and developed for vulnerability assessment. These include: the comprehensive evaluation method (Gowrie, 2003); the artificial neural-network evaluation method (Park et al., 2004); the landscape evaluation method (Aspinall and Pearson, 2000); and the principal component analysis (PCA) method (Li et al., 2006). Among them, the analytic hierarchy process (AHP) is one of the most commonly used method of assessment (Li et al., 2007; Xiong et al., 2007), which works on a premise that decisionmaking of complex problems can be handled by structuring the complex problem into a simple and comprehensible hierarchical structure. Despite of its wide range of applications, the conventional AHP approach may not fully reflect a style of human thinking, in which human’s judgments are represented as exact numbers. However, in many practical situations, decision makers usually feel more confident to give interval judgments rather than expressing their judgments in the form of exact numeric values. Therefore, AHP technique involves subjectivity in pair-wise comparisons and vagueness and uncertainty dominate in this process. In view of that, Laarhoven and Pedrycz (1983) have evolved AHP into the FAHP, bringing the triangular fuzzy number of the fuzzy set theory directly into the pair-wise comparison matrix of the AHP. The purpose is to solve vague problems, which occur during the analysis of criteria and the judgment process. FAHP should be able to tolerate vagueness or ambiguity (Mikhailov and Tsvetinov, 2004), and should thus be more appropriate and effective than conventional AHP in real practice The objectives of this study were: (1) to develop an ecoenvironmental vulnerability assessment method integrating fuzzy AHP and GIS, (2) to identify the key factors of eco-environmental vulnerability for the DRA, and (3) to describe the characteristics and major distribution of eco-environmental vulnerability at different scales and suggest appropriate measures to immediately address environmental issues.

2. Methodology 2.1. Study area The Danjiangkou Reservoir Area (DRA), is situated between 31◦ 20 –34◦ 10 N and 106◦ –112◦ E, and covers an area of 95,200 km2 (Fig. 1). Elevations within the DRA range from 150 to 3612 m. The DRA is characterized by a typical subtropical monsoon climate. The mean annual precipitation is about 873 mm and the annual average temperature is 13.7 ◦ C, both of which have clear seasonal variation. Soil types consist of yellow brown soil, brown soil, yellow cinnamon soil, calcareous soil, paddy soil, chao soil, and purple soil according to Chinese soil classification system (National Soil Survey Office, 1992), which corresponds respectively to Alfisols, Ultisols, Aquepts, and Inceptosols in the USA Soil Taxonomy (Soil Survey Staff, 1999). The vegetation is typically subtropical. Evergreen forests occur at altitudes below 1500 m and subalpine meadows occur at altitudes above 2500 m, whilst conifer and broad-leaf mixed forests are distributed at altitudes between 1500 and 2500 m. Residual forests are located only in the mountainous areas in the DRA. The major crops are rice (Oryza sativa L.), wheat (Triticum aestivum L.) and rape (Brassica napus L.). The DRA includes 48 counties in Shanxi, Hubei and Henan province. The majority of the population is agricultural and the population density is about 130 people km−2 . 2.2. Data The basic data used for this study included: (1) remote sensing (RS) data, including TM images (September 2007); (2) DEM data and road data, supplied by the State Bureau of Surveying and Cartography, from 1:250,000 standard maps; (3) soil type data from the data-sharing network of earth system science compiled by the Institute of Soil Science Chinese Academy of Sciences and geological data from the data-sharing platform for land and resource science developed by the China Geological Survey; (4) water-heat meteorological data, from the National Eco-environmental Background Water-heat Database, developed by the Natural Resource and Agricultural Region Research Institute of the Chinese Academy of Agricultural Sciences, including ≥10 ◦ C accumulated temperature, annual average temperature, annual average precipitation, aridity index and moisture index; (5) socio-economic data, from the Annual Statistics of Shanxi, Henan and Hubei Province. Source data were further processed as follows: (1) Subjects for eco-environmental vulnerability assessment were determined from the original data. Land use and vegetation data were derived from the RS data using a user-computer interactive interpreta-

Fig. 1. Location of the study area and its DEM.

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tion method. This was integrated with on site investigation for some typical regions. Elevation and slope data were generated from DEM. (2) Unification of the geo-reference of subjects. All data were projected using the Albers projection system, the Krasovsky ellipsoid. (3) Rasterization of the vector data. All data were transferred to 1000 m × 1000 m raster data, on which spatial logic and algebra computation were calculated at each subject layer. (4) Large value differences between assessment factors and the different units employed make indirect assessment of eco-environmental vulnerability extremely difficult. All variables must thus be normalized and directionalized to allow a uniform measurement system (Locantore et al., 2004). First all variables was standardized to a uniform suitability rating scale, then the membership degree in fuzzy mathematics was applied to the assessment criteria. A detailed description about this will be provided in Section 2.3.3. 2.3. The fuzzy analytic hierarchy process A method for synthesized multi-index analysis using fuzzy AHP analysis to define index weight was developed. An eight-step procedure describing fuzzy AHP is provided in Fig. 2 whilst a detailed description of the methodology is presented below. 2.3.1. Development of the hierarchical structures Using the AHP method of multi-objective decision-making, referring to relevant literature, synthesizing expert opinion and qualitative analysis of the environment in the study area, the ultimate objective can be broken down into four levels, as shown in Fig. 3. The first level is the integrated environment vulnerability. The second level is composed of subsystems: land resource conditions; water-heat meteorological conditions; geological and topographical conditions; and human impact. The third level consists of the concrete factors which affect the eco-environmental vulnerability. The fourth level is comprised of each assessment unit (cell). 2.3.1.1. Land resource conditions (B1 ). Land is a key resource for human activities and environmental changes are deeply embedded in how land is used. For the land resource condition subsystem, vegetation cover (C1 ), proportion of cultivated area (C2 ) and soil type (C3 ) were selected as the factors. Vegetation cover is an important factor for protecting biological diversity and improving environment quality. Soil type is fundamental to the resource base for eco-environmental systems. 2.3.1.2. Water-heat meteorological conditions (B2 ). Five factors were selected for the water-heat meteorological conditions subsystem. Average temperature (C4 ), >10 ◦ C accumulated temperatures

Fig. 2. The proposed methodology for fuzzy AHP.

(C5 ) and average precipitation (C6 ) strongly influence plant growth; aridity index (C7 ) and moisture index (C8 ) strongly influence soil moisture and plant growth. 2.3.1.3. Geological and topographical conditions (B3 ). The elevation (C9 ), terrain slope (C10 ) and geological conditions (C11 ) were selected as the factors of B3 . Slope is a crucial factor for construction control and soil erosion. Variation in elevationcan has an impact on soils, transportation, local climatic effects, and other processes that could impact environment vulnerability (Vadrevu et al., 2008). 2.3.1.4. Human impact (B4 ). For B4 , population density (C12 ), GDP per capita (C13 ) and road density (C14 ) were selected. They indicate the degree of development of land resources, and also reflect the change or deterioration of the ecological system. Generally speaking, high population density can directly result in traffic jams, environmental pollution and other serious problems, whilst greater poverty generally brings greater environmental pressures (Fan and Chan-Kang, 2005; Yu and Wei, 2003).

Fig. 3. Hierarchical structure of environmental vulnerability estimation.

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Table 1 Random index (RI). N RI

1 0

2 0

3 0.58

4 0.96

5 1.12

6 1.24

7 1.32

8 1.41

9 1.45

10 1.49

11 1.51

2.3.2. Determining index weight 2.3.2.1. Development of a judgment matrix using pair-wise comparisons. In this study, six experts were invited as the decision makers. Concerning the type of experts, they were ecologist, pedologist, geographer, meteorologist, land resource expert and environmental protection expert, respectively. The six experts have all performed related studies of eco-environment in study area and are all familiar with Danjiangkou reservoir area, therefore, they could directly give the scores of pair-wise comparison matrices. Each decision maker was asked to express the relative importance of two decision elements from the same level using a nine-point scale. The scores of pair-wise comparisons were collected and used to form pair-wise comparison matrices for each decision makers. 2.3.2.2. Checking for consistency. The matrices were analysed for consistency. The priority of the elements was compared by the computation of eigenvalues and eigenvectors: R · w = max · w

(1)

where w is the eigenvector, the weight vector, of matrix R, and max is the largest eigenvalue of R. The consistency property of the matrix is then checked to ensure the consistency of judgments in the pairwise comparison. The consistency index (CI) and consistency ratio (CR) are defined as follows (Satty, 1977): CI =

max − n n−1

(2)

CI CR = RI

2.3.2.4. Calculation of fuzzy weights. Based on the Lambda–Max method proposed by Csutora and Buckley (2001), fuzzy weights of the decision elements were calculated using the following procedures: • Application of an ˛-cut. Let ˛ = 1 to obtain the positive matrix ˜ k = (˜rij )k , and let ˛ = 0 to obtain the lower of decision maker k, R b b bound and upper bound positive matrices of decision maker k, k k k k ˜ a = (˜rij ) and R ˜ c = (˜rij ) . Based on the weight calculation proR a c cedure proposed in AHP, the weight matrix is calculated, Wbk =

(wi )kb , Wak = (wi )ka and Wck = (wi )kc , i = 1, 2, ..., n. • In order to minimize the fuzziness of the weight, two constants, Mak and Mck , were chosen as follows:

 Mak

 Mck

2.3.2.3. Construction of fuzzy positive matrices. The scores of the pair-wise comparisons were transformed into linguistic variables, which were represented by positive triangular fuzzy numbers as listed in Table 2. According to Buckley (1985), the fuzzy positive reciprocal matrix can be defined as: ˜ k = [˜rij ]k R

(4)

˜ k : a positive reciprocal matrix of decision maker k; r˜ij : relWhere R ative importance between decision elements i and j; r˜ij = 1, ∀i = j; and r˜ij = 1/˜rij , ∀i, j = 1, 2, ......, n.

= max

k wia k wib k wic

 |1 ≤ i ≤ n

(5)

 |1 ≤ i ≤ n

(6)

The upper bound and lower bound of the weight are defined as ∗k wia

k = Mak wia

(7)

∗k wic

k Mck wic

(8)

=

The upper bound and lower bound weight matrices are wa∗k

= (wi∗ )ka ,

i = 1, 2, ..., n

(9)

wc∗k = (wi∗ )kc ,

i = 1, 2, ..., n

(10)

• By combining wa∗k , w∗k and wc∗k , the fuzzy weight matrix for b ˜k= decision maker k can be obtained and is defined as W ∗k , w ∗k , w ∗k ), (wia ib ic

(3)

where n is the number of items being compared in the matrix, and RI is a random index, the average consistency index of randomly generated pair-wise comparison matrix of similar size, as shown in Table 1. If the consistency test is not passed, the original values in the pair-wise comparison matrix must be revised by the decision maker.

= min

k wib

i

i = 1, 2, ..., n.

2.3.2.5. Integration of the opinions of decision makers. A geometric average was applied to combine the fuzzy weights of different decision makers: ˜¯ = W

 K 

˜k W i

1/K

, ∀k = 1, 2, ...K

(11)

k=1

˜¯ : combined fuzzy weight of decision element i of K decision where W ˜ k : fuzzy weight of decision element i of decision maker makers, W i k, K: number of decision makers. 2.3.2.6. Defuzzification. Based on the equation proposed by Chen (2000), a proximity coefficient was defined to obtain the ranking order of the decision elements. The proximity coefficient is defined as follows: CCi =

˜¯ i , 0) d− (W , ¯ ˜ i , 1) + d− (W ˜¯ i , 0) d∗ (W

i = 1, 2, ..., n,

0 ≤ CCi ≤ 1

Table 2 Triangular fuzzy numbers. Linguistic variables Extremely strong Intermediate Very strong Intermediate Strong Intermediate Moderately strong Intermediate Equally strong

Positive triangular fuzzy numbers (9, 9, 9) (7, 8, 9) (6, 7, 8) (5, 6, 7) (4, 5, 6) (3, 4, 5) (2, 3, 4) (1, 2, 3) (1, 1, 1)

Positive reciprocal triangular fuzzy numbers (1/9, 1/9, 1/9) (1/9, 1/8, 1/7) (1/8, 1/7, 1/6) (1/7, 1/6, 1/5) (1/6, 1/5, 1/4) (1/5, 1/4, 1/3) (1/4, 1/3, 1/2) (1/3, 1/2, 1) (1, 1, 1)

(12)

L. Li et al. / Ecological Modelling 220 (2009) 3439–3447 Table 3 Weight of each assessment index for the eco-environment. First grade

Second grade no.

Weight

Third grade no.

Weight

A

B1

0.159

C1 C2 C3

0.079 0.023 0.053

B2

0.212

C4 C5 C6 C7 C8

0.044 0.048 0.077 0.015 0.024

B3

0.327

C9 C10 C11

0.174 0.106 0.040

B4

0.327

C12 C13 C14

0.149 0.070 0.104

cal guidelines from the related national codes and literature were used to determine the boundary values. As a general guideline, a positive correlation between the value awarded and vulnerability is employed. The class boundaries and standardized measurements employed for each factor were shown in Table 4. The integer numbers ranging from 1 to 5 were assigned to slight, light, moderate, heavy, and extreme classes, respectively. The next step involved assigning a new value for degree of membership of every attribute to each index at each level. Suppose that a standard attribute value of positive index ui at level m is Pij (i = 1, 2, . . ., n, j = 1, 2, . . ., m), and at spatial position k the practical attribute value of ui is Cki , and rij represents degree of membership of index i to level j, the membership degree function of each assessment factor to each level function is as follows:When j = 1,

rij = where ˜¯ d− (W

CCi

i , 0)

=

is the weight for decision element i, and 

˜ ia − 0)2 + (W ˜ ib − 0)2 + (W ˜ ic − 0)2 ], (1/3)[(W 2

2

⎧ 1 ⎪ ⎪ ⎨

Cki > Pi1

Cki − Pi2

Pi2 − Pi1 ⎪ ⎪ ⎩ 0

Pi1 ≤ Cki ≤ Pi2

(13)

Cki < Pi2

When j = 2, 3, . . ., m − 1,

2

˜¯ i , 0) = ˜ ia − 1) + (W ˜ ib − 1) + (W ˜ ic − 1) ], d∗ (W (1/3)[(W ˜¯ , 0) and d∗ (W ˜¯ , 0) are the distance measurements between d− (W i

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i

two fuzzy numbers. 2.3.2.7. Total ranking. Matrixes for layers B and C and for C and D were generated using the same method as that for layers A and B. Based on the results of a series of simple rankings the weights of all elements in each level of the hierarchy relative to the entire level directly above were obtained. These in turn were all ranked, and were carried from the upper layer to the lower layer. After the above analytic process, the weight of each assessment factor was determined for integrated assessment of eco-environmental vulnerability of the DRA (Table 3). 2.3.3. Membership value standardization for assessment variables In the process of environmental vulnerability assessment, a primary step is to ensure a standardized measurement system for all factors considered. Since most images hold cell values for the original map codes, they have to be standardized to a uniform rating scale, in this case between 1 and 5 for ease of analysis. Assigning values to specific factors requires specific decision rules in the shape of thresholds for each factor. Various statistical and empiri-

rij =

⎧ Pij−1 − Cki ⎪ Pij ≤ Cki ≤ Pij−1 ⎪ ⎪ ⎪ Pij−1 − Pij ⎨ Cki − Pij+1

⎪ ⎪ Pij − Pij+1 ⎪ ⎪ ⎩ 0

(14)

Pij+1 ≤ Cki ≤ Pij Cki > Pij−1 , Cki < Pij+1 ,

When j = m,

rij =

⎧ 0 ⎪ ⎪ ⎨ ⎪ ⎪ ⎩

Cki > Pim−1

Pim−1 − Cki Pim−1 − Pim

Pim ≤ Cki ≤ Pim−1

1

Cki > Pim

(15)

The membership degree function for negative indices of ecoenvironmental vulnerability assessment is constructed in the same manner. 2.3.4. Assessment of eco-environmental vulnerability The degree of membership within different levels of different indices was integrated using weight CCi and the total degree of membership for different values of eco-environmental vulnerability was calculated as follows: V = CCi ⊗ R

(16)

Table 4 Standardized rates of assessment factors. Factors

Vegetation cover Proportion of cultivated area Soil typea Average temperature (◦ C) >10 ◦ C accumulated temperatures (◦ C) Average precipitation (mm) Aridity index Moisture index Elevation (m) Slope gradient (◦ ) Geological conditionsb Population density (person/km2 ) GDP per capita (ten thousand RMB/km2 ) Road density (km/km2 )

Rating 1

2

3

4

5

>0.82 <0.03 MMS, AS >15 >4800 >1100 <0.7 >45 <400 6 VR <2 <20 <0.3

0.75–0.82 0.03–0.1 PAS, PUS 14–15 4500–4800 900–1100 0.7–0.8 30–45 400–800 6–15 SR, MR 2–50 20–40 0.3–0.4

0.65–0.75 0.1–0.3 YBS,BS,CIS 13–14 4200–4500 800–900 0.8–0.9 15–30 800–1200 15–25 DR 50–100 40–60 0.4–0.5

0.3–0.65 0.3–0.5 HGS 12–13 3800–4200 750–800 0.9–1.1 5–15 1200–1700 25–40 CR 100–250 60–100 0.5–0.6

<0.3 >0.5 SAS,CAS <12 <3800 <750 >1.1 <5 >1700 >40 RR >250 >100 >0.6

a Note soil type: MMS, mountain meadow soil; AS, alluvial soil; PAS, paddy soil; PUS, purple soil; YBS, yellow brown soil; BS, brown soil; CIS, cinnamon; HGS, huanggang soil; SAS, saline-alkali soil; CAS, calcareous soil. b Note geological conditions: VR, volcanic rock; SR, sedimentary rock; MR, metamorphic rock; DR, diorite rock, etc.; CR, carbonatite rock; RR, ranite rock.

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Fig. 4. Grade maps of eco-environmental vulnerability sub-indices.

The maximum membership principle was adopted to define the comprehensive classification of eco-environmental vulnerability. If Vi0 = max Vi (1 ≤ I ≤ n), the comprehensive classification is Vi0 . Supported by ArcGIS, each sub-index classification and integrated classification of eco-environmental vulnerability for the study area was obtained. 3. Results 3.1. Eco-environmental vulnerability of the sub-indices Based on the method mentioned above, the vulnerability of the subsystem indices, were classified into five levels (potential, light, medium, heavy, very heavy) respectively, shown in Fig. 4. Table 5 lists the evaluated results of the eco-environmental vulnerability sub-index. Table 5 showed that land resource areas with potential, light and medium level classification accounted for 77.7% of the total area, which denotes a good land resource situation for the DRA.

Most of these areas were located at the northwest and the south of the DRA, namely the southern sections of the Qinling Mountains, Daba Mountains and Shennongjia forest region, where an abundance of plant life occurs. Whereas, the middle section along the Han River, the prime agricultural band, has lower vegetation cover. The spatial distribution of land resource types (Fig. 4(a)), presented a clear differentiation. The land resource situation in the northwest and southern regions were better than those in the middle section, and those regions classified as heavy or very heavy occurred around Danjiangkou Reservoir and on the plain in the western part of Shanxi Province. With respect to the water-heat meteorological condition, areas classified as potential, light and medium covered 71.3% of the total area of the DRA (Table 5). The spatial distribution of water-heat meteorological conditions (Fig. 4(b)), presented a clear zonal distribution pattern. Water-heat meteorological condition gradually deteriorated from South to North. Regions with better classification were located in southwest and south of the DRA, where rain and sunshine are plentiful with the continental monsoon climate.

Table 5 Statistics of eco-environmental vulnerability sub-index assessment in the DRA. Eco-environmental vulnerability sub-index

Vulnerability level Potential

Light

Medium

Heavy

Very heavy

Land resource condition

Area (km2 ) Percentage

11233 11.74

21496 22.46

41655 43.52

18392 19.22

2129 2.22

Water-heat meteorological condition

Area (km2 ) Percentage

5723 5.98

25634 26.78

36925 38.58

17918 18.72

8705 9.09

Geological and topographical condition

Area (km2 ) Percentage

12847 16.42

29332 30.65

26388 27.57

18097 18.91

8241 8.61

Human impact

Area (km2 ) Percentage

32761 34.23

24821 25.93

18740 19.58

14414 15.06

4169 4.36

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Fig. 5. Percentage area occupied by different vulnerability levels. Fig. 6. Map depicting the integrated assessment of the eco-environmental vulnerability of the DRA.

Another good region was around Danjiangkou Reservoir, which was influenced by the microclimate effect of the Danjiangkou Reservoir itself. Regions classified as bad or worse with respect to water-heat were focused in the north of the Qinling Mountain, where mountain conditions lead to the lack of light and heat. The assessment results for geological and topographical conditions (Table 5) showed that regions classified as medium, heavy and very heavy covered an area of 55.1%. The spatial distribution of geological and topographical conditions (Fig. 4(c)), was nearly the opposite of that of land resource condition. The south and north were worse than the middle with respect to geological and topographical condition. In particular the terrain structure in the Qin-ba Mountains, of two mountain ranges clamping a river, was marked. The mountain areas had characteristic deep valleys, steep slopes, and complicated geological structure, leading to the low level in terms of geological and topographical conditions. Alternatively, the Han River valley, consisting of alluvial-diluvial plains with low elevation and flat topography, is very favorable for both vegetation growth and human habitation. The human impact indicators revealed that population density, GDP per capita and road density ascended in sequence and demonstrated the influence degree of the human activities. The greater the value of each index is, the greater the effect of human impact overall is. The total area classified as potential, light and medium human impact was 79.7% (Table 5). The map of human impact (Fig. 4(d)) showed that there was a significant difference in human impact between the mountainous region and the plain and in different provinces. The concentration of population, cities, towns, roads and industry occurred in Henan and Hubei province. More than 40 percent of regions with the heavy and very heavy levels of human impact were in Henan and Hubei province. The worst regions were largely distributed over the plain in the western part of Shanxi Province and Henan Province, where high industrialization, and high densities of population and roads results in environmental damage and pollution.

vulnerability of the DRA exhibited an asymmetrical normal distribution centered on a moderate vulnerability level. From the map of integrated eco-environmental vulnerability (Fig. 6), the areas with potential and light eco-environmental vulnerability were located in three regions: the Qinling Mountain area in the northwest, the Daba Mountain area in the south and in the surroundings of the Danjiangkou Reservoir in the east. In the Qin-ba Mountain area low levels of environmental vulnerability were due to the higher vegetation condition and lower intensity of human activities. However, blocks with high or very high environmental vulnerability were visible within these areas, due steep slopes resulting in less forest protection and serious soil erosion. It was notable that the eco-environmental vulnerability in the areas immediately surrounding Danjiangkou Reservoir was most frequently potential to light, with only a few areas with high or very high vulnerability. Whilst better vegetation conditions and lower levels of anthropogenic interference were again factors underlying this pattern, the relatively low hypsography and the microclimate around the reservoir were also important in providing better waterheat conditions. Two regions with very high vulnerability were located north of Danjiangkou Reservoir in Henan province and on the plain in the western part of Shanxi Province. These areas were urban, with high densities of buildings and limited vegetation cover or bad geological conditions, which increased the eco-environmental vulnerability. Areas with higher vulnerability were generally distributed in the north. Most areas with medium vulnerability, where eco-environment and human activity intensity were moderate, were located in the basin of Shanxi province and the southern part of Hubei province. These are agricultural areas with the main land use type being paddy fields and dry land, along with some grassland and woodland. The eco-environment of these areas was affected mostly by human activity.

3.2. Analysis of eco-environmental vulnerability

4. Discussion

The evaluated results for eco-environmental vulnerability are shown in Fig. 5. Overall regions with potential, light and medium status were made up 85.7% of the total area of the DRA, indicating moderate overall integrated eco-environmental vulnerability. An area of 10,777 km2 , accounting for 11.26% of the total area of DRA, was classified as having heavy vulnerability, and 2048 km2 (2.14%) as very heavy vulnerability. Thus one seventh of the total area of the DRA is very vulnerable. The moderately vulnerable area made up 35.86% (34,326 km2 ), whilst the area of light vulnerability and potential vulnerability accounted for 27.67% (26,487 km2 ) and 22.22% (21,267 km2 ), respectively. In general, the environmental

4.1. Analysis of the method presented A structured methodology based on analysis and classification of factors influencing environmental vulnerability and combining FAHP with GIS was developed. The developed method offered a quite creative and comprehensive way to combine fuzzy set theory and decision-making science for an eco-environmental vulnerability assessment. The similarity between the results generated and real situation in the DRA validate this method and suggest that it is an effective approach which could be applied to the assessment of environmental vulnerability in other regions.

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It should be noted that the accuracy and reliability of the evaluation results were dependent on many factors including data quality, data processing and the weight of factors. Uncertainties regarding data sources may be compounded and introduce even larger uncertainties within environmental evaluations (Shi et al., 2009). Sensitivity analyses indicated that the results of the proposed method are highly sensitive to the weights applied. The determination of weights for the various factors is thus one of the greatest challenges, as is frequently encountered in more conventional evaluation methods (Van der Merwe, 1997). In additional, the proposed method has the additional potential for reliable dynamic estimation of eco-environmental vulnerability. In the present study, however, this was limited to current status only due to the lack of suitable historic data. This capacity thus requires further evaluation. 4.2. Major problems of the DRA eco-environment In the DRA, most of the 13 million residents in the upper basin are farmers, and agricultural activities are intensive and concentrated along the river and streams, resulting in non-point pollution of the water (Zhang et al., 2009). In addition, with the excessive consumption of natural resources and the gradual increase in population, some prominent eco-environmental problems need to be solved urgently. Among them, deforestation and forest degradation are the major environmental and ecological issues in the DRA (Fu et al., 2004; Liang et al., 2005). A substantial amount of forest has been lost due to the conversion of forest to farmlands, high grading and other logging practices. Despite the high levels of current effort in forest conservation, degradation of forests caused by unsound exploitation is still a serious threat. In addition, soil erosion has also seriously affected the sustainable development of the eco-environment in DRA (Hu and Zhang, 2003). The area of soil erosion is about 39,515 km2 , 41.51% of the national total area. Currently, this area is increasing at a rate of 1000 km2 /year. Deforestation is the chief cause of soil erosion in the DRA, and the adverse geology and climatic conditions intensify erosion. Moreover, the Qin-ba Mountain area is one of the main impoverished regions of China with economic decline and poor environmental awareness. Over the years, with population increase, demand for food, fuel and timber has exceeded local production levels, leading to an imbalance between humans and their environment. Inappropriate land use has generated a huge area of sloped farmland, vegetation destruction, and soil loss, leading to gross deterioration of the environment. 4.3. Conservation implications An important goal of environmental assessment is to provide assistance to policy makers and practitioners in environmental protection. Areas of higher ecological vulnerability should be protected over all others, and appropriate laws and regulations regarding environmental protection such as “convert slope farmland into forest or pasture” should be established and implemented (Zhang et al., 2004). For moderately vulnerable areas, integrated small watershed management should purposefully focus on sustainable utilization of water and soil and sustainable protection of the eco-environment (Liu, 2005). In addition, it should develop ecological agriculture combining traditional and modern agricultural practices to realize the coordinated development of both the environment and economy (Shi, 2002). In the areas immediately surrounding Danjiangkou Reservoir, the importance of environmental protection should also be emphasized because of its significant geographical position. In addition to increasing vegetation coverage, enhancing the capacity for soil and

water preservation and strengthening controls on non-point pollution, the establishment of special ecologically functional reserves, such as the Dan River national wetland nature reserve, is a matter of priority. Human migration due to the water project itself must also be noted. Ecological protection measures must be adopted to prevent soil erosion, prevent and control pollution, and to protect water quality during emigrant movement and settlement within the DRA. However, strengthening environmental protection alone without alleviating poverty and environmental ignorance can only be a temporary measure. Therefore, it is necessary to address the problems of poverty and raise public environmental awareness as well as scientific understanding. Generally speaking, proper protection of the ecological environment of the DRA would be significant not only for the protection of water resources, the ecological system and biodiversity in China, but also for social progress, economic development and improvement in standards of living, national prosperity of the water source areas and the region at large. 5. Conclusions The current condition of eco-environmental vulnerability for the DRA based on an integrated FAHP and GIS method indicated: (1) Eco-environmental vulnerability in the study area as a whole was moderate. Nevertheless, geographic variation in eco-environmental vulnerability was apparent and exhibited regional features. This suggested that specific environmental protection measures should be conducted in different regions with differing eco-environmental vulnerability. (2) This study demonstrated that the proposed method is an effective approach to assessment of environmental vulnerability, since the results gained closely reflect the reality of the ecoenvironmental vulnerability of DRA. However, this method still requires development to further reduce subjectivity in judgments due to the high sensitivity of evaluated results to the weights applied. Acknowledgements Financial support for this project was provided by the National Science and Technology Supporting Programs under Project No. 2006BAC10B02 and the Natural Science Foundation of China (No. 40671178). References Adger, W.N., 2006. Vulnerability. Glob. Environ. Change 16, 268–281. Aspinall, R., Pearson, D., 2000. Integrated geographical assessment of environmental condition in water catchments: Linking landscape ecology, environmental modelling and GIS. J. Environ. Manag. 59, 299–319. Buckley, J.J., 1985. Fuzzy Hierarchical Analysis. Fuzzy Sets Syst. 17, 233–247. Chen, C.T., 2000. Extensions of TOPSIS for group decision–making under fuzzy environment. Fuzzy Sets Syst. 114, 1–9. Chen, H., Guo, S.L., Xu, C.Y., Singh, V.P., 2007. Historical temporal trends of hydroclimatic variables and runoff response to climate variability and their relevance in water resource management in the Hanjiang basin. J. Hydrol. 344, 171–184. Csutora, R., Buckley, J.J., 2001. Fuzzy hierarchical analysis: The Lambda–Max method. Fuzzy Sets Syst. 120, 181–195. Eakin, H., Luers, A.L., 2006. Assessing the vulnerability of social-environmental systems. Annu. Rev. Environ. Resour. 31, 365–394. Fan, S.C., Chan-Kang, C., 2005. Road development, economic growth and poverty reduction in China. International Food Policy Research (IFPRI) Report No. 138. Washington, USA. Ford, J.D., Smit, B., Wandel, J., 2006. Vulnerability to climate change in the arctic: a case study from arctic bay canada. Glob. Environ. Change 16 (2), 145–160. Fu, B.J., Liu, G.H., Wang, X.K., Ouyang, Z.Y., 2004. Ecological issues and risk assessment in China. Int. J. Sust. Dev. World 11, 143–149. Gowrie, M.N., 2003. Environmental Vulnerability Index for the Island of Tobago, West Indies. Conserv. Ecol. 7 (2), 11.

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