Regional water conservation is the key to maintain water supply. Based on the data of 2000, 2010 and 2020, this paper takes Jingyu County in the Changbai Mountain as the study area. By land use conversion matrix, geostatistical analysis, InVEST model and scenario analysis, this paper aims to find out the variation of water conservation and the its related influencing factors in Changbai Mountain region. From 2000 to 2020, forest and cultivated land decreased, while grassland and developed land increased. The water conservation increased from 2.94 × 108 m3 to 4.83 × 108 m3, with a total increase of 64.29%. Forest has the highest average water conservation, followed by grassland, cultivated land and developed land. Climatic factors were important to 91.92% of the study area, whereas 6.22% of it was influenced by land use. When forest decrease in some areas, the main control factor of 48.71% of the area shifted into land use, that could lead to a sharp decrease in water conservation. Climate determines the overall spatial pattern of water conservation; land use determines the local characteristics by controlling runoff. The increase of water conservation is beneficial to improve the reserves and development potential of mineral water.

  • Application of InVEST model and scenario analysis in Changbai Mountain area.

  • Climate change is a dominant factor impacting water conservation.

  • Change of water conservation is more severe in area of land use conversion.

  • High water conservation capacity is conducive to mineral water exploitation.

  • Suggestions are put forward for mineral water resource management.

Graphical Abstract

Graphical Abstract

Water conservation is an important ecological service function, playing important role in improving the climate, regulating runoff, and maintaining balance and stability of the ecosystem (Sharafatmandrad & Mashizi 2021). It refers to the amount of water resources retained after evaporation and runoff and can be preserved in the ecosystem through plants, interception of litter and soil storage in a specific spatio-temporal range (Costanza et al. 2004). The amount of water conservation can reflect the capacity of water supply, soil and water retention and runoff regulation in the region (Xu et al. 2019).

The methods of precipitation storage, comprehensive storage capacity and water budget are widely used to evaluate water conservation. However, the precipitation storage method didn't distinguish the evapotranspiration by different vegetation (Hou et al. 2018), and the comprehensive water storage capacity method required a large demand for data (Zhang et al. 2012). While the water budget method utilized precipitation, evapotranspiration and runoff to estimate the water conservation by vegetation and soil with multiple advantages such as easy data acquisition and good adaptability to a Geographic Information System (GIS), thus applicable to wide range (Sun et al. 2018; Hu et al. 2021).

The InVEST model (Integrated Valuation of Ecosystem Services and trade-offs) developed by Stanford University, The Nature Conservancy (TNC) and World Wide Fund for Nature (WWF) (https://naturalcapitalproject.stanford.edu/software/invest), aims at providing a scientific basis for decision makers to weigh the benefits and impacts of human activities by simulating changes in the amount of ecosystem goods and values under different land cover scenarios (Sharp et al. 2020). The Water Yield module of InVEST model based on water budget method is widely used currently (Donohue et al. 2012; Gao et al. 2017). Scholars at home and abroad can use it to classify water conservation levels, discuss the characteristics of its intra-annual variation (Su & Fu 2013; Kim & Jung 2020), analyse the particularity of water conservation capacity in special climate and geomorphic areas (Lang et al. 2018; Yang et al. 2020), evaluate the importance of its influencing factors (Lang et al. 2017; Dai et al. 2020; Lian et al. 2020), and predict the future development trend (Shrestha et al. 2020). In addition, the model is applied to analyse the impact of cultivated conversion and afforestation on water conservation (Kim et al. 2017; Qi et al. 2019; Daneshi et al. 2021), explore the changes in water yield of the watershed by ecological management measures (such as the developed of protected areas) (Shoyama & Yamagata 2014; Mafios & Burney 2017). In addition, its combination with other models (such as CA-Markov) and parameters correction conducted recently makes it a more comprehensive approach. (Chu et al. 2018; Yang et al. 2019).

Among the above studies, forest ecosystem has always been the major focus of scholars’ attention due to its strong water conservation capacity important role in controlling the regional water cycle (Yu et al. 2017). When the changes of forest ecosystem happen, they will have great impact on the ecological environment, especially on the drinking water source, which may further affect its water supply function (Zhang et al. 2020). In China, balancing the protection and exploitation of water resources in forest ecosystems is a critical concern for policy makers. Changbai Mountain area is an important distribution area of forest and wetland ecosystem in China with rich mineral water resources. Jingyu County in this region is one of the three major mineral spring cities in the world, owning large spring flow and excellent water quality (Yan et al. 2015, 2016). At present, studies on water conservation in Changbai Mountain are mainly static analyses of annual water conservation, investigation on changes of different periods, and analysis involving dynamic law of water conservation capacity under climate or land use change scenarios are very limited. Therefore, in order to understand and optimize the further exploitation of mineral water resources in Changbai Mountain, it is important to clarify the mechanism of water conservation changes in the region first.

In view of the current research status and gaps, as well as urgent needs for future water resources and ecological protection, this paper calculated the water conservation of the study area and analysed its spatio-temporal change rules based on InVEST model. Then, three different climate and land use scenarios were created based on the data of 2000 and 2020, so as to study the impact of climatic and land use factors on water conservation capacity. Finally, the relationship between water conservation capacity and local mineral water resources was discussed.

Study area

The study area is Jingyu County in the west of Changbai Mountain, located in the southeast of Jilin Province, with a geographic range of E126°30′ ∼ 127°16′, N42°06′ ∼ 42°48′, and an area of 3,094.4 km2 (Figure 1). It's a humid zone in the cold temperate zone, with an average annual temperature of 4.1 °C, average annual precipitation of 792 mm, and average annual sunshine duration of 2,303 hours. The river network in this area is dense and the surface water resources are abundant.
Figure 1

Spatial location of Jingyu Country and its topographic features.

Figure 1

Spatial location of Jingyu Country and its topographic features.

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Jingyu is an important source of mineral water in China. A total of 84 springs have been discovered in this county with large amount of mineral water and good water quality (Bian et al. 2019; Wang et al. 2021). Some mineral water also contains strontium and other trace elements. The southwest part of study area is the concentrated distribution area of mineral water. The number of mineral springs in the area is large, and the water quality is good. The local famous mineral springs such as Qinglong Spring and Baijing spring are exposed here.

Research methods

First, a land use conversion matrix was established to analyse the change of land use types. Second, the InVEST software was used to quantitatively calculate the water conservation in different periods and analyse the change trend. Then, the influencing factors of water conservation were analysed, and the influencing mechanism was determined by statistical analysis of ArcGIS and correlation analysis of SPSS. The relative importance of climatic and land use factors was determined by scenario analysis with InVEST and ArcGIS, and the impacts of climate change and human activities on water conservation were analysed. Finally, the relationship between water content and mineral water resources in the study area was discussed. The research roadmap is shown in Figure 2.
Figure 2

Research roadmap.

Figure 2

Research roadmap.

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Calculation of water yield

In this paper, the InVEST software (version 3.9.0) was used to calculate the water yield. The model is based on the Budyko curve and annual average precipitation (Budyko & Miller 1974), and the water yield is assumed to be the water collected to the outlet section of the basin by runoff. The model treats surface water, groundwater, and baseflow as a whole and calculates water yield by subtracting actual evaporation from precipitation (Sharp et al. 2020). Figure 3 shows the working principle of the model.
Figure 3

Working principle of the model.

Figure 3

Working principle of the model.

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The calculation formula of water yield is as follows:
(1)
where, Yx is the annual water yield of grid x; AETx is the annual actual evapotranspiration of grid x; Px is the annual precipitation of grid x. AETx is determined by the following formula:
(2)
where, PETx is the annual potential evapotranspiration of grid x; ωx is the non-physical parameters of natural climate-soil properties; PETx and ωx are calculated by the following formula:
(3)
(4)
where, ET0x is the reference evapotranspiration of grid x; is the evapotranspiration coefficient of specific land use types in grid x; AWCx represents soil effective water content of grid x; Z is the seasonal coefficient. AWCx is calculated as follows:
(5)
(6)
where, PAWC represents the plants available water content; SAN, SIL, CLA and C are the contents of sand, silt, clay and organic matter of soil respectively.
The ET0 can be calculated by the improved Hargreaves formula:
(7)
where, Tmax and Tmin are monthly maximum temperature and minimum temperature (°C); Ra is the amount of extra-terrestrial radiation (mm/d). In Hargreaves’ study, the annual monthly radiation values in each latitude zone were given (Hargreaves 1994). C, E and T are empirical parameters, which are determined according to the correction values of Hu et al. (2011).

Calculation of water conservation

The water yield calculated by InVEST model includes surface runoff, soil water, plant interception and litter capacity. According to the principle of water balance, the amount of water that pixel x can keep in a year is water conservation, which is the amount of water yield minus the surface runoff. The surface runoff is calculated according to precipitation and runoff coefficient. The calculation formula is as follows:
(8)
(9)

where, WRx is the amount of water conservation of grid x; Rsx is the surface runoff of grid x; α is runoff coefficient.

Scenario analysis

Scenario analysis provides a clear analysis of the impact of various influencing factors on the results. In this paper, the relative importance index (RII) is used to calculate the relative impact of climatic and land use factors on ecological services (Yang et al. 2019). The essence of this method is scenario analysis. Three scenarios were set up in this study. Scenario 1: Environmental conditions in 2000, i.e., climate and land use data are the actual conditions in 2000; Scenario 2: Land use conditions within 20 years are the only drivers of water conservation changes, i.e. climate data in 2000 and land use data in 2020; Scenario 3: Climate conditions within 20 years are the only driving factors affecting water conservation, i.e., climate data in 2020 and land use data in 2000. In these three scenarios, Equation (10) is used to calculate RII. RII greater than 0 means that land use change is more important than climate change; RII less than 0 means that climate change is more important than land use change; RII equal to 0 means that these two factors have equal influence (Bai et al. 2019).
(10)
where, ES is the amount of water conservation in each scenario.

Data requirements

The data needed to run InVEST model are annual precipitation grid, annual reference evapotranspiration grid, land use type grid, soil maximum root depth grid, PAWC (Plants Available Water Content) grid, watershed vector map, biophysical series table and seasonal coefficient (Z coefficient) of Jingyu from 2000 to 2020. The data sources are shown in Table 1.

Table 1

Data sources

Data typeData sourcesAccuracy
Annual precipitation China Meteorological Data Service Centre (http://data.cma.cn/14 stations 
Annual reference evapotranspiration 
Land use GlobeLand30 (http://globeland30.org/) 30 m 
Watershed Geospatial Data Cloud site, Chinese Academy of Sciences. (http://www.gscloud.cn30 m 
Maximum root depth National Tibetan Plateau Data Center (http://www.tpdc.ac.cn/) (Meng & Wang 20181 km 
PAWC 
Data typeData sourcesAccuracy
Annual precipitation China Meteorological Data Service Centre (http://data.cma.cn/14 stations 
Annual reference evapotranspiration 
Land use GlobeLand30 (http://globeland30.org/) 30 m 
Watershed Geospatial Data Cloud site, Chinese Academy of Sciences. (http://www.gscloud.cn30 m 
Maximum root depth National Tibetan Plateau Data Center (http://www.tpdc.ac.cn/) (Meng & Wang 20181 km 
PAWC 

In addition, based on previous research results on water conservation capacity of forest ecosystem in Northeast China and empirical data, biophysical table and runoff coefficient are determined as shown in Table 2 (Zhang et al. 2016; Zhang 2019).

Table 2

Biophysical table and runoff coefficient

Land use codeLand use typeLULC_vegaRoot depth (mm)Evapotranspiration coefficientRunoff coefficient
10 Cultivated 1,500 0.65 0.6 
20 Forest 3,500 0.2 
30 Grassland 2,000 0.65 0.35 
50 Wetlands 0.8 
60 Water 1.1 0.9 
80 Developed 0.3 0.85 
Land use codeLand use typeLULC_vegaRoot depth (mm)Evapotranspiration coefficientRunoff coefficient
10 Cultivated 1,500 0.65 0.6 
20 Forest 3,500 0.2 
30 Grassland 2,000 0.65 0.35 
50 Wetlands 0.8 
60 Water 1.1 0.9 
80 Developed 0.3 0.85 

aLULC_veg Values must be 1 for vegetated land use except wetlands, and 0 for all other land uses.

The InVEST model requires that all input files have the same projection coordinate system. Therefore, before ArcGIS related data processing, all data are projected into the WGS_1984_UTM_Zone_52N coordinate system, and the raster-unit is m. Since the raster sizes of data from various sources are different, the resampling tool of ArcGIS is used to resampling the pixel sizes of all input data to 100 m. Figure 4 shows the data in the table after ArcGIS processing.
Figure 4

ArcGIS processing diagram of input data required by InVEST (a. Precipitation; b. Evapotranspiration; c. Land use; d. Watershed; e. Maximum root depth; f. PAWC).

Figure 4

ArcGIS processing diagram of input data required by InVEST (a. Precipitation; b. Evapotranspiration; c. Land use; d. Watershed; e. Maximum root depth; f. PAWC).

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Parameter calibration and model validation

In previous studies, seasonal coefficient (Z coefficient) had a large influence on the result of model. Researchers usually used the measured runoff data in the study area to conduct parameter calibration for InVEST model (Daneshi et al. 2021). In this paper, the total runoff in the study area (including surface runoff and underground runoff) is extracted from the Water Resources Bulletin of Baishan City published by the Water Resources Department of China. In 2010, the total runoff in the study area was 10.65 × 108m3. Using the 2010 data, the relative errors of the calculated and measured values were compared at different Z-factors as shown in Figure 5. The results show that the relative error is the smallest at Z = 25, which is 0.79%. Therefore, Z = 25 can better simulate the spatio-temporal variation of water conservation in this region.
Figure 5

Relative error of the model at varying Z values.

Figure 5

Relative error of the model at varying Z values.

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Land use change

The area of land use types in the study area from large to small is forest, followed by cultivated land, grassland, developed land, water and wetlands. In 2020, the forest area was 2,460.21 km2, accounting for 79.75% of the total area. The arable land area was 453.65 km2, accounting for 14.71% of the total area. From 2000 to 2020, the transition between cultivated land and forest was significant. Based on the land use conversion matrix (Tables 3 and 4), from 2000 to 2010, the forest area decreased from 2,560.58 km2 to 2,400.79 km2, accounting for 5.18%, amongst which the conversion area from forest to cultivated land and grassland was 51.36 km2 and 100.6 km2 respectively. From 2010 to 2020, the forest area has increased, with a total area of 2,400.88 km2, and the area of cultivated land and grassland converted to forest was 40.9 km2 and 19.65 km2 respectively. In addition, cultivated land area decreased by 56.26 km2, with an abandonment rate of 11.03%, Although the forest area remains smaller than in 2000, the policy of returning farmland to forest has achieved good results.

Table 3

Land use conversion matrix 2000–2010 (unit: km2)

2010\2000CultivatedDevelopedForestGrasslandWaterWetlandsTotal
Cultivated 458.26 0.68 2.92 1.42 0.32 463.61 
Developed 0.05 17.03 0.01 17.09 
Forest 51.36 1.65 2,397.62 100.6 7.03 2.32 2,560.58 
Water 0.23 0.04 0.24 0.05 42.81 0.01 43.39 
Wetlands 
Total 509.90 19.41 2,400.79 102.07 50.18 2.33 3,084.67 
2010\2000CultivatedDevelopedForestGrasslandWaterWetlandsTotal
Cultivated 458.26 0.68 2.92 1.42 0.32 463.61 
Developed 0.05 17.03 0.01 17.09 
Forest 51.36 1.65 2,397.62 100.6 7.03 2.32 2,560.58 
Water 0.23 0.04 0.24 0.05 42.81 0.01 43.39 
Wetlands 
Total 509.90 19.41 2,400.79 102.07 50.18 2.33 3,084.67 
Table 4

Land use conversion matrix 2010–2020 (unit: km2)

2020\2010CultivatedDevelopedForestGrasslandWaterWetlandsTotal
Cultivated 422.23 12.97 63.91 7.64 3.12 509.88 
Developed 1.26 17.69 0.12 0.25 0.10 19.41 
Forest 23.01 1.15 2,349.02 24.38 3.25 0.07 2,400.88 
Grassland 5.79 0.56 44.03 51.32 0.35 0.02 102.08 
Water 1.32 0.25 2.93 2.00 43.61 0.02 50.13 
Wetlands 0.00 0.09 0.00 0.02 2.21 2.33 
Total 453.62 32.62 2,460.10 85.59 50.45 2.33 3,084.71 
2020\2010CultivatedDevelopedForestGrasslandWaterWetlandsTotal
Cultivated 422.23 12.97 63.91 7.64 3.12 509.88 
Developed 1.26 17.69 0.12 0.25 0.10 19.41 
Forest 23.01 1.15 2,349.02 24.38 3.25 0.07 2,400.88 
Grassland 5.79 0.56 44.03 51.32 0.35 0.02 102.08 
Water 1.32 0.25 2.93 2.00 43.61 0.02 50.13 
Wetlands 0.00 0.09 0.00 0.02 2.21 2.33 
Total 453.62 32.62 2,460.10 85.59 50.45 2.33 3,084.71 

The developed land is primarily distributed in the central area of Jingyu Town. During the 20 years, the area increased from 17.09 to 32.62 km2, accounting for 90.81%, indicating that the villages and towns were developed greatly, and the development was the most rapid in the last decade. The area of cultivated and forest converted to developed land was 12.45 km2 and 2.98 km2 respectively, accounting for 80.21% and 19.22% of the increment respectively. In the study area, the land use changes indicate a decrease in forest and cultivated land and an increase in grassland and developed land.

Results of water yield and water conservation

Temporal distribution of water conservation

InVEST model was used to obtain the distribution of water yield and water conservation in 2000, 2010 and 2020 (Figure 6(a) and 6(b)). The average water yield of the three periods was 266.21 mm, 347.11 mm, 368.10 mm, respectively, and the annual average was 327.14 mm. The total water yield was 8.25 × 108m3, 10.74 × 108m3, and 11.36 × 108m3 respectively, showing an increasing trend. The average water conservation of the three periods was 95.16 mm, 141.42 mm, 156.34 mm, respectively, and the annual average was 130.97 mm. The total water conservation was 2.94 × 108m3, 4.36 × 108m3 and 4.83 × 108m3, which also shows an increasing trend. The proportion of water yield to water conservation was 40%, and the water conservation capacity was strong, which is consistent with the forest-oriented land use type in the study area.
Figure 6

Distribution map of water yield and water conservation (a. Water yield; b. Water conservation; c. Water conservation in the nature reserve).

Figure 6

Distribution map of water yield and water conservation (a. Water yield; b. Water conservation; c. Water conservation in the nature reserve).

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Data of the concentrated distribution area of mineral water were extracted from the distribution of water conservation, and the results are shown in Figure 6(c). The average water conservation in the distribution area of mineral water in the three periods was 99.79 mm, 167.91 mm and 176.37 mm, and the perennial average was 148.02 mm, which is higher than the average of the study area in the same period. Thus, this value is conducive to the supply of mineral water.

Spatial distribution of water conservation

Spatially, water yield is correlated with land use type distribution. In combination with town distribution in the study area, the water conservation amount and contribution of each town are presented in Figure 7. The average water conservation from high to low rank is Sandaohu Town, Jingshan town, Na‘erhong Town, Mengjiang town, Huayuankou Town, Chisong Town, Longquan town, Jingyu town. The forest coverage rate of the first four towns is high, which provides conditions for water conservation. The water conservation of the last four towns is relatively small due to the large area of cultivated land, among which Jingyu Town is mainly occupied by developed land and cultivated land, and the water conservation is the lowest. There is a mineral water reserve in Mengjiang Town, and the town's contribution rate to water conservation is the largest, up to 20.4%, ensuring the full supply of mineral water. Jingyu Town contributed the smallest, at just 0.7%.
Figure 7

Statistical chart of water conservation and contribution rate of each town in study area. (a. the average water conservation capacity of each township; b. Contribution rate of each township to total water conservation.).

Figure 7

Statistical chart of water conservation and contribution rate of each town in study area. (a. the average water conservation capacity of each township; b. Contribution rate of each township to total water conservation.).

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Influencing factors of water conservation

Climatic factors

The average precipitation was 719.82 mm, 799.05 mm and 830.81 mm in 2000, 2010 and 2020 respectively, showing an increasing trend, whereas the water yield and water conservation also increased. Comparing the distribution maps of precipitation, water yield and water conservation in the three stages, the overall distribution pattern of water yield and water conservation in the whole study area is the same as that of precipitation. Meanwhile, SPSS was used to calculate the correlation amongst water yield, precipitation and PET in three stages and further analyse the influence mechanism of meteorological factors. A positive correlation was observed between water yield and precipitation, with a correlation coefficient of 0.883. By contrast, a negative correlation was found between water production and PET, with a correlation coefficient of −0.770.

Land use factors

Precipitation often causes a continuous transition of water conservation from high to low in space, but water conservation actually has a discontinuous distribution area, that is, a sudden change from high to low. Comparing the land use type map, the phenomenon is similar to land use distribution. Figure 8 shows the average water yield and water conservation of each land use type. Except for wetlands and water area, evident differences in water yield and water conservation are observed in different land use types. Grassland has the highest average water yield, followed by cultivated land, forest and developed land, with a range of 358.6 mm. Forest has the highest average water conservation, followed by grassland, cultivated land and developed land, with a range of 160.9 mm.
Figure 8

Statistical chart of water yield and water conservation of different land use types.

Figure 8

Statistical chart of water yield and water conservation of different land use types.

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Response of water conservation to climate and land use change

Influence mechanism of climatic and land use factors

With regard to climatic factors, compared with the precipitation map of 2000, 2010 and 2020, the distribution law of precipitation is not evident. This finding is due to the small size of the study area and the large randomness of annual precipitation distribution in different areas, which leads to the different spatial distribution of precipitation in different years and the inconsistent spatial distribution of annual water yield and water conservation in 2000, 2010 and 2020. However, the spatial distribution of water yield and water conservation is consistent with that of precipitation in the same year. Therefore, climatic factors determine the overall spatial and temporal distribution pattern of water and water conservation in the study area.

For land use factors, the water yield of cultivated land and developed land is high, but the water conservation is low. This finding is due to the low vegetation coverage and root depth of these two land use types, resulting in less precipitation intercepted and utilised by vegetation, and less transpiration loss than that of forest and grassland. Thus, most of the precipitation is converted into water yield. In addition, the infiltration condition of cultivated land and developed land is poor, and the ability of vegetation to retain water is low. Therefore, the runoff coefficient is relatively large, and the water yield is completely lost through the surface runoff out of the region, resulting in low water conservation. However, for forests, the canopy and litter are broad and thick, respectively, which result in an evident interception effect on runoff and large amount of water conserved (Julian & Gardner 2014).

For wetlands and water area, this study considers that precipitation falling into the water body will directly form runoff out of the study area, which cannot be used in determining the statistics of water conservation in the study area. Given the scattered distribution of cultivated land and developed land, local high-value or low value areas appear in the distribution map of water yield and water conservation. Therefore, land use affects water conservation by influencing runoff, which determines the local characteristics of water conservation in the study area.

Relative importance analysis

Scenario analysis was used to calculate the RII of climate and land use to water conservation. As shown in Figure 9, climatic factors were important to 91.92% of the study area, whereas only 6.22% of the study area was significantly influenced by land use factors and the remaining 1.86% was balanced. If the areas with changes in land use types are considered, then the pixel point where land use factors are more important than climatic factors will increase to 48.71%, and the pixel point where climatic factors are more important will decrease to 49.08%. Notably, most of these land use changes occur in areas where forests are decreasing and cultivated and developed land are increasing, all of which are due to human activities. Therefore, under natural conditions, climatic factors play a dominant role in water conservation. By contrast, when land use types change dramatically, land use will be of great importance.
Figure 9

Distribution diagram of relative importance of factors and changes of water conservation.

Figure 9

Distribution diagram of relative importance of factors and changes of water conservation.

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In particular, from 2000 to 2020, water conservation increased with the increase of precipitation. However, the spatial distribution of the increase was different, and it decreased in some areas (particularly in the southern part of the study area, Figure 9). The areas where water conservation decreased were consistent with those where land use factors dominated. Considering that the land use change is evident from 2010 to 2020, ArcGIS was used to extract areas with land use change from 2010 to 2020, make statistics of the changes in water conservation and analyse the response of water conservation to land use change. The water conservation obtained in 2010 is subtracted from that in 2020 to represent the change in water conservation under the combined influence of climate and land use. Then, the climatic conditions in 2010 and land use conditions in 2020 were simultaneously input into the InVEST model, and the water conservation in 2010 was subtracted from the result to represent the change in water conservation without considering the climatic effect. The results are shown in Figure 10.
Figure 10

Statistical chart of water conservation under different factors from 2010 to 2020.

Figure 10

Statistical chart of water conservation under different factors from 2010 to 2020.

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As shown in Figure 10, if the climatic effect is not considered, when the forest changes to cultivated, developed land and grassland, then the water conservation will decrease. The reduction is the largest when forest changes to cultivated land (172 mm), and it is the least when forest changes to grassland (49 mm). Most of the crops in northeast China, such as rice and corn, are relatively low. These crops conserve water and soil only by themselves and their shallow roots; but forest can conserve water through canopy, understory shrubs, humus and roots. Thus, the conversion of forest to cultivated leads to a sharp decline in water conservation. Although Section 3.3.2 considers that developed land has a worse water conservation capacity than cultivated land, the area of forest converted into cultivated land from 2010 to 2020 is 23.01 km2, which is larger than the area converted into construction land (1.15 km2). Therefore, the loss of water conservation caused by the conversion of forest to farmland is the largest. In addition, the Changbai Mountain is a national key construction tourism area. The newly built towns in this region are small with a large internal green area, and some areas retain the characteristics of forest or grassland. When cultivated land and developed land are converted to forest and grassland, respectively, water conservation will increase, and the increment is the largest when the cultivated land changes to forest (149 mm). When the climatic effect is considered, water conservation under different categories (Figure 10) fluctuates because of differences in precipitation and evapotranspiration in different areas, with variation ranging from −19 to 34 mm, which is significantly smaller than that under the influence of land use only −172 to 149 mm. Therefore, although climatic factors are the main factors affecting water conservation in the study area, large-scale land use change, particularly deforestation and reclamation caused by human activities, will change the amount of runoff loss. Such changes in water conservation are often more dramatic than those caused by climate. At present, the expansion of cultivated land and developed land remains the main inhibiting factor of water conservation in the area. Therefore, the policy of returning farmland to forest should be implemented for the sustainable exploitation of mineral water resources.

The effect of water conservation on mineral water resources

The Changbai Mountain is a world-famous mineral water source. It is of reference significance for other water sources in the world to clarify the influence of water conservation on mineral water resources and establish the relationship between them. Based on previous studies, mineral water in the Changbai Mountain comes from a single source and it is primarily supplied by atmospheric precipitation (Bian et al. 2019; Li et al. 2022). Precipitation initially enters into the aquifer through soil and then into the vadose zone, where it is fully exchanged with basalt to form high-quality mineral water. The mineral water in this area has strong renewability, and it is sensitive to changes in atmospheric precipitation. Water conservation in the InVEST model is directly or indirectly generated by atmospheric precipitation, and it is the residual part of water yield through runoff loss. In this paper, the mineral water concentrated distribution area is densely forested, the ecosystem is well protected, and the runoff loss is less, which is conducive to the storage of water resources. The water conservation of the mineral water concentrated distribution area is higher than the average level of the study area, which is conducive to sufficient water–rock reaction between mineral water and basalt in the basin. Consequently, the supply and storage of mineral water will increase, and the water quantity and quality of mineral water, suitable for the development and utilisation of mineral water.

Given its location in the middle temperate zone of China, the Changbai Mountain has a high altitude (average altitude of 1,640–1,826 m), and the snow and ice cover period last for 9 months every year. The local forest ecosystem can effectively hold the meltwater, allowing it to recharge mineral water slowly. The recharge time of snow meltwater for runoff in the Changbai Mountain is 1 month, which indicates also reflects the strong water conservation capacity of forest ecosystem (Feng 2021). However, in areas with low vegetation coverage, ice and snow meltwater often flows out of the ecosystem in ice flood, resulting in regional water resource loss. Therefore, the forest ecosystem can effectively ensure not only high water conservation in the Changbai Mountain area, but also mineral water supply.

Suggestions on the management of local mineral water resources

This study is can clarify the causes of gains and losses of water conservation services, which can be further extended to ecosystem protection policies in related areas. At the end of the past century, large-scale deforestation and land reclamation activities in China severely affected the ecological environment, leading to decline of river flow in many areas, reduction of forest area and weakening of ecological services. In solving the abovementioned problems, the Chinese government has vigorously implemented the ecological policy of returning farmland to forests, grasslands and lakes, which has achieved remarkable effects (Tang et al. 2020). However, with the rapid economic development and large-scale urban expansion from the 21st century, the contradiction between construction land and natural area (such as forest and grassland) has to be put on the urgent agenda. Hence, policy makers must identify the contribution of various land uses to ecological service functions and identify areas that are most in need of protection or improvement.

In this paper, the change map of water conservation can directly reflect the gains and losses of water conservation services under the influence of climate and land use, and scenario analysis provides the separate effect of two influencing factors on water conservation. Areas with reduced water conservation should be improved, that is, areas with reduced forest and grassland in areas where land use change is the main factor. Considering the actual content of the study area, most of these areas are urban and road construction land in recent years. Therefore, the relationship between urban expansion and ecological protection should be weighed.

The formulation of the ecological policy should not only consider the overall changes of water conservation in the region, but also reflect the particularity of different functional areas to make the policy more localised. The central part of the research area is the urban centre, and economic development is the main goal. Therefore, the development of construction land and cultivated land can be carried out without affecting the ecology, thereby ensuring the normal operation of the ecosystem. However, for mineral water protection areas in the southwest of the study area, farming and animal husbandry activities should be prohibited. Furthermore, mineral water sources and surrounding rivers should be protected, particularly the upper reaches of rivers, to prevent the establishment of mineral water plant and related human activities on the water conservation function caused by negative effects.

This paper makes a comprehensive analysis of the change mechanism of water conservation in the Changbai mountain area and evaluated the influence of water conservation on mineral water resources. The results show that from 2000 to 2020, the changes of land use in the study area decreased in forest and cultivated land and increased in grassland and developed land. Water conservation increased from 2.94 × 108m3 to 4.83 × 108m3, total conservation increased by 64.29%, and the proportion of water production into water conservation was 40%. Under natural conditions, climatic factors played a decisive role in water conservation in 91.92% of the study regions. However, when forest decreased, cultivated and construction land increase occurred in some areas, the main control factor of 48.71% of the area shifted into land use factor, and these behaviours will cause a sharp decrease in water conservation. The change of land use factors may greatly reduce water conservation, or even directly go to 0, which is more severe than the impact of climatic factors.

This study showed that climatic factors determine the overall spatial pattern of water conservation, while land use factors determine the local characteristics of water conservation by controlling runoff. In addition, forest ecosystems can effectively intercept precipitation and store snow, so that water resources can be well stored. For mineral water source, this is an important supply source, conducive to the sustainable exploitation of high-quality mineral water. Therefore, in the process of mineral water mining and economic development, the conflict between urban construction, arable land expansion, and forest protection should be weighed to avoid forest destruction. This is conducive to the steady and healthy development of the local economy dominated by mineral water. The methods and results used in this study will be helpful to better understand the change mechanism of water conservation and reveal the significance of water conservation function to mineral water resources, playing a guiding role in decision making of local policy development and ecological protection.

The study was supported by the National Key Research and Development Program of China (grant number 2019YFC0409103) and the Key Projects of Jilin Provincial Department of Science and Technology (grant number 20190303076SF). We sincerely appreciate the funding of the above projects and the work of each author.

The study was supported by:

  • 1.

    National Key Research and Development Program of China (grant number 2019YFC0409103);

  • 2.

    Key Projects of Jilin Provincial Department of Science and Technology (grant number 20190303076SF).

Sun Wenhao: Conceptualization; Formal Analysis; Methodology; Investigation; Writing – Original Draft. Bian Jianmin: Funding Acquisition; Methodology; Writing – Review & Editing. Li Yihan: Resources; Data Curation. Li Jialin: Investigation; Resources.

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 paper.

All relevant data are available from an online repository or repositories. The URL or references of all data sources have been noted in the relevant positions in the text.

All relevant data are available from an online repository or repositories. China Meteorological Data Service Centre (http://data.cma.cn/) GlobeLand30 (http://globeland30.org/) Geospatial Data Cloud site, Chinese Academy of Sciences. (http://www.gscloud.cn) National Tibetan Plateau Data Center (http://www.tpdc.ac.cn/).

The authors declare there is no conflict.

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