RS-Based Fuzzy Multiattribute Assessment of Eco-Environmental Vulnerability in the Source Area of the Lishui River of Northwest

Nat Hazards DOI 10.1007/s11069-015-1762-2

ORIGINAL PAPER

RS-based fuzzy multiattribute assessment of eco- environmental vulnerability in the source area of the Lishui River of northwest Hunan Province, China

1 2 Guifang Yang • Zhenghong Chen

Received: 2 February 2015 / Accepted: 13 April 2015 Ó Springer Science+Business Media Dordrecht 2015

Abstract Intrinsic uncertainties within geo-hazards affecting the source area of the Lishui River region exhibit chief challenges for environmental management and regional development. This study employs a fuzzy multiattribute approach to rank the eco-environmental vulnerability to geo-hazards for 38 given geographic units in the study area. The closeness coefﬁcients for alternatives are applied to sort the eco-environmental vulnerability based on the distances of alternatives from the fuzzy positive-ideal and fuzzy negative-ideal solutions. The multiple attributes used in the ranking are geological characteristics, geological hazards, and human impacts. In general, results of the multiattribute method are consistent with those of a synthetic index method. Our results suggest that geological conﬁgurations/characteristics and the associated range and density of geo- hazards are the critical determinants of eco-environmental vulnerability. Human activities, however, slightly increase the overall vulnerability of geographic units. Results of the study aid preliminary resource exploitation and eco-environmental protection, yielding support for the long-term sustainable development of the region.

Keywords Eco-environmental vulnerability Á Fuzzy multiattribute assessment (FMA) Á Geo-hazards Á Source area of the Lishui River

& Guifang Yang [email protected] & Zhenghong Chen [email protected]

1 School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China 2 China Meteorological Administration Training Centre, Beijing 100081, China 123 Nat Hazards

1 Introduction

Reasonable assessment of environmental vulnerability and establishment of effective rehabilitation strategies are critical challenges in the design of regional development plans (Chen and Yang 2014). Concerns for environmental and societal security are especially pronounced in those parts of the world where development pressures are high, environmental threats are considerable, and spectacular geoheritages and significant water resources must be simultaneously protected (Yang et al. 2011a, 2012; Chen and Yang 2013). These issues lie at the heart of the sustainability agenda. Over the past couple of decades, effective assessment of ecological conditions in large river catchments over the world has been increasingly associated with questions of global change, hazard mitigation, and long- term catchment management (e.g., Aspinall and Pearson 2000; Prato 2003; Solaimani et al. 2009). Systematic appraisal of anthropogenic disturbances relative to geological characteristics is a key issue in the assessment of eco-environmental vulnerability (Anbalagan 1992; Collins et al. 2009; Chen and Yang 2014). Various analyses of eco-environmental vulnerability have been introduced for large river basins using multiple attribute and subjective assessments (Villa and MacLeod 2002; Wei et al. 2004; Solaimani et al. 2009). Despite these achievements, the complex interactions of multiple parameters and inherent uncertainties have limited the application of procedures to evaluate vulnerability to geological hazards and human activities (Malheir 2006; Chen et al. 2008). Analogy-based approaches can be used to incorporate preference patterns of multiattribute decision makers with unquantifiable, incomplete, unobtainable information, and even partially unknown facts and/or attributes (Cheng and Zhang 1999; Mao 2003) by comparisons with an existing reference database. This approach has been criticized due to its inability to adequately incorporate new information (Mao 2003). At this point, when vulnerability analysts are working in new regions, they typically lack sufficient knowledge of the degree to which the spatial variability of vulnerability corresponds to variations in natural and anthropogenic factors. To address this problem, assessment results are primarily based on evaluation of previous cases, but this is often unable to capture the differential patterns of vulnerability in a reliable manner (Emmi and Horton 1993). Optionally, different estimation indicators are treated as having equal weights in their contribution to the overall vulnerability of eco-environment (Cutter 1996; Cutter et al. 2000). Clearly, both options have drawbacks. The synthetic index analysis (SIA) is another common approach. In this method, subjective assessments of the importance of attributes in a given evaluation process are derived using a numerical scale that generates a comprehensive/synthetic index (Gowrie 2003; Xu et al. 2005). A group of similar or different individual indicators with different units can be normalized and cumulated into a comprehensive/synthetic index through statistical analysis. The synthetic index largely measures the overall performances of all individual indicators and their associated weights. This method, with ratings and the weights of the attributes being given as crisp values, is far from satisfactory due to its incapability of tackling inherent subjectivity and uncertainty involved in the multiattribute decision-making processes. In particular, these procedures are unable to derive reasonable results when individual parameters are characterized by vague boundaries for grading. As a result, the synthetic index method is incompetent to model real-world situations because discrepancies in environmental patterns cannot be easily identified (Xia 2007; Su and Su 2007). These procedures are also highly sensitive to the weights selected for all the attributes and sub-attributes that are cumulated to obtain an index of total vulnerability. In

123 Nat Hazards addition, various quantitative methods have been developed to assess overall eco-environmental vulnerability by assigning different weights to selected attributes (Zhou et al. 1999; Guzzetti et al. 1999; Fedeski and Gwilliam 2007; Li et al. 2007; Xiong et al. 2007;Li et al. 2009). This allows the outcomes of various alternatives to be properly modeled for multiple attributes. However, many compromised solutions may be yielded, presenting insigniﬁcant options that fail to assist (and may hinder) decision-making processes (Deng and Yang 2005). Given these issues, several of these methodologies cannot be reliably applied in analysis of eco-environmental vulnerability, and alternative approaches must be designed to address the inherently subjective and uncertain nature of vulnerability assessment. The present study characterizes the vulnerable behavior of selected attributes for eco- environmental assessment and management in the source area of the Lishui River basin (a tributary of the middle Yangtze River) in northwest Hunan Province. Owing to its spectacular sandstone peak forest landform and unique water resource, this region has received a growing attention (Yang et al. 2011a, b, 2012), yet little is known concerning how to balance such spectacular landscape and its eco-geological environmental potentials, as compared with the widespread karst landscapes elsewhere in China (Yang et al. 2011c). For this reason, multiattribute analysis and fuzzy set theory are used to represent the multidimensional nature of eco-environmental problems in the region. RS applications are used to quantitatively determine the combined risk and underlying causes of eco-environmental vulnerability and their spatial variability. The approach developed here can reduce subjectivity and imprecision in the decision-making processes, aiding the establishment of countermeasures to alleviate environmental vulnerability and enhancing the development of more strategic approaches to resource management in this region.

2 Study area

The source area of the Lishui River in northwest Hunan principally refers to the Sangzhi County, part of Zhangjiajie City (Yang et al. 2011a). It is located at the north foot of Wuling Mountain and the southern tip of the western Hubei, and covers 38 towns and around 410 rivers (Yang et al. 2011a, b; Fig. 1). The mean annual temperature in the region is about 17 °C. Precipitation averages approximately 1400 mm per year of which about 75 % falls between May and September (China Meteorological Administration 2010; Yang et al. 2011a, c). The study area extends across two ﬁrst-order tectonic units, Jiangnan ancient land and Yangtze paraplatform (Hunan Bureau of Geology and Mineral Resources 1988; Hunan Bureau of Land and Resource 2003; Yang et al. 2011a, b, 2012). In general terms, Sangzhi County is located in the Wuling Mountain and thus characterized by undulating hills, steep slopes, deep cutting terrain, well-developed network of rivers, and intensive rock weathering. It is also one of the rainstorm centers of Hunan Province, therefore becoming one area of geological disasters. In particular, the already-accelerated human activities have induced frequent severe geological hazards that have halted to a large degree of the socioeconomic development of the region (e.g., Wang et al. 2002; Yuan and Wu 2004). Before the 1990s, little was known about the hazard characteristics and their geological origins due to limited economic development and extremely poor accessibility of the region (Chen and Xiao 1993; Yang and Li 2000). Initial work by the Hunan Geo-environmental Monitoring Centre (1988) described and documented some of the geological

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Fig. 1 Geographical location of the source area of the Lishui River of northwest Hunan Province hazards in Hunan Province. Major advances in the understanding of geo-hazards emerged in the mid–late 1990s in association with the development of the hydrogeological and engineering geological survey (Chen and Xiao 1993; Yang and Li 2000). Research by Yuan and Wu (2004) identified landslide hazards and their relation to topographic nature in Sangzhi. Simultaneously, various authors explained the distribution and types of geo- hazards in the study area in relation to their genesis (Yang and Li 2000; Wang et al. 2002; Liu et al. 2006; Yao 2010), providing significant guidance for regional geo-hazard mitigation. Related investigations had assessed how natural and anthropogenic factors influence geo-ecological conditions (Wang et al. 2002; Fu and Chen 2007). In addition, a few engineering applications and concerns for the sustainable development of the region had been reported in various studies (Ma 1989;He2005; Yang et al. 2011d). The source area of the Lishui River region was selected for evaluation in this study because of its unique geological setting, spectacular sandstone landform, and the potential for water resource development in the region (Chen and Xiao 1993; Yang and Li 2000). The study area has undergone rapid development of tourism and hydropower over the past

123 Nat Hazards two decades (He 2005; Yang et al. 2012). Given its sensitive eco-geological environment, changing topography, unique climatic and hydrological patterns, and increasing anthropogenic impacts, the source area of the Lishui River basin is vulnerable to several disturbances that have negatively impacted upon geoheritages, water resources, and eco- geological environment. These issues have become a major concern for basin residents and pose signiﬁcant challenges for sustainable development of the region.

3 Materials and methods

3.1 Materials

A total of 38 geographic units (A1–A38) were chosen for this study, as indicated on Figs. 1 and 2. In order to meet the primary goals of our present study, yearly measurements of various geo-hazards were recorded through 2014 in various references and reports (Chen and Xiao 1993; Yang and Li 2000; Wang et al. 2002;He2005; Liu et al. 2006; Fu and Chen 2007; Yao 2010). Available annual data on landslide, collapse, and debris ﬂow, along with strata and active tectonic characteristics, were incorporated in this study (Hunan Institute of geological survey 2002). Population growth or anthropogenic engineering development, and possibly urbanization were simultaneously collected (He 2005). We also incorporated the published database in recent decades from previous and ongoing studies to ensure a reliable dataset (Hunan Bureau of Geology and Mineral Resources 1988; Hunan Geo-environmental Monitoring Center 1988; Yang and Li 2000; Hunan Institute of geological survey 2002; Hunan Bureau of Land and Resource 2003; Yang et al. 2011a). With this dataset, effective assessment can be performed for alleviating environmental vulnerability while mitigating the management of resource protection and human activities.

3.2 Brief outline of fuzzy multiattribute method

The fuzzy multiattribute analysis (FMA) has been widely accepted in real-world realm for the decision making (Chen and Klein 1997; Chen 2000; Rashed and Weeks 2003; Yang et al. 2013). Multiattribute analysis is well suited to evaluate the vulnerability of the

Fig. 2 The hierarchical structure of the evaluation problem 123 Nat Hazards available units to geo-hazards and human activities. The general multiattribute problem usually consists of a number of alternatives Aiði ¼ 1; 2; ...; nÞ to be evaluated against a set of attributes CjðÞj ¼ 1; 2; ...; m . Subjective assessments are often required for determining the performance of each alternative Aiði ¼ 1; 2; ...; nÞ with respect to each attribute CjðÞj ¼ 1; 2; ...; m , denoted as xij, and the relative importance of the each criterion, represented as wj, with respect to the overall objective of the problem. We used the fuzzy multiattribute assessment method defined by Deng and Yang (2005) and reshaped by Yang et al. (2013). A closeness coefficient changing between zero and one for each alternative is defined as: À di Pi ¼ þ À i ¼ 1; 2; ...; n di þ di

As the closeness coefﬁcient approaches 1, the attributes for that alternative approach the attributes for the fuzzy positive-ideal solution and depart from the attributes for the fuzzy negative-ideal solution. Therefore, the closer the closeness coefﬁcient is to one, the more preferred the alternative.

3.3 Formulation of FMA

An overall evaluation of available alternative units for potential water resource exploration and landform protection with respect to various attributes is required to effectively prioritize the eco-environmental vulnerability to geo-hazards and human activities. Based on systematic consultations with a large numbers of stakeholders, geological characteristics (C1), geological hazards condition (C2), and human impact (C3) were chosen as three major attributes for evaluating the eco-environmental vulnerability of geographic units from northwest Hunan Province. The hierarchical structure of the evaluation problem we used here is illustrated in Fig. 2. The geological characteristic (C1) is an indication of the extent to which the potential tectonic activities are present (C11) and the lithologic, slope, vegetation coverage, and river system density impacts can occur (C12,C13,C14,C15). Geological hazards condition (C2) can represent the potential instability of units. Hazards such as landslide (C21), collapse and debris flow (C22) occur infrequently, yet they can cause regional instability and considerable loss of lives and capital, if handled inappropriately. Hence, it is crucial to assess the likelihood and intensity of these disasters in order to develop appropriate risk management and loss mitigation strategies. The human impact (C3) reflects the intensity to human activities or human presence influence the instability of the geological setting and their impacts upon the occurrence of geo-hazards in the given region. This is closely associated with relative population density (C31) and underlying anthropogenic engineering construction and irrational agriculture practices. Individual variables that reflect the potential influence of socioeconomic status (C32) are perhaps more obvious, where an increase in the intensity can be related simply to increases in population size and geo-hazards. These above-mentioned attributes and their associated sub-attribute will, to a certain degree, alter the morphologic shape of the slope and trigger geo-hazards such as debris flows, landslides, and collapse, showing a negative contribution to eco-environmental stability with a relatively higher vulnerability. As a consequence, the units with higher closeness coeffi- cients are usually not suitable for future hydropower station construction and tourism development.

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In addition, the social and economic effects that occur in response to water resource exploration and landform protection are carefully considered because these play as an important contributor to the future regional development and human well-being in the study area. For this analysis, however, each unit is uniformly considered to be affected by future planning and construction, such that the positive range of social, environmental, and political beneﬁts for the regional development and improved well-being of local people are balanced across all units. As such, this paper is concerned primarily with the negative factors that inﬂuence eco-environmental vulnerability.

4 Results and discussion

4.1 Results

Figure 3 shows the results of the overall geo-hazards distribution patterns for 38 geographic units in the source area of the Lishui River using the hierarchical structure of attributes and sub-attributes show in Fig. 2. Subjective assessments of the weights for the three attributes and their associated sub-attributes and the vulnerability of each unit with regard to each sub-attribute based on the linguistic terms deﬁned in Tables 1 and 2 are presented in Table 3. Table 4 presents the fuzzy weights for the attributes and sub-attributes. In general, the more severe the geo-hazard, the more important the alternative unit is. Therefore, the different grades for each attributes and their associated sub-attribute can be assigned different values (Tables 3, 4). For instance, active fractures and plate collision zone are vulnerable to geological instability and are given a higher level (VH or H, Fig. 3a). Geographic units along indistinct fractures are characterized as relatively stable (F or L). The lithologic property dominated by mudstone, shale, and soft rock types is considered favorable for geo-hazard occurrence and higher eco-environmental vulnerability (range from VH to F), while the hard lithologic property such as sandstone and limestone are resistant to weathering process and favor eco-environmental stability (Fig. 3b). Similarly, relatively higher slope ([40°), lower vegetation coverage (\50 %), and higher river density are attributed to higher eco-environmental vulnerability (range from VH to H), while the lower slope (\20°), higher plant coverage ([60 %), and lower river density should be indicative of high eco-environmental stability (Fig. 3c, d, e). Likewise, the landslide with a scale of [10,00,000 m3, 1,00,000–10,00,000 m3, 10,000–1,00,000 m3,\1,00,000 m3, or no landslide is represented as very high (VH), high (H), fair (F), low (L), and very low (VL), respectively (Fig. 3f). In Fig. 3g, the units without geo-hazards are assigned a value of VL. The units typical of severe, serious, moderate, and negligible geo-hazards are assigned as VH, H, F, and L, respectively (Fig. 3g). When grouping the distribution density of these types of geo-hazards, their values are upgraded to a higher level to reﬂect higher density. Human activities are associated with population density and potential activities regarding engineering construction and agriculture practices (referring to He 2005; Fig. 3h). In general, the ranking of alternatives based on FMA is in line with the ranking ac- cording to the synthetic index (Fig. 4). A total of 55 % from all 38 geographic units in the source area of the Lishui River region have very low, low, or fair vulnerability. Obviously, these areas have favorable eco-environmental conditions with low–moderate risk. These intact areas with extremely good and good environmental conditions are evident along upper segments of the north source of the Lishui River, as shown in green and blue on Fig. 4b. The upper Lishui River region and southeast part of study area have relatively 123 Nat Hazards

Fig. 3 Overall attribute and subattribute distribution patterns of the study area. a fracture system; b lithologic property; c slope; d vegetation coverage; e river density; f landslide; g other geo-hazards including debris ﬂow, mass movement, and collapse; h population density moderate vulnerability, as indicated in white on Fig. 4b. In contrast, river conﬂuence areas are more sensitive to geo-hazards and thus have high to very high vulnerability levels, as indicated in red and purple in Fig. 4b.

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Table 1 Linguistic variables used to describe the effects of alternatives on attributes Linguistic variables Very low (VL) Low (L) Fair (F) High (H) Very high (VH)

Fuzzy numbers (1, 1, 3) (1, 3, 5) (3, 5, 7) (5, 7, 9) (7, 9, 9)

Table 2 Linguistic terms used by the weighting vectors Linguistic variables Least important Less important Important More important Most important

Fuzzy numbers (1, 1, 3) (1, 3, 5) (3, 5, 7) (5, 7, 9) (7, 9, 9)

Despite the good correlation between FMA and SIA, some local discrepancies are still evident (as shown in Fig. 4). For example, the previous SIA demonstrates that the eco- environmental vulnerability of areas along the middle source of the Lishui River is high as shown in dotted line in Fig. 4a, whereas the FMA presented here classiﬁed these regions as having low eco-environmental vulnerability (Fig. 4b).

4.2 Advantages of the FMA method

The developed RS-based fuzzy multiattribute evaluation method is a useful way to utilize fuzzy decision making in the assessment of eco-environmental vulnerability. The similarity occurring in the ranking of alternatives for different geographic units demonstrates that the FMA can yield a visual rational evaluation in the intensity of the considered attributes. As a result, this method can provide a more meaningful and usable assessments of vulnerability, especially when there are rather fuzzy boundaries or limited data. Therefore, the proposed methodology (FMA) uses the linguistic assessments instead of numerical values and can aid further theoretical understanding of eco-environmental vulnerability of the region. To ensure a more reasonable assessment, we also consult a large number of experts for various criteria such as applicability, similarity, efficiency, and the overall performance of the approach. It is of interest that each of these criteria and the overall performance of the abovementioned model were rated in a range between good and very good, largely supporting the effectiveness and practicability of FMA method. Results indicate that the vulnerabilities of Kuzhuping, Sifangxi, and Longtanping (A20, A21, and A22, respectively) are underestimated, whereas vulnerability of Shangdongjia (A35) and Kongqiaoshu (A3) are overestimated in comparison with those from the tradi- tional SIA. Also, the vulnerability of the Qiaoziwan (A16) is relatively high because it is subject to numerous geo-hazards and characterized by lower vegetation coverage as well as higher population density. Yet, the results for this geographic unit using the synthetic index method are inconsistent with the intuitive visual indication and ranking of the variation in the intensity of the considered data. The fuzzy multiattribute method presented here can minimize subjectivity in appraisal of all controlling attribute to eco-environment variation, while optimizing flexibility to consider additional factors that influence vulnerability. Obviously, the accuracy and reliability of assessment results are largely influenced by amount of data, size of weights, and associated indices (e.g., Rashed and Weeks 2003; Guzzetti et al. 2005). The RS-based fuzzy multiattribute assessment method, however, can incorporate dominant factors and inherent uncertainty into the analytical procedures, thus

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Table 3 Fuzzy grading of different attributes, sub-attributes, and their weights Attribute Weight Sub-attribute Weight Grading Categories principle

Geological VH (7, Fracture system VH (7, Comprehensive VH characteristics 9, 9) 9, 9) Relatively H (C1) active General F Stable L Little or none VL Lithologic property H (5, 7, Mudstone, shale VH 9) Limestone– H mudstone Limestone, F dolomite Limestone– L sandstone Sandstone VL Slope VH (7, [40° H 9, 9) 20–40° F \20° L Vegetation coverage F (3, 5, Perfect ([70 %) VH 7) Good H (60–70 %) General F (50–60 %) Poor (40–50 %) L Very poor VL (\40 %) River density F (3, 5, Very high VH 7) High H Moderate F Slight L Little or none VL Geo-hazard F (3, 5, Landslide H (5, 7, Very severe VH condition (C2) 7) 9) Serious H Fair F Little L None VL Other geo-hazards F (3, 5, Very severe VH 7) Serious H Fair F Little L None VL

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Table 3 continued

Attribute Weight Sub-attribute Weight Grading Categories principle

Human factor (C3) VL (1, Population density F (3, 5, Extreme VH 3, 5) 7) intensive Intensive H Slightly F intensive Little L None VL Anthropogenic engineering and F (3, 5, Very high VH irrational practice 7) High H Fair F Lower L Little or none VL

Where: VH = (7, 9, 9); H = (5, 7, 9); F = (3, 5, 7); L = (1, 3, 5); VL = (1, 1, 3) presenting a rather simple assessment process that ensures rational estimation of eco- environmental vulnerability. Given the agreement with intuitive ranking schemes, the approach developed here can produce a more reasonable ranking order for vulnerability, as shown in Table 5 and Fig. 4b.

4.3 Fuzzy vulnerability ranking

As mentioned previously, the fuzzy multiattribute estimation is feasible in evaluating subjective and uncertain eco-environmental vulnerability and generates meaningful outputs in the case of the considered data. RS analyses have enabled rational interpretation of underlying factors (for instance, slope and vegetation coverage) that cause eco-environmental vulnerability in the source area of the Lishui River basin. Geographic units of highest eco-environmental vulnerability are shown in red or purple on Fig. 4b. These areas are subject to abrupt, large-scale geological disasters, such as landslides, mass movement, and collapse, as well as ongoing river erosion. These processes are common along the north source of the Lishui River region (Figs. 3, 4b). The unique geological, tectonic, and geomorphologic conditions should increase the geological vulnerability in these areas. The units with medium risk are principally located along the Lishui River and southeast part of Sangzhi County, as shown in white on Fig. 4b. These areas are featured by moderate geological disasters, such as small- or medium-scale landslides. The higher vegetation coverage and less human activities limit the risk of serious ﬂuvial erosion in these areas. Although these were areas of active faulting and higher slope, they have been so far relatively stable. Areas with stable lithology units, little active tectonic activity, and low levels of anthropogenic interference are characterized as areas of low risk. Limited, small-scale river erosion and almost no landslide occur in these stable areas due to the good environmental conditions. Minimal risk actually reﬂects the absence of human disturbance and/or infre- quent geo-hazards. Obviously, the range of geological setting and geo-hazards are the primary determinants of risk in this eco-environment. Considering the lower population density and relatively

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Table 4 Linguistic assessment of alternatives for the study area

Subattribute Unit name C11 C12 C13 C14 C15 C21 C22 C31 C32

A1 Liyuan H H VH F VH VH F VH VH

A2 Ruitapu H H H F H H F H H

A3 Kongqiaoshu F H H F F F L H F

A4 Mihu F F F H L F L F F

A5 Zhuyeping H H F H L L F L L

A6 Baishi H F F H F F L VL VL

A7 Renchaoxi F F L H F F L L L

A8 Zoumaping F F F H L F L F F

A9 Changtanping F F F H L F L F F

A10 Guandiping F F F H L F L H F

A11 Mahekou F F F H L F L H F

A12 Maidiping F F F H L F L H F

A13 Furongqiao F F F H L F L H F

A14 Linxihe F F L H L L L L L

A15 Hongjiaguan F F F H L H F F F

A16 Qiaoziwan F F F H L F L F F

A17 Liangshuikou F F F H L F L F F

A18 Guluoshan F F F H L F L F F

A19 Shataping F F F F L F L F F

A20 Kuzhuping L L H H F L L L L

A21 Longtanping L F F F L L L L L

A22 Sifangxi L F F F L L L L L

A23 Bamaoxi L F L F L L L VL VL

A24 Wudaoshui L L L F L VL VL VL VL

A25 Xishaping L L L F L VL VL VL VL

A26 Badagongshan L L L L L VL VL VL VL

A27 Dunjiapo F F L F L L L L VL

A28 Hekou L L F F L L VL L L

A29 Yanwukou L L F H VL F VL L L

A30 Shanghexi L L L F VL F VL F L

A31 Chenjiahe L L F L VL F VL F L

A32 Lianghekou F F F H L H F H F

A33 Daguquan H H VH F VH H F F F

A34 Liaojiacun F F F H L H F F F

A35 Shangdongjie F F F F L F F F F

A36 Lifuta H F F L L F F H F

A37 Xilian H F F H F F L VL VL

A38 Liujiaping H H H F H H F H H

evenly spread population, human factors might play a secondary role in this sensitive environment. Landslides and active fractures inﬂuence the spatial variability of risk in these subsystems. The greater the value of each index, the higher the variability of 123 Nat Hazards

Fig. 4 Hazard ranking for eco-environment priority management. VH very high, H high, F fair, L low, VL very low. a Refers to results from the synthetic index analysis; b refers to results from fuzzy multiattribute assessment approach vulnerability. Hence, of the biophysical factors considered in this study, overall com- paratively weak and almost uniformly distributed river erosion is considered to have the least impact on eco-environmental change. This likely reveals the resistant rock types and limited sediment availability in the study area. Difference between the SIA and the FMA occurred in a few geographic units (Fig. 4). The unit of Qiaoziwan is considered to be vulnerable area because of the various large- scale geological disasters and obvious faulting activities. The fuzzy multiattribute assessment method, based on a rational ranking of estimation, produces a helpful guide to geo-hazard identiﬁcation for individual units.

4.4 Prioritization of environmental management issues

FMA provides a scientiﬁc base for decision making in relation to hydropower station construction and geo-hazard relief. The method ranks the eco-environmental vulnerability of regions to geo-hazards and anthropogenic disturbance. As such, it is of great importance in analyzing the vulnerability performance of eco-environment and the main factors controlling these changes in the region. In particular, this method based on fuzzy theory highlights the importance of multiattribute estimation for vague boundaries contained within some sub-attributes. Therefore, the utilization of FMA offers a proper and helpful visual indication in the given data and generates reasonable estimates. Results of this study can be applied to prioritize management countermeasures to reduce eco-environmental vulnerability in these areas. Units with higher vulnerability should receive priority for appropriate regulations to reduce geo-hazards. Practical measures can be carried out to increase vegetative land coverage to reduce river erosion and possibly establish an ecological protection zone in this fragile and sensitive eco-environment. As a result, impacts of anthropogenic disturbance, water resource, and geoheritage exploitation can be largely minimized in areas of strong geo-hazard potential. In the areas of moderate vulnerability, water and geological resources should be framed for sustainable development. Vegetation rehabilitation techniques can be employed to sustain eco-environmental values in these units. In the region of low vulnerability, comprehensive practices should connect effective eco-environmental protection with efforts to develop the regional economy. Our results indicate that future water resource utilization and tourism development in the source area of the Lishui River region should be targeted in regions with low

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Table 5 Ranking of alternatives based on closeness coefﬁcients Alternatives Unit name Closeness Grade Ranking based Results coefﬁcient (Pi) on FMA from SIA

A1 Liyuan 0.4915 1 VH H

A2 Ruitapu 0.4767 2 VH H

A3 Kongqiaoshu 0.4225 11 H F

A4 Mihu 0.395 18 F F

A5 Zhuyeping 0.3755 22 F F

A6 Baishi 0.3549 24 F F

A7 Renchaoxi 0.3646 23 F F

A8 Zoumaping 0.395 19 F F

A9 Changtanping 0.395 20 F F

A10 Guandiping 0.4074 13 H H

A11 Mahekou 0.4074 14 H H

A12 Maidiping 0.4074 15 H F

A13 Furongqiao 0.4074 16 H H

A14 Linxihe 0.3375 27 L F

A15 Hongjiaguan 0.4263 6 H H

A16 Qiaoziwan 0.4263 7 H F

A17 Liangshuikou 0.4263 8 H H

A18 Guluoshan 0.4263 9 H H

A19 Shataping 0.3864 21 F F

A20 Kuzhuping 0.3452 26 L F

A21 Longtanping 0.3284 28 L L

A22 Sifangxi 0.3284 29 L F

A23 Bamaoxi 0.2874 35 VL L

A24 Wudaoshui 0.2381 36 VL L

A25 Xishaping 0.2381 37 VL L

A26 Badagongshan 0.2281 38 VL L

A27 Dunjiapo 0.3134 33 L H

A28 Hekou 0.3028 34 L H

A29 Yanwukou 0.3273 30 L H

A30 Shanghexi 0.3225 32 L H

A31 Chenjiahe 0.3234 31 L H

A32 Lianghekou 0.438 5 H H

A33 Daguquan 0.4616 4 VH H

A34 Liaojiacun 0.4263 10 H H

A35 Shangdongjie 0.4007 17 H F

A36 Lifuta 0.4139 12 H H

A37 Xilian 0.3549 25 F F

A38 Liujiaping 0.4767 3 VH H geo-hazard vulnerability. On basis of these abovementioned insights, future measurements can balance water resource utilization as well as landform tourism with regional sustainable development. 123 Nat Hazards

5 Conclusive remarks

Fuzzy vulnerability assessment of ecological environment in the source area of the Lishui River basin is a comprehensive issue due to the existing subjective assessments and multiple attributes. This paper develops a fuzzy multiattribute approach to incorporate the dominant factors in an integrated analysis, generating a more rational ranking order of eco- environmental vulnerability in the region in terms of geological setting, geo-hazards, and anthropogenic disturbance. This study ensures the inclusion of various variables, rather than arbitrary weightings. In addition, the FMA highlights that the data can be easily acquired and used by regional decision makers. In this sense, this analysis offers an effective analytical tool in the identiﬁcation of different vulnerability levels for the local planners. Supplementary RS analyses aid explanation of controlling reasons in the study area. Results from this case suggest that the eco-environmental vulnerability for each unit is largely controlled by the geological settings and associated geo-hazards, and slightly altered by human activities. The agreement between FMA and SIA implies that the fuzzy approach to eco-environmental vulnerability assessment is rational and the procedures can be amended by different users to support decision-making procedures. This is of sig- niﬁcance in promoting sustainable development within a strategy of ‘‘development with conservation.’’

Acknowledgments We gratefully acknowledge the Fundamental Research Funds for the Central Universities (No. 2652014058), the National Natural Science Foundation of China (Nos. 41002036, 41172067, 41320003, and 41220001), projects funded by China Postdoctoral Science Foundation (General Financial Grant No. 2012M520220 and Special Financial Grant No. 2014T70059). We extend our thanks to Mr. Xiuming Li for his assistance in partial data preparation. We greatly appreciate the comments of the anonymous reviewer(s).

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