1. Introduction
Different ecosystems are a unified whole formed by interacting with specific natural environments and living things. Meanwhile, different ecosystems have different ecological structures, with different ESS under specific backgrounds. ESS is a research field that has gradually developed in recent decades. The Study of Critical Environmental Problems (SCEP) first mentioned the concept of environmental service in 1970 [
1]. In 1981, Ehrlich formally proposed the concept of EES, and it was widely recognized and used. Since then, numerous studies have been carried out from various aspects [
2,
3,
4]. As a matter of fact, the quantitative assessment of EES needs to solve a series of basic theoretical problems and also needs the support of relevant methodologies. The interaction between biological elements and environmental elements and their ecological, economic, and social effects are the important bases for measuring ESS. At the same time, the ecological processes and mechanisms of material circulation, energy flow, and information transfer in different ecosystems are the important theoretical basis for defining the concept of EES. At present, ESS assessment methods can be divided into two categories, one is the common accounting methods, such as value quantity assessment method, material quality assessment method, emergy analysis method; the other is the ecological model method, these methods have their own advantages and disadvantages in the research and application. So far, modeling is becoming more and more important in the quantitative assessment of ESS. Common ecosystem models include InVEST, Aries, SoLVES, and other models, among which InVEST model is widely used [
5]. Global climate change and human exploitation have adversely affected ESS. Numerous studies have also pointed out that climate change has negative effects on most ESS and will continue to increase in the future [
6].
The Qilian Mountains are rich in various natural resources. The Qilian Mountain Ecosystem (QLME) undertakes many important EES in China, such as water conservation, biodiversity, water and soil conservation, and other important functions [
7]. It plays an extremely important strategic role in maintaining local ecological security. In recent years, affected by climate change and frequent human activities, problems such as vegetation degradation of natural forests and grass, water and soil erosion, shrinking snow and ice-covered areas, and reduction of biodiversity have occurred in the Qilian Mountains [
8]. Therefore, research on climate change under the background of the QLME and ESS dynamic change can provide scientific support and effective guidance for the health of the QLME protection and social-economic sustainable development.
Ecohydrological service function is an important function of ESS, whether water conservation or soil conservation, which is important in QLME. Although there are many types of ESS, this study focuses on these functions. As an important ecological barrier in the arid region of western China, it is of great practical significance for the ecological construction, environmental protection, and social and economic development of this region and related regions to maintain the regional ecosystem function under the background of climate change and intense human activities. Although temperature controls soil organic carbon storage in middle and high latitudes, the hydrological climate in tropical regions may be the main driving factor of soil carbon storage [
9]. The carbon pool of the global forest ecosystem is extremely complex [
10], and forest water use efficiency is closely related to atmospheric carbon dioxide [
11]. Model simulation methods play an important role in estimating ecosystem parameters such as Net Primary Productivity (NPP) [
12,
13]. At present, many methods have been explored around the evaluation of ESS value and the estimation of ecosystem carbon storage [
14]. The value-quantity evaluation method and material-quality evaluation method are important evaluation methods, while emergy, shrinking ice and snow, and reduced biodiversity have seriously restricted the no-analysis method. In recent years, the ecological model law has become an active hotspot. With the development of ecological information science and geographic information on science, assessment methods of ESS and estimation methods of carbon storage, carbon budget, and carbon source and sink are gradually associated with ecological models and are further deepened [
15]. At present, the modules of InVEST model are sorted into different types. The different EES can directly contribute to human society [
16,
17].
The ecological system is the foundation of human survival and development. Some research shows the impact of climate change on streamflow and hydrology of a mountainous region in Canada [
18]. Meanwhile, the research estimates the soil moisture of a watershed in the Qilian mountain ranges via vegetation and land surface temperature [
19]. Global change research has an important status and role, while the mountain ecosystem, with its unique geographical perpendicular band landscape characteristics contains a unique topography, soil, vegetation, hydrology, climate, and other natural geographical factors, have become an important area global change research. The QLME is an important ecological barrier in China because of the artificial vegetation habitat damage produced by intense activity, natural grassland degradation, soil erosion, shrinking ice and snow and reduce biodiversity, has seriously restricted the normal play of its function; however, it also seriously restricted regional low carbon and green development. In recent years, the state has made great efforts to improve the ecological environment of the Qilian Mountains. In particular, relying on the original nature reserve of the Qilian Mountains, the state has continuously promoted the pilot work of the national park, which has greatly accelerated the process of ecological restoration. At present, with the attention of ecological and environmental issues, the QLME has also been concerned about the impact of climate change and frequent human activities. In this context, the research background of climate change, the QLME response to climate change, but also the health of the Qilian mountains, ecological environment protection, and social, sustainable economic development will provide a scientific basis for policymaking, pilot project construction, and management of the QLME in China, which has important theoretical significance and practical application value. In the first section of this paper, the main progress of the research on ecohydrological services function caused by climate change is described. In the second section, the data sources and methods are introduced. In the third section, the simulation of ecohydrological services under future scenarios is mainly described.
Section 4 and
Section 5, respectively, discuss and present the study’s conclusions.
As for climate change scenario models, many models worldwide have certain applicability to simulate the change of relevant climate factors at different scales. In particular, recent developments in NDCs and Shared Socioeconomic Paths (SSPs) scenarios below 2 °C have provided important insights for estimating QLME ecohydrological service functions [
20]. Based on the RCP4.5 and RCP8.5 scenarios of the BCC model, this study estimated the future trends and spatial distribution of water conservation and soil conservation functions in the QLME.
2. Data and Methods
2.1. Geographical Position of the QLME
The QLME is located at the intersection of the Qinghai-Tibet Plateau, Loess Plateau, and Mongolia-Xinjiang Plateau. The geographic coordinates are 95°06′~103°01′ E, 36°40′~39°42′ N, spans Qinghai and Gansu provinces, and its total area is 50,200 km
2, as shown in
Figure 1. The whole study area belongs to mountainous ravines, and the average elevation is 3799 m. The landscape includes woodland, grassland, wetland, desert, and snow glaciers and is quite rich. They are important areas for biodiversity conservation in China and also help with water runoff to the inland river system. The study area is an important ecological barrier in China (even in Central Asia) and one of the newly established pilot nature reserves with national parks as the main body, which has extremely important ecological value. At the same time, the application of scenario simulation in mountain ecosystem research is relatively weak, and this study is prospective.
The northern part of the study area has a typical temperate continental climate along the Hexi Corridor, while the southern part has an alpine and semi-arid climate, with annual precipitation ranging from 96.4 mm/year to 729.6 mm/year. As a watershed area of the Heihe River, Shule River, Shiyang River, and other water systems, the glacier, snow cover, woodland, and other ecosystems provide important water conservation services. The QLME is rich in soil resources, covering more than 30 soil types, such as black felt soil, cold calcium soil, and cold frozen soil. Its vegetation presents a vertical distribution rule in different elevation gradient regions. From bottom to top, it can be roughly divided into mountain forest zone, mountain grassland zone, sub-alpine shrub zone, sub-alpine sub-snow, and ice sparse vegetation zone. The vegetation population mainly consists of trees such as Qinghai spruce and Qilian cedar, as well as shrubs such as flagellaria and willow [
21].
2.2. Data Source and Processing
This study needs the support of multi-source data. In addition to the basic information obtained from the actual survey, a series of data are adopted in the estimation and analysis of carbon storage. The raster data of land use/cover, plant available water content, and soil maximum root depth were obtained from the National Tibetan Plateau Scientific Data Center and were reclassified and resampled by ArcGIS software. The raster data of annual precipitation and annual potential evapotranspiration were obtained from China Meteorological Science Data Sharing Service Network and were interpolated by Anusplin software and calculated by the Penman–Monteith method. The secondary watershed boundary vector data and Digital Elevation Model (DEM) raster data are derived from the resource and environmental science data cloud platform and processed by the ArcGIS software hydrological analysis module. Slope and slope direction raster data were obtained based on DEM data by ArcGIS software 3D Analyst tool. Vector data of roads, railways, and urban settlements are derived from the national basic geographic information database, and the distance analysis module of the ArcGIS software is used to obtain the Euclidian distance of the elements. According to the basic data requirements of the InVEST and Geosos-Flus model, all kinds of data were preprocessed in different ways and then related ecological elements were further retrieved through the model.
The research used meteorological data, future climate scenario data, land use/cover data, topographic data, soil data, and literature statistical data, and obtained input data of the InVEST and Geosos-FLUS models after various preprocessing. The reliability of input data is closely related to the reliability of output data.
2.3. Feature of the InVEST Model
InVEST is a suite of models used to map and value the goods and services from nature that sustain and fulfill human life. It helps explore how changes in ecosystems can lead to changes in the flows of many different benefits to people. If properly managed, ecosystems yield a flow of services that are vital to humanity, including the production of goods, life-support processes, and life-fulfilling conditions, and the conservation of options. The modules of the InVEST model can be divided into two categories. As mentioned above, one is the module that supports ESS, such as habitat quality and habitat risk assessment. The other type is the final ESS module, including carbon storage, water conservation, water purification, soil conservation, and other service functions, which can directly benefit human society [
22]. Despite its importance, this natural capital is poorly understood, scarcely monitored, and in many cases, undergoing rapid degradation and depletion.
In the QLME, due to the strong influence of natural and human activities, the ecosystem structure and function have undergone a series of changes in recent years, and the ESS has been seriously disturbed. In this context, applying InVEST principles and methods to estimate the current, especially future changes in ecosystem functions, plays an important role in ecological restoration and sustainable development. In fact, InVEST includes a number of services, according to the connotation and classification of InVEST, the service value of InVEST is estimated mainly from the aspects of soil and water conservation, and the possible trend of the service value in the next 30 years is predicted under the future scenario.
2.3.1. Water Conservation Function Assessment in QLME
In the InVEST model, the water conservation service function comprehensively considers influencing factors such as precipitation, potential evapotranspiration, vegetation cover, topographic factors, and soil water conductivity in the QLME. Among them, many studies believe that precipitation, potential evapotranspiration, and vegetation cover have a greater impact on the change of water conservation function. Under the background of climate change, the change of precipitation directly affects the total amount of water resources in the QLME, while the change of temperature indirectly affects its water production and water conservation function by affecting evapotranspiration. Vegetation cover affects water conservation function through transpiration and interception. The water conservation service function is evaluated based on the water yield module in the InVEST model, which is based on the water balance estimation method, i.e., the difference between precipitation and evaporation on the grid cell to obtain the water supply, by assuming that the water yield of each grid is collected from the collection point through different channels. Then the total amount and average value of water yield in the secondary basin are calculated.
2.3.2. Soil Conservation Function Assessment in the QLME
The soil conservation module of InVEST model generates the spatial distribution of sediment yield in the catchment area based on the input DEM data [
23]. The model first calculates the potential soil erosion amount, i.e., the soil erosion amount under the protection of nature and vegetation (
RKLS), and then calculates the actual soil erosion amount, i.e., the soil erosion amount under the artificial management and conservation measures (
USLE). The soil retention amount (
SD) is the difference between the potential soil erosion amount and the actual soil erosion amount. The equation is as follows:
where
R is the rainfall erosivity factor,
K is the soil erodibility factor.
LS is the slope length factor;
C is the coverage and management factor;
P is the factor of soil conservation measures.
2.4. Features of the Geosos-Flus Model
The Geosos-Flus model is a multi-type land-use change scenario simulation model developed based on the principle of the Flus model [
24]. It is the development and inheritance of its predecessor, geographic simulation, and optimization system Geosos. The Geosos-Flus model provides users with the function of simulating spatial land-use change. When simulating future land-use change, users need to apply other methods (System Dynamics model, or Markov chain) or use preset scenarios to determine the amount of future land-use change as the input of Geosos-Flus.
The Geosos-Flus model is mainly divided into two modules. One is the suitability probability calculation module based on Artificial Neuron Network (ANN). The other part is the cellular automata module based on the adaptive inertia mechanism. Firstly, the classified data of land use and the driving factors of land-use change (such as terrain, location, road distribution) were input into the suitability module, and the suitability probability of each region was calculated by ANN. The model principle is expressed as follows:
In Equations (4) and (5),
p(
n,
l,
t) represents the suitability probability of the crop class
l at time
t on the grid pixel
n;
Wi,t and
sigmoid are the weights and excitation functions of the hidden layer and the output layer;
Neti (
n,
t) is the response value of pixel
n of the class
l hidden layer in time.
where
I is the adaptive inertia coefficient of the difference between the number of land terminations and the number of land decisions;
Mlt−1 and
Mlt−2 represent the difference between the actual grid number and the number of termination targets at the time of
t − 2 and
t − 1, respectively.
In Equation (7),
TPtn,l represents the total probability that grid
n at time
t is transformed into land type
l, and
SCc−t represents the cost that land use type
c is transformed into land type
l.
Otn,l stands for the domain role.
where
represents the total number of grids in class
l after the end of the last iteration of the Mole Domain window of
N ×
N, and
wl is the weight of the influence degree of the field.
4. Discussion
Under the setting of future scenarios, the water conservation function in the QLME was lower than that in RCP4.5 under the higher emission scenario; however, the water conservation in the QLME was still increased compared with that in 2018 due to the increase in vegetation cover under the nature conservation scenario. These results indicate that if certain environmental protection policies are adopted in the future to increase vegetation, the impact of climate change on water conservation function can be alleviated to a certain extent. InVEST is the model of soil conservation service function that mainly calculated the soil erosion caused by rainfall, topography, soil properties, vegetation cover, and land-use. Among them, soil and topography greatly influence water erosion, but they are relatively stable. Therefore, some scholars attributed the EES of soil conservation mainly to precipitation and land-use/cover. The greater the vegetation density, the greater the interception of raindrops, and the smaller the influence of rainfall on soil surface particles. Therefore, land-use and vegetation cover exceed the influence of rainfall intensity and topographic factors to some extent. It is considered to be the key factor leading to the change of soil erosion and soil retention.
Space-time environmental heterogeneities for predicting the multiscale ecological and environmental dynamics underlying the patterns are considered in ecohydrology. Model-checking is a research and testing process to determine the correctness, validity, and credibility of the model. Its essence is to verify that the built model can reflect the behavior characteristics of the real system. In the QLME study, both the InVEST model and RCP model are uncertain due to natural and anthropogenic impacts. With further study of the model mechanism, the reliability of prediction will be further improved.
At present, the EES research concept and method are gradually improved and deepened. A number of experts have systematically studied and combed the state of ESS research on an international scale, revealing eight themes and 28 approaches to ESS [
28]. Biodiversity conservation, economic value, ecosystem services research, human well-being, landscape planning, land-use are the main topics, and research and assessment methods are more diverse such as measurement, computation, decision support, economics, matrix, meta-analysis/bibliometric analysis, non-monetary valuation, software models, spatially explicit, statistical, and conceptual, mixed/integrated, and indicators. Around method exploration, it also involves conservation management, ecosystem-based governance, operation, planning, and stakeholder participation. More methods involve the interaction between people, the environment, landscape ecology, etc. No matter which aspects are focused on, the purpose of the methods is to solve the problems related to EES, deepen people’s understanding of EES, better improve ESS, and continuously guarantee human well-being. For a long time, human activities such as illegal mining and construction in the QLME have resulted in reducing natural vegetation and serious damage to the ecological environment. Since 2015, the state has formulated policies to carry out a series of ecological restoration projects in the Qilian Mountains, including vegetation restoration and protection, environmental governance in mining areas, and transfer of residents in the protected areas. The restoration measures such as artificial afforestation and mountain closure for forest cultivation have greatly increased the forest area, significantly changed the land cover in the study area, and the ecological environment has also begun to recover and improve, and the water conservation and soil conservation functions have been significantly improved. As in recent years, establish a national park as the main body of the suggestion of the natural reserve system and the implementation of the related ecological protection engineering (i.e., natural protection scenario), the ecological environment of the QLME may be further improved, which makes the ecological environment problems such as soil erosion can be further solved. The development of ESS-related theories and methods has important implications for understanding the function of the QLME ecohydrological service and maintaining and improving water conservation and soil conservation.
GSUA typically assigns probability distribution functions to all model factors and propagates that into model outputs [
29]. That is useful for assessing input factor importance and interaction, regimes, and scaling laws between model input factors and outcomes. The sensitivity of the output to an input variable is therefore measured by the amount of variance in the output caused by that input. Variance-based methods fully explore the input space, accounting for interactions and nonlinear responses. For these reasons, they are widely used when it is feasible to calculate them. Sensitivity Analysis (SA) investigates how the variation in the output of a numerical model can be attributed to variations of its input factors. SA is increasingly being used in environmental modeling for a variety of purposes, including uncertainty assessment, model calibration and diagnostic evaluation, dominant control analysis, and robust decision-making.
In fact, the reasons for the temporal and spatial differences of the QLME ecohydrological services are complex, with different impacts of natural factors and human activities. It is subject to the adjustment of national ecological environment protection policy and the long-term adaptation of regional hydrological, climatic, and soil conditions, and the biomass has a cumulative effect. Under this background, the ecohydrological service function is increasing. However, the QLME disturbed by artificial intensity will be affected by the lag of ecological effect, and it will take a certain period of time to increase ecohydrological service function. At present, the QLME study area is covered by an integrated information network and monitoring sites, greatly facilitating the restoration of ecological functions. With the development of green, low-carbon, and high-quality development models, the QLME’s ecohydrological services are expected to be further enhanced in the future.
5. Conclusions
The QLME has a variety of resources and plays an extremely important strategic role in maintaining ecological security. In recent years, under the influence of climate change, especially overgrazing, illegal construction, mineral mining, tourism and sightseeing, and other intensive human activities, vegetation degradation of natural forests and grass, water and soil erosion, shrinking snow and ice-covered areas, and reduction of biodiversity have occurred in the QLME. Under the background of a series of strategic decisions to implement, and further research dynamic change with climate change scenarios, and estimates and analyses the different scales of ESS spatiotemporal changes in the QLME and the comprehensive environmental effect have important theory value. At the same time, this study to explore the QLME influence factors of carbon storage and reveal the carbon cycle and biogeochemical cycle, improve the carbon storage estimation technology, ESS value estimation technology, ecosystem habitat quality evaluation technology, and a series of ecosystem of the ecological environment evolution and the quality evaluation system of technology also has important theoretical value. In the long run, it is of great practical significance to advocate the construction of natural protected areas and low-carbon development.
Based on the InVEST model, this paper analyzed the spatiotemporal evolution of water conservation and soil conservation services in the QLME and estimated the changes of ESS under the future scenarios by coupling the Geosos-Flus model and reached the following conclusions.
- (1)
The ESS of water conservation and soil conservation in the QLME showed an increasing trend in general from 1985 to 2018 but showed spatial heterogeneity. The water conservation in the QLME first decreased and then fluctuated and increased, and the spatial distribution was higher in the northwest and the southeast. The spatial distribution pattern of soil conservation services was high in the eastern and central regions and low in the western regions, with an overall upward trend in time. However, due to illegal mining of mineral resources and construction, soil conservation volume decreased sharply from 2010 to 2015.
- (2)
In the future scenario, water conservation and soil conservation functions in the QLME will increase to varying degrees in 2050. In 2050, although the water conservation amount will increase compared with 2018, the average water conservation amount will decrease. The water conservation amount under the RCP4.5 scenario is higher than that under the RCP8.5 scenario, and the EES of water conservation will decline under a higher emission scenario. However, the growth rate of the soil conservation function was greater under the RCP8.5 scenario.
In the context of climate change, there are common points in many studies on the understanding of climate change in the Qilian Mountain region, and there are also some controversies. Due to the differences in research spatial and temporal scales and evaluation methods, different scholars have different discussions on climate characteristics in the Qilian Mountain region. Although the climate effects of different ecosystems have been understood through a large number of monitoring, analysis, and simulation estimates, and the facts and effects of relevant climate change have been confirmed, people still need to understand further the process, severity, and impacts of climate change on different regions. This study systematically studied the temperature and precipitation in the QLME from 1960 to 2018 and summarized the characteristics and effects of the main factors of climate change in the QLME.
- (3)
The ESS in the QLME were different in different land types. From the perspective of different land-use/cover, the order of water conservation is grassland, glacier and snow, forest land, bare land, wetland, arable land, and construction land. However, water conservation capacity is as follows: glacier snow cover, grassland, woodland, wetland, arable land, etc. The land type with the highest soil conservation intensity was woodland, followed by grassland, cultivated land, and glacial snow cover. The soil conservation ability of bare land was the weakest.
- (4)
In the future scenario, water conservation and soil conservation functions in the QLME will increase to varying degrees in 2050. In 2050, although the water conservation amount will increase compared with 2018, the average water conservation amount will decrease. The water conservation amount under the RCP4.5 scenario is higher than that under the RCP8.5 scenario, and EES of water conservation will decline under a higher emission scenario. However, the growth rate of the soil conservation function was greater under the RCP8.5 scenario.