The aim of this study was to identify the impacts of different driving factors on terrestrial ecosystem evolution. The Yellow River Basin was selected as the study area, of which terrestrial ecosystem was deeply affected by climatic change and human activities. We constructed four scenarios (including without any impacts, affected by climate change, by human activities and by both impacts), and the discrepancies between them reflected the impacts of climate change or human activities. Based on this, the future land use simulation model was used to simulate the land use distribution under the four scenarios, and then, the ecosystem services values (ESV) and landscape patterns index were evaluated. The results indicated that affected by climate change during 1995–2015, the Mean Patch Area of the forestland decreased by 0.19% and the landscape patterns became fragmented. Meanwhile, the total ESV decreased by 0.03 billion dollars and the ecosystem regulation services were weakened. Under the influences of human activities, the Contagion index decreased by 1.71% and the landscape patterns became dispersed. Simultaneously, the total ESV increased by 0.56 billion dollars, but the function tends to be unitary. In addition, these effects showed great spatial heterogeneity. This study provides scientific support for ecological protection in the Yellow River Basin.
Under the dual effects of climate change and human activities, the composition and structure of ecosystem have been undergone tremendous changes [1,2], which exerted profound impacts on landscape patterns and ecosystem services function [3,4,5]. Observed climatic warming has led to changes in the spatial–temporal of precipitation and frequent occurrence of extreme events . All of these changes have altered the potential evapotranspiration and the soil hydrothermal properties, promoted or restrained the growth of vegetation and affected the vegetation ecosystem further [7,8]. In addition, with the accretion of population and rapid economic development, the impacts of anthropogenic activities on the ecosystem have intensified. The vegetation coverage has been increased by forest planting and hill closing afforestation, and the vegetation ecosystem services have been improved [9,10], but the urbanization expansion and unreasonable farming have been destroyed the ecosystem and weakened the ecosystem services function [11,12]. Under the background of intensified global warming and man-made activities, it has guiding significance to quantify the impacts of distinct driving mechanisms on landscape patterns and ecosystem services function for regional ecological construction and sustainable development.
The development of remote sensing technology made it possible to study the effects of climate change and human activities on terrestrial ecosystem [13,14,15]. Scholars have conducted extensive studies, which mainly focused on the correlation analysis based on historical meteorological data and vegetation coverage data , analyzing the effects of human activities on ecosystem services , as well as quantifying the contribution of multiple driving factors . Climate change was closely related to vegetation ecosystem; Zheng et al. analyzed the relationship between the net primary production (NPP) and normalized difference vegetation index (NDVI) and found that climate change contributed 57.65% to the grassland NDVI in the Loess Plateau , but studies have shown that the impacts of climate change on vegetation ecosystem presented great regional difference, which was related to regional altitude, precipitation and vegetation types . The temperature rising led to increased vegetation cover in the middle and high latitudes of the Northern Hemisphere, which improved the ecological service function [21,22]. While in some arid areas, increased potential evapotranspiration and soil water shortage caused by temperature rising has resulted in the decreasing of vegetation coverage and then impaired the landscape patterns [23,24]. Extreme events, such as severe droughts and heavy rains, may augment the mortality of trees and reduce the forest ecosystem services [25,26]. The impacts of human activities on ecosystems were dramatic. Wang et al. observed that the urbanization expansion in Dongying City led to the ecosystem services values (ESV) was decreased by 16.1% based on the historical land use data in 1995–2015, and this trend was expected to continue for a long time in the future . Similar problems have also arisen in other regions, such as the Pearl River Delta  and the Yangtze River Delta . Liu et al. wrote that the vegetation restoration projects in the agro-pastoral ecotone of northern China in the past 20 years significantly improved ecosystem services based on the NPP and scenarios simulation, which resulted in a net increase of ESV by 8.18 billion dollars . However, some scholars proposed that the projects of vegetation restoration in the Loess Plateau have also brought about some ecological problems, such as the singularization of ecological service function and the attenuation of landscape diversity [31,32]. In recent years, the effects of multiple driving factors on the ecosystem were carried out [33,34]. Ling et al. selected remote sensing data, including vegetation cover, human disturbance index and temperature vegetation drought index, and pointed out that the contributions of these three driving factors to the total ESV in Manas River Basin were 38, 31.6 and 30.4%, respectively . Wang et al. noted that the ecosystem services function in Shaanxi province showed an increasing trend from 2000 to 2013 based on different spatial models, and human activities contributed the most to the restoration of vegetation ecosystem (45.8%), followed by climate factors (38.0%) and climate and human factors (16.2%) . However, few studies have considered the following two scientific issues: (1) how to identify the driving mechanism of climate change and human activities on terrestrial ecosystem and (2) how to quantify the contribution of different driving factors.
The Yellow River Basin was a fundamental ecological security barrier in China, as well as a central area of population activities and economic development, which shouldered a pivotal strategic position in the national development and socialist modernization . In recent years, climate change, vegetation restoration projects and urbanization have had huge impacts on watershed ecosystem . The objective of this article is to conduct the attribution identification of terrestrial ecosystem evolution in the Yellow River Basin, which is based on the quantitative analysis of the impacts of climate change and human activities on landscape patterns and ecosystem services function.
2 Data and methods
2.1 Study area
The Yellow River basin (95°53′–119°05′E, 32°10′–41°50′N) is the second largest river in China, occupying approximately 795,000 km2, which accounts for about 8.3% of China’s continent area (Figure 1). The watershed is located between northwest arid zones and southeast humid zones, most of the areas belong to arid and semi-arid continental monsoon climate. The average annual temperature is 6.74°C, with the characteristics of increasing from west to east and decreasing from north to south. The average annual precipitation is 487 mm, mainly concentrated in June to September and exhibited a decreasing trend from southeast to northwest. The Yellow River Basin covers 9 provinces (autonomous regions) of China, including more than 66 cities and 340 counties, with a total population of 116 million and a population density of 147 people/km2. The terrain is high in the west and low in the east, with a highest elevation of 6,212 m and a minimum of −8 m. The basin is divided into three parts, the Plateau Glaciers in the upper reaches, the Loess Plateau in the middle reaches and the Yellow River Delta in the lower reaches.
The data used in this article include the historical land use data, the historical meteorological data, digital elevation model (DEM) data, the socioeconomic data, the roads data and the statistics data of the Yellow River Basin (Table 1).
|Land use||Land use||1995/2015||Resources and Environmental Sciences and Data Center (https://www.resdc.cn/)||With a resolution of 1 km|
|Meteorological||Temperature||1980–2015||Resources and Environmental Sciences and Data Center (https://www.resdc.cn/)||With a resolution of 1 km|
|Socioeconomic||GDP||2015||Resources and Environmental Sciences and Data Center (https://www.resdc.cn/)||With a resolution of 1 km|
|Terrain data||DEM||2000||Geospatial Data cloud (http://www.gscloud.cn/)||With a resolution of 1 km|
|Slope||—||Generated from DEM data|
|Roads data||Road||—||Open Street Map (https://www.openstreetmap.org/)||Convert from the shp file, with a resolution of 1 km|
|Statistics||Grain yield||2000–2015||Statistical yearbook sharing platform (https://www.yearbookchina.com/)||Average grain yield and average grain price|
The research workflow includes data preparation, scenarios setting, land use simulation, index calculation and attribution identification (Figure 2). First, based on the historical meteorological data and land use data, the Mann–Kendall test and land use transfer matrix were used to detect the influence of climate change and human activities. Then, the scenarios of without any impacts (Scenarios 1), affected by climate change (Scenarios 2), by human activities (Scenarios 3) and by both impacts (Scenarios 4) were constructed. Second, the future land use simulation (FLUS) model was calibrated and verified, which was used to simulate the land use distribution in 2015 under the four scenarios. Third, the landscape patterns index and the ESV were calculated. Finally, according to the discrepancies of evaluation results between the scenarios, the impacts of climate change and human activities on terrestrial ecosystem were quantitatively evaluated.
The Mann–Kendall test is a nonparametric test method, which is widely used to detect the long-term change trend and mutation point of the precipitation and temperature [38,39]. The formulas were as follows:
where k = 1, 2, …, n; , . was the statistical series calculated by the time series, and all the values would form a curve; then, the sequence was arranged in reverse order and repeated the above calculation process to get . The significance level in this article was 0.05, and the critical value . When the curve of UF and UB intersected at a certain point, which called the mutation point.
According to the annual precipitation and temperature data of the Yellow River Basin from 1981 to 2015, we found that the average annual precipitation showed a decreasing trend with a decreasing rate of −2.1 mm/10a, but the change trend showed no significance (p > 0.05). While the average annual temperature showed a significant increasing trend (p < 0.05) with a rising rate of 0.4°C/10a. Since the temperature change was significant, and which would affect the frequency and intensity of precipitation , the temperature numerical data were used to detect the mutation point of climate change, and the mutation year appeared in 1994. Based on this, we divided the baseline period climate (1981–1994) and the variation period climate (1995–2015).
We used the Arcgis 10.2 software to calculate the land use transfer matrix of the Yellow River Basin from 1995 to 2015. The changes in forestland, grassland, water bodies and other areas were mainly related to cultivated land and construction land. If the conversion of cultivated land and construction land to other land use types was prohibited, the changes of forestland, grassland, water area and unutilized land affected by human activities will be very small . Therefore, the impacts of human activities were determined by whether the cultivated land and construction land changed in land use conversion. If the impacts of human activities were absence, the cultivated land and construction land were consistent with that in 1995. On the contrary, the conversion of cultivated land and construction land with other land use types was permitted.
In the light of whether the land use types were affected by climate change and human activities, four scenarios were constructed, including without any impacts (Scenario 1), affected by climate change (Scenario 2), by human activities (Scenario 3) and by both impacts (Scenario 4). Where the discrepancies between Scenario 1 and Scenario 2 reflected the impacts of climate change, the discrepancies between Scenario 1 and Scenario 3 reflected the impacts of human activities and the discrepancies between Scenario 1 and Scenario 4 reflected the combined impacts of climate change and human activities.
The FLUS model simulation and validation
In this article, the FLUS model was used to simulate and predict the land use distribution (Figure 3), which was an integrated model for multi-type land use scenario simulation by coupling anthropogenic and nature effects [42,43]. First, the system dynamics model was used to predicted the land use demand under various climatic and socioeconomical driving factors. Then, the artificial neural network was used to calculate the adaptability probability, which aimed to obtain the sinuous relationships between land use types and various human and nonhuman driving factors. In addition, the model adopted an adaptive inertia competition mechanism based on roulette wheel selection method, which dealt with the uncertainty and complexity of land use conversion effectively, and made the FLUS model with a high simulation accuracy [44,45,46].
Based on the land use data in 1995, we selected natural factors (such as DEM, slope and aspect), social and economic factors (such as population density and GDP), meteorological factors (such as temperature and precipitation) and other driving factors to calculate the adaptability probability of the land use types (Figure 3) and then simulated land use distribution in 2015. The actual data of 2015 were used to verify the simulation results. The Kappa coefficient was 0.85, which indicated that the FLUS model could better simulate the spatial distribution of the land use in the Yellow River Basin. In the next work, we simulated and predicted the land use distribution under the four scenarios by adjusting the input driving force and parameters of the model.
According to the landscape patterns features and the practical principle in the study area, the watershed ecological landscape patterns indices were analyzed from the type level and landscape level. Where the type level includes the number of patches (NP), the edge density (ED), the landscape shape index (LSI) and the mean patch area (AREA_MN), the landscape level comprises the ED, the LSI, the Contagion (CONTAG) and the Shannon’s diversity index (SHDI). The value of each landscape indices was calculated by Fragstats 4.2 software, and the ecological significance and calculation method of each index were detailed in the references [47,48].
The equivalent coefficient method was the most representative method to quantitatively evaluate the ecosystem services functions. In this article, the ecosystem services types and the equivalent coefficient were determined based on the research results of Costanza et al. and Xie et al. [49,50]. Combined with the value calculation method of the equivalent coefficients proposed by Xie et al. , we obtained the ESV per unit area of different land use types in the Yellow River Basin (Table 2).
|Primary classification||Secondary classification||Farmland||Forestland||Grassland||Water bodies||Unutilized land|
|Provisioning services||Food supply||219.06||79.89||56.70||206.17||0.00|
|Raw material supply||103.08||182.98||85.05||59.27||0.00|
|Regulating services||Air quality regulation||172.67||605.62||293.79||198.44||5.15|
|Regulation of water flows||69.58||904.57||569.54||26348.52||7.73|
|Supporting services||Maintenance of soil||30.93||56.70||28.35||18.04||0.00|
|Fertility habitat services||33.50||530.89||327.29||657.17||5.15|
|Cultural services||Aesthetic landscape provision||15.46||293.79||144.32||487.08||2.58|
3.1 Land use simulation of different scenarios
The land use distribution and the proportions of different land use categories in the Yellow River Basin by 2015 under the four scenarios are shown in Figure 4. Compared with scenario 1 (Figure 4a), the forestland area in scenario 2 (Figure 4b) was decreased by 0.1%, the grassland area was increased, the water bodies and unutilized land were changed slightly, which indicated that climate change led to a decrement in the forestland and an increment in the grassland. The area of water bodies and construction land in scenario 3 (Figure 4c) and scenario 4 (Figure 4d) were consistent, with an increase of 1.3 and 20.6%, while the other land use types showed diverse trends. In scenario 3, the area of cultivated land and grassland were decreased by 2.0 and 0.3%, and the area of forestland was increased by 1.2%. In scenario 4, the area of cultivated land and grassland were decreased by 1.8 and 0.3% and the area of forestland was increased by 1.1%. These differentially showed that under the dual effects of climate change and human activities, the area of cultivated land was decreased sharply, while the forestland and construction land were increased drastically. Diverse climatic conditions led to different variations in forestland, cultivated land, grassland and unutilized land.
3.2 Impacts of climate change and human activities on landscape patterns
The impacts on the type level
In terms of type level, the landscape pattern indices of each land use types under different scenarios are shown in Figure 5. Compared with scenario 1, the ED and LSI were changed little in scenario 2, the NP of forestland and unutilized land were increased by 0.09 and 0.77%, the AREA_MN were decreased by 0.19 and 0.76%, the NP of grassland was decreased by 0.67% and the AREA_MN was increased by 0.70%. The results showed that the fragmentation of vegetation landscape was increased due to climate change. Scenario 3 and Scenario 4 showed a similar trend, the NP and AREA_MN of cultivated land as well as construction land were changed the most, the LSI of cultivated land, water bodies and construction land were increased significantly and the ED of grassland was decreased the most. These alterations showed that the dominant species of grassland landscape was decreased, the shape of cultivated land, water bodies and construction land became more irregular and the boundary length was decreased due to human activities.
The impacts on the landscape level
At the landscape level, the landscape pattern indices of different scenarios are shown in Figure 6. Compared with scenario 1, landscape pattern indices of scenario 2 was changed slightly. Scenario 3 and Scenario 4 were displayed homologous change characteristics, in which the LSI was increased by 0.46 and 0.44%, the CONTAG was decreased by 1.71 and 1.70% and the ED and SHDI were changed slightly. The results indicated that the entirety landscape patterns in the Yellow River Basin were less affected by climate change, while the ecological function of the Yellow River Basin was impaired under the action of human activities, and the landscape patterns tended to be complicated, fragmented and dispersed.
3.3 Impacts of climate change and human activities on ESV
Changes of the ESV under different scenarios
The ESV per unit area as well as the proportions of different ecosystems under four scenarios are shown in Figure 7. The total ESV of scenario 1 (Figure 7a) was expected to be 237.87 billion dollars by 2015. Compared with scenario 1, the ESV of scenario 2 (Figure 7b) was decreased by 0.03 billion dollars, the proportion of ESV in the grassland was increased and forestland was decreased. Scenario 3 (Figure 7c) and Scenario 4 (Figure 7d) were increased by 0.56 and 0.50 billion dollars, respectively. In Scenario 3, the cultivated land and grassland were reduced by 0.43 and 0.28 billion dollars, while the forestland and water bodies were increased by 0.72 and 0.55 billion dollars. In Scenario 4, the cultivated land and grassland were decreased by 0.40 and 0.31 billion dollars, and the forestland and water bodies were increased by 0.67 and 0.55 billion dollars. These discrepancies indicated that the ESV of forestland decreased due to the impacts of climate change. Under the combined influences of climate change and human activities, the ESV of forestland and water area were increased, while the cultivated land and grassland were decreased.
Changes in the values of different ecosystem services
Figure 8 shows the changes in the values of different ecosystem services in the Yellow River Basin under four scenarios. Compared with Scenario 1, the regulating services such as erosion prevention were reduced in Scenario 2 and the other ecosystem services functions were changed small. The provisioning services in Scenario 3 and Scenario 4 were weakened, which reduced by about 0.3%. While the regulation services were improved significantly, especially the function of the climate regulation and regulation of water flows.
4.1 Effects of climate change on landscape patterns and ecosystem services function
Since 1995, climate change was notable in the Yellow River Basin. The average temperature was increased by 0.4°C/10a, which was significantly higher than the growth rate in China (0.25°C/10a) . The average precipitation was decreased by −2.1 mm/10a but presented the characteristic of large spatial-temporal difference. Concurrently, the frequency of extreme events was increased [4,40], such as the agricultural and ecological droughts. In accordance with the simulation results of scenario 1 and scenario 2, under the impacts of climate change, the forestland area was decreased by 0.1% and the grassland area was increased in the Yellow River Basin from 1995 to 2015, but these changes showed great spatial heterogeneity. In the upper reaches of the Yellow River, the forestland area was decreased by 0.2%, the grassland and unutilized land area were increased. While in the middle and lower reaches, the impacts of climate change were weakened, the forestland was reduced by 0.04%.
There were several reasons for these complex spatial–temporal changes. In the upstream high-altitude regions, the vegetation ecosystems were extremely sensitive and fragile in respond to climate variation. The rising temperature prolonged the growing season of vegetation and strengthened the photosynthesis, which improved the vegetation coverage, and led to the enhancement of ecosystem services function and landscape patterns [53,54]. While in the other arid and semi-arid areas, climate warming induced an increase in evaporation, coupled with the decreased precipitation, all of which aggravated the shortage of soil moisture and resulted in the decrease of vegetation coverage, then impaired the ecosystem services [24,55,56]. In addition, extreme drought events were occurred frequently. Studies have proved that prolonged drought may cause trees to die due to the lack of moisture for a long time and reduce the survival rate of trees [25,57]. According to Bulletin of Flood and Drought Disaster in China , there were five large-scale droughts in the Yellow River Basin during 1995–2015. Especially the North China Plain drought in 2009, with the features of long duration and wide range, which had serious impacts on the vegetation ecosystems. In conclusion, the vegetation ecosystem was damaged due to climate change in the Yellow River Basin, the fragmentation of vegetation landscape was intensified, which resulted in the reduction of ESV, and the ecosystem regulation services became weakened, such as the erosion prevention.
4.2 Effects of human activities on landscape patterns and ecosystem services function
During the study period, the population density in the Yellow River Basin increased from 121.11 people/km2 (1995) to 149.62 people/km2 (2015), and the GDP per unit area increased from 467.7 yuan/km2 (1995) to 715.0 yuan/km2 (2015). In line with the simulation results of scenario 1 and scenario 3, under the impacts of human activities, the area of forestland, water bodies and construction land were increased by 1.2, 1.3 and 20.6% while the cultivated land and grassland were decreased by 2.0 and 0.3%. However, these variations exhibited different characteristics in the upper, middle and lower reaches of the Yellow River Basin. In Qinghai, Gansu and other mid-upstream cities, the economic development was relatively backward, and with a low population concentration, while the soil erosion and ecological degradation were serious. The local government had implemented large-scale projects of the Grain for Green Project to protect the ecological environment, which resulted in the forestland area increased rapidly, the arable land and grassland were decreased [30,59]. According to statistics, since the implementation of the Grain for Green Project in 1999, the cumulative afforestation area reached 18.9 million hm2. The ecological services functions such as the soil and water conservation in the mid-upstream area were significantly improved, but resulted in the singularization of ecological function and weakened the landscape diversity .
In Henan, Shandong and other mid-downstream cities, many large irrigation districts have been built due to the merits of population concentration, flat terrain and convenient irrigation, such as the Yellow River irrigation district, which resulted in a large number of grassland and water bodies converted into arable land . Simultaneously, to cooperate with the population growth and economic development, a large number of cultivated land and ecological land were occupied by the construction land . In addition, some reservoirs have been built for the purpose of irrigation and flood prevention, such as the Xiaolangdi Hydro Project (total storage capacity 12.65 billion m3), as well as some rubber dams have been built for restoring the aquatic ecosystem, such as the water surface engineering ; all of these measures have increased the area of water bodies area, which improved the ecosystem regulation of water flows function significantly. The expansion of arable land and construction land in mid-downstream impaired the ecosystem function, but these losses were offset by the increased water bodies and forestland. In general, the total ESV were increased by 0.56 billion dollars affected by human activities, in which the provisioning services function was weakened, and the regulating services and supporting services were improved, but the ecosystem services function tends to be unitary. In terms of landscape patterns, the ecological landscape patterns became more complex and irregular, landscape diversity became more weakened and the anti-interference ability decreased.
4.3 Relationship between landscape patterns and ESV
Studies have shown that there was a close relationship between landscape patterns and ESV. Landscape patterns changed the material circulation and the energy flow in landscape by affecting the types, area and spatial distribution of various ecosystems and then affected the supply and maintenance of ecosystem services function [61,62]. However, the emphases of the two analysis method were different. The landscape patterns index was an important quantitative indicator reflecting the landscape structure composition, spatial configuration and landscape heterogeneity, such as the landscape fragmentation degree and landscape anti-interference ability, which highly concentrated the landscape patterns information and described the spatially discontinuous data [10,47]. While the ESV was a monetary method to quantify the value of ecosystem services function and evaluate the production and living services provided by the ecosystem, including provisioning services, regulating services, supporting services and cultural services [48,49]. Therefore, our study selected the landscape patterns and ESV as the evaluation indicator, which would have a more comprehensive understanding of the impacts of climate change and human activities on the ecosystem of the Yellow River Basin.
Our study selected the Yellow River Basin as study area and quantitatively evaluated the contributions of climate change and human activities on landscape patterns and ecosystem services function from 1995 to 2015 by scenarios simulation. The main conclusions were as follows:
Under the combined action of climate change and human activities, the total ESV in the Yellow River Basin increased by 0.5 billion dollars during 1995–2015, the ecological service function increased, but the overall landscape patterns tended to be complicated, fragmented and decentralized.
Climate change has had great impacts on the composition and pattern of vegetation ecosystems in the Yellow River Basin, but these impacts showed great spatial heterogeneity, which presented greater impacts in the upper reaches, where the forestland area was decreased by 0.2%.
The ESVs were increased by 0.56 billion dollars due to human activities, which means that the ecological situation of the Yellow River Basin has been improved, but the contradiction between ecological environment protection and economic development still existed, especially in the middle and lower reaches of the Yellow River, which has brought considerable pressure to local governments.
In the following work, the dynamic evolution between climate change and vegetation ecosystems will be further studied.
This research was supported by the National Science Fund Project (51725905; 52130907; 51879275) and Five Major Excellent Talent Programs of IWHR (WR0199A012021). We acknowledge the Resources and Environmental Sciences and Data Center and China Energy Modeling Forum. Moreover, we would like to thank the reviewers for their constructive and detailed comments.
Author contributions: All the authors contributed to the completion of this article. JH conducted the model building, data processing and article writing. TQ reviewed and revised the comments of the article. SL and SY designed the structure of the manuscript. XZ and CL polished the English expression of the article. JW and JF provided assistance in model building and data processing. All authors have read and agreed to the published version of the manuscript.
Conflicts of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.
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