Next Article in Journal
Using Tree Height, Crown Area and Stand-Level Parameters to Estimate Tree Diameter, Volume, and Biomass of Pinus radiata, Eucalyptus globulus and Eucalyptus nitens
Previous Article in Journal
Computer Vision-Based Wood Identification: A Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 13
Issue 12
10.3390/f13122039
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

How Does the Water Conservation Function of Hulunbuir Forest–Steppe Ecotone Respond to Climate Change and Land Use Change?

by
Pu Ma
1,
Shihai Lyu
2,
Zhaoyan Diao
2,
Zhirong Zheng
2,
Jing He
1,*,
Derong Su
1,* and
Jingru Zhang
3
1
School of Grassland Science, Beijing Forestry University, Beijing 100083, China
2
State Environmental Protection Key Laboratory of Regional Eco-Process and Function Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
3
School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China
*
Authors to whom correspondence should be addressed.
Forests 2022, 13(12), 2039; https://doi.org/10.3390/f13122039
Submission received: 3 November 2022 / Revised: 26 November 2022 / Accepted: 28 November 2022 / Published: 30 November 2022
(This article belongs to the Section Forest Meteorology and Climate Change)
Browse Figures
Review Reports Versions Notes

Abstract

:
The scarcity of water resources is becoming a global focus, and water conservation has become one of the most crucial service functions in the water security and sustainable development of ecosystems. Hulunbuir forest–steppe ecotone, as an important water conservation area in the northeastern provinces of China, plays an irreplaceable role in Northeastern China. However, the water yield and water conservation are rarely understood in the ecotone. In this study, the InVEST model was employed to analyze the spatiotemporal dynamics of water yield and water conservation from 2000 to 2020. Meanwhile, we explored the response of water conservation to climatic factors and human disturbance. The results demonstrated that water yield and water conservation presented a decreasing trend in the first decade and then increased. The land use transfer obvious from 2000 to 2010, and most vegetation types were converted into unused land. This transition intensified reduction of water conservation. The main factor affecting the water conservation was climate Precipitation had the greatest impact on water conservation. The findings of this study have great and important implications for regional sustainable water resource management and ecological protection policies and provide a convenient method for evaluating water conservation in other areas that are lacking climate, hydrology, and geological data.

1. Introduction

Ecotone is a transitional zone between adjacent ecological systems, with a series of characteristics uniquely defined by time, space, and the strength of the interactions between the adjacent zone [1,2]. The utility of the ecotone concept has been described in a series of international meetings [3] and a famous joint publication by the Ecological Society of America and the Man and Biosphere Program [4]. It is more sensitive to changes in environmental conditions often associated with climatic change and human disturbances because of relatively dynamic and unstable zones compared to their neighboring ecosystems [5,6,7]. The Hulunbuir forest–steppe ecotone, located in the east of Inner Mongolia, China, and adjacent to Mongolia and Russia, is a typical ecological transition zone from steppe to forest. In recent decades, due to the overgrazing of grassland and the development of the regional economy, the severe grazing overload in the grassland makes the grazing pressure in the grassland area shift towards the forest–steppe ecotone, where a large number of grasslands have been reclaimed and crops or forage crops have been planted, resulting in the decline or even loss of the ecological service function of the forest–steppe ecotone [8,9]. Daxing’anling forest region is an essential part of the northeast forest belt. It has critical ecological functions such as water conservation and has been recognized as one of China’s important ecological barrier areas by the Ministry of ecological environment. However, there are few research reports on the water conservation capacity of the forest–steppe ecotone connecting Hulunbuir grassland and Daxing’anling forest region, and how much weight it occupies in this ecological barrier area. The water conservation capacity of the ecotone has an irreplaceable role in guaranteeing the water supply and ecological stability of 180 million people downstream and promoting ecological sustainable socio-economic development in the northern region of China.
In recent years, data from several studies suggested that the forest–steppe ecotone was disturbed by global climate change and human activities resulting in significant variations in landscape patterns, severe aridity of habitats, drying of lakes, shrinking wetlands, and decreasing water yield [10,11,12,13,14,15]. These phenomena have become significant ecological problems restricting the sustainable development of regional resources, environment, and social economy. Therefore, it is of great significance to investigate how the water conservation capacity in the Hulunbuir forest–steppe ecotone responds to past climate changes and human disturbances, in order predict and guide future water resource management of the ecotone.
As a critical output of ecosystem services, water conservation is considered the most valuable ecosystem service [16,17]. Climate change and land use change have been identified as two main factors driving water conservation [18]. Climatic factors are crucial in maintaining ecosystem stability and providing water conservation capacity [19]. Studies have demonstrated that meteorological elements affect water conservation by changing precipitation, temperature, and evapotranspiration [20,21]. The temporal and spatial fluctuation of precipitation has significant impacts on water yield [22]. Climate change may be responsible for increases in the severity and frequency of extreme events, such as extreme precipitation, temperature waves, and high winds. A previous study concluded that water yield was very sensitive to extreme weather [23]. Climate change influences ecosystem services by altering the biophysical processes of ecosystems, and it is expected to become a more severe threat to ecosystem services in the next decade [24,25]. Land use change is another essential driver affecting the distribution of water conservation by modifying ecosystem type, landscape pattern, and ecological processes [26]. Land use change significantly affects the hydrologic process and characteristics of regional hydrology by modifying the vegetation cover [27]. Jin, et al. [26] conducted a meta-analysis of land use and demonstrated that land use change severely affected regional hydrological ecosystems and human well-being. Similarly, Bai, et al. [28] found that land use change in Kentucky (e.g., converting woodland into pasture, construction, and agricultural land) reduced water conservation. In fact, a number of studies applied land use as a factor of ecosystem services to predict potential future developments and to assess environmental change [29].
The InVEST model and the SWAT model are currently the most popular methods for researching water yield and water conservation [30,31,32,33,34]. The InVEST model is a comprehensive assessment model for evaluating ecosystem services, and is developed by Stanford University, The Nature Conservancy (TNC), and World Wide Fund for Nature (WWF) [35]. It can visually represent the assessment results of ecosystem service functions, which were abstractly expressed in words. The InVEST model has been widely adopted in more than 20 countries and regions to evaluate ecosystem services, including water yield, carbon storage, biodiversity, and habitat risk assessment [36,37,38,39]. The model requires little data to evaluate the water yield based on the Budyko hydrothermal coupling equilibrium hypothesis [40,41]. It is suitable for applications in areas where data are difficult to acquire. To date, the water yield assessment of the InVEST model has been widely used in different rivers, wetlands, mountains, and hills worldwide. However, there was little discussion about its applications in assessing water yield in the ecotone [34,42,43]. The InVEST model can quantify the water yield under different driving factors, such as climate variables and watershed characteristics (e.g., land use change). A large and growing body of literature has reported that meteorological factors are the most influential parameters in assessing regional water yield using the InVEST model [44,45,46]. However, a large volume of published studies on how land use affects water yield has demonstrated that land-use change alters regional water yield by affecting the biophysical characteristics of vegetation cover [22,42,47]. Collectively, the InVEST model was widely used to explore the change in water yield caused by land use or meteorological factors and performed well under different geographical and climatic characteristics.
In this study, we used the InVEST model to calculate and simulate water yield and water conservation of Hulunbuir forest–steppe ecotone in northeast China from 2000 to 2020. The aims of this study are (1) to visually and quantitatively analyze the spatiotemporal distribution of water yield and water conservation of the ecotone, and (2) to clarify the response of climate change and human disturbance (e.g., land use change and landscape patterns) to water conservation function and to identify the main influencing factors. The results help to deeply understand the water conservation capacity of the forest–steppe ecotone, remind people to pay more attention to the ecotone, and provide basic information on the water conservation capacity for local ecological management.

2. Materials and Methods

2.1. Study Area

Hulunbuir forest–steppe ecotone is located at the Western edge of the Daxing’an Mountains in northeastern China. Its geographical extent is 119°06′ E to 121°51′ E and 47°19′ N to 50°56′ N. There are three county level cities (Genhe City, Erguna City, and Yakeshi City), three counties (Chen Barag Banner, Ewenki Autonomous Banner, and New Barag Left Banner), one district (Hailar District) at Hulunbuir City, Inner Mongolia Autonomous Region. The total area is 45,000 km2 and accounts for 17.8% of the Hulunbuir City. The location of Hulunbuir forest–steppe ecotone is shown in Figure 1. The geological structure of the ecotone belongs to a transitional zone from the western section of the Daxing’an Mountains Fold System to Hulunbuir Plateau, with high terrain in the southeast and low in the northwest, and the relative altitude difference is 500 m. This study area is under a continental arid and semi-arid climate, with an annual average temperature ranging from −3.1 °C to 0 °C. The average annual rainfall ranges from 300 mm to 450 mm with precipitation from June to August accounting for more than 70% of the whole year. In addition, the precipitation in the ecotone increases with the elevation. The average annual potential evapotranspiration ranges from 1200 mm to 1700 mm, which is 4 or 5 times larger than precipitation. Soil types are Haplic Greyzems, Haplic Chernozems, and calcic Chernozems [48]. The vegetation is characterized by a distinct transitional region of forests-steppes, changing gradually from the closed canopy forests in the northeast to the meadows-steppes in the southwest along the transect. Farmland, residential area, water, and unused land mainly lie in the transitional area between grassland and forest. The dominant tree species in woodland are Larix gmelinii (Rupr.) Kuzen, Pinus sylvestris var. mongolica, and Betula platyphylla Suk. Examples of typical herbabaceous species in grassland are Leymus chinensis (Trin.) Tzvel, Stipa baicalensis Roshev, Carex pediformis C. A. Mey [49], and Thermopsis lanceolata R. Br. The transitional region offer a better habitat for Spiraea salicifolia L., Geum aleppicum Jacq., Pyrola incarnata, and Carex pediformis. The forest-steppe ecotone is rich in water resources. There are many rivers, lakes, and wetlands, which are an essential part of the Argun water system. Genhe River and Hailar River flow through the region.

2.2. Calculation Method of Water Conservation

2.2.1. InVEST Model Water Yield Module

The water yield module is based on the Budyko hydrothermal coupling equilibrium hypothesis [50,51]. The module runs on a gridded map and acquires the input in raster format, which in turn helps the spatial heterogeneity of gird data influencing the water yield to be understood. We calculated annual water yield Y(x) for each grid x unit as follows:
Y x = 1 − A E T x P x × P x
where, AET(x) is the annual actual evapotranspiration of the grid unit, and P(x) is the annual precipitation of the grid unit. AET(x)/P(x) is the vegetation evapotranspiration of land use/cover type.
A E T ( x ) P ( x ) = 1 + P E T ( x ) P ( x ) − 1 + A E T ( x ) ω P ( x ) 1 / ω
where, PET(x) is the potential evapotranspiration and ω ( x ) is a non-physical parameter that characterizes the natural climatic-soil properties. PET(x) and ω ( x ) are calculated as (3) (4):
P E T x = K c ι x × E T 0 x
where, E T 0 ( x ) is the reference crop evapotranspiration of the grid unit, and K c ι x is the plant evapotranspiration coefficient of the specific land use/cover type in the grid unit.
ω x = Z A W C x P x + 1.25
where, Z is an empirical constant that can represent the regional precipitation distribution and other hydrogeological characteristics [35]. This study obtained Z by repeatedly verifying the simulated water production and actual water resources. A W C x is the effective soil water content (mm) of the grid unit, which is mainly determined by the plant available water content ( P A W C x ), the maximum root burial depth of the soil and the minimum value of the plant root depth [35].
A W C x = M i n R e s t . l a y e r . d e p t h ,     r o o t . d e p t h × P A W C
where, the maximum root burial depth of the soil is the maximum depth to which plant roots can extend in the soil due to the different physical and chemical characteristics of the environment. Plant root depth refers to the depth of the soil layer at which 95% of the root biomass of a specific plant type is present. P A W C represents the plant available water content, expressed as the difference between the field water holding capacity and the wilting point.

2.2.2. Calculation of Water Conservation

Water conservation function characterizes the ability of the ecosystem to retain water in the study area [43]. The calculation formula is listed as follows:
R e n t e n t i o n = M i n 1 ,     249 V e l o c i t y   × M i n 1 ,     0.9 × T I 3   × 1 ,     K s a t 300   × Y i e l d
where, R e n t e n t i o n is the water conservation capacity (mm). Y i e l d is the water yield calculated by the Invest model (mm). TI is the topographic index. V e l o c i t y is the velocity coefficient, and K s a t is the soil saturated hydraulic conductivity.

2.3. Data Source and Processing Method

The water yield module demands some spatial and non-spatial data to calculate water yield, including average annual precipitation, average annual potential evapotranspiration, land use and vegetation cover, altitude, soil data, plant available water content, watersheds, parameter Z, and a biophysical table [35]. The assessment of water conservation requires data such as topographic index, soil saturated hydraulic conductivity, and velocity coefficient [52,53]. Relevant basic data sources are shown in Table 1. In this study, prior to computing water yield, all of the data were resampled at a spatial resolution of 30 m and projected using the WGS_ 1984_ Albers. All of the raster data formats were TIFF. The biophysical coefficients are shown in Table 2, which provides information about land use categories, plant evapotranspiration coefficient ( k c ), and root depth [35,52].

2.4. Scenario Settings for Differentiating Effects of Land Use and Climate Change

We designed four scenarios through controlling two influential factors of land use and climate to analyze LULC, climate change, and their interactive effects on water conservation. First, we selected the LULC raster data for 2000 and 2010 to represent LULC conditions. Then, we used the climate maps for 2010 and 2015 to represent climate conditions. Significant variations in the four raster data can be better used to set scenarios. Finally, we generated four simulation scenarios by integrating land use and climate data (Table 3) and used the InVEST model to assess water conservation under the four simulated scenarios.
In order to analyze these two driving factors, we determined the effects of LULC change on water conservation by using Equation (7).
W l = 1 2 W l 1 + W l 2
where, W l represents the change in water conservation due to the separate effect of LULC change. W l 1 and W l 2 represent the changes in water conservation calculated as the difference between the outputs of S1 and S2 in 2010, and S3 and S4 in 2015, respectively. We used a similar approach to quantify the effects of climate change on water conservation by using the following Equation (8)
W c = 1 2 × W c 1 + W c 2
where, W c is the change in water conservation due to the separate effect of climate variability. W c 1 and W c 2 are the variations in water conservation calculated as the difference between outputs of S1 and S3 under LULC of 2000 and S2 and S4 under LULC of 2010, respectively. We determine the combined land use and climate effects by Equation (9).
W l c = 1 2 W l c 1 + W l c 2 − W l − W c
where W l c is the variations in water conservation due to the combined effects of LULC and climate change. W l c 1 represents the changes in water conservation calculated as the difference between the output of S4 and S1. W l c 2 is the difference between scenarios of S2 and S3.
Previous studies have applied similar approaches to evaluate the separate effects of LULC and climate change [58,59,60,61,62,63,64]. In this study, we replaced the general representation of water conservation (W) with the specific hydrological component under consideration.

2.5. Data Analysis Method

Firstly, spatial data analysis was used to assess the spatial distribution patterns of water yield and water conservation capacity. The quantile classification in Arcgis 10.6 (ESRI, Redlands, CA, USA) was used to visualize these raster data. Secondly, the Markov space transfer matrix method was used to generate the land use transition matrix that reflects the changes of land cover and land use types from 2000 to 2020. Moreover, we used python 3.7 to draw the Sankey diagram of the transfer matrix to make the transfer matrix more visualizable. Thirdly, the M-K abrupt change test has been widely used to quantify the slope of trends in meteorological time series. Therefore, we calculated the M-K abrupt change test for temperature, precipitation, and evapotranspiration using python 3.7 to analyze the effect of climate factors on water conservation capacity. Fourthly, we used ArcGIS 10.6 to generate 100 random sampling points and extracted data from Δ W c , Δ W l , and Δ W l c scenarios and we applied ANOVA to compare the effects of LULC and climate change on water production. Finally, we produced a heat map of the Pearson correlation analysis to explore the effects of climate change and other factors on water conservation.

3. Results

3.1. Variation Characteristics of Water Yield and Water Conservation

3.1.1. Variation Characteristics of Water Yield

Figure 2 shows the temporal and spatial distribution of water yield in Hulunbuir forest–steppe ecotone from 2000 to 2020. The interannual variation of water yield was obvious, but its spatial distribution characteristics were basically similar. The water yield of the forest–steppe ecotone gradually decreased from southeast to northwest. The high water yield area was mainly distributed in the southeastern region of Yakeshi City. The middle water yield area was mainly at Hailar District, Yakeshi City and Erguna City. The low water yield area was located at the northwest district of Erguna City and the west of Hailar District (Figure 2a–e). The interannual variation of water yield in the ecotone firstly demonstrated a decrease and then increased. The water yield demonstrated a significant decline about 57.9% from 2000 to 2010, and the water yield was lowest in 2010 with 33.12 × 108 m3. The water yield increased significantly with an increase of 73.4% from 2010 to 2020, and the water yield was highest in 2015, at about 81.47 × 108 m3 (Figure 2f).

3.1.2. Variation Characteristics of Water Conservation

Figure 3 shows the temporal and spatial distribution of water conservation in the Hulunbuir forest–steppe ecotone from 2000 to 2020. The spatial distribution of water conservation was basically consistent with the water yield. The high water conservation area was mainly distributed in the southeastern region of Yakeshi City. The low water conservation area was located at the northwest district of Erguna City and the west of Hailar District. The middle water conservation area was mainly among Hailar District, Yakeshi City and Erguna City (Figure 3a–e). The interannual variation of water conservation in the ecotone demonstrated a trend of decreasing and then increasing. Water conservation demonstrated a significant decline with a decline range of 58.6% from 2000 to 2010. Water conservation was lowest in 2010 about 17.16 × 108 m3. Water conservation increased significantly with an increase rate of 87.6% from 2010 to 2020. Water conservation was highest in 2015 about 46.33 × 108 m3 (Figure 3f).

3.2. Variations of Land Use and Climate Change in the Forest–Steppe Ecotone

3.2.1. Variations of Land Use

Taking the spatial distribution characteristics of land use in 2020 as an example, the main land use in this area was woodland, grassland, and farmland, which account for 84.9% of the total study area. Woodland was distributed in the eastern high-altitude area of the ecotone. Grassland was distributed in the western low-altitude area of the ecotone. Farmland was distributed in the central area of the ecotone, which was scattered between woodland and grassland (Figure S2). Table 4 and Figure 4 show the interannual changes of land use in the forest–steppe ecotone from 2000 to 2020. In the past 20 years, the areas of farmland, woodland, and unused land had increased significantly. The areas of these land use had increased by 0.13 × 104 km2, 0.15 × 104 km2, and 0.45 × 104 km2, respectively, with an increase rate of 24.5%, 11.3% and 281.3%. The area of grassland decreased significantly by 0.75 × 104 km2 with a reduction of 30.7%. The change of land use was most obvious from 2005 to 2010. A large area of grassland became farmland, woodland, and unused land, and the transfer rates of grassland were 7.7%, 16.2%, and 17.5% respectively.
The water conservation of the land use in the ecotone was statistically calculated from 2000 to 2020. The water conservation of the three main land uses in the five study periods was: woodland > grassland > farmland (Table S1). Grassland, woodland, and farmland all exhibited highest water conservation in 2015: woodland (172.51 mm) > grassland (98.1 mm) > farmland (63.15 mm), while their water conservation was lowest in 2010: woodland (51.69 mm) > grassland (43.75 mm) > farmland (31.56 mm).

3.2.2. Variations of Climate Change

Figure 5 shows the interannual variation of temperature, evapotranspiration, and precipitation from 1996 to 2020. The results indicated that the changes of annual mean temperature (Figure 5a) and annual evapotranspiration (Figure 5c) showed an overall upward trend, while the change trend of annual precipitation (Figure 5e) presented a slightly increasing trend. By comparing the average values of meteorological factors every 5 years, it was found that precipitation reached the maximum in 2011–2015 with 398.28 mm, and temperature and evapotranspiration were at a relatively low level with −0.64 °C and 850.51 mm, respectively. Therefore, under the influence of various meteorological factors, the water conservation reached the maximum in 2015. Precipitation reached a minimum value of 291.01 mm in 2001–2005, and temperature and evapotranspiration were at relatively high levels with −0.19 °C and 878.59 mm, respectively. Therefore, the water conservation was at a low level in 2005.
Figure 5b,d,f shows the mutability analysis of meteorological factors in the ecotone from 1996 to 2020. The results demonstrated that there were no significant trends in temperature and evapotranspiration. There was a significant decreasing trend in precipitation from 2000 to 2010. Therefore, the water conservation declined in 2005 and 2010.

3.3. Response of Water Conservation to Driving Factors

3.3.1. Response of Water Conservation to Driving Factors

Climate change increased water conservation by 66.09 mm for the forest–steppe ecotone from 2015 to 2010, with most pronounced effects in the southeast mountainous area (Figure 6a,d). Land use decreased water conservation by 9.25 mm for the whole region from 2010 to 2000 (Figure 6d). The changes in water conservation are mainly distributed in areas where the LULC is shifting to unused land (Figure 6b). Interactive effects between LULC and climate change had significant impacts on water conservation in the Hulunbuir forest–steppe ecotone with inhibitory effects on water conservation. Interactive effects decreased water conservation by 66.09 mm in the scenario analysis, with the most pronounced inhibitory impact occurring in the woodland region (Figure 6c,d). In short, climate change was the main factor influencing water conservation changes in the forest–steppe ecotone, followed by LULC and interactive effects.

3.3.2. Main Driving Factors of Water Conservation

We adopted the Pearson correlation analysis to explore the impact of various driving factors on water conservation (Figure 7). The correlation results demonstrated that climate factors have a more significant impact on water conservation. Precipitation (Pre) exhibited significant positive correlations with water conservation, and temperature (Tem) and potential evapotranspiration (ET0) exhibited significant negative correlations. This suggested that an increase in precipitation would promote water conservation, while changes to other climate factors would have an inhibitory effect on water conservation. In addition, elevation and velocity coefficient demonstrated a significant positive correlation with water conservation, illustrating that soil texture and topography could be important driver factors to water conservation. Overall, precipitation has a stronger correlation with water conservation than other driver factors.

4. Discussion

4.1. Temporal and Spatial Distribution of Water Yield, Water Conservation, and Model Verification

4.1.1. Temporal and Spatial Distribution of Water Yield and Water Conservation

This study provides a reference for understanding the temporal and spatial distribution of water-ecological service functions and the analysis of drivers in the fragile area of the ecotone. By comparing the model with the actual data, the parameters of the InVEST model were revised, which can accurately simulate the water ecological service function of the ecotone. The temporal and spatial distribution of water yield and water conservation in the ecotone had changed considerably within the last 20 years, and the interannual variations initially decreased and then increased. The water yield and water conservation demonstrated a decreasing trend with a decrease of 57.9% and 58.6% respectively, from 2000 to 2010. There was an increasing trend for water yield and water conservation with increase of 73.4% and 87.6%, respectively, from 2010 to 2020. Water yield and water conservation were lowest in 2010 with 33.12 × 108 m3 and 17.16 × 108 m3, respectively, and were highest in 2015 with 81.47 × 108 m3 and 46.33 × 108 m3, respectively (Figure 2f and Figure 3f). This result was similar with the results of the previous study [65]. For the spatial distribution of water yield and water conservation, the water ecological service function of mountains and hills in the southeast was higher than that of plains and platforms in the northwest. This result was consistent with the study of Dennedy-Frank, et al. [31]. Water yield and water conservation were closely related to the nature of ecosystem types. Due to the spatial heterogeneity of geographical location, climate conditions, and vegetation cover, the water conservation of each land use also demonstrated obvious differences. The water conservation of the ecotone reached the highest in 2015, and the water conservation of the main land uses was: woodland (172.51 mm) > grassland (98.1 mm) > farmland (63.15 mm). There was a minimum value in 2010, and the water conservation of the main land uses was: woodland (51.69 mm) > grassland (43.75 mm) > farmland (31.56 mm) (Table S1). This result was the same as that of the previous study [66].

4.1.2. Parameter Z and Model Validation

The water conservation function is an important part of the regulation function of the watershed ecosystem, which involves the sustainable development and ecological security of regional water resources [22]. In this study, the water yield module of the InVEST model was used to assess the water conservation function of the ecotone. The model utilized a simplified confluence process that ignored the interaction between surface water and groundwater. The water yield was the precipitation in each grid unit minus the actual evapotranspiration. Meanwhile, the water yield was corrected by the Topographic index, soil saturated hydraulic conductivity, and velocity coefficient to obtain the water conservation [52,67]. Compared with the traditional water yield estimation method, the InVEST model requires less data, which is easy to obtain, and the model can also well express the results of spatial heterogeneity caused by various factors in actual experience. When the model is widely used in ecological and hydrological research, its good performance must be guaranteed [68]. The accuracy and validity of the InVEST model largely depend on the parameter Z, which can represent regional precipitation distribution and other hydrogeological characteristics. However, as an empirical constant, the choice of the parameter Z is uncertain and has a wide range of choices, which has a significant impact on the results [35]. Using the invest model manual to calculate the parameter Z, there is a large gap with the actual data, so the accuracy of the model still cannot be well expressed. Based on the data of the Inner Mongolia Water Resources Bulletin, this study repeatedly verified the water yield results of the Invest model. After running the model 50 times, the water yield difference of the Hulunbuir forest–steppe ecotone was obtained from 2000 to 2020. Then statistical fitting was performed (R2 = 0.99), and it was found that when the parameter Z was 1.7, the difference between the simulated water yield and the actual data was the smallest (Figure S1). The simulation results demonstrated that the range of water yield in the Hulunbuir forest–steppe ecotone was 33.12 × 108 m3–81.47 × 108 m3 (Figure 2f), and the range of water conservation was 17.16 × 108m3–46.33 × 108 m3 (Figure 3f) in the last 20 years. The parameter Z in the results of this study can be applied to other research areas with similar geographical location [53].

4.2. The Main Driving Factors of Water Conservation in the Forest–Steppe Ecotone

Water conservation is one of the important regulating services provided by the ecosystem. On the spatial scale, the water conservation function can realize the mutual transformation of surface runoff, soil water conservation, and underground runoff, thus regulating regional water circulation [69,70]. On the temporal scale, the function can replenish water in dry seasons, reduce surface runoff in flood seasons, effectively prevent sedimentation of rivers and lakes, and ensure the safety of the drinking water sources [69,71,72]. However, regional water conservation is mainly affected by climate factors and human activities [62,63]. In order to explore the impact of these driver factors on water conservation, this study compared the no LULC change scenario (climate change) and the LULC change scenario (no climate change). The results demonstrated that climate changes in water conservation were more than LULC changes (Figure 6). This finding is consistent with previous results [18,73,74]. There are some reasons to explain the smaller contribution of LULC change. Firstly, the scale of LULC change is small and the change process is complex, which has little effect on water conservation [75]. Furthermore, different land use conservation patterns cause both negative and positive impacts on water conservation. The conversion of grassland to farmland decreased water conservation, while the conversion of farmland to woodland increased water conservation (Table S1). In contrast, climate change could directly modify surface runoff and have a significant effect on water conservation [18]. The results of the correlation demonstrated that precipitation was an important driver of water conservation. It was consistent with many other studies that emphasized the importance of precipitation to the InVEST model [38,45,46,53,76]. At the same time, it was also found that the impact of meteorological factors on water conservation was greater than that of human interference, which was similar with the previous study [18,73,74]. According to the principle of water balance, precipitation and actual evapotranspiration were the important links that determined the water conservation, and precipitation was mainly controlled by natural conditions, while human activities had little influence on precipitation. However, actual evapotranspiration was influenced by land use, temperature, wind speed, relative humidity, and sunshine duration [77]. Land use was greatly affected by human activities, but their change process was complex and has little impact on water conservation [75]. The conversion between different land uses may increase or reduce water conservation resulting in an overall insignificant change. The conversion of farmland to woodland increased water conservation, while the conversion of grassland to farmland decreased water conservation. In addition, the land use change in the Hulunbuir forest–steppe ecotone in the recent 10 years was not obvious, and the influence of climate factors can easily exceed that of land use.

4.3. Significance and Limitations of This Study

Invest model is widely used to evaluate water yield in different rivers, wetlands, mountains, and hills all over the world, but it is still not used to study the water yield evaluation of ecotones [34,42,43]. In this study, the changes of water conservation in the Hulunbuir forest–steppe ecotone from 2000 to 2020 were studied, and various factors affecting the water conservation of the ecotone were analyzed, which filled the research gap of the water conservation of ecotone. The improvement of water conservation demonstrated that the water storage and regulation function of the regional ecosystem were enhanced, which promoted the positive feedback of hydrological process, and it was conducive to the development of ecosystem services. On the contrary, it will interfere the sustainable development of ecosystem and services [78]. The Hulunbuir forest–steppe ecotone is a channel for water resources exchange between two important ecological function areas and an important ecological barrier area in the north of China, which is of strategic importance for maintaining sustainable development and water resources management planning in northeast and even north China. However, the water conservation of the Hulunbuir forest–steppe ecotone has decreased in the past 20 years, which indicated the deterioration of the ecological environment in this area [10,11,12,13]. Therefore, it is necessary to strengthen the awareness of the ecological environment and water resources protection and take effective measures in time. At present, there still exists some limitations in the study of water conservation in the Hulunbuir forest–steppe ecotone based on the Invest model, which mainly include: (1) The response of each driving factor to water conservation is discussed in the research, but the research on the specific mechanism of how each factor affects water conservation is limited. (2) The applicability of the model in the whole Hulunbuir region needs further verification. Thus, the model has better application scenarios and increases the general applicability of the model.
Authors should discuss the results and how they can be interpreted from the perspective of previous studies and of the working hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted.

5. Conclusions

This study used the InVEST model to analyze the water yield and water conservation of the Hulunbuir forest–steppe ecotone from 2000 to 2020, and determined the comprehensive effects and relative importance of land use and climate change in determining the water conservation of the ecosystem. The overall trend of water yield and water conservation in the forest–steppe ecotone first decreased and then increased in the past 20 years. The water yield and water conservation reached the lowest level in 2010 and reached the highest level in 2015. The land use transferred obviously from 2000 to 2010. Most of the land use types with the high water conservation capacity were converted into unused land. These transformations intensified the reduction of the water conservation. Climatic factors were the main factors affecting the water conservation of the forest–steppe ecotone. Precipitation had a greater impact on water conservation than potential evapotranspiration and temperature. In addition, the model parameters used in this study can also provide reference for the study of related regions under similar climatic conditions. The modified InVEST model can be applied to other regions with similar climatic and hydrogeological backgrounds when lacking the relevant data.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f13122039/s1, Figure S1: The difference between the simulated and measured water resources; Figure S2: Spatial distribution of land use in forest–steppe ecotone in 2020; Table S1: Water conservation of land use in Hulunbuir forest–steppe ecotone from 2000 to 2020.

Author Contributions

P.M.: Conceptualization, Methodology, Writing—Original Draft, Writing—Review and Editing. S.L.: Funding acquisition and Writing—Review and Editing. Z.D.: Resources and Investigation. Z.Z.: Formal analysis. J.H.: Conceptualization, Methodology and Writing—Review and Editing. D.S.: Conceptualization, Methodology and Writing—Review and Editing. J.Z.: Writing—Review and Editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the Fundamental Research Funds for the Central Universities (BLX202019).

Data Availability Statement

Not applicable.

Acknowledgments

The experiment was carried out at the National Environmental Protection Hulunbuir Forest–steppe Ecotone Observation and Research Station of China. Thanks staff from Huihe National Nature Reserve bureau for offering their help.

Conflicts of Interest

The authors declare that they have no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

References

  1. Holland, M.M. SCOPE/MAB Technical Consultations on Landscape Boundaries: Report of a SCOPE/MAB Workshop on Ecotones. Biol. Int. 1988, 17, 106. [Google Scholar]
  2. Shea, M.E.; Mladenoff, D.J.; Clayton, M.K.; Berg, S.; Elza, H. Pattern of Tree Species Co-Occurrence in an Ecotone Responds to Spatially Variable Drivers. Landsc. Ecol. 2022, 37, 2327–2342. [Google Scholar] [CrossRef]
  3. Di Castri, F. A New Look at Ecotones: Emerging International Projects on Landscape Boundaries. Biol. Int. 1988, 1–163. [Google Scholar]
  4. Holland, M. Ecotones: The Role of Landscape Boundaries in the Management and Restoration of Changing Environments; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2012. [Google Scholar]
  5. Myster, R.W. Tree Invasion and Establishment in Old Fields at Hutcheson Memorial Forest. Bot. Rev. 1993, 59, 251–272. [Google Scholar] [CrossRef]
  6. Myster, R.W. Post-Agricultural Succession in the Neotropics; Springer: Berlin/Heidelberg, Germany, 2008. [Google Scholar]
  7. Oliveras, I.; Malhi, Y. Many Shades of Green: The Dynamic Tropical Forest–Savannah Transition Zones. Philos. Trans. R. Soc. B Biol. Sci. 2016, 371, 20150308. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Li, S.-H.; Sun, J. Optical Dating of Holocene Dune Sands from the Hulun Buir Desert, Northeastern China. Holocene 2006, 16, 457–462. [Google Scholar] [CrossRef]
  9. Zhang, R.; Yeh, E.T.; Tan, S. Marketization Induced Overgrazing: The Political Ecology of Neoliberal Pastoral Policies in Inner Mongolia. J. Rural Stud. 2021, 86, 309–317. [Google Scholar] [CrossRef]
  10. Bai, M.; Mo, X.; Liu, S.; Hu, S. Detection and Attribution of Lake Water Loss in the Semi-Arid Mongolian Plateau—A Case Study in the Lake Dalinor. Ecohydrology 2021, 14, e2251. [Google Scholar] [CrossRef]
  11. Tao, S.; Fang, J.; Zhao, X.; Zhao, S.; Shen, H.; Hu, H.; Tang, Z.; Wang, Z.; Guo, Q. Rapid Loss of Lakes on the Mongolian Plateau. Proc. Natl. Acad. Sci. USA 2015, 112, 2281–2286. [Google Scholar] [CrossRef] [Green Version]
  12. Xuan, W.; Ziqian, X.; Mindi, L.; Guiting, Z.; Xinzhe, N.; Honghua, R. Dynamic Analysis of the Wetlands in Hulunbeier Region Basedon Remote Sensing from 1999 to 2010. Wetl. Sci. Manag. 2014, 2, 53–57. [Google Scholar]
  13. Zheng, Y.; Liu, H.; Zhuo, Y.; Li, Z.; Liang, C.; Wang, L. Dynamic Changes and Driving Factors of Wetlands in Inner Mongolia Plateau, China. PLoS ONE 2019, 14, e0221177. [Google Scholar] [CrossRef] [Green Version]
  14. Wei, M.; Li, H.; Akram, M.A.; Dong, L.; Sun, Y.; Hu, W.; Gong, H.; Zhao, D.; Xiong, J.; Yao, S.; et al. Quantifying Drought Resistance of Drylands in Northern China from 1982 to 2015: Regional Disparity in Drought Resistance. Forests 2022, 13, 100. [Google Scholar] [CrossRef]
  15. Sun, Y.; Sun, Y.; Yao, S.; Akram, M.A.; Hu, W.; Dong, L.; Li, H.; Wei, M.; Gong, H.; Xie, S.; et al. Impact of Climate Change on Plant Species Richness across Drylands in China: From Past to Present and into the Future. Ecol. Indic. 2021, 132, 108288. [Google Scholar] [CrossRef]
  16. Huntington, T.G. Evidence for Intensification of the Global Water Cycle: Review and Synthesis. J. Hydrol. 2006, 319, 83–95. [Google Scholar] [CrossRef]
  17. Chen, Z.; Zhu, R.; Yin, Z.; Feng, Q.; Yang, L.; Wang, L.; Lu, R.; Fang, C. Hydrological Response to Future Climate Change in a Mountainous Watershed in the Northeast of Tibetan Plateau. J. Hydrol. Reg. Stud. 2022, 44, 101256. [Google Scholar] [CrossRef]
  18. Lang, Y.; Song, W.; Zhang, Y. Responses of the Water-Yield Ecosystem Service to Climate and Land Use Change in Sancha River Basin, China. Phys. Chem. Earth Parts ABC 2017, 101, 102–111. [Google Scholar] [CrossRef]
  19. Barros, V.R.; Field, C.B.; Dokken, D.J.; Mastrandrea, M.D.; Mach, K.J.; Bilir, T.E.; Chatterjee, M.; Ebi, K.L.; Estrada, Y.O.; Genova, R.C.; et al. Climate Change 2014 Impacts, Adaptation, and Vulnerability Part B: Regional Aspects: Working Group II Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In Climate Change 2014: Impacts, Adaptation and Vulnerability: Part B: Regional Aspects: Working Group II Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2014; pp. 1–1820. [Google Scholar]
  20. Legesse, D.; Vallet-Coulomb, C.; Gasse, F. Hydrological Response of a Catchment to Climate and Land Use Changes in Tropical Africa: Case Study South Central Ethiopia. J. Hydrol. 2003, 275, 67–85. [Google Scholar] [CrossRef]
  21. Liu, H.; Li, Y.; Josef, T.; Zhang, R.; Huang, G. Quantitative Estimation of Climate Change Effects on Potential Evapotranspiration in Beijing during 1951–2010. J. Geogr. Sci. 2014, 24, 93–112. [Google Scholar] [CrossRef]
  22. Pessacg, N.; Flaherty, S.; Brandizi, L.; Solman, S.; Pascual, M. Getting Water Right: A Case Study in Water Yield Modelling Based on Precipitation Data. Sci. Total Environ. 2015, 537, 225–234. [Google Scholar] [CrossRef]
  23. Cavalcante Júnior, R.G.; Vasconcelos Freitas, M.A.; da Silva, N.F.; de Azevedo Filho, F.R. Sustainable Groundwater Exploitation Aiming at the Reduction of Water Vulnerability in the Brazilian Semi-Arid Region. Energies 2019, 12, 904. [Google Scholar] [CrossRef] [Green Version]
  24. Lopez, A.B.; Martin, A.; Killeen, B.; Iversen, C.; Russo, G.; Andersen, H.K.; Daniell, J.; Galea, L.; Giannini, M.; Jol, A.; et al. The European Environment State and Outlook 2020. Eur. Environ. 2020, 2021. [Google Scholar]
  25. Song, W.; Deng, X.; Yuan, Y.; Wang, Z.; Li, Z. Impacts of Land-Use Change on Valued Ecosystem Service in Rapidly Urbanized North China Plain. Ecol. Model. 2015, 318, 245–253. [Google Scholar] [CrossRef]
  26. Jin, G.; Wang, P.; Zhao, T.; Bai, Y.; Zhao, C.; Chen, D. Reviews on Land Use Change Induced Effects on Regional Hydrological Ecosystem Services for Integrated Water Resources Management. Phys. Chem. Earth Parts ABC 2015, 89, 33–39. [Google Scholar] [CrossRef]
  27. Deng, X.; Li, Z.; Huang, J.; Shi, Q.; Li, Y. A Revisit to the Impacts of Land Use Changes on the Human Wellbeing via Altering the Ecosystem Provisioning Services. Adv. Meteorol. 2013, 2013, 907367. [Google Scholar] [CrossRef]
  28. Bai, Y.; Ochuodho, T.O.; Yang, J. Impact of Land Use and Climate Change on Water-Related Ecosystem Services in Kentucky, USA. Ecol. Indic. 2019, 102, 51–64. [Google Scholar] [CrossRef]
  29. Chuai, X.; Huang, X.; Wu, C.; Li, J.; Lu, Q.; Qi, X.; Zhang, M.; Zuo, T.; Lu, J. Land Use and Ecosystems Services Value Changes and Ecological Land Management in Coastal Jiangsu, China. Habitat Int. 2016, 57, 164–174. [Google Scholar] [CrossRef]
  30. Cong, W.; Sun, X.; Guo, H.; Shan, R. Comparison of the SWAT and InVEST Models to Determine Hydrological Ecosystem Service Spatial Patterns, Priorities and Trade-Offs in a Complex Basin. Ecol. Indic. 2020, 112, 106089. [Google Scholar] [CrossRef]
  31. Dennedy-Frank, P.J.; Muenich, R.L.; Chaubey, I.; Ziv, G. Comparing Two Tools for Ecosystem Service Assessments Regarding Water Resources Decisions. J. Environ. Manag. 2016, 177, 331–340. [Google Scholar] [CrossRef] [Green Version]
  32. Kim, G.; Lim, C.-H.; Kim, S.; Lee, J.; Son, Y.; Lee, W.-K. Effect of National-Scale Afforestation on Forest Water Supply and Soil Loss in South Korea, 1971–2010. Sustainability 2017, 9, 1017. [Google Scholar] [CrossRef] [Green Version]
  33. Ochoa, V.; Urbina-Cardona, N. Tools for Spatially Modeling Ecosystem Services: Publication Trends, Conceptual Reflections and Future Challenges. Ecosyst. Serv. 2017, 26, 155–169. [Google Scholar] [CrossRef]
  34. Su, C.; Fu, B. Evolution of Ecosystem Services in the Chinese Loess Plateau under Climatic and Land Use Changes. Glob. Planet. Chang. 2013, 101, 119–128. [Google Scholar] [CrossRef]
  35. Sharp, R.; Tallis, H.; Ricketts, T.; Guerry, A.; Wood, S.; Chaplin-Kramer, R.; Nelson, E.; Ennaanay, D.; Wolny, S.; Olwero, N.; et al. InVEST 3.3. 3 User’s Guide; The Natural Capital Project; Stanford University: Stanford, CA, USA; University of Minnesota: Minneapolis, MN, USA; The Nature Conservancy: Arlington, VA, USA; World Wildlife Fund: Gland, Switzerland, 2016. [Google Scholar]
  36. Di Febbraro, M.; Sallustio, L.; Vizzarri, M.; De Rosa, D.; De Lisio, L.; Loy, A.; Eichelberger, B.; Marchetti, M. Expert-Based and Correlative Models to Map Habitat Quality: Which Gives Better Support to Conservation Planning? Glob. Ecol. Conserv. 2018, 16, e00513. [Google Scholar] [CrossRef]
  37. Willaert, T.; García-Alegre, A.; Queiroga, H.; Cunha-e-Sá, M.A.; Lillebø, A.I. Measuring Vulnerability of Marine and Coastal Habitats’ Potential to Deliver Ecosystem Services: Complex Atlantic Region as Case Study. Front. Mar. Sci. 2019, 6, 199. [Google Scholar] [CrossRef]
  38. Yang, D.; Liu, W.; Tang, L.; Chen, L.; Li, X.; Xu, X. Estimation of Water Provision Service for Monsoon Catchments of South China: Applicability of the InVEST Model. Landsc. Urban Plan. 2019, 182, 133–143. [Google Scholar] [CrossRef]
  39. Zhao, M.; He, Z.; Du, J.; Chen, L.; Lin, P.; Fang, S. Assessing the Effects of Ecological Engineering on Carbon Storage by Linking the CA-Markov and InVEST Models. Ecol. Indic. 2019, 98, 29–38. [Google Scholar] [CrossRef]
  40. Budyko, M.I. The Heat Balance of the Earth’s Surface. Sov. Geogr. 1961, 2, 3–13. [Google Scholar] [CrossRef]
  41. Dou, P.; Zuo, S.; Ren, Y.; Rodriguez, M.J.; Dai, S. Refined Water Security Assessment for Sustainable Water Management: A Case Study of 15 Key Cities in the Yangtze River Delta, China. J. Environ. Manag. 2021, 290, 112588. [Google Scholar] [CrossRef]
  42. Hu, W.; Li, G.; Li, Z. Spatial and Temporal Evolution Characteristics of the Water Conservation Function and Its Driving Factors in Regional Lake Wetlands—Two Types of Homogeneous Lakes as Examples. Ecol. Indic. 2021, 130, 108069. [Google Scholar] [CrossRef]
  43. Wang, L.-J.; Ma, S.; Jiang, J.; Zhao, Y.-G.; Zhang, J.-C. Spatiotemporal Variation in Ecosystem Services and Their Drivers among Different Landscape Heterogeneity Units and Terrain Gradients in the Southern Hill and Mountain Belt, China. Remote Sens. 2021, 13, 1375. [Google Scholar] [CrossRef]
  44. Goyal, M.K.; Khan, M. Assessment of Spatially Explicit Annual Water-Balance Model for Sutlej River Basin in Eastern Himalayas and Tungabhadra River Basin in Peninsular India. Hydrol. Res. 2017, 48, 542–558. [Google Scholar] [CrossRef] [Green Version]
  45. Sánchez-Canales, M.; López Benito, A.; Passuello, A.; Terrado, M.; Ziv, G.; Acuña, V.; Schuhmacher, M.; Elorza, F.J. Sensitivity Analysis of Ecosystem Service Valuation in a Mediterranean Watershed. Sci. Total Environ. 2012, 440, 140–153. [Google Scholar] [CrossRef] [PubMed]
  46. Terrado, M.; Acuña, V.; Ennaanay, D.; Tallis, H.; Sabater, S. Impact of Climate Extremes on Hydrological Ecosystem Services in a Heavily Humanized Mediterranean Basin. Ecol. Indic. 2014, 37, 199–209. [Google Scholar] [CrossRef]
  47. Li, S.; Yang, H.; Lacayo, M.; Liu, J.; Lei, G. Impacts of Land-Use and Land-Cover Changes on Water Yield: A Case Study in Jing-Jin-Ji, China. Sustainability 2018, 10, 960. [Google Scholar] [CrossRef] [Green Version]
  48. IUSS Working Group WRB. World Reference Base for Soil Resources. World Soil Resour. Rep. 2006, 103. [Google Scholar] [CrossRef]
  49. He, P.; Fontana, S.; Ma, C.; Liu, H.; Xu, L.; Wang, R.; Jiang, Y.; Li, M.-H. Using Leaf Traits to Explain Species Co-Existence and Its Consequences for Primary Productivity across a Forest–steppe Ecotone. Sci. Total Environ. 2022, 2022, 160139. [Google Scholar] [CrossRef]
  50. Donohue, R.J.; Roderick, M.L.; McVicar, T.R. Roots, Storms and Soil Pores: Incorporating Key Ecohydrological Processes into Budyko’s Hydrological Model. J. Hydrol. 2012, 436, 35–50. [Google Scholar] [CrossRef]
  51. Zhang, L.; Hickel, K.; Dawes, W.; Chiew, F.H.; Western, A.; Briggs, P. A Rational Function Approach for Estimating Mean Annual Evapotranspiration. Water Resour. Res. 2004, 40, 2710. [Google Scholar] [CrossRef]
  52. Bao, Y.-B.; Li, T.; Liu, H.; Ma, T.; Wang, H.; Liu, K.; Shen, X.; Liu, X. Spatial and Temporal Changes of Water Conservation of Loess Plateau in Northern Shaanxi Province by InVEST Model. Geogr. Res. 2016, 35, 664–676. [Google Scholar]
  53. Hamel, P.; Guswa, A.J. Uncertainty Analysis of a Spatially Explicit Annual Water-Balance Model: Case Study of the Cape Fear Basin, North Carolina. Hydrol. Earth Syst. Sci. 2015, 19, 839–853. [Google Scholar] [CrossRef] [Green Version]
  54. Hu, W.; Li, G.; Gao, Z.; Jia, G.; Wang, Z.; Li, Y. Assessment of the Impact of the Poplar Ecological Retreat Project on Water Conservation in the Dongting Lake Wetland Region Using the InVEST Model. Sci. Total Environ. 2020, 733, 139423. [Google Scholar] [CrossRef]
  55. Guo, B.; Zhang, J.; Meng, X.; Xu, T.; Song, Y. Long-Term Spatio-Temporal Precipitation Variations in China with Precipitation Surface Interpolated by ANUSPLIN. Sci. Rep. 2020, 10, 81. [Google Scholar] [CrossRef] [Green Version]
  56. Fan, J.; McConkey, B.; Wang, H.; Janzen, H. Root Distribution by Depth for Temperate Agricultural Crops. Field Crops Res. 2016, 189, 68–74. [Google Scholar] [CrossRef] [Green Version]
  57. Feng, H.; Zhou, J.; Zhou, A.; Bai, G.; Li, Z.; Chen, H.; Su, D.; Han, X. Grassland Ecological Restoration Based on the Relationship between Vegetation and Its Below-Ground Habitat Analysis in Steppe Coal Mine Area. Sci. Total Environ. 2021, 778, 146221. [Google Scholar] [CrossRef]
  58. Fang, X.; Ren, L.; Li, Q.; Zhu, Q.; Shi, P.; Zhu, Y. Hydrologic Response to Land Use and Land Cover Changes within the Context of Catchment-Scale Spatial Information. J. Hydrol. Eng. 2013, 18, 1539–1548. [Google Scholar] [CrossRef]
  59. Guo, H.; Hu, Q.; Jiang, T. Annual and Seasonal Streamflow Responses to Climate and Land-Cover Changes in the Poyang Lake Basin, China. J. Hydrol. 2008, 355, 106–122. [Google Scholar] [CrossRef]
  60. Ma, X.; Xu, J.; Luo, Y.; Prasad Aggarwal, S.; Li, J. Response of Hydrological Processes to Land-Cover and Climate Changes in Kejie Watershed, South-West China. Hydrol. Process. Int. J. 2009, 23, 1179–1191. [Google Scholar] [CrossRef]
  61. Mekonnen, D.F.; Duan, Z.; Rientjes, T.; Disse, M. Analysis of Combined and Isolated Effects of Land-Use and Land-Cover Changes and Climate Change on the Upper Blue Nile River Basin’s Streamflow. Hydrol. Earth Syst. Sci. 2018, 22, 6187–6207. [Google Scholar] [CrossRef] [Green Version]
  62. Mekonnen, Z.; Kassa, H.; Woldeamanuel, T.; Asfaw, Z. Analysis of Observed and Perceived Climate Change and Variability in Arsi Negele District, Ethiopia. Environ. Dev. Sustain. 2018, 20, 1191–1212. [Google Scholar] [CrossRef]
  63. Woldesenbet, T.A.; Elagib, N.A.; Ribbe, L.; Heinrich, J. Catchment Response to Climate and Land Use Changes in the Upper Blue Nile Sub-Basins, Ethiopia. Sci. Total Environ. 2018, 644, 193–206. [Google Scholar] [CrossRef]
  64. Yang, L.; Feng, Q.; Yin, Z.; Wen, X.; Si, J.; Li, C.; Deo, R.C. Identifying Separate Impacts of Climate and Land Use/Cover Change on Hydrological Processes in Upper Stream of Heihe River, Northwest China. Hydrol. Process. 2017, 31, 1100–1112. [Google Scholar] [CrossRef]
  65. Li, S.; Chen, J.; Xiang, J.; Pan, Y.; Huang, Z.; Wu, Y. Water Level Changes of Hulun Lake in Inner Mongolia Derived from Jason Satellite Data. J. Vis. Commun. Image Represent. 2019, 58, 565–575. [Google Scholar] [CrossRef]
  66. Pan, T.; Zuo, L.; Zhang, Z.; Zhao, X.; Sun, F.; Zhu, Z.; Liu, Y. Impact of Land Use Change on Water Conservation: A Case Study of Zhangjiakou in Yongding River. Sustainability 2020, 13, 22. [Google Scholar] [CrossRef]
  67. Fu, B.; Xu, P.; Wang, Y.; Peng, Y.; Ren, J. Spatial Pattern of Water Retention in Dujiangyan County. Acta Ecol. Sin. 2013, 33, 789–797. [Google Scholar]
  68. Power, M. The Predictive Validation of Ecological and Environmental Models. Ecol. Model. 1993, 68, 33–50. [Google Scholar] [CrossRef]
  69. Biao, Z.; Wenhua, L.; Gaodi, X.; Yu, X. Water Conservation of Forest Ecosystem in Beijing and Its Value. Ecol. Econ. 2010, 69, 1416–1426. [Google Scholar] [CrossRef]
  70. Núñez, D.; Nahuelhual, L.; Oyarzún, C. Forests and Water: The Value of Native Temperate Forests in Supplying Water for Human Consumption. Ecol. Econ. 2006, 58, 606–616. [Google Scholar] [CrossRef]
  71. Vose, J.M.; Sun, G.; Ford, C.R.; Bredemeier, M.; Otsuki, K.; Wei, X.; Zhang, Z.; Zhang, L. Forest Ecohydrological Research in the 21st Century: What Are the Critical Needs? Ecohydrology 2011, 4, 146–158. [Google Scholar] [CrossRef]
  72. Wang, X.; Shen, H.; Li, X.; Jing, F. Concepts, Processes and Quantification Methods of the Forest Water Conservation at the Multiple Scales. Acta Ecol. Sin. 2013, 33, 1019–1030. [Google Scholar] [CrossRef]
  73. Lian, X.; Qi, Y.; Wang, H.; Zhang, J.; Yang, R. Assessing Changes of Water Yield in Qinghai Lake Watershed of China. Water 2019, 12, 11. [Google Scholar] [CrossRef] [Green Version]
  74. Jiang, C.; Li, D.; Wang, D.; Zhang, L. Quantification and Assessment of Changes in Ecosystem Service in the Three-River Headwaters Region, China as a Result of Climate Variability and Land Cover Change. Ecol. Indic. 2016, 66, 199–211. [Google Scholar] [CrossRef]
  75. Nie, W.; Yuan, Y.; Kepner, W.; Nash, M.S.; Jackson, M.; Erickson, C. Assessing Impacts of Landuse and Landcover Changes on Hydrology for the Upper San Pedro Watershed. J. Hydrol. 2011, 407, 105–114. [Google Scholar] [CrossRef]
  76. Boithias, L.; Acuña, V.; Vergoñós, L.; Ziv, G.; Marcé, R.; Sabater, S. Assessment of the Water Supply: Demand Ratios in a Mediterranean Basin under Different Global Change Scenarios and Mitigation Alternatives. Sci. Total Environ. 2014, 470, 567–577. [Google Scholar] [CrossRef]
  77. Shirmohammadi, B.; Malekian, A.; Salajegheh, A.; Taheri, B.; Azarnivand, H.; Malek, Z.; Verburg, P.H. Impacts of Future Climate and Land Use Change on Water Yield in a Semiarid Basin in Iran. Land Degrad. Dev. 2020, 31, 1252–1264. [Google Scholar] [CrossRef]
  78. Li, M.; Liang, D.; Xia, J.; Song, J.; Cheng, D.; Wu, J.; Cao, Y.; Sun, H.; Li, Q. Evaluation of Water Conservation Function of Danjiang River Basin in Qinling Mountains, China Based on InVEST Model. J. Environ. Manag. 2021, 286, 112212. [Google Scholar] [CrossRef]
Forests 13 02039 g001 550
Figure 1. Study area of the Hulunbuir forest–steppe ecotone in China.
Figure 1. Study area of the Hulunbuir forest–steppe ecotone in China.
Forests 13 02039 g001
Forests 13 02039 g002 550
Figure 2. Temporal and spatial distribution of water yield in Hulunbuir forest–steppe ecotone from 2000 to 2020. (a–e) represent the spatial distribution of water yield in the Hulunbuir forest–steppe ecotone in 2000, 2005, 2010, 2015, and 2020, respectively. The water yield was reclassified to obtain 4 intervals according to quantiles, among which >186 mm was high water yield area, <123 mm was low water yield area (here referred to relatively high, medium, and low water yield areas), and (f) represents the time series of water yield in the Hulunbuir forest–steppe ecotone from 2000 to 2020.
Figure 2. Temporal and spatial distribution of water yield in Hulunbuir forest–steppe ecotone from 2000 to 2020. (a–e) represent the spatial distribution of water yield in the Hulunbuir forest–steppe ecotone in 2000, 2005, 2010, 2015, and 2020, respectively. The water yield was reclassified to obtain 4 intervals according to quantiles, among which >186 mm was high water yield area, <123 mm was low water yield area (here referred to relatively high, medium, and low water yield areas), and (f) represents the time series of water yield in the Hulunbuir forest–steppe ecotone from 2000 to 2020.
Forests 13 02039 g002
Forests 13 02039 g003 550
Figure 3. Temporal and spatial distribution of water conservation in Hulunbuir forest–steppe ecotone from 2000 to 2020. (a–e) represent the spatial distribution of water conservation in the Hulunbuir forest–steppe ecotone in 2000, 2005, 2010, 2015, and 2020, respectively. The water conservation was reclassified according to quantiles to obtain 4 intervals, among which >95 mm was a high water conservation area, <58 mm was a low water conservation area (here referred to relatively high, medium, and low water conservation areas), and (f) represents the time series of water conservation in the Hulunbuir forest–steppe ecotone from 2000 to 2020.
Figure 3. Temporal and spatial distribution of water conservation in Hulunbuir forest–steppe ecotone from 2000 to 2020. (a–e) represent the spatial distribution of water conservation in the Hulunbuir forest–steppe ecotone in 2000, 2005, 2010, 2015, and 2020, respectively. The water conservation was reclassified according to quantiles to obtain 4 intervals, among which >95 mm was a high water conservation area, <58 mm was a low water conservation area (here referred to relatively high, medium, and low water conservation areas), and (f) represents the time series of water conservation in the Hulunbuir forest–steppe ecotone from 2000 to 2020.
Forests 13 02039 g003
Forests 13 02039 g004 550
Figure 4. The sankey diagram of land use transfer matrix in Hulunbuir forest–steppe ecotone from 2000 to 2020.
Figure 4. The sankey diagram of land use transfer matrix in Hulunbuir forest–steppe ecotone from 2000 to 2020.
Forests 13 02039 g004
Forests 13 02039 g005 550
Figure 5. Time series variation and Mann-Kendall mutation detection of main climate indexes in forest–steppe ecotone. (a,c,e) showed the interannual variation of temperature, potential evapotranspiration, and precipitation, respectively. (b,d,f) showed Mann-Kendall mutation detection of temperature, potential evapotranspiration, and precipitation, respectively.
Figure 5. Time series variation and Mann-Kendall mutation detection of main climate indexes in forest–steppe ecotone. (a,c,e) showed the interannual variation of temperature, potential evapotranspiration, and precipitation, respectively. (b,d,f) showed Mann-Kendall mutation detection of temperature, potential evapotranspiration, and precipitation, respectively.
Forests 13 02039 g005
Forests 13 02039 g006 550
Figure 6. Spatial distributions and effect sizes of climate change, LULC and their interactive effects. (a–c) represented the spatial change in water conservation due to the separate effect of LULC change, climate change, and the combined effects of LULC change and climate change. (d) represented the analysis of variance (ANOVA) for water conservation in three scenarios. “****” represents the node.
Figure 6. Spatial distributions and effect sizes of climate change, LULC and their interactive effects. (a–c) represented the spatial change in water conservation due to the separate effect of LULC change, climate change, and the combined effects of LULC change and climate change. (d) represented the analysis of variance (ANOVA) for water conservation in three scenarios. “****” represents the node.
Forests 13 02039 g006
Forests 13 02039 g007 550
Figure 7. Correlation analysis of the driving factors of water conservation. Tem is temperature; Pre is precipitation; ET0 is potential evapotranspiration; DEM is digital elevation; PAWC is plant available water content.
Figure 7. Correlation analysis of the driving factors of water conservation. Tem is temperature; Pre is precipitation; ET0 is potential evapotranspiration; DEM is digital elevation; PAWC is plant available water content.
Forests 13 02039 g007
Table
Table 1. Acquisition and preprocessing of the data.
Table 1. Acquisition and preprocessing of the data.
DataData Source and Processing Method
PrecipitationThe meteorological data were obtained from the National Meteorological Information Center (http://data.cma.cn/) (accessed on 20 September 2021). We selected the monthly data of 41 meteorological stations in northeast China from 1996 to 2020, and obtained the required annual value data for each station by calculation. Then we calculated the average precipitation for each station from 1996–2000, 2001–2005, 2006–2010, 2011–2015 and 2016–2020 [54]. Finally, interpolation calculations were performed using Anusplin 4.3 software to generate precipitation raster data for the years 2000, 2005, 2010, 2015 and 2020 [55].
Potential evapotranspirationWe calculated the monthly potential evapotranspiration at each meteorological station using the Penman-Monteith formula recommended by Food and Agriculture Organization of the United Nations (FAO), and obtained the required annual data for each station by calculation. Then we calculated the average potential evapotranspiration for each station from 1996–2000, 2001–2005, 2006–2010, 2011–2015 and 2016–2020 [54]. Finally, interpolation calculations were performed using Anusplin 4.3 software to generate precipitation raster data for the years 2000, 2005, 2010, 2015 and 2020 [55].
Soil dataThe data mainly include soil type, soil texture (%clay, % silt, % sand, and % organic carbon) and soil depth, which are derived from the world soil database (HWSD v1.2) (https://www.fao.org) (accessed on 10 October 2021) constructed by Food and Agriculture Organization of the United Nations (FAO) and International Institute for Applied Systems Analysis (IIASA).
Plant available water content (PAWC)Calculated from soil texture: PAWC = 54.509 − 0.132sand% − 0.003(sand%)2 − 0.055silt% − 0.006(slit%)2 − 0.738clay% + 0.007(clay%)2 − 2.688OM% + 0.501(OM%)2, where OM% represents the content of soil organic matter [52].
Land use The data were obtained from the Resource and Environment Science and Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/) (accessed on 12 October 2021). We download the land use data of the study area in 2000, 2005, 2010, 2015, and 2020 from the website.
Digital elevation modelThe data were obtained from the Geospatial Data Cloud (http://www.gscloud.cn) (accessed on 15 October 2021), from which ASTER GDEM 30 m resolution elevation data were downloaded.
Topographic indexBased on the data of DEM and soil depth, the spatial analysis tool in ArcGIS was used for calculation.
Velocity coefficientData were obtained from the relevant studies and the InVEST model manual.
Soil saturated hydraulic conductivityIt was calculated using SPAW software based on soil texture data.
The parameter ZThe simulated water yield and the actual water resources in the Inner Mongolia Water Resources Bulletin were verified 10 times a year (50 times in total). When Z = 1.7, the simulated water yield is consistent with the public data (Figure S1).
Table
Table 2. Table of biophysical coefficients for the water yield module of the InVEST model.
Table 2. Table of biophysical coefficients for the water yield module of the InVEST model.
LucodeLULC_DescLULC_VegkcRoot_Depth (mm)
1Farmland10.65400 [56]
2Woodland113500
3Grassland10.65600 [57]
4Water011
5Residential00.31
6Unused land011
Note: The biophysical coefficient table was mainly for each land use attribute rather than grid unit attribute. Lucode was the land category code in each land use, which was consistent with the above land use grid data. LULC_desc was a descriptive name for land use. LULC_veg was vegetation covered land use, excluding wetlands, and zero was other land use, including wetlands, urban land, and water bodies. k c was the plant evapotranspiration coefficient for each land use. Root_depth was the maximum root depth of vegetation covered land use, and the unlabeled data in the table referred to the InVEST model manual [35].
Table
Table 3. Land use and climate scenario settings.
Table 3. Land use and climate scenario settings.
Land Use 2000Land Use 2010
Climate 2010S1S2
Climate 2015S3S4
Table
Table 4. Land use change in forest–steppe ecotone from 2000 to 2020.
Table 4. Land use change in forest–steppe ecotone from 2000 to 2020.
LULC Types20002005201020152020
104 km2%104 km2%104 km2%104 km2%104 km2%
Farmland0.5311.8%0.5412.0%0.6815.1%0.6815.1%0.6614.6%
Woodland1.3329.5%1.3429.7%1.4832.8%1.4832.8%1.4832.8%
Grassland2.4454.1%2.4153.4%1.6636.8%1.6536.6%1.6937.5%
Unused land0.163.5%0.173.8%0.6414.2%0.6414.2%0.6113.5%
Residential area0.030.7%0.030.7%0.030.7%0.040.9%0.051.1%
Water0.020.4%0.020.4%0.020.4%0.020.4%0.020.4%
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Ma, P.; Lyu, S.; Diao, Z.; Zheng, Z.; He, J.; Su, D.; Zhang, J. How Does the Water Conservation Function of Hulunbuir Forest–Steppe Ecotone Respond to Climate Change and Land Use Change? Forests 2022, 13, 2039. https://doi.org/10.3390/f13122039

AMA Style

Ma P, Lyu S, Diao Z, Zheng Z, He J, Su D, Zhang J. How Does the Water Conservation Function of Hulunbuir Forest–Steppe Ecotone Respond to Climate Change and Land Use Change? Forests. 2022; 13(12):2039. https://doi.org/10.3390/f13122039

Chicago/Turabian Style

Ma, Pu, Shihai Lyu, Zhaoyan Diao, Zhirong Zheng, Jing He, Derong Su, and Jingru Zhang. 2022. "How Does the Water Conservation Function of Hulunbuir Forest–Steppe Ecotone Respond to Climate Change and Land Use Change?" Forests 13, no. 12: 2039. https://doi.org/10.3390/f13122039

APA Style

Ma, P., Lyu, S., Diao, Z., Zheng, Z., He, J., Su, D., & Zhang, J. (2022). How Does the Water Conservation Function of Hulunbuir Forest–Steppe Ecotone Respond to Climate Change and Land Use Change? Forests, 13(12), 2039. https://doi.org/10.3390/f13122039

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop