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Journal of Resources and Ecology >

2021 , Vol. 12 >Issue 5: 650 - 657

DOI: https://doi.org/10.5814/j.issn.1674-764x.2021.05.008

Ecosystem Assessment

Assessing the Health of Inland Wetland Ecosystems over Space and Time in China

  • YAO Yunxiao ,
  • WANG Wen , * ,
  • YANG Wenting ,
  • ZHANG Qihao
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  • School of Environment and Nature Resources, Renmin University of China, Beijing 100872, China
*WANG Wen, E-mail:

YAO Yunxiao, E-mail:

Received date: 2020-10-12

  Accepted date: 2021-02-25

  Online published: 2021-11-22

Supported by

Ministry of Education Focus on Humanities and Social Science Research Base(17JJD910001)

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Abstract

Wetland is a unique natural landscape pattern, which provides a variety of important functions and services for human societies. With the rapid develop of the economy and accelerated urbanization, the inland wetlands are faced with series of problems, including reduced area, weakened wetland functions, and deterioration of the wetland ecosystem environment. Therefore, it is necessary to quantitatively assess the ecological health of China’s inland wetlands, which is key to the sustainable development of ecosystems. However, most assessments of wetland ecosystems only examine single wetlands or watershed wetlands, and there are few assessments of wetland health at the national level. In this paper, based on land cover data, climate data, and social and economic data, an assessment system of inland wetland health is established by using the Pressure-State-Effect-Response (PSER) model, which includes 15 assessment indicators. Analytic hierarchy process (AHP) was used to define the indicator weights. Then we assessed the ecosystem health of the inland wetlands of China in 2010 and 2018, which produced three main results. (1) Unlike ecosystem health evaluated by administrative districts, wetland ecosystem health (WEH) evaluation based on the grid could provide additional details of wetland health. (2) The area of inland wetlands increased by 16328 km 2 in 2018 compared to 2010, and the average wetland ecosystem health index in 2018 was 3.45, compared to an index value of 3.24 in 2010. (3) In 2018, wetlands in the better, good, moderate and poor conditions represented about 26.3%, 46.4%, 26.9% and 0.5% of the total, respectively. These results provide a practical guide for protecting and managing wetland system resources and reliable information for land use planning and development.

Key words： wetland; ecosystem health; Pressure-State-Effect-Response; AHP

Cite this article

YAO Yunxiao , WANG Wen , YANG Wenting , ZHANG Qihao . Assessing the Health of Inland Wetland Ecosystems over Space and Time in China[J]. Journal of Resources and Ecology, 2021 , 12(5) : 650 -657 . DOI: 10.5814/j.issn.1674-764x.2021.05.008

1 Introduction

Wetland is a transitional area of biodiversity and productivity between land and open water. It is characterized by shallow water covering flooded soil and scattered with submerged or emergent vegetation (Lee et al., 2006). In providing various Ecosystem Services (ES), such as the mitigation of cultural values and climate changes, replenishment of groundwater, flood control, biodiversity conservation, and many others, the wetlands have a very crucial role for humans (Decleer et al., 2016, Pattison-Williams et al., 2018). By impacting the ecological stability of regional river basins, the health of the related aquatic and terrestrial ecosystems are also influenced by the health of the inland wetlands (Rapport et al., 1998).
Despite the benefit to humans from wetland ecosystem services, wetland conservation still faces many problems. The health outlook of China’s inland wetlands is not optimistic due to rapid economic development, increased urbanization, inefficient investment in environmental protection, and poor policy implementation (Sun et al., 2016; Mao et al., 2018; Mao et al., 2020). In the Yangtze-Yellow- Lancang River Plain, 87% of the total wetlands have disappeared, and degraded water in Haihe-Zhujiang-Yangtze- Yellow-Liaohe-Huai-Songhua River accounts for 63.1% due to pollution caused by human activities. Furthermore, as reported in the first and second surveys of national wetland resources, the wetlands in China have been diminishing for the past decade, with a total decrease of 339.63 million ha, representing annual average and overall reduction rates of 0.92% and 8.82%, respectively (Geng, 2014). A study on the ecological status of 1413 wetlands in China shows that the ecological status of 341 nationally important wetlands is either moderate or poor (Qian et al., 2019). The health of the wetlands needs to be restored and protected, along with a comprehensive assessment of the complete ecosystem (Ekumah et al., 2020). Therefore, assessing the ecosystem health of China’s inland wetlands is important for the development, utilization, and protection of wetlands.
Wetland ecosystem health is an important part of ecosystem health (Yu et al., 2013), and some researchers have expended great effort in ecosystem health studies. One the one hand, there are wetland ecosystem health assessments based on field observation data or models. For example, water birds (Péron et al., 2013; Ogden et al., 2014), macro-invertebrates, fishes (Sharma and Rawat, 2009), plants (Albert and Minc, 2004), and water quality (Mo et al., 2009) data have been used to reflect the health status of wetlands. Nevertheless, with the development of ecosystem assessments, the utility of field observation data is limited since such data cannot be widely applied on a large spatial scale (Chen and Wang, 2005) and suffer from difficulty in providing spatially and temporally explicit assessments (Kerr and Ostrovsky, 2003). The high potential for monitoring and assessing the health of the ecosystem at various spatial and temporal scales across wide areas is enabled by the enhanced usage of remote sensing data to estimate the health of the wetland ecosystems (Ludwig et al., 2007). The wetland ecosystem health of the Liaohe River Delta in China was assessed at the watershed scale, through Geographical Information System (GIS), the PSR model, and remote sensing in 2005 by Jiang et al. (2005). The extremely useful global land cover maps (Globe Land30) for 2000 and 2010, with a spatial resolution of 30 m, were produced for the evaluation of regional ecosystems by Chen et al. (2015). On the other hand, Rapport et al. (1985) first proposed that the concept for ecosystem health is defined by the sustainability and stability of the ecosystem, which includes the ability to recover after stress, self-regulation, and the ability to maintain its organizational structure. Since then, to evaluate wetland ecosystem health, several indicators framed by multi-layered assessment systems have been proposed. These indicators included the Vigor-Organization-Resilience (VOR) model (Costanza, 1992), the Pressure-State-Response (PSR) framework (Walz, 2000) and its extended frameworks, including driving forces-state-response (DSR) (Tung et al., 2005), pressure state-impact-response (PSIR) (Turner et al., 1998), driving forces-pressure state-impact-response (DPSIR) (Smeets and Weterings, 1999) and Driving forces-Pressure- State-Response-Control (DPSRC) (Yang et al., 2008). Some models, such as PSR, were originally developed to solve environmental problems, but changes in ecosystem health are also influenced by social development. Therefore, the “effect” index was integrated into some models, such as the Pressure-State-Effect-Response (PSER) model, to consider the effects of social problems.
In this paper, we assessed the ecosystem health of China’s inland wetlands using the PSER assessment model and remote sensing data based on grids. The indicators for the health assessment of the wetland ecosystem were firstly established according to the PSER model. To determine the weight of each indicator, the Analytic Hierarchy Process was utilised. In order to address the deviation resulting from assessment units, we adopted the grid as a replacement for administrative districts as the assessment units. Specifically, we obtained the spatial distributions of indices, such as population density and Gross Domestic Product, when evaluating wetland ecosystem health, and the grid data increased assessment accuracy. Through this assessment, we obtained a spatial distribution of the ecosystem health of China’s inland wetlands. Our results provide important information for coordinating the development, utilization, and protection of wetlands in China.

2 Materials and methods

2.1 Study area

The total area of inland wetlands in China was 320440 km2 in 2018, a slight increase from the 304112 km2 of inland wetlands in 2010. The inland wetland area is 3.2% of all land-based areas, which includes water, on the land use and cover map derived from Landsat TM. The wetland types involved in this study include lakes, reservoir ponds, streams and swamps. Inland wetlands are classified as either natural or artificial, with natural wetlands occupying 3.8% of the overall wetlands in China. In the past 50 years, natural wetlands in China have been greatly reduced and degraded because of wetland reclamation, population pressure and poor environmental stewardship. Moreover, the Chinese government has taken steps to protect remaining wetlands, such as establishing wetland reserves and restoring degraded wetlands.

2.2 Data resources

For geographic data, we used the Land Cover Mapping geographic data at 30 m resolution provided by Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC), Digital Elevation Model (DEM) and thirty-year averaged annual precipitation data to evaluate wetland ecosystem health (WEH) in China, with the geographic data including rivers, railways, national highways, national secondary roads, and watersheds. Landcover data were produced for the year 2018. By calculating the proportion of each land cover type in every 1 km grid, the land cover map was resampled to 1 km resolution. The 1 km resolution DEM data can be seen at http://www.cgiar-csi.org. Thirty-years mean annual precipitation data, from 1990 to 2010, were obtained from the global weather data for SWAT data sharing platforms (http://globalweather.tamu.edu). We obtained the spatial distribution of the human population and GDP at a resolution of 1 km2, and according to the relationship between land use and population, we proposed the relationship between land use and GDP.

2.3 Methods

2.3.1 PSER assessment model
The Pressure-State-Response model was proposed by Rapport (Rapport et al., 1985; Yao et al., 2018) and has been widely used for analyses in the field of environmental issues. The PSR model was designed to reflect the degradation of wetland ecosystems due to human pressure on the ecosystem. However, the PSR model is unable to account for social problems. Therefore, the “effect” index has been integrated into some of the previous models, so the Pressure-State-Effect-Response (PSER) model (Liang et al., 2017) was proposed to incorporate social problems. Based on the PSER conceptual model, Zhou et al. (2019) assessed water resource security in Guizhou Province. The PSER model separates the indicators into four categories: 1) The pressure indicator reflects human activities and natural factors affecting ecosystem health, which include natural and artificial pressures; 2) The state indicators are used to describe the wetland ecosystem composition, and the structure and function of the wetland under pressure; 3) The effect indicators reflect the ecosystem service function and the recovery capability, including resilience and natural response; and 4) The response indicator measures the vitality of wetlands. The indicators were selected to consider the health status of the inland wetlands, and for computation of the inland wetland ecological health index by using the analytical hierarchy method.
In this paper, we chose the PSER model to establish the wetland ecological evaluation indicators. Human pressure can cause various changes in the wetland environment and natural resources, which were the response indicators. The change rate of the proportion of wetland area was chosen as the response indicator.
2.3.2 Assessment indicators for wetland health
Numerous conceptual frameworks have been designed by researchers to meet the needs of environmental decision making and management. The Pressure-State-Response model have reached an extensive adoption due to its clarity of causal relationships through these frameworks.
Regarding the PSR model as our conceptual framework, the basic PSR model was revised according to our needs and the situations which exist in our system (Table 1). For example, the secondary indicators of natural response and resilience were incorporated into the revised model with an index, wherein the primary indicator “effect” indicated the ability of an ecosystem to self-recover. Thus, while determining the response index, the self-recovery of the ecosystem was taken into consideration. Moreover, the state index was subdivided into the organizational structure and vigour (Table 1). Hence, the revised model was found to have a better pertinence for the concept of good health of the ecosystem.
Table 1 Indicator system for assessment of wetland ecosystem health
Primary indicators Secondary indicators Measures Positive/ Negative
Pressure Artificial pressure Population density (people.km-2) ‒
GDP per unit area (yuan km-2) ‒
Urbanization rate (%) ‒
Pressure of cultivated land (%) ‒
Distance to road (m) +
State Vigor Average annual precipitation (mm) +
Geomorphic types +
Shrub and grass rate (%) +
Terrain slope (°) ‒
Organizational structure Patch density +
Mean fractal dimension +
Effect Resilience Surrounding patch types +
Nature
response		 Land degradation rate (%) ‒
Soil erosion (t) ‒
Response Wetland response Wetland area change rate (%) +

Notes: ‘+’ represents a positive correlation between the indicator and WEH; ‘‒’ represents a negative correlation between the indicator and WEH.

The pressure indicator reflects human activities and natural factors affecting ecosystem health. Human economic activities have a greater impact on wetland change. In this paper, we chose population density, GDP, urbanization rate, cultivated land, and the distance between wetlands and highways to indicate ecosystem pressure.
The functions of the wetland under pressure, and the ecosystem composition were described using the state indicators, including vigour and organization structure. Therefore, for vigour the indicators of the average annual precipitation, geomorphic types (Li et al., 2007), shrub and grass rate, and terrain slope were chosen to determine the status of the health of the wetland ecosystem (Wang et al., 2011). Meanwhile, for organization structure, the mean fractal dimension and the patch density were chosen for quantifying the state of the wetland ecosystem health.
The effect indicators reflected the ecosystem service function and recovery capability. The effect indicators chosen included resilience and natural response. Resilience was characterized by surrounding patch types in this paper. Natural response reflected a change in the function caused by outside stress, and it is characterized by the rate of land degradation and soil erosion. The land degradation rate was used to express the impact of human activities. The classification of soil erosion in this paper (Table 2) was based on the standard criteria of the Chinese Ministry of Water Resources.
Table 2 Soil erosion criteria
Vegetation coverage (%) Slope
< 5° 5°-8° 8°-15° 15°-25° 25°-35° > 35°
> 75 Not obvious Micro Micro Micro Micro Micro
60-75 Micro Mild Mild Mild Moderate High
45-60 Micro Mild Mild Moderate High Strong
30-45 Micro Mild Moderate High Strong Extremely strong
< 30 Micro Moderate High Strong Extremely strong Severe
2.3.3 Weights of assessment factors
We determined the weights of assessment indicators using AHP, which is a method that consists of qualitative and quantitative analyses. As a structured technique for organizing and analyzing complex decisions, AHP has often been used in a wide variety of situations where decisions need to be made, like in fields such as education, shipbuilding, industry, healthcare, business, and government. The Analytic Hierarchy Process has specific application potential in the group decision making processes.
The AHP method employs a semantic scale, which relates numbers to judgments, to assign priority values. It shows the possible results of qualitative comparisons. With a homogenous measurement scale, different elements can be weighted. In this way, the weight that is assigned to every single standard reflects the importance of each expert involved in the project. With the advantage of simplicity, the discrete scale of this method is easy to master.
2.3.4 Assessment of wetland ecosystem health
WEH is complex and contains many different factors, so the use of both qualitative and quantitative methods for system evaluation is most effective. By following the linear function below, the integrated indicators of state, response, effect, and pressure for each unit were obtained, along with the weights and standardized values of each indicator:
$WEHI=\sum\limits_{i=1}^{n}{{{W}_{i}}\times {{C}_{i}}}$
where, Wetland Ecological Health Index (WEHI) represents the value of the response, the effect, the state, and the pressure indicator. Ci is the standardized value of the i-th indicator, and Wi indicates the weight of the i-th indicator. The sum of the response WEHI, the effect, state, and the pressure, constitute the WEHI. The reclassification standards of WEHI are given in Table 3.
Table 3 WEHI reclassification standards
Rank Better Good Moderate Poor
WEHI WEHI≥4 4>WEHI≥3 3>WEHI≥2 2>WEHI
Area (km2) 84275.72 148684.2 86198.36 1602.2

3 Results

3.1 Determination of factor weights by AHP

We used AHP to calculate the weights of the WEH evaluation factors; the final weights of the estimated metrics for this study are given in detail in Table 4. It should be noted that the positive numbers represent positive correlations, while the negative numbers represent negative correlations. The results show that the population and GDP had larger weights among the pressure indicators, while the average precipitation had a larger weight in the state index.
Table 4 Weights of evaluation factors
Goal Criteria Alternative Comprehensive weight
Indicator Weight
Wetland ecosystem health Pressure (0.3701) Artificial pressure (1.0000) Population density 0.3130 0.1158
GDP 0.3130 0.1158
Urbanization rate 0.0988 0.0366
Pressure index of cultivated land 0.0988 0.0366
Distance to road 0.1765 0.0653
State (0.1850) Vigor (0.5000) Average annual precipitation 0.4547 0.0421
Geomorphic types 0.1411 0.0131
Terrain slope 0.1411 0.0131
Shrub and grass rate 0.2630 0.0243
Organization structure (0.5000) Patch density 0.5000 0.0463
Mean fractal dimension 0.5000 0.0463
Effect (0.0998) Resilience (0.5000) Surrounding patch types 0.5000 0.0499
Nature response (0.5000) Land degradation rate 0.2500 0.0250
Soil erosion 0.2500 0.0250
Response (0.3451) Wetland area changing rate 1.0000 0.3451

Notes: Values in parentheses are the weights of the evaluation factors.

3.2 Assessment results of the PSER model

The weight overlay tool in ARCGIS was used to calculate the weights of the selected assessment indicators; for example, Pressure was calculated by Population density, GDP, Urbanization rate, Pressure of cultivated land and Distance to road. Their weights are 0.3130, 0.3130, 0.0988, 0.0988 and 0.1765, respectively. Then based on the weights and standardized values of the assessment indicators, we obtained the spatial distributions of the four integrated indicators of pressure, state, effect and response (Fig. 1).
Fig. 1 Distributions of pressure, state, effect and response index values.
3.2.1 Wetland health pressure
Figure 1 shows that wetlands in the eastern region of China generally suffered greater pressure. A lower indicator value of pressure denotes higher pressure and means worse wetland ecosystem health. The pressure indicator gradually increased from western to eastern China, which is closely related to the patterns of high population density and urbanization rate.
3.2.2 Wetland health state: The pressure values of inland wetlands
The wetland ecosystem health state of central and eastern China was generally better than western China (Fig. 1). A relatively higher indicator value for the state means that the wetland ecosystem is healthier. The spatial pattern observed is just due to the average annual precipitation decreasing gradually from eastern to western China, and the poor vegetation cover and steep terrain in western China. So, as a result, the wetland health of eastern China was better than that of western China (Fig. 1).
3.2.3 Wetland health effect
Higher values of the effect indicator were mainly distributed in eastern China (Fig. 1). A higher indicator value of an effect reflects better wetland ecosystem health. Regarding the patch types of wetlands, the southeastern areas were surrounded by grassland and forest land, while northwestern wetlands were accompanied by desert and glacier areas. Besides, the land degradation rate in the northwest was higher than that of the southeast because of terrain, water loss and soil erosion. The highest land degradation rate was about 1.3% (NBS, 2011), and this was located in Inner Mongolia. In western China, the land degradation rate was low, and the green vegetation region was relatively high. In China, soil erosion gradually increased from east to west and from south to north. The northwestern regions of the Yangtze-Yellow-Lancang River area have suffered serious land degradation because of freeze-thaw erosion. The effect indicator value in the eastern Tibetan Plateau was the maximum value for the whole study area. These areas were situated along the south bank of the Yellow River with relatively abundant water resources. The most effective way to increase the values of regional effect indicators was to convert wetlands to woodland and grassland.
3.2.4 Wetland health response
We found that the wetland in western China increased from 2010 to 2018, while the wetlands in northern and southern China decreased from 2010 to 2018 because of long-term overgrazing and grassland deterioration.

3.3 Wetland health assessment

The spatial distribution of wetland ecosystem health in China was determined using equation (1). Figure 2 shows that the environmental conditions were severe in many parts of China. Areas with scores greater than 4 were indicated as having excellent ecological health, but these areas constituted only 18% of the entire region. The wetland ecosystem was healthy in the Zoige Wetland Nature Reserve in Sichuan Province, which was distant from any farm or town ecosystems. This zone was near the Yangtze and the Yellow River and had the most suitable habitats for rare water birds and migratory birds. Scores ranging from 3 to 4 indicated relatively good wetland ecological health, and such areas were located in the Qiangtang Plateau and the basin in northwest Tibet. This zone had relatively lower stress from human activities and biological invasion. The main step for improving regional ecological health conditions is to reduce water loss and soil erosion. In contrast, poor ecological health areas (indicated by scores of less than 3) were mainly distributed near the continental rivers in Inner Mongolia and Hexi Inland with poor natural conditions. To strengthen their poor ecological health, the prevention of soil erosion using windbreaks and sand-fixation is necessary.
Fig. 2 Distribution of inland wetland ecosystem health in 2010 and 2018

4 Discussion

The PSR model has been widely used to assess ecosystem health because of the clear causal relationship between nature and human disturbances, such as pressure, state, and response (Sun et al., 2016; Niu et al., 2017). However, it has some deficiencies. For example, the indicators are difficult to classify and quantify. Also, the PSR model cannot easily be used to analyze social problems. Therefore, it is necessary to modify the PSR model by introducing an effective index, and this resulted in the development of the PSER model.
(1) The modified PSER model was more rational than the original PSR model. The effect index demonstrated ecosystem self-recovery potential, and this contained both resilience and natural response. The pressure index was divided into artificial pressure and natural pressure components, and the state index was divided into vigour and organizational structure. The revised model was mainly used in the indicator selection process. It eliminated the uncertainty in the index selection process and could explain the reasons for changes in wetland ecosystems.
(2) This study adopted a grid as the evaluation unit, so population density and GDP were divided into grids. The results clearly showed differences within the internal administrative units. The gridded GDP and population density data more accurately reflected the regional economic effects caused by human activities. Also, when using administrative units, the scale of the statistical data was inconsistent, and this problem was minimized by using grid data (Hou et al., 2016). We assessed the WEH by administrative units, and the values of WEH indexes ranged from 1.56 to 2.33. By comparison, the values evaluated by grids ranged from 1.91 to 4.81, which were between 1 and 5, so the result assessed by grids helps to divide the grades of WEH. The results can provide guidance for wetland management, and a more reliable scientific basis for land use planning and development.
(3) Some experts have evaluated wetland ecosystems in China. Qian et al. (2019) has summarized 1413 wetlands nationwide, which were evaluated in terms of the definition of wetland ecological status evaluation, the ecological field status of key wetlands nationwide and the main evaluation methods. The results indicated that the ecological condition of the wetlands is poor and moderate at 24.85%, good at 41.33% and better at 33.82%. In this paper, inland wetlands in better, good, moderate and poor conditions accounted for about 26.3%, 46.4%, 26.9% and 0.5% of the total respectively, which verifies that the research results of this paper are reasonable.
(4) It should be mentioned that the method in this study is not simpler than other ecosystem assessment methods. Taking into account the limitations of obtaining inland wetland field experimental data, this study has certain limitations. However, using multi-scale spatiotemporal remote sensing data can provide up-to-date information for the ecological security and management decisions needed for wetland reserves (Chen et al., 2019). High-resolution remote sensing data and detailed social statistics are also necessary to properly assess wetland ecosystem health.

5 Conclusions

We established assessment indicators based on the PSER model and assessed the ecosystem health of China’s inland wetlands using a grid system. This study considered four classified indicators, which are wetland pressure, state, effect, and response. We obtained a spatial distribution of wetland health in China. The results of this study lead to four main conclusions. 1) The area of inland wetlands increased by 16328 km2 in 2018 compared to 2010. 2) Among the inland wetlands, only 72.6% of the wetland health evaluation results were above moderate. Thus, there are still 27.4% of wetlands with moderate or poor health assessment results. 3) The average wetland ecological health index in 2018 was 3.45, which was 0.21 higher than that of 2010, showing a slight upward trend. 4) The combination of remote sensing data and statistical data can provide more possibilities for the quantitative assessment of ecosystem health. The results presented here provide a practical guide for protecting and managing wetland system resources and reliable information for land use planning and development.

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