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Marsh wetland degradation risk assessment and change analysis: A case study in the Zoige Plateau, China
Ecological Indicators 82 (2017) 316–326
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Original Articles
Marsh wetland degradation risk assessment and change analysis: A case study in the Zoige Plateau, China
MARK
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Weiguo Jianga,b,c, , Jinxia Lvb,c, Cuicui Wangd, Zheng Chena,c, Yinghui Liua,c a
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China c Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China d Tobacco Town People’s Government, Liaocheng City, Shandong Province 252665, China b
A R T I C L E I N F O
A B S T R A C T
Keywords: Wetland degradation Risk assessment Wetland hazard index Wetland vulnerability index Zoige wetland
Wetlands play an important role in regional development and environmental protection. Under the impact of natural and artificial factors, the plateau wetlands have degenerated and even disappeared, resulting in serious problems for society and the ecological environment. It is necessary to establish a reasonable risk assessment method to evaluate the risk of wetland degradation, and then to analyze changes in the range and features of risk. For this work, the Zogie Plateau wetland was selected as the study area. For this site, a wetland degradation risk assessment method was established based on the conceptual model of Ecological Risk Assessment (ERA). The method included nine indicators used to analyze the wetland hazard index, wetland vulnerability index, and wetland degradation risk synthetically. From the spatial-temporal pattern, the wetland degradation risk was analyzed using data from 2000 to 2014. The calculated results revealed the following: (1) from 2000 to 2014, the wetland hazard index (WHI) showed a trend of increase, the value of which increased from 0.29 to 0.42, with a growth rate of 44.83%. Similarly, the wetland vulnerability index (WVI) significantly increased from 0.30 to 0.54, with a growth rate of 80%. Over the same time, the total wetland area decreased from 3910.25 km2 to 2777.38 km2, a reduction of 28.97%. (2) Using the equidistant method, the risk value was divided into three risk grades. The wetland degradation risk in the whole region is increasing, and the risk rank has changed from the low risk zone (0.092) to the medium risk zone (0.25). The degradation risk becomes greater with distance from the center to fringe areas.
1. Introduction Wetland is well known as one of the three major ecosystems (with forest and ocean) that are essential to human survival and development (Chatterjee et al., 2015). Furthermore, wetland ecosystems have many important functions and provide a wide range of ecosystem services, including water storage, flood control, irrigation, climate regulation, and prevention of soil erosion (Zhang et al., 2014; Chatterjee et al., 2015; Beuel et al., 2016). However, with the spread of urbanization, industrialization, and rapid growth in population, wetland areas are in decline and their functions are degraded in many parts of the world (Malekmohammadi and Blouchi, 2014). In the past 150 years, more than half of the global wetlands have been modified or degraded due to human activities (Sica et al., 2016). In South Africa alone, 35–50% of wetlands were affected by social and economic pressures, suffered complete loss or serious destruction (Oberholster et al., 2014). In 1981/ 1982, National Wetlands Inventory (NWI) determined 233 wetlands,
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about 40% of which have been destroyed by human activities or disappeared due to drought (Holland et al., 1995). As wetland degradation has become more severe and public awareness of wetlands has deepened, wetlands have become the focus of studies and of global concern. Wetlands degradation is the process by which wetland area, structure, and function have degenerated even disappeared under the effect of natural and human activities (U.S. EPA, 1998). Previous studies have demonstrated that wetland degradation is caused by natural threats and by human activities (Wang et al., 2012). Current wetland research includes characterization of wetland degradation, assessment of wetland degradation (Chatterjee et al., 2015; Wanda et al., 2016), factors driving wetland degradation (Song et al., 2014; Zhang et al., 2014), and the restoration and management of wetlands (Jiang et al., 2015). Wetland degradation caused reduction of wetland area, water pollution, environmental degradation, and biodiversity loss among other issues. At the same time, these studies showed that increased
Corresponding author at: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China. E-mail address: [email protected] (W. Jiang).
http://dx.doi.org/10.1016/j.ecolind.2017.06.059 Received 4 April 2017; Received in revised form 28 June 2017; Accepted 30 June 2017 1470-160X/ © 2017 Elsevier Ltd. All rights reserved.
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the largest plateau-type wetland in the world. It covers a large peat deposit and exhibits abundant biodiversity (Fei et al., 2006; Gandarillas et al., 2016). The Zoige has a sub-frigid and sub-humid climate, and features a very cold temperature range. The annual mean temperature is 0.96 °C; with means of −10.7 °C in January and 10.7 °C in July. The annual precipitation range is 600–750 mm. The rainy season roughly April to October every year, and accounts for 90% of annual precipitation (Dong et al., 2010; Huo et al., 2013). There is an obvious vertical change in the soil of the region, which includes swamp soil, meadow soil, plateau cinnamon, and artificial grassland soil (Tian et al., 2004). The major vegetation types in the region are swamp vegetation, meadow vegetation, shrub vegetation, forest vegetation, and cultivated vegetation. The landforms are a combination of low mountains, hills, valleys, and terraces. With the flat, low-lying ground and poor drainage of surface water, large swampy areas form readily on lowlands, terraces, and some underground stream gully zones.
urbanization and agricultural activities are the reasons for wetland degradation (Jiang et al., 2015; Wang et al., 2012). A study of wetland degradation in the Yellow River Delta (China) proposed that water shortage and expanding urban area resulted in shrinkage of the wetland area (Wang et al., 2012). One analysis was conducted of the forces driving change on the Sanjiang Plain from 1975 to 2005, and the results indicated that the main reason for wetland degradation was human activity (Song et al., 2014). Wetland risk assessment is critical for wetlands development. Wetlands degradation risk assessment is generally based on ecological risk assessment (Walker et al., 2001). Ecological Risk Assessment methods include the “Three-Step Framework” formulated by the United States Environmental Protection Agency (USEPA) (U.S. EPA., 1998), ecological level of risk assessment (PETAR) (Moraes and Molander, 2004), and the relative risk model (RRM) (O’Brien and Wepener, 2012; Li et al., 2015). The relative risk model (RRM) was used in the assessment of risk to coastal habitats, rivers, and basins (Landis and Wiegers, 1997; WHO, 2011). The ecological risk assessment was conducted in Florida wetlands to predict the risk of wetland habitat loss (Gutzwiller and Flather, 2011). The ecological risk assessment was conducted in China's Taihu Lake Basin by risk stratification ecological assessment (Xu et al., 2016). However, the previous studies have mainly focused on coastal wetlands, plains wetlands, and specific regions. The study of plateau wetlands degradation risk assessment has become more popular in recent years due to human activities and natural processes. Plateau wetland is special because of its special geographical location and has an irreplaceable role in maintaining biological diversity, soil and water conservation, flood and drought control, and climate regulation. However, due to the impact of natural and human factors, plateau wetlands are constantly degraded especially in degradation of swamp wetlands environments (Malekmohammadi and Blouchi, 2014). Many studies have been carried out on the Zoige Plateau by ecologists and geographers. Some researchers studied the effect of wetland degradation on the bacterial community and humic acids (Song et al., 2011; Tang et al., 2012; Tian et al., 2012). The current status of wetland degradation was also analyzed and a perspective provided for future ecosystems restoration in Zogie Marsh (Jiang et al., 2015). The ecological influence of ditches was determined in Zoige Peatland (Zhang et al., 2014). However, each of these studies concentrated on the effect of a single factor, and lacked systematic analysis of wetland degradation risk assessment. Therefore, for the work reported in this paper, a scientific assessment model was built by selecting typical indicators to conduct a wetland degradation risk assessment. The model was built according to the conceptual model of ecological risk assessment (ERA), which has a reliable theory support. Furthermore, the model constructed the wetland hazard and vulnerability index from wetland area, structure and function aspects. It built a multi-indicators assessment system and a new perspective to study plateau wetland. Zogie Plateau wetland was selected as the study area, with the aim to make a quantitative evaluation of wetland degradation ecological risk for the interval from 2000 to 2014. Therefore, the specific objectives were (1) to establish a model of wetland hazards, vulnerability, and risk assessment index based on the conceptual model of ecological risk assessment; (2) to achieve quantitative evaluation of wetland degradation risk; and (3) to analyze change in the degree of wetland degradation risk.
2.2. Data sources For this study, we collected Landsat images and MODIS images of the years 2000 and 2014, which were downloaded from the National Aeronautics and Space Administration (NASA) of the United States. The monthly values of normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface temperature (LST) were gained using the maximum data synthesis method derived from MODIS data. Land use data for 2000 and 2014 was extracted by Support Vector Machine (SVM) using Landsat images, which were classified into seven types: forest land, grassland, marsh, non-wetlands, cultivated land, artificial surfaces and unclassified. The classification accuracy is in line with the accuracy requirements. Furthermore, the study area was divided into 42 small watersheds for analysis based on the DEM data. This is because the small watersheds are independent landform units and the ecological system is integral. In this way, we could reduce the interference of some fine-scale information and avoid the influence of scale on the result. Other data were collected mainly to determine the forces driving wetland degradation risk. These included administrative maps, topographic maps, meteorological data, disaster data, grazing statistics data, related natural and economic statistical yearbooks, statistical bulletins, and literature. 2.3. Assessment indicators of wetland degradation risk In this paper, wetland degradation risk assessment was based on changes in external threats and internal features. The Wetland Hazard Index (WHI) was used to describe the risk sources: natural disasters (ND) and human activities (HA). Natural disasters were represented by the temperature vegetation dryness index (TVDI), which shows the condition of wetland drought and is good at monitoring surface soil moisture and agricultural drought. This paper selected EVI and LST to calculate TVDI. Human activities (HA) are important causes of wetland degradation, and were expressed by the degree of anthropogenic disturbance (AD) and grazing intensity (GI). This paper used farmland and construction land to express the influence of anthropogenic disturbance (AD). The grazing intensity (GI) was indicated by the amount of grazing overload. Simultaneously, the Wetland Vulnerability Index (WVI) describes the resistance to natural disaster and artificial activities. Three wetland attribute indicators (Area (A), Structure(S), and Function (F)) were selected to analyze quantitatively the wetland vulnerability. The degradation of area (A) was described by the wetland area variability index (AI) which compares the current actual wetland area with the area in 1995. The degradation of structure (S) was represented by the landscape fragmentation index (LFI) and fractal dimension (FRAC). LFI mainly showed the degree of landscape elements fragmentation affected by human activities. FRAC indicated the degree of interference
2. Materials and methods 2.1. Study area The Zoige region is located in the northeast area of the QinghaiTibet Plateau (between 31° 50′–34° 30′ N and 100° 40′–103° 40′ E). It covers an area of 16670.6 km2 and is at an elevation of about 3500 m. The study area includes Zoige, Hongyuan, and Aba Counties in Sichuan Province, and Maqu County in Gansu Province (Fig. 1). Zoige wetland is 317
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Fig. 1. Location of the study area.
the processed indicator data is shown in Fig. 2.
affected by human activities by quantitative description of the landscape and plaque size of the core area, and boundary line tortuosity in the study area. LFI and FRAC were calculated by Fragstat 4.2 software. The degradation of function (F) is represented by the water conservation function (WC), habitat suitability index (HSI), and wetland productivity function (WP). The water conservation function is an important function of wetlands, and is mainly performed by inhibition of surface evaporation, increase in precipitation and soil infiltration, and reduction of surface runoff (Potter et al., 1993). In this paper, WC was calculated by the water balance method, including the precipitation, soil conservation capacity and evaporation. The habitat suitability index (HSI) model (Roloff and Kernohan, 1999) has been widely used to describe the condition of animal and plant habitat, which is possible to reflect the maintenance function of biodiversity indirectly. This paper selected the land use type, the distance to the protected area, the elevation, the slope, the distance to the road and the distance to the water to analyze HSI. This paper used net primary productivity (NPP) (Ruimy et al., 1994; Field et al., 1995; Los, 1998) to represent wetland productivity functions. The CASA model was used to estimate the NPP value, which is mainly based on the absorbed photosynthetic active radiation (APAR) and actual light energy utilization (Sannigrahi, 2017). The description of the nine indicators sources is shown in Table 1 and
2.4. Methods The overall method used for assessment of wetland degradation risk is outlined in Fig. 3. The definition and formulation of the wetland degradation risk problem is the first step. The conceptual model for Ecological Risk Assessment (ERA) is the theoretical basis of this paper, and is used to describe the interaction and relationships of ecological components: exposure of sources and stressors that release the hazard, effects of endpoints and receptors that bear the risk and impact (Landis and Wiegers, 1997; U.S. EPA, 1998). The models of wetland hazard, vulnerability, and risk assessment were established. The wetland degradation risk value was calculated and divided into different levels. Then, in order to provide a more reliable result, uncertainty analysis was carried out on the model. Finally, the change in wetland degradation risk was analyzed in the different phase. 2.4.1. Conceptual model of ecological risk assessment (ERA) The conceptual model of Ecological Risk Assessment (ERA) describes key relationships between some several stressors and assessment endpoints. Exposure characterization examines the sources of risk
Table 1 The description of the nine indicators sources. Indicators
Data source and Description
Temperature vegetation dryness index (TVDI) Anthropogenic disturbance (AD) Grazing intensity (GI) Wetland area variability index (AI) Landscape fragmentation index (LFI) Fractal dimension (FRAC) Water conservation function (WC) Habitat suitability index (HSI) Wetland productivity function (WP)
Generated Generated Generated Generated Generated Generated Generated Generated Generated
from from from from from from from from from
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MODIS product (MOD11A2, MOD13Q1) statistical bulletins and expressed by the proportion of farmland and construction land statistical bulletins and indicated by the amount of grazing overload Land use data Land use data and calculated by Fragstat 4.2 software Land use data and calculated by Fragstat 4.2 software Land use data and calculated by the water balance method Land use data and calculated by HSI model MODIS data and calculated by CASA model
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Fig. 2. The indicators of wetland degradation in the year of 2000 and 2014; (a1,a2) Temperature Vegetation Dryness Index (TVDI) in 2000 and 2014; (b1, b2) Anthropogenic Disturbance (AD) in 2000 and 2014; (c1,c2) Grazing Intensity (GI) in 2000 and 2014; (d1,d2) Wetland Area Variability Index (AI) in 2000 and 2014; (e1,e2) Landscape Fragmentation Index (LFI) in 2000 and 2014; (f1,f2) Fractal Dimension (FRAC) in 2000 and 2014; (g1,g2) Water Conservation Function (WC) in 2000 and 2014; (h1,h2) Habitat Suitability Index (HSI) in 2000 and 2014 (i1,i2) Wetland Productivity Function (WP) in 2000 and 2014.
statement. With the ecological risk assessment framework, the problems are formulated first, the exposure and ecological effects are characterized next, and then the exposure/response profile is developed. Furthermore, to implement the risk estimation and description, the results should be discussed between the risk assessor and risk manager.
stressors, along with their distribution and contact with risk receptors in the wetland ecosystem. Ecological effects characterization describes the adverse response of risk receptors when stressors act directly on the assessment endpoint (U.S. EPA, 1998). The overall ecological risk assessment involves hazard assessment, comparative risk assessment, cumulative ecological risk assessment, and environmental impact 319
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Fig. 3. Regional wetland degradation risk assessment procedure.
quantitatively.
It is critical that exposure characterization describes potential or actual contact or co-occurrence of stressors, and that the profile also describes the impact of variability. In addition, the characterization ecological effects are used to describe the effects elicited by a stressor; links them to the assessment endpoints; and evaluates how they change with varying stress levels (U.S. EPA, 1998). According to the analysis plan, risk assessment is compiled based on stressor-response profiles using many techniques.
V = Aw Av + Sw Sv + Fw Fv
Where V = Wetland Vulnerability Index (WVI), and the subscripts W and V indicate the important weights and the corresponding ratings. Similarly, using the analytic hierarchy process (AHP) method, we determined the weight values of area (A), structure (S), and function (F), (0.54, 0.16, and 0.30, respectively). The degradation of area (A) was described by wetland area variability index (AI) and the weight value was 1.00. The degradation of structure (S) was represented by the landscape fragmentation index (LFI) and fractal dimension (FRAC), and the weight values were 0.75 and 0.25, respectively. The degradation of function (F) was represented by the water conservation function (WC), habitat suitability index (HSI), and wetland productivity function (WP); and the weight values were 0.43, 0.43, and 0.14, respectively. The wetland degradation risk assessment (R) considered the degree of exposure of the risk sources and the degree of vulnerability of the risk receptors. Then, the R was calculated by multiplying the wetland hazard index and wetland vulnerability index.
2.4.2. Analysis method of wetland degradation risk This is the most important of the wetland degradation risk assessments, and it aims to identify the degree of exposure to risk sources and the degree of vulnerability of the risk receptor. The risk sources are external impact factors, which include natural disaster (ND) and human activities (HA). The risk receptors are wetland internal attributes, which include area, structure, and function for wetland ecosystem level endpoints (U.S. EPA, 2003). The wetland hazard index can represent the exposure to risk sources, which release stress and impact the wetland ecosystem to a certain extent. Drought disaster (D), land use change (L), and grazing intensity (G) were serious, major hazard factors, among those that affected the marsh wetland degradation on the Zoige Plateau. The wetland hazard index, known as H, is the composite of the three indicators.
H = NDw NDv + HAw HAv
(2)
R=H×V
(3)
Where R = Wetland Degradation Risk Assessment, and subscripts H and V indicate the Wetland Hazard Index (WHI) and Wetland Vulnerability Index (WVI).
(1)
2.4.3. Analysis method of wetland degradation risk change Change analysis is a quantitative method for determining the characteristics and processes of wetland degradation risk in different periods. Time series analysis has been widely used for environments, land resources, ecological change, and vegetation change. Moreover, the comparative method is based on the risk index and risk rank. The part based on the risk index of the comparative method is mainly intended to reflect subtle changes in the evaluation units of the Risk Index. At the same time, the part based on the risk rank of the comparative method could distinguish qualitative changes in the risk rank. In this paper, we analyzed the change in the wetland degradation risk by combining the two methods. The part of the comparative method based on the risk index is mainly to calculate the difference between the risk values of two specific times. According to the
Where H = Wetland Hazard Index (WHI), subscripts W and V indicate the important weights and the corresponding ratings. We selected the analytic hierarchy process (AHP) (Saaty, 2008) method to determine the weight values of wetlands degradation risk assessment system (Table 2). The weight values of Natural disaster (ND) and human activities (HA) were 0.33 and 0.67, respectively. Temperature vegetation dryness index (TVDI) is used to describe the natural disaster (ND) and the weight value was 1.00. Human activities (HA) was expressed by the degree of anthropogenic disturbance (AD) and grazing intensity (GI), and those weight values were 0.50 and 0.50. The Wetland Vulnerability Index (WVI) described the internal wetland resistance to natural disaster and artificial activities. Three wetland attribute indicators, area (A), structure (S), and function (F) were selected to analyze wetland vulnerability in the model 320
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Table 2 The weight values of wetlands degradation risk assessment index system. Risk
Hazard/Vulnerability
External impact/internal attribute
Assessment Indicators
Wetland Degradation Risk
Wetland Hazard Index (WHI)
Natural Disaster (ND,0.33) Human Activities (HC,0.67)
Wetland Vulnerability index (WVI)
Area(A, 0.54) Structure (S, 0.16) Function(F, 0.30)
Temperature Vegetation Dryness Index (TVDI,1.00) Anthropogenic Disturbance (AD,0.50) Grazing Intensity(GI,0.50) Wetland Area Variability Index (AI,1.00) Landscape Fragmentation Index(LFI,0.75) Fractal Dimension (FRAC,0.25) Water Conservation Function(WC,0.43) Habitat Suitability Index (HSI,0.43) Wetland Productivity Function(WP,0.14)
3.2. Analysis of wetland vulnerability index
calculated results, a positive value, a negative value, or zero respectively indicated increase, reduction, or no change, in the wetland degradation risk. The part of the comparative method based on the risk rank used the Markov model to describe the different risk rank conversions (Jarrow et al., 1997; Jiang et al., 2008).
Wetlands degradation mainly indicated the degradation of area, structure, and function. Therefore, according to the Weight Values of Wetlands Degradation Risk Assessment System, the Wetland Vulnerability Index (WVI) was calculated from 2000 to 2014. As shown in Fig. 5, the mean value of WVI significantly increased from 0.30 to 0.54, with a growth rate of 80%. Furthermore, the total wetland area decreased from 3910.25 km2 to 2777.38 km2, with a reduction rate of 28.97%. Some areas of the Zoige Plateau wetland were completely degraded in 2014, and accounted for 29.13% of the total area (the highest value defined). From the spatial pattern of distribution, the highest value of WHI was distributed in Zoige and Aba County. The upper degradation area was mainly distributed in the northwest region. Fringe wetland areas had higher degrees of degradation. The west of Maqu County and the south of Hongyuan County had high degrees of degradation of wetlands, which increased significantly.
3. Result 3.1. Analysis of wetland hazard index The Wetland Hazard Index (WHI) is affected by natural disaster and human activities. Fig. 4 shows the Wetland Hazard Index in the year 2000 and 2014. It was determined that WHI had a trend of increase and the value increased from 0.29 to 0.42, with a growth rate of 44.83%. Some areas in Zoige Plateau wetland were completely degraded in 2014, and accounted for 29.13% of the total area (this was the highest value defined). From the spatial pattern of distribution, most areas have a certain degree of degradation and the degradation became more serious from the center toward the fringe areas. The highest values of WHI were distributed in Zoige and Aba County. The medium values of WHI were mostly distributed in the central part of the study area, because these areas included the majority of wetlands and human activities had more disturbance strength. Furthermore, the wetlands distributed in Hongyuan County were seriously degraded or vanished.
3.3. Analysis of wetland degradation risk According to the wetland degradation risk assessment system, the small watersheds were selected as evaluation units. The risk was calculated by multiplying wetland hazard index and wetland vulnerability index. According to the equidistant method, the risk value was divided into three risk grades: low risk zone (0–0.16), medium risk zone
Fig. 4. Wetland Hazard Index in the year of 2000 and 2014.
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Fig. 5. Wetland Vulnerability Index in the year of 2000 and 2014.
Fig. 6. The Wetland degradation risk index in the year of 2000 and 2014.
concentrated wetland area extended from the surrounding area to the center. The wetland degradation risk was higher in Maqu County and Aba County. In 2000, the low risk areas were distributed in the southeast and northwest (in Maqu and Hongyuan Counties), and the middle and high risk areas were distributed in Aba County and the northwest edge of Zogie County. However, in 2014, the study area was mainly in the low risk zone. The medium risk areas were located in the west of Maqu County and the northwest corner of Zogie County. In the marginal areas, the wetland was in the high risk zone, and it gradually degenerated, even disappeared in some areas.
(0.16–0.32), and high risk zone (0.32–0.46). The Wetland degradation risk index and risk rank distribution are shown in Figs. 6 and 7 from 2000 to 2014. From the time series, the wetland degradation risk index of the study area increased significantly from 2000 to 2014, and the average value of wetland degradation risk was from 0.092 to 0.25. The risk rank changed from the low risk zone to the medium risk zone. Furthermore, the marsh area of the low risk area decreased from 2428.94 km2 to 1617.56 km2; and the marsh area of the middle risk area increased from 63.5 km2 to 98.06 km2. From the spatial distribution pattern, the area of wetland degradation risk showed an increasing trend. The degradation risk of the 322
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Fig. 7. The Wetland degradation risk rank in the year of 2000 and 2014.
In the past 14 years, the area of increase in the wetland degradation risk index was located in the northwest and center of the study area and the common boundary of the four counties. The area of decreased wetland degradation risk index was in the southwest of Aba County, because the marsh wetland was mainly distributed in the middle and north of the study area. There was less marsh wetlands distributed in the marginal areas. From 2000–2014, the mean value of the degradation risk index was 0.158 in the Zoige Plateau, which indicates that the wetland degradation risk index is increasing on the whole. However, it showed a status both increase and decrease in local region.
3.4. Analysis of change in the wetland degradation risk index As shown in Fig. 8, after subtracting the 2000 and 2014 wetland degradation risk data, the wetland degradation index of each assessment unit was determined. The values of wetland degradation risk index were changed from −0.164 to 0.565 (from 2000 to 2014), where the negative value indicated that the risk of wetland degradation was gradually decreased, and the positive value indicated that the risk of wetland degradation was gradually increased. The risk of wetland degradation has an increasing trend and the area of increased risk constituted 87.12%. Especially, the area of risk index (0–0.1) has increased significantly constituted 47.14%.
3.5. Analysis of change in the wetland degradation risk rank Using the wetland degradation risk assessment model, the wetland degradation risk rank was calculated from 2000 to 2014. As shown in Fig. 9, in 2000 the wetland degradation risk level was mainly in the low risk zone. In 2014, the low risk zones were mainly located at the border of four counties (northwest Maqu County, east Zogie County, and east Hongyuan County), and the middle risk zone was surrounded by the low risk zone. The result obtained the area of wetland degradation risk rank (Fig. 9) and the transferred area of wetland degradation risk (Table 3). The transition probability matrix of the wetland degradation risk rank from 2000 to 2014, was based on the evolution of assessment units. The structural characteristics of wetland degradation risk rank were
Fig. 8. The change of Wetland Degradation Risk Rank (2000–2014).
Fig. 9. The area of wetland degradation risk rank from 2000 to 2014.
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In addition, wetlands are not only affected by natural hazards and human activities, but are also affected by the internal features of area, structure, and function. Fig. 11 shows that the wetland areas continued to decrease from 2000 to 2014, and this was matched the wetland degradation. Regarding the structure of the wetland, the indicators LFI and FRAC increased, which showed that the wetlands became more dispersed, which is not good for wetland development. Regarding the function of the wetland, the indicator WC showed a trend of decrease, and NPP and HSI had stable trends overall. Change existed at a medium date, but compared with 2000, the indicators WC, NPP, and HSI displayed a trend of decrease. The six indicators showed that the wetland was degraded to some extent and that it had changed due to the protection measures. We analyzed the driving forces using nine indexes, but the wetlands are influenced by many factors. This work showed only some of the factors. In the future, we could do more research to discuss the driving forces in more detail.
Table 3 The transferred area of wetland degradation risk from 2000 to 2014 (km2). Risk rank
The low risk zone
The medium risk zone
The high risk zone
The low risk zone The medium risk zone The high risk zone
19068.49 585.65
3765.47 2347.68
10360.03 1239.88
0
830.29
0
as follows. The medium risk area and the high risk area were increased, and the low risk area was decreased significantly from 2000 to 2014. The transition probability matrix of the wetland degradation risk level showed that from 2000 to 2014, the proportions of the low risk zone transformed to low risk zone, medium risk zone, and high risk zone were 57.45%, 11.34%, and 31.21%, respectively. The proportions of the old medium risk zone transformed to low risk zone, medium risk zone, and high risk zone were 14.03%, 56.25%, and 29.71%, respectively. In 2014, the high risk area increased significantly where the wetlands degraded, or even disappeared. The transformation of the wetland degradation risk level showed that unequal transformation among the risk ranks was the main reason for the change of the structural characteristics of the risk hierarchy.
4.3. Uncertainties and prospects This wetland degradation system and the risk calculated are subject to complexity and uncertainty. In addition, the spatial information used for risk assessment also has the characteristics of multiplicity, complexity, and uncertainty. Therefore, some problems exist in the wetland degradation risk assessment of Zoige Plateau, which need to be further studied. Second, some socio-economic data and more detailed statistical data in the study area are difficult to obtain in some counties or villages; which has a certain impact on the spatialization of the data. The assessment units were small watersheds, which ignored changes in the wetland degradation risk within the basin. Third, in this paper, we evaluated the wetland degradation risk and analyzed the risk change on the Zogie Plateau, but did not analyze the driving mechanism, or simulate future wetland degradation risk. In future study, a risk-driven mechanism will be added and explored using a wetland degradation risk simulation model, so as to provide a reference for wetland protection.
4. Discussion 4.1. Contribution of the wetland degradation risk assessment system This paper established the Wetland Degradation Risk Assessment System based on the conceptual model of the US EPA exposure-response mechanism, which is used to describe the external stress and internal characteristics of wetlands. The external stress is usually affected by natural and human disturbance. The changes of internal features are reflected in the area, structure and function. In this paper, the assessment system is more diversified and rational in the index selection of wetland degradation risk. We selected nine indexes to analyze the wetland hazard index and wetland vulnerability index, and combined them to calculate the wetland degradation risk. Furthermore, the previous studies focused on the characteristics of wetland degradation and put less attention on the degradation risk index and risk rank change. This paper concentrated in the Zogie Plateau wetland to do an exploratory research that could provide a reference for similar studies. In addition, the wetland degradation risk assessment can show dynamic changes in multiple periods by time series analysis, and makes the quantitative analysis more reasonable and feasible. Through the multi-period change of WHI, WVI, and risk assessment, we can determine the change characteristics of wetlands, and analyze the factors driving wetland degradation. This should provide some guidance for wetland protection and management.
5. Conclusions In this paper, a wetland degradation risk assessment system was established to evaluate change in the degradation risk in the Zogie Plateau wetland from 2000 to 2014. In this system, nine indicators were selected to show the wetland degradation. The risk distribution pattern and change process were analyzed on temporal and spatial scales. From this, we learned that: (1) From 2000 to 2014, wetland hazard index showed a trend of increase, and the value increased from 0.29 to 0.42, with a growth rate of 44.83%. The highest value of WHI was in Zoige County territory and in Aba County. The medium values of WHI were distributed mostly in the center of the study area. (2) From 2000 to 2014, the wetland vulnerability index significantly increased from 0.30 to 0.54, with a growth rate of 80%. The degradation risk increased from the internal area toward the fringe. Furthermore, the total wetland area decreased from 3910.25 km2 to 2777.38 km2 (reduction of 28.97%). Some areas in the Zoige Plateau wetland were completely degraded in 2014, and accounted for 29.13% of the total area. The highest WHI values were in Zoige County territory and in Aba County. The west of Maqu County and south of Hongyuan County had significantly higher degrees of degradation of wetlands. (3) For the equidistant method, the risk value was divided into three risk grades. The wetland degradation risk index of the study area increased significantly from 2000 to 2014, and the average value of wetland degradation risk rose from 0.092 to 0.25. The risk rank changed from the low risk zone to the medium risk zone in the
4.2. The driving factors of wetland degradation Wetland degradation became more severe from 2000 to 2014, as indicated by positive risk index values. In some areas, especially in the high risk zone, marsh wetlands disappeared completely where the wetlands had higher degradation risk. We selected the driving forces of the wetland hazard index and wetland vulnerability index to analyze the wetland degradation. Fig. 10 shows the trends of TVDI, AD, and GI. From 2000–2010, TVDI continued to increase, which indicated that the soil moisture was in decline and that this was bad for the growth of vegetation and for wetland maintenance. However, in 2014, TVDI showed an increasing trend and this could have affected the conservation. Furthermore, the values of AD and GI increased steadily, which could indicate that human interference caused the rapid decline of Zogie swamp (Zhang et al., 2014). 324
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Fig. 10. The driving forces of wetland hazard index including Temperature Vegetation Dryness Index (TVI), Anthropogenic Disturbance (AD) and Grazing Intensity (GI).
Fig. 11. The driving forces of wetland vulnerability including wetland area, Landscape Fragmentation Index (LFI), Fractal Dimension (FRAC), Water Conservation Function (WC), Net Primary Productivity (NPP) and Habitat Suitability Index (HSI).
index also increased and decreased simultaneously in different local areas. (5) Regarding the change of the risk rank, the area of the medium risk zone and high risk zone of the study area increased significantly. From 2000 to 2014, the high risk areas were transformed with the low and medium risk area completely. The low risk area accounted for 57.45% did not change. The transformation of wetland degradation risk rank showed that the unequal transformation is the
whole study area. Furthermore, the marsh area of the low risk area decreased from 2428.94 km2 to 1617.56 km2; the marsh area of the middle risk area increased from 63.5 km2 to 98.06 km2. (4) From the change analysis of the risk index, the mean value of the wetland degradation risk from 2000 to 2014 was 0.158, which indicates that the wetland degradation risk had a trend of increase. The area of increased risk constituted 87.12%. The area of risk index significantly increased (from 0 to 0.1) by 47.14%, but the risk 325
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Li, X., Zuo, R., Teng, Y., Wang, J., Wang, B., 2015. Development of relative risk model for regional groundwater risk assessment: a case study in the lower Liaohe river plain, China. PLoS One 10, e0128249. Los, S.O., 1998. Linkages Between Global Vegetation and Climate: an Analysis Based on NOAA Advanced Very High Resolution Radiometer Data. NASA Center for AeroSpace Information. Malekmohammadi, B., Blouchi, L.R., 2014. Ecological risk assessment of wetland ecosystems using multi criteria decision making and geographic information system. Ecol. Indic. 41, 133–144. Moraes, R., Molander, S., 2004. A procedure for ecological tiered assessment of risks (PETAR). Hum. Ecol. Risk Assess. 10, 349–371. O’Brien, G., Wepener, V., 2012. Regional-scale risk assessment methodology using the Relative Risk Model (RRM) for surface freshwater aquatic ecosystems in South Africa. Water SA 38, 153–166. Oberholster, P., McMillan, P., Durgapersad, K., Botha, A., De Klerk, A., 2014. The development of a Wetland Classification and Risk Assessment Index (WCRAI) for nonwetland specialists for the management of natural freshwater wetland ecosystems. Water Air Soil Pollut. 225, 1833. Potter, C.S., Randerson, J.T., Field, C.B., Matson, P.A., Vitousek, P.M., Mooney, H.A., Klooster, S.A., 1993. Terrestrial ecosystem production: a process model based on global satellite and surface data. Glob. Biogeochem. Cycles 7, 811–841. Roloff, G.J., Kernohan, B.J., 1999. Evaluating reliability of habitat suitability index models. Wildl. Soc. Bull. 973–985. Ruimy, A., Saugier, B., Dedieu, G., 1994. Methodology for the estimation of terrestrial net primary production from remotely sensed data. J. Geophys. Res. Atmos. 99, 5263–5283. Saaty, T.L., 2008. Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 1, 83–98. Sannigrahi, S., 2017. Modeling terrestrial ecosystem productivity of an estuarine ecosystem in the Sundarban Biosphere Region, India using seven ecosystem models. Ecol. Model. 356, 73–90. Sica, Y., Quintana, R., Radeloff, V., Gavier-Pizarro, G., 2016. Wetland loss due to land use change in the Lower Paraná River Delta, Argentina. Sci. Total Environ. 568, 967–978. Song, C., Wang, L., Guo, Y., Song, Y., Yang, G., Li, Y., 2011. Impacts of natural wetland degradation on dissolved carbon dynamics in the Sanjiang Plain, Northeastern China. J. Hydrol. 398, 26–32. Song, K., Wang, Z., Du, J., Liu, L., Zeng, L., Ren, C., 2014. Wetland degradation: its driving forces and environmental impacts in the Sanjiang Plain, China. Environ. Manage. 54, 255–271. Tang, J., Ding, X., Wang, L., Xu, Q., Yang, Z., Zhao, J., Sun, Q., Feng, S., Zhang, J., 2012. Effects of wetland degradation on bacterial community in the Zoige Wetland of Qinghai-Tibetan Plateau (China). World J. Microbiol. Biotechnol. 28, 649–657. Tian, Y.-B., Xiong, M.-B., Song, G.-Y., 2004. Study on change of soil organic matter in the process of wetland ecological restoration in Ruoergai Plateau. Wetland Sci. 2, 88–93. Tian, J., Zhu, Y., Kang, X., Dong, X., Li, W., Chen, H., Wang, Y., 2012. Effects of drought on the archaeal community in soil of the Zoige wetlands of the Qinghai-Tibetan plateau. Eur. J. Soil Biol. 52, 84–90. U.S. EPA, 1998. Guidelines for Ecological Risk Assessment. Risk Assessment Forum, Washington, DC (EPA/630/R-95/002F). U.S. EPA, 2003. Generic Ecological Assessment Endpoints (GEAEs) for Ecological Risk Assessment. Risk Assessment Forum, Washington, DC (EPA/630/P-02/004F.). WHO(world-Health-Organization), 2011. Report on Integrated Risk Assessment[S]. World-Health-Organization, Geneva. Switzerland. Walker, R., Landis, W., Brown, P., 2001. Developing a regional ecological risk assessment: a case study of a Tasmanian agricultural catchment. Hum. Ecol. Risk Assess. 7, 417–439. Wanda, E.M., Mamba, B.B., Msagati, T.A., Msilimba, G., 2016. Determination of the health of Lunyangwa wetland using Wetland classification and risk assessment index. Phys. Chem. Earth Parts A/B/C 92, 52–60. Wang, M., Qi, S., Zhang, X., 2012. Wetland loss and degradation in the yellow river delta, Shandong province of China. Environ. Earth Sci. 67, 185–188. Xu, X., Yang, G., Tan, Y., Zhuang, Q., Li, H., Wan, R., Su, W., Zhang, J., 2016. Ecological risk assessment of ecosystem services in the Taihu Lake Basin of China from 1985 to 2020. Sci. Total Environ. 554, 7–16. Zhang, W., Lu, Q., Song, K., Qin, G., Wang, Y., Wang, X., Li, H., Li, J., Liu, G., Li, H., 2014. Remotely sensing the ecological influences of ditches in Zoige Peatland, eastern Tibetan Plateau. Int. J. Remote Sens. 35, 5186–5197.
main reason for the change of the structural characteristics of the risk hierarchy. Conflicts of interest The authors declare no conflict of interest. Author contributions Weiguo Jiang and Jinxia Lv conceived and designed the study. Jinxia Lv and Weiguo Jiang performed the experiments and wrote the paper. Cuicui Wang, Zheng Chen and Yinghui Liu aid to perform the experiments and write the paper. Weiguo Jiang, Jinxia Lv, Cuicui Wang, Zheng Chen and Yinghui Liu reviewed and edited the manuscript. All authors read and approved the manuscript. Acknowledgments This work was supported by the National Natural Science Foundation of China (41571077), the National Key Research and Development Program of China (2016YFC0503002) and State Key Laboratory of Earth Surface Processes and Resource Ecology. References Beuel, S., Alvarez, M., Amler, E., Behn, K., Kotze, D., Kreye, C., Leemhuis, C., Wagner, K., Willy, D.K., Ziegler, S., 2016. A rapid assessment of anthropogenic disturbances in East African Wetlands. Ecol. Indic. 67, 684–692. Chatterjee, K., Bandyopadhyay, A., Ghosh, A., Kar, S., 2015. Assessment of environmental factors causing wetland degradation using Fuzzy Analytic Network Process: a case study on Keoladeo National Park, India. Ecol. Model. 316, 1–13. Dong, Z., Hu, G., Yan, C., Wang, W., Lu, J., 2010. Aeolian desertification and its causes in the Zoige Plateau of China’s Qinghai-Tibetan Plateau. Environ. Earth Sci. 59, 1731–1740. Fei, S., Cui, L., He, S., Chen, X., Jiang, J., 2006. A background study of the wetland ecosystem research station in the Ruoergai plateau. J. Sichuan For. Sci. Technol. 27, 21–29. Field, C.B., Randerson, J.T., Malmström, C.M., 1995. Global net primary production: combining ecology and remote sensing. Remote Sens. Environ. 51, 74–88. Gandarillas, V., Jiang, Y., Irvine, K., 2016. Assessing the services of high mountain wetlands in tropical Andes: a case study of Caripe wetlands at Bolivian Altiplano. Ecosyst. Serv. 19, 51–64. Gutzwiller, K.J., Flather, C.H., 2011. Wetland features and landscape context predict the risk of wetland habitat loss. Ecol. Appl. 21, 968–982. Holland, C.C., Honea, J., Gwin, S.E., Kentula, M.E., 1995. Wetland degradation and loss in the rapidly urbanizing area of Portland, Oregon. Wetlands 15, 336–345. Huo, L., Chen, Z., Zou, Y., Lu, X., Guo, J., Tang, X., 2013. Effect of Zoige alpine wetland degradation on the density and fractions of soil organic carbon. Ecol. Eng. 51, 287–295. Jarrow, R.A., Lando, D., Turnbull, S.M., 1997. A Markov model for the term structure of credit risk spreads. Rev. Financ. Stud. 10, 481–523. Jiang, W.-G., Ssheng, S.-X., Zzhu, X.-H., Zuo, W., 2008. Change and spatial pattern of flood disaster risk. Geogr. Res. 3, 005 (in Chinese). Jiang, T.-T., Pan, J.-F., Pu, X.-M., Wang, B., Pan, J.-J., 2015. Current status of coastal wetlands in China: degradation, restoration, and future management. Estuar. Coast. Shelf Sci. 164, 265–275. Landis, W.G., Wiegers, J.A., 1997. Design considerations and a suggested approach for regional and comparative ecological risk assessment. Hum. Ecol. Risk Assess. 3, 287–297.
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Original Articles
Marsh wetland degradation risk assessment and change analysis: A case study in the Zoige Plateau, China
MARK
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Weiguo Jianga,b,c, , Jinxia Lvb,c, Cuicui Wangd, Zheng Chena,c, Yinghui Liua,c a
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China c Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China d Tobacco Town People’s Government, Liaocheng City, Shandong Province 252665, China b
A R T I C L E I N F O
A B S T R A C T
Keywords: Wetland degradation Risk assessment Wetland hazard index Wetland vulnerability index Zoige wetland
Wetlands play an important role in regional development and environmental protection. Under the impact of natural and artificial factors, the plateau wetlands have degenerated and even disappeared, resulting in serious problems for society and the ecological environment. It is necessary to establish a reasonable risk assessment method to evaluate the risk of wetland degradation, and then to analyze changes in the range and features of risk. For this work, the Zogie Plateau wetland was selected as the study area. For this site, a wetland degradation risk assessment method was established based on the conceptual model of Ecological Risk Assessment (ERA). The method included nine indicators used to analyze the wetland hazard index, wetland vulnerability index, and wetland degradation risk synthetically. From the spatial-temporal pattern, the wetland degradation risk was analyzed using data from 2000 to 2014. The calculated results revealed the following: (1) from 2000 to 2014, the wetland hazard index (WHI) showed a trend of increase, the value of which increased from 0.29 to 0.42, with a growth rate of 44.83%. Similarly, the wetland vulnerability index (WVI) significantly increased from 0.30 to 0.54, with a growth rate of 80%. Over the same time, the total wetland area decreased from 3910.25 km2 to 2777.38 km2, a reduction of 28.97%. (2) Using the equidistant method, the risk value was divided into three risk grades. The wetland degradation risk in the whole region is increasing, and the risk rank has changed from the low risk zone (0.092) to the medium risk zone (0.25). The degradation risk becomes greater with distance from the center to fringe areas.
1. Introduction Wetland is well known as one of the three major ecosystems (with forest and ocean) that are essential to human survival and development (Chatterjee et al., 2015). Furthermore, wetland ecosystems have many important functions and provide a wide range of ecosystem services, including water storage, flood control, irrigation, climate regulation, and prevention of soil erosion (Zhang et al., 2014; Chatterjee et al., 2015; Beuel et al., 2016). However, with the spread of urbanization, industrialization, and rapid growth in population, wetland areas are in decline and their functions are degraded in many parts of the world (Malekmohammadi and Blouchi, 2014). In the past 150 years, more than half of the global wetlands have been modified or degraded due to human activities (Sica et al., 2016). In South Africa alone, 35–50% of wetlands were affected by social and economic pressures, suffered complete loss or serious destruction (Oberholster et al., 2014). In 1981/ 1982, National Wetlands Inventory (NWI) determined 233 wetlands,
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about 40% of which have been destroyed by human activities or disappeared due to drought (Holland et al., 1995). As wetland degradation has become more severe and public awareness of wetlands has deepened, wetlands have become the focus of studies and of global concern. Wetlands degradation is the process by which wetland area, structure, and function have degenerated even disappeared under the effect of natural and human activities (U.S. EPA, 1998). Previous studies have demonstrated that wetland degradation is caused by natural threats and by human activities (Wang et al., 2012). Current wetland research includes characterization of wetland degradation, assessment of wetland degradation (Chatterjee et al., 2015; Wanda et al., 2016), factors driving wetland degradation (Song et al., 2014; Zhang et al., 2014), and the restoration and management of wetlands (Jiang et al., 2015). Wetland degradation caused reduction of wetland area, water pollution, environmental degradation, and biodiversity loss among other issues. At the same time, these studies showed that increased
Corresponding author at: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China. E-mail address: [email protected] (W. Jiang).
http://dx.doi.org/10.1016/j.ecolind.2017.06.059 Received 4 April 2017; Received in revised form 28 June 2017; Accepted 30 June 2017 1470-160X/ © 2017 Elsevier Ltd. All rights reserved.
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the largest plateau-type wetland in the world. It covers a large peat deposit and exhibits abundant biodiversity (Fei et al., 2006; Gandarillas et al., 2016). The Zoige has a sub-frigid and sub-humid climate, and features a very cold temperature range. The annual mean temperature is 0.96 °C; with means of −10.7 °C in January and 10.7 °C in July. The annual precipitation range is 600–750 mm. The rainy season roughly April to October every year, and accounts for 90% of annual precipitation (Dong et al., 2010; Huo et al., 2013). There is an obvious vertical change in the soil of the region, which includes swamp soil, meadow soil, plateau cinnamon, and artificial grassland soil (Tian et al., 2004). The major vegetation types in the region are swamp vegetation, meadow vegetation, shrub vegetation, forest vegetation, and cultivated vegetation. The landforms are a combination of low mountains, hills, valleys, and terraces. With the flat, low-lying ground and poor drainage of surface water, large swampy areas form readily on lowlands, terraces, and some underground stream gully zones.
urbanization and agricultural activities are the reasons for wetland degradation (Jiang et al., 2015; Wang et al., 2012). A study of wetland degradation in the Yellow River Delta (China) proposed that water shortage and expanding urban area resulted in shrinkage of the wetland area (Wang et al., 2012). One analysis was conducted of the forces driving change on the Sanjiang Plain from 1975 to 2005, and the results indicated that the main reason for wetland degradation was human activity (Song et al., 2014). Wetland risk assessment is critical for wetlands development. Wetlands degradation risk assessment is generally based on ecological risk assessment (Walker et al., 2001). Ecological Risk Assessment methods include the “Three-Step Framework” formulated by the United States Environmental Protection Agency (USEPA) (U.S. EPA., 1998), ecological level of risk assessment (PETAR) (Moraes and Molander, 2004), and the relative risk model (RRM) (O’Brien and Wepener, 2012; Li et al., 2015). The relative risk model (RRM) was used in the assessment of risk to coastal habitats, rivers, and basins (Landis and Wiegers, 1997; WHO, 2011). The ecological risk assessment was conducted in Florida wetlands to predict the risk of wetland habitat loss (Gutzwiller and Flather, 2011). The ecological risk assessment was conducted in China's Taihu Lake Basin by risk stratification ecological assessment (Xu et al., 2016). However, the previous studies have mainly focused on coastal wetlands, plains wetlands, and specific regions. The study of plateau wetlands degradation risk assessment has become more popular in recent years due to human activities and natural processes. Plateau wetland is special because of its special geographical location and has an irreplaceable role in maintaining biological diversity, soil and water conservation, flood and drought control, and climate regulation. However, due to the impact of natural and human factors, plateau wetlands are constantly degraded especially in degradation of swamp wetlands environments (Malekmohammadi and Blouchi, 2014). Many studies have been carried out on the Zoige Plateau by ecologists and geographers. Some researchers studied the effect of wetland degradation on the bacterial community and humic acids (Song et al., 2011; Tang et al., 2012; Tian et al., 2012). The current status of wetland degradation was also analyzed and a perspective provided for future ecosystems restoration in Zogie Marsh (Jiang et al., 2015). The ecological influence of ditches was determined in Zoige Peatland (Zhang et al., 2014). However, each of these studies concentrated on the effect of a single factor, and lacked systematic analysis of wetland degradation risk assessment. Therefore, for the work reported in this paper, a scientific assessment model was built by selecting typical indicators to conduct a wetland degradation risk assessment. The model was built according to the conceptual model of ecological risk assessment (ERA), which has a reliable theory support. Furthermore, the model constructed the wetland hazard and vulnerability index from wetland area, structure and function aspects. It built a multi-indicators assessment system and a new perspective to study plateau wetland. Zogie Plateau wetland was selected as the study area, with the aim to make a quantitative evaluation of wetland degradation ecological risk for the interval from 2000 to 2014. Therefore, the specific objectives were (1) to establish a model of wetland hazards, vulnerability, and risk assessment index based on the conceptual model of ecological risk assessment; (2) to achieve quantitative evaluation of wetland degradation risk; and (3) to analyze change in the degree of wetland degradation risk.
2.2. Data sources For this study, we collected Landsat images and MODIS images of the years 2000 and 2014, which were downloaded from the National Aeronautics and Space Administration (NASA) of the United States. The monthly values of normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface temperature (LST) were gained using the maximum data synthesis method derived from MODIS data. Land use data for 2000 and 2014 was extracted by Support Vector Machine (SVM) using Landsat images, which were classified into seven types: forest land, grassland, marsh, non-wetlands, cultivated land, artificial surfaces and unclassified. The classification accuracy is in line with the accuracy requirements. Furthermore, the study area was divided into 42 small watersheds for analysis based on the DEM data. This is because the small watersheds are independent landform units and the ecological system is integral. In this way, we could reduce the interference of some fine-scale information and avoid the influence of scale on the result. Other data were collected mainly to determine the forces driving wetland degradation risk. These included administrative maps, topographic maps, meteorological data, disaster data, grazing statistics data, related natural and economic statistical yearbooks, statistical bulletins, and literature. 2.3. Assessment indicators of wetland degradation risk In this paper, wetland degradation risk assessment was based on changes in external threats and internal features. The Wetland Hazard Index (WHI) was used to describe the risk sources: natural disasters (ND) and human activities (HA). Natural disasters were represented by the temperature vegetation dryness index (TVDI), which shows the condition of wetland drought and is good at monitoring surface soil moisture and agricultural drought. This paper selected EVI and LST to calculate TVDI. Human activities (HA) are important causes of wetland degradation, and were expressed by the degree of anthropogenic disturbance (AD) and grazing intensity (GI). This paper used farmland and construction land to express the influence of anthropogenic disturbance (AD). The grazing intensity (GI) was indicated by the amount of grazing overload. Simultaneously, the Wetland Vulnerability Index (WVI) describes the resistance to natural disaster and artificial activities. Three wetland attribute indicators (Area (A), Structure(S), and Function (F)) were selected to analyze quantitatively the wetland vulnerability. The degradation of area (A) was described by the wetland area variability index (AI) which compares the current actual wetland area with the area in 1995. The degradation of structure (S) was represented by the landscape fragmentation index (LFI) and fractal dimension (FRAC). LFI mainly showed the degree of landscape elements fragmentation affected by human activities. FRAC indicated the degree of interference
2. Materials and methods 2.1. Study area The Zoige region is located in the northeast area of the QinghaiTibet Plateau (between 31° 50′–34° 30′ N and 100° 40′–103° 40′ E). It covers an area of 16670.6 km2 and is at an elevation of about 3500 m. The study area includes Zoige, Hongyuan, and Aba Counties in Sichuan Province, and Maqu County in Gansu Province (Fig. 1). Zoige wetland is 317
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Fig. 1. Location of the study area.
the processed indicator data is shown in Fig. 2.
affected by human activities by quantitative description of the landscape and plaque size of the core area, and boundary line tortuosity in the study area. LFI and FRAC were calculated by Fragstat 4.2 software. The degradation of function (F) is represented by the water conservation function (WC), habitat suitability index (HSI), and wetland productivity function (WP). The water conservation function is an important function of wetlands, and is mainly performed by inhibition of surface evaporation, increase in precipitation and soil infiltration, and reduction of surface runoff (Potter et al., 1993). In this paper, WC was calculated by the water balance method, including the precipitation, soil conservation capacity and evaporation. The habitat suitability index (HSI) model (Roloff and Kernohan, 1999) has been widely used to describe the condition of animal and plant habitat, which is possible to reflect the maintenance function of biodiversity indirectly. This paper selected the land use type, the distance to the protected area, the elevation, the slope, the distance to the road and the distance to the water to analyze HSI. This paper used net primary productivity (NPP) (Ruimy et al., 1994; Field et al., 1995; Los, 1998) to represent wetland productivity functions. The CASA model was used to estimate the NPP value, which is mainly based on the absorbed photosynthetic active radiation (APAR) and actual light energy utilization (Sannigrahi, 2017). The description of the nine indicators sources is shown in Table 1 and
2.4. Methods The overall method used for assessment of wetland degradation risk is outlined in Fig. 3. The definition and formulation of the wetland degradation risk problem is the first step. The conceptual model for Ecological Risk Assessment (ERA) is the theoretical basis of this paper, and is used to describe the interaction and relationships of ecological components: exposure of sources and stressors that release the hazard, effects of endpoints and receptors that bear the risk and impact (Landis and Wiegers, 1997; U.S. EPA, 1998). The models of wetland hazard, vulnerability, and risk assessment were established. The wetland degradation risk value was calculated and divided into different levels. Then, in order to provide a more reliable result, uncertainty analysis was carried out on the model. Finally, the change in wetland degradation risk was analyzed in the different phase. 2.4.1. Conceptual model of ecological risk assessment (ERA) The conceptual model of Ecological Risk Assessment (ERA) describes key relationships between some several stressors and assessment endpoints. Exposure characterization examines the sources of risk
Table 1 The description of the nine indicators sources. Indicators
Data source and Description
Temperature vegetation dryness index (TVDI) Anthropogenic disturbance (AD) Grazing intensity (GI) Wetland area variability index (AI) Landscape fragmentation index (LFI) Fractal dimension (FRAC) Water conservation function (WC) Habitat suitability index (HSI) Wetland productivity function (WP)
Generated Generated Generated Generated Generated Generated Generated Generated Generated
from from from from from from from from from
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MODIS product (MOD11A2, MOD13Q1) statistical bulletins and expressed by the proportion of farmland and construction land statistical bulletins and indicated by the amount of grazing overload Land use data Land use data and calculated by Fragstat 4.2 software Land use data and calculated by Fragstat 4.2 software Land use data and calculated by the water balance method Land use data and calculated by HSI model MODIS data and calculated by CASA model
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Fig. 2. The indicators of wetland degradation in the year of 2000 and 2014; (a1,a2) Temperature Vegetation Dryness Index (TVDI) in 2000 and 2014; (b1, b2) Anthropogenic Disturbance (AD) in 2000 and 2014; (c1,c2) Grazing Intensity (GI) in 2000 and 2014; (d1,d2) Wetland Area Variability Index (AI) in 2000 and 2014; (e1,e2) Landscape Fragmentation Index (LFI) in 2000 and 2014; (f1,f2) Fractal Dimension (FRAC) in 2000 and 2014; (g1,g2) Water Conservation Function (WC) in 2000 and 2014; (h1,h2) Habitat Suitability Index (HSI) in 2000 and 2014 (i1,i2) Wetland Productivity Function (WP) in 2000 and 2014.
statement. With the ecological risk assessment framework, the problems are formulated first, the exposure and ecological effects are characterized next, and then the exposure/response profile is developed. Furthermore, to implement the risk estimation and description, the results should be discussed between the risk assessor and risk manager.
stressors, along with their distribution and contact with risk receptors in the wetland ecosystem. Ecological effects characterization describes the adverse response of risk receptors when stressors act directly on the assessment endpoint (U.S. EPA, 1998). The overall ecological risk assessment involves hazard assessment, comparative risk assessment, cumulative ecological risk assessment, and environmental impact 319
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Fig. 3. Regional wetland degradation risk assessment procedure.
quantitatively.
It is critical that exposure characterization describes potential or actual contact or co-occurrence of stressors, and that the profile also describes the impact of variability. In addition, the characterization ecological effects are used to describe the effects elicited by a stressor; links them to the assessment endpoints; and evaluates how they change with varying stress levels (U.S. EPA, 1998). According to the analysis plan, risk assessment is compiled based on stressor-response profiles using many techniques.
V = Aw Av + Sw Sv + Fw Fv
Where V = Wetland Vulnerability Index (WVI), and the subscripts W and V indicate the important weights and the corresponding ratings. Similarly, using the analytic hierarchy process (AHP) method, we determined the weight values of area (A), structure (S), and function (F), (0.54, 0.16, and 0.30, respectively). The degradation of area (A) was described by wetland area variability index (AI) and the weight value was 1.00. The degradation of structure (S) was represented by the landscape fragmentation index (LFI) and fractal dimension (FRAC), and the weight values were 0.75 and 0.25, respectively. The degradation of function (F) was represented by the water conservation function (WC), habitat suitability index (HSI), and wetland productivity function (WP); and the weight values were 0.43, 0.43, and 0.14, respectively. The wetland degradation risk assessment (R) considered the degree of exposure of the risk sources and the degree of vulnerability of the risk receptors. Then, the R was calculated by multiplying the wetland hazard index and wetland vulnerability index.
2.4.2. Analysis method of wetland degradation risk This is the most important of the wetland degradation risk assessments, and it aims to identify the degree of exposure to risk sources and the degree of vulnerability of the risk receptor. The risk sources are external impact factors, which include natural disaster (ND) and human activities (HA). The risk receptors are wetland internal attributes, which include area, structure, and function for wetland ecosystem level endpoints (U.S. EPA, 2003). The wetland hazard index can represent the exposure to risk sources, which release stress and impact the wetland ecosystem to a certain extent. Drought disaster (D), land use change (L), and grazing intensity (G) were serious, major hazard factors, among those that affected the marsh wetland degradation on the Zoige Plateau. The wetland hazard index, known as H, is the composite of the three indicators.
H = NDw NDv + HAw HAv
(2)
R=H×V
(3)
Where R = Wetland Degradation Risk Assessment, and subscripts H and V indicate the Wetland Hazard Index (WHI) and Wetland Vulnerability Index (WVI).
(1)
2.4.3. Analysis method of wetland degradation risk change Change analysis is a quantitative method for determining the characteristics and processes of wetland degradation risk in different periods. Time series analysis has been widely used for environments, land resources, ecological change, and vegetation change. Moreover, the comparative method is based on the risk index and risk rank. The part based on the risk index of the comparative method is mainly intended to reflect subtle changes in the evaluation units of the Risk Index. At the same time, the part based on the risk rank of the comparative method could distinguish qualitative changes in the risk rank. In this paper, we analyzed the change in the wetland degradation risk by combining the two methods. The part of the comparative method based on the risk index is mainly to calculate the difference between the risk values of two specific times. According to the
Where H = Wetland Hazard Index (WHI), subscripts W and V indicate the important weights and the corresponding ratings. We selected the analytic hierarchy process (AHP) (Saaty, 2008) method to determine the weight values of wetlands degradation risk assessment system (Table 2). The weight values of Natural disaster (ND) and human activities (HA) were 0.33 and 0.67, respectively. Temperature vegetation dryness index (TVDI) is used to describe the natural disaster (ND) and the weight value was 1.00. Human activities (HA) was expressed by the degree of anthropogenic disturbance (AD) and grazing intensity (GI), and those weight values were 0.50 and 0.50. The Wetland Vulnerability Index (WVI) described the internal wetland resistance to natural disaster and artificial activities. Three wetland attribute indicators, area (A), structure (S), and function (F) were selected to analyze wetland vulnerability in the model 320
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Table 2 The weight values of wetlands degradation risk assessment index system. Risk
Hazard/Vulnerability
External impact/internal attribute
Assessment Indicators
Wetland Degradation Risk
Wetland Hazard Index (WHI)
Natural Disaster (ND,0.33) Human Activities (HC,0.67)
Wetland Vulnerability index (WVI)
Area(A, 0.54) Structure (S, 0.16) Function(F, 0.30)
Temperature Vegetation Dryness Index (TVDI,1.00) Anthropogenic Disturbance (AD,0.50) Grazing Intensity(GI,0.50) Wetland Area Variability Index (AI,1.00) Landscape Fragmentation Index(LFI,0.75) Fractal Dimension (FRAC,0.25) Water Conservation Function(WC,0.43) Habitat Suitability Index (HSI,0.43) Wetland Productivity Function(WP,0.14)
3.2. Analysis of wetland vulnerability index
calculated results, a positive value, a negative value, or zero respectively indicated increase, reduction, or no change, in the wetland degradation risk. The part of the comparative method based on the risk rank used the Markov model to describe the different risk rank conversions (Jarrow et al., 1997; Jiang et al., 2008).
Wetlands degradation mainly indicated the degradation of area, structure, and function. Therefore, according to the Weight Values of Wetlands Degradation Risk Assessment System, the Wetland Vulnerability Index (WVI) was calculated from 2000 to 2014. As shown in Fig. 5, the mean value of WVI significantly increased from 0.30 to 0.54, with a growth rate of 80%. Furthermore, the total wetland area decreased from 3910.25 km2 to 2777.38 km2, with a reduction rate of 28.97%. Some areas of the Zoige Plateau wetland were completely degraded in 2014, and accounted for 29.13% of the total area (the highest value defined). From the spatial pattern of distribution, the highest value of WHI was distributed in Zoige and Aba County. The upper degradation area was mainly distributed in the northwest region. Fringe wetland areas had higher degrees of degradation. The west of Maqu County and the south of Hongyuan County had high degrees of degradation of wetlands, which increased significantly.
3. Result 3.1. Analysis of wetland hazard index The Wetland Hazard Index (WHI) is affected by natural disaster and human activities. Fig. 4 shows the Wetland Hazard Index in the year 2000 and 2014. It was determined that WHI had a trend of increase and the value increased from 0.29 to 0.42, with a growth rate of 44.83%. Some areas in Zoige Plateau wetland were completely degraded in 2014, and accounted for 29.13% of the total area (this was the highest value defined). From the spatial pattern of distribution, most areas have a certain degree of degradation and the degradation became more serious from the center toward the fringe areas. The highest values of WHI were distributed in Zoige and Aba County. The medium values of WHI were mostly distributed in the central part of the study area, because these areas included the majority of wetlands and human activities had more disturbance strength. Furthermore, the wetlands distributed in Hongyuan County were seriously degraded or vanished.
3.3. Analysis of wetland degradation risk According to the wetland degradation risk assessment system, the small watersheds were selected as evaluation units. The risk was calculated by multiplying wetland hazard index and wetland vulnerability index. According to the equidistant method, the risk value was divided into three risk grades: low risk zone (0–0.16), medium risk zone
Fig. 4. Wetland Hazard Index in the year of 2000 and 2014.
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Fig. 5. Wetland Vulnerability Index in the year of 2000 and 2014.
Fig. 6. The Wetland degradation risk index in the year of 2000 and 2014.
concentrated wetland area extended from the surrounding area to the center. The wetland degradation risk was higher in Maqu County and Aba County. In 2000, the low risk areas were distributed in the southeast and northwest (in Maqu and Hongyuan Counties), and the middle and high risk areas were distributed in Aba County and the northwest edge of Zogie County. However, in 2014, the study area was mainly in the low risk zone. The medium risk areas were located in the west of Maqu County and the northwest corner of Zogie County. In the marginal areas, the wetland was in the high risk zone, and it gradually degenerated, even disappeared in some areas.
(0.16–0.32), and high risk zone (0.32–0.46). The Wetland degradation risk index and risk rank distribution are shown in Figs. 6 and 7 from 2000 to 2014. From the time series, the wetland degradation risk index of the study area increased significantly from 2000 to 2014, and the average value of wetland degradation risk was from 0.092 to 0.25. The risk rank changed from the low risk zone to the medium risk zone. Furthermore, the marsh area of the low risk area decreased from 2428.94 km2 to 1617.56 km2; and the marsh area of the middle risk area increased from 63.5 km2 to 98.06 km2. From the spatial distribution pattern, the area of wetland degradation risk showed an increasing trend. The degradation risk of the 322
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Fig. 7. The Wetland degradation risk rank in the year of 2000 and 2014.
In the past 14 years, the area of increase in the wetland degradation risk index was located in the northwest and center of the study area and the common boundary of the four counties. The area of decreased wetland degradation risk index was in the southwest of Aba County, because the marsh wetland was mainly distributed in the middle and north of the study area. There was less marsh wetlands distributed in the marginal areas. From 2000–2014, the mean value of the degradation risk index was 0.158 in the Zoige Plateau, which indicates that the wetland degradation risk index is increasing on the whole. However, it showed a status both increase and decrease in local region.
3.4. Analysis of change in the wetland degradation risk index As shown in Fig. 8, after subtracting the 2000 and 2014 wetland degradation risk data, the wetland degradation index of each assessment unit was determined. The values of wetland degradation risk index were changed from −0.164 to 0.565 (from 2000 to 2014), where the negative value indicated that the risk of wetland degradation was gradually decreased, and the positive value indicated that the risk of wetland degradation was gradually increased. The risk of wetland degradation has an increasing trend and the area of increased risk constituted 87.12%. Especially, the area of risk index (0–0.1) has increased significantly constituted 47.14%.
3.5. Analysis of change in the wetland degradation risk rank Using the wetland degradation risk assessment model, the wetland degradation risk rank was calculated from 2000 to 2014. As shown in Fig. 9, in 2000 the wetland degradation risk level was mainly in the low risk zone. In 2014, the low risk zones were mainly located at the border of four counties (northwest Maqu County, east Zogie County, and east Hongyuan County), and the middle risk zone was surrounded by the low risk zone. The result obtained the area of wetland degradation risk rank (Fig. 9) and the transferred area of wetland degradation risk (Table 3). The transition probability matrix of the wetland degradation risk rank from 2000 to 2014, was based on the evolution of assessment units. The structural characteristics of wetland degradation risk rank were
Fig. 8. The change of Wetland Degradation Risk Rank (2000–2014).
Fig. 9. The area of wetland degradation risk rank from 2000 to 2014.
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In addition, wetlands are not only affected by natural hazards and human activities, but are also affected by the internal features of area, structure, and function. Fig. 11 shows that the wetland areas continued to decrease from 2000 to 2014, and this was matched the wetland degradation. Regarding the structure of the wetland, the indicators LFI and FRAC increased, which showed that the wetlands became more dispersed, which is not good for wetland development. Regarding the function of the wetland, the indicator WC showed a trend of decrease, and NPP and HSI had stable trends overall. Change existed at a medium date, but compared with 2000, the indicators WC, NPP, and HSI displayed a trend of decrease. The six indicators showed that the wetland was degraded to some extent and that it had changed due to the protection measures. We analyzed the driving forces using nine indexes, but the wetlands are influenced by many factors. This work showed only some of the factors. In the future, we could do more research to discuss the driving forces in more detail.
Table 3 The transferred area of wetland degradation risk from 2000 to 2014 (km2). Risk rank
The low risk zone
The medium risk zone
The high risk zone
The low risk zone The medium risk zone The high risk zone
19068.49 585.65
3765.47 2347.68
10360.03 1239.88
0
830.29
0
as follows. The medium risk area and the high risk area were increased, and the low risk area was decreased significantly from 2000 to 2014. The transition probability matrix of the wetland degradation risk level showed that from 2000 to 2014, the proportions of the low risk zone transformed to low risk zone, medium risk zone, and high risk zone were 57.45%, 11.34%, and 31.21%, respectively. The proportions of the old medium risk zone transformed to low risk zone, medium risk zone, and high risk zone were 14.03%, 56.25%, and 29.71%, respectively. In 2014, the high risk area increased significantly where the wetlands degraded, or even disappeared. The transformation of the wetland degradation risk level showed that unequal transformation among the risk ranks was the main reason for the change of the structural characteristics of the risk hierarchy.
4.3. Uncertainties and prospects This wetland degradation system and the risk calculated are subject to complexity and uncertainty. In addition, the spatial information used for risk assessment also has the characteristics of multiplicity, complexity, and uncertainty. Therefore, some problems exist in the wetland degradation risk assessment of Zoige Plateau, which need to be further studied. Second, some socio-economic data and more detailed statistical data in the study area are difficult to obtain in some counties or villages; which has a certain impact on the spatialization of the data. The assessment units were small watersheds, which ignored changes in the wetland degradation risk within the basin. Third, in this paper, we evaluated the wetland degradation risk and analyzed the risk change on the Zogie Plateau, but did not analyze the driving mechanism, or simulate future wetland degradation risk. In future study, a risk-driven mechanism will be added and explored using a wetland degradation risk simulation model, so as to provide a reference for wetland protection.
4. Discussion 4.1. Contribution of the wetland degradation risk assessment system This paper established the Wetland Degradation Risk Assessment System based on the conceptual model of the US EPA exposure-response mechanism, which is used to describe the external stress and internal characteristics of wetlands. The external stress is usually affected by natural and human disturbance. The changes of internal features are reflected in the area, structure and function. In this paper, the assessment system is more diversified and rational in the index selection of wetland degradation risk. We selected nine indexes to analyze the wetland hazard index and wetland vulnerability index, and combined them to calculate the wetland degradation risk. Furthermore, the previous studies focused on the characteristics of wetland degradation and put less attention on the degradation risk index and risk rank change. This paper concentrated in the Zogie Plateau wetland to do an exploratory research that could provide a reference for similar studies. In addition, the wetland degradation risk assessment can show dynamic changes in multiple periods by time series analysis, and makes the quantitative analysis more reasonable and feasible. Through the multi-period change of WHI, WVI, and risk assessment, we can determine the change characteristics of wetlands, and analyze the factors driving wetland degradation. This should provide some guidance for wetland protection and management.
5. Conclusions In this paper, a wetland degradation risk assessment system was established to evaluate change in the degradation risk in the Zogie Plateau wetland from 2000 to 2014. In this system, nine indicators were selected to show the wetland degradation. The risk distribution pattern and change process were analyzed on temporal and spatial scales. From this, we learned that: (1) From 2000 to 2014, wetland hazard index showed a trend of increase, and the value increased from 0.29 to 0.42, with a growth rate of 44.83%. The highest value of WHI was in Zoige County territory and in Aba County. The medium values of WHI were distributed mostly in the center of the study area. (2) From 2000 to 2014, the wetland vulnerability index significantly increased from 0.30 to 0.54, with a growth rate of 80%. The degradation risk increased from the internal area toward the fringe. Furthermore, the total wetland area decreased from 3910.25 km2 to 2777.38 km2 (reduction of 28.97%). Some areas in the Zoige Plateau wetland were completely degraded in 2014, and accounted for 29.13% of the total area. The highest WHI values were in Zoige County territory and in Aba County. The west of Maqu County and south of Hongyuan County had significantly higher degrees of degradation of wetlands. (3) For the equidistant method, the risk value was divided into three risk grades. The wetland degradation risk index of the study area increased significantly from 2000 to 2014, and the average value of wetland degradation risk rose from 0.092 to 0.25. The risk rank changed from the low risk zone to the medium risk zone in the
4.2. The driving factors of wetland degradation Wetland degradation became more severe from 2000 to 2014, as indicated by positive risk index values. In some areas, especially in the high risk zone, marsh wetlands disappeared completely where the wetlands had higher degradation risk. We selected the driving forces of the wetland hazard index and wetland vulnerability index to analyze the wetland degradation. Fig. 10 shows the trends of TVDI, AD, and GI. From 2000–2010, TVDI continued to increase, which indicated that the soil moisture was in decline and that this was bad for the growth of vegetation and for wetland maintenance. However, in 2014, TVDI showed an increasing trend and this could have affected the conservation. Furthermore, the values of AD and GI increased steadily, which could indicate that human interference caused the rapid decline of Zogie swamp (Zhang et al., 2014). 324
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Fig. 10. The driving forces of wetland hazard index including Temperature Vegetation Dryness Index (TVI), Anthropogenic Disturbance (AD) and Grazing Intensity (GI).
Fig. 11. The driving forces of wetland vulnerability including wetland area, Landscape Fragmentation Index (LFI), Fractal Dimension (FRAC), Water Conservation Function (WC), Net Primary Productivity (NPP) and Habitat Suitability Index (HSI).
index also increased and decreased simultaneously in different local areas. (5) Regarding the change of the risk rank, the area of the medium risk zone and high risk zone of the study area increased significantly. From 2000 to 2014, the high risk areas were transformed with the low and medium risk area completely. The low risk area accounted for 57.45% did not change. The transformation of wetland degradation risk rank showed that the unequal transformation is the
whole study area. Furthermore, the marsh area of the low risk area decreased from 2428.94 km2 to 1617.56 km2; the marsh area of the middle risk area increased from 63.5 km2 to 98.06 km2. (4) From the change analysis of the risk index, the mean value of the wetland degradation risk from 2000 to 2014 was 0.158, which indicates that the wetland degradation risk had a trend of increase. The area of increased risk constituted 87.12%. The area of risk index significantly increased (from 0 to 0.1) by 47.14%, but the risk 325
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main reason for the change of the structural characteristics of the risk hierarchy. Conflicts of interest The authors declare no conflict of interest. Author contributions Weiguo Jiang and Jinxia Lv conceived and designed the study. Jinxia Lv and Weiguo Jiang performed the experiments and wrote the paper. Cuicui Wang, Zheng Chen and Yinghui Liu aid to perform the experiments and write the paper. Weiguo Jiang, Jinxia Lv, Cuicui Wang, Zheng Chen and Yinghui Liu reviewed and edited the manuscript. All authors read and approved the manuscript. Acknowledgments This work was supported by the National Natural Science Foundation of China (41571077), the National Key Research and Development Program of China (2016YFC0503002) and State Key Laboratory of Earth Surface Processes and Resource Ecology. References Beuel, S., Alvarez, M., Amler, E., Behn, K., Kotze, D., Kreye, C., Leemhuis, C., Wagner, K., Willy, D.K., Ziegler, S., 2016. A rapid assessment of anthropogenic disturbances in East African Wetlands. Ecol. Indic. 67, 684–692. Chatterjee, K., Bandyopadhyay, A., Ghosh, A., Kar, S., 2015. Assessment of environmental factors causing wetland degradation using Fuzzy Analytic Network Process: a case study on Keoladeo National Park, India. Ecol. Model. 316, 1–13. Dong, Z., Hu, G., Yan, C., Wang, W., Lu, J., 2010. Aeolian desertification and its causes in the Zoige Plateau of China’s Qinghai-Tibetan Plateau. Environ. Earth Sci. 59, 1731–1740. Fei, S., Cui, L., He, S., Chen, X., Jiang, J., 2006. A background study of the wetland ecosystem research station in the Ruoergai plateau. J. Sichuan For. Sci. Technol. 27, 21–29. Field, C.B., Randerson, J.T., Malmström, C.M., 1995. Global net primary production: combining ecology and remote sensing. Remote Sens. Environ. 51, 74–88. Gandarillas, V., Jiang, Y., Irvine, K., 2016. Assessing the services of high mountain wetlands in tropical Andes: a case study of Caripe wetlands at Bolivian Altiplano. Ecosyst. Serv. 19, 51–64. Gutzwiller, K.J., Flather, C.H., 2011. Wetland features and landscape context predict the risk of wetland habitat loss. Ecol. Appl. 21, 968–982. Holland, C.C., Honea, J., Gwin, S.E., Kentula, M.E., 1995. Wetland degradation and loss in the rapidly urbanizing area of Portland, Oregon. Wetlands 15, 336–345. Huo, L., Chen, Z., Zou, Y., Lu, X., Guo, J., Tang, X., 2013. Effect of Zoige alpine wetland degradation on the density and fractions of soil organic carbon. Ecol. Eng. 51, 287–295. Jarrow, R.A., Lando, D., Turnbull, S.M., 1997. A Markov model for the term structure of credit risk spreads. Rev. Financ. Stud. 10, 481–523. Jiang, W.-G., Ssheng, S.-X., Zzhu, X.-H., Zuo, W., 2008. Change and spatial pattern of flood disaster risk. Geogr. Res. 3, 005 (in Chinese). Jiang, T.-T., Pan, J.-F., Pu, X.-M., Wang, B., Pan, J.-J., 2015. Current status of coastal wetlands in China: degradation, restoration, and future management. Estuar. Coast. Shelf Sci. 164, 265–275. Landis, W.G., Wiegers, J.A., 1997. Design considerations and a suggested approach for regional and comparative ecological risk assessment. Hum. Ecol. Risk Assess. 3, 287–297.
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