Large spatial variations in the distributions of and factors affecting forest water retention capacity in China

Ecological Indicators 113 (2020) 106152

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Large spatial variations in the distributions of and factors affecting forest water retention capacity in China ⁎

Xi Wua,b,c, Wenjiao Shia,d, , Bin Guoc, Fulu Taoa,d,

T

⁎

a Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China b Jiangsu Province Surveying & Mapping Engineering Institute, Nanjing 210013, China c College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China d College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

A R T I C LE I N FO

A B S T R A C T

Keywords: China Forest water retention Meta-analysis Path analysis Spatial distribution Factor

The estimation of forest water retention based on observation and experimental data can be used to accurately analyze large variations in the spatial distributions of the water retention capacity across China and explore factors affecting these large spatial variations at different scales. Therefore, we reviewed the relevant peerreviewed literature to extract water retention parameters and their influencing factors throughout China via a meta-analysis approach. Furthermore, an empirical model and path analysis were used to explore the spatial distributions of and significant factors affecting the canopy interception capacity (CIC), litter maximum waterholding capacity (LWHC), soil water storage capacity (SSC) and forest water retention capacity (WRC) at the national and basin scales. The results showed that the spatial distributions of the CIC and LWHC exhibited opposite trends in China. The average CIC values in the Pearl River Basin (PRB), Southeast Rivers Basin (SERB), Southwest Rivers Basin (SWRB) and Yangtze River Basin (YTRB) were two times higher than those in the Songhua River Basin (SHRB), Liao River Basin (LRB) and Northwest Rivers Basin (NWRB). In addition, the SSC values accounted for approximately 60–85% of the WRC values in the different basins, soil types and forest types. The average SSC values in semi-alfisol and ferralsol were 2.5 times higher than those in semi-hydromorphic soil and pedocal. Moreover, the effects of some factors such as location, terrain and canopy structure on the CIC, LWHC, SSC and WRC values were significant in only certain basins, but these effects were not significant throughout China. The indirect effects of some climatic, location and terrain factors on the CIC, LWHC, SSC and WRC values determined the total effects. These results can contribute to a better understanding of the large spatial variations in WRC and provide a scientific basis for the improvement of the forest water retention function across China.

1. Introduction Water supply, which represents a major ecosystem service, is the provision of water through watersheds, reservoirs, and aquifers (Costanza et al., 1997). Forest ecosystems, which are one of the most important terrestrial ecosystems on Earth, can provide ecosystem services by supplying water to humans (Nunez et al., 2006). Under a

scenario of global climate change and terrestrial water storage depletion, forest water retention, including the interception and storage of precipitation, regulation of runoff and purification of water, has become a focus of studies on ecosystem services (Liu et al., 2003; Andreassian, 2004; Moiwo et al., 2013). The methods for water retention research are mostly based on water-balance theory or empirical models. The water-balance theory

Abbreviations: CIC, canopy interception capacity; LWHC, litter maximum water-holding capacity; SSC, soil water storage capacity; WRC, forest water retention capacity; SHRB, Songhua River Basin; LRB, Liao River Basin; NWRB, Northwest Rivers Basin; HRB, Hai River Basin; YRB, Yellow River Basin; YTRB, Yangtze River Basin; HuRB, Huai River Basin; SERB, Southeast Rivers Basin; SWRB, Southwest Rivers Basin; PRB, Pearl River Basin; CTMNF, cold and temperate mountainous needleleaf forest; TNF, temperate needleleaf forest; TDBF, temperate deciduous broadleaf forest; SNF, subtropical needleleaf forest; STMNF, subtropical and tropical mountainous needleleaf forest; SDBF, subtropical deciduous broadleaf forest; SEBF, subtropical evergreen broadleaf forest; TMR, tropical monsoon rainforest; STBF, subtropical and tropical bamboo forest; SL, shrubland ⁎ Corresponding authors at: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China. E-mail addresses: [email protected] (W. Shi), taofl@igsnrr.ac.cn (F. Tao). https://doi.org/10.1016/j.ecolind.2020.106152 Received 2 July 2019; Received in revised form 23 January 2020; Accepted 28 January 2020 1470-160X/ © 2020 Elsevier Ltd. All rights reserved.

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Fig. 1. The distributions of forest types (a), soil types (b), elevations of observation sites extracted from the literature (c) and the highest daily precipitation (d) in China.

retention research via hydrological models (Liang et al., 1994; Liu et al., 2005), the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model (Zheng et al., 2016) and remote sensing products (Rodell et al., 2018). Hydrological models are commonly used to

(Hay and Mccabe, 2002; Sun et al., 2005; Caldwell et al., 2016), in which water yield is the balance of precipitation minus evapotranspiration (ET) ignoring soil water changes and groundwater recharge on the annual scale, has been successfully applied in water 2

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Fig. 1. (continued)

Integration (TOPKAPI) model to estimate soil water volume, but the forcing variables and physical parameters could not be adequately calibrated. In addition, the InVEST model is widely used in ecological service research (Vigerstol and Aukema, 2011; Su and Fu, 2013; Jiang et al., 2016; Ouyang et al., 2016), but it cannot adequately describe the

simulate water retention (Liang et al., 1996; Liu and Todini, 2002; Sinclair and Pegram, 2013), but each model has limitations. For example, the Water Assessment Tool (SWAT) model does not perform well during base flow periods (Ahl et al., 2008). Sinclair and Pegram (2013) successfully applied the TOPographic Kinematic APproximation and 3

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tropical needleleaf forest, temperate needleleaf and broadleaf forest, subtropical needleleaf and broadleaf forest and grassland (Fig. 1a). The area occupied by each forest type and the spatial distribution of each forest type in each basin are shown in Appendix A and Fig. 1a, respectively. According to the 1:1000000-scale soil maps produced by the National Soil Census Office, the soil types are classified into 12 groups: alfisol, semi-alfisol, pedocal, xerosol, desert soil, primarosols, semihydromorphic soil, hydromorphic soil, saline-alkaline soil, anthrosol, alpine soil and ferralsol (Fig. 1b).

complex water-balance process. Moreover, satellite-based products (Moiwo et al., 2013; Reager and Famiglietti, 2013) have been used in recent years as a feasible way to simulate large-scale water retention, but some important local parameters cannot be derived from the products, such as canopy interception capacity (CIC), litter maximum water-holding capacity (LWHC) and soil water storage capacity (SSC). Therefore, the accuracy of these methods based on water-balance theory requires verification based on observation data (Zhai and Tao, 2017). Some empirical models, including the soil storage method (Sampson and Allen, 1999) and canopy interception method (Deng et al., 2002), are based on observation data from a single layer in the soil or canopy. In contrast, an empirical model of the forest water retention capacity (WRC), representing the sum of CIC, LWHC and SSC and considering the canopy, litter and soil layers based on observation data (Zhang et al., 2011), is a relatively comprehensive method for estimating water retention. However, due to the difficulty in obtaining observation CIC, LWHC and SSC data, this method is usually applied over small spatial scales, such as in the Dianchi watershed (Li et al., 2015), the Bipenggou Nature Reserve (Wang et al., 2016) and Beijing (Zhang et al., 2011). In addition, a systematic analysis of the distributions of and factors affecting WRC based on observation data related to CIC, LWHC and SSC parameters is lacking at both national and regional scales especially in China. For example, precipitation and temperature are positively correlated with WRC in China (Gong et al., 2017), but the effects of terrain factors on WRC differ among different regions. For example, elevation and the WRC exhibit a positive correlation in the Qiongjiang River Basin (Liu et al., 2013), a negative correlation in the Wujiang River Basin (Tang and Shao, 2016), and a nonlinear relationship in the Yangtze River Basin (Li et al., 2011). The differences in the spatial distribution of forest water retention over large scales depend on the comprehensive effects of multiple influencing factors. Niu and Cui (2015) found that canopy structure factors (leaf area index (LAI), forest density, forest age and canopy density) and climatic factors (precipitation and temperature) significantly affect the CIC, while in the litter layer, litter storage was negatively correlated with latitude and elevation, and positively correlated with the annual average temperature and canopy density (Ling et al., 2009). Therefore, to explore the factors driving these large spatial variations in CIC, LWHC, SSC and WRC, we collected data from site-specific experiments across China for the first time and analyzed the spatial distributions of and factors affecting the CIC, LWHC, SSC and WRC throughout China using a meta-analysis method, empirical model and path analysis at different scales. Specifically, the aims of our study were to (i) analyze the large variations in the spatial distributions of the CIC, LWHC, SSC and WRC values in different forest types and soil types at the national and basin scales and (ii) explore the factors affecting the large spatial variations in CIC, LWHC, SSC and WRC at both scales.

2.2. Data 2.2.1. Meta-analysis data A comprehensive literature search was conducted to collect articles reporting on the parameters related to water retention, including canopy interception rate, litter storage, litter maximum water-holding rate, soil depth and soil noncapillary porosity. The China National Knowledge Infrastructure (CNKI) and Web of Science Core Collection Databases were used to search for these articles. A total of 740 studies were retrieved according to the following search: (“storage capacity” OR “storage rate” OR “interception capacity” OR “interception rate” OR “water-holding* capacity” OR “water-holding* rate” OR “retention capacity” OR “retention rate” OR “conservation capacity” OR “conservation rate” OR “containment capacity” OR “containment rate” OR “capillary porosity”) AND (canopy AND litter AND soil). We read the abstracts of these 740 papers to determine whether they met the requirements for inclusion in this study. There were 3 types of studies that did not include the required parameters. First, we excluded reviews or conceptual papers and articles that did not include specific data. Second, our criteria required the studies to have conducted site-specific experiments or measurements of the parameters related to forest water retention. These criteria excluded articles that researched forest water retention based on remote sensing products. Third, articles on hydrological processes were also excluded. The 334 papers that met our criteria were read in detail to extract the data. The required parameters could not be extracted from 104 of these studies, which were therefore excluded from further analysis, leaving a total of 230 studies (Fig. 2). We extracted the data on annual temperature, longitude, latitude, elevation, aspect, slope, forest age, tree height, tree breast diameter, tree crown width, forest density, canopy density, litter thickness and soil bulk density from these articles. A total of 1045 observation sites, including 264 sites analyzing a single layer, 445 sites analyzing two layers and 336 sites analyzing three layers, were used for this analysis

2. Materials and methods 2.1. Study area In China, there are 10 basins (from north to south): the Songhua River Basin (SHRB), the Liao River Basin (LRB), the Northwest Rivers Basin (NWRB), the Hai River Basin (HRB), the Yellow River Basin (YRB), the Yangtze River Basin (YTRB), the Huai River Basin (HuRB), the Southeast Rivers Basin (SERB), the Southwest Rivers Basin (SWRB) and the Pearl River Basin (PRB). According to the 1:1000000-scale vegetation maps, there are 14 main forest types: cold and temperate mountainous needleleaf forest (CTMNF), temperate needleleaf forest (TNF), temperate deciduous broadleaf forest (TDBF), subtropical needleleaf forest (SNF), subtropical and tropical mountainous needleleaf forest (STMNF), subtropical deciduous broadleaf forest (SDBF), subtropical evergreen broadleaf forest (SEBF), tropical monsoon rainforest (TMR), subtropical and tropical bamboo forest (STBF), shrubland (SL),

Fig. 2. The framework of the literature search. 4

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Table 1 Description of the spatial data. Data

Data description

Data source

The highest daily precipitation

Annual average highest daily precipitation from 2000 to 2015 with 1-km spatial resolution Annual average LAI from 2000 to 2015 with 1-km spatial resolution Annual average NDVI from 2000 to 2015 with 1-km spatial resolution Annual average FVC from 2000 to 2015 with 1-km spatial resolution DEM with 1-km spatial resolution

China Meteorological Sharing Service System.

Leaf Area Index (LAI) Normalized Difference Vegetation Index (NDVI) Fractional Vegetation Cover (FVC) Digital Elevation Model (DEM)

SSCi = Sdi × Si

2.3.2. Z-score standardization Due to differences in dimensionality among the factors, we normalized the CIC, LWHC, SSC, WRC and their influencing factors using the z-score standardization method to make the data comparable.

z=

(x − μ) σ

(5)

where x is the value of CIC, LWHC, SSC, WRC or each influencing factor, μ is the mean of x, and σ is the standard deviation of x. 2.3.3. Statistical analysis Path analysis (Wright, 1921; Leithead et al., 2012; Helm et al., 2017) was used to refine the initial hypothesized models by removing nonsignificant direct paths between variables to achieve the best fit. First, we used the Pearson correlation coefficient (R) to assess the impacts of climatic factors, terrain factors, location factors, forest types, and soil types, as well as canopy, litter and soil characteristics on the CIC, LWHC, SSC and WRC, and the significance of each correlation was considered at the p < 0.05 level. Because some of the factors were ecologically (and statistically) correlated, we used path analysis to determine the significant direct and indirect pathways through which the factors affected the CIC, LWHC, SSC and WRC. Direct path coefficients indicated the factors had direct effects, and indirect path coefficients indicated the factors had indirect effects via changes in other factors. The total path coefficients of the factors equaled the sum of the direct and indirect effects. In addition, the effects of the factors on the CIC, LWHC, SSC and WRC were high when the absolute value of the decision coefficient was high (Sackett et al., 2013). Path analysis fits (Chapman and Mcewan, 2018) were assessed by the root mean square error (RMSE; values < 0.06 indicate a good fit), coefficient of determination (R2; values close to 1 indicate that much of the total variance is explained) and F-value (the higher the F-value is, the higher the significance of the model). The path analysis was performed using the software package AMOS 21.0. Due to the differences in the CIC, LWHC, SSC and WRC values among basins, forest types and soil types, the one-way ANOVA with least significant difference (LSD) post hoc test (with an α level for all tests of 0.05) was applied to reveal the basins, forest types or soil types in which the differences in CIC, LWHC, SSC or WRC were significant. If the differences in CIC, LWHC, SSC and WRC among basins, forest types or soil types were significant based on the one-way ANOVA test, the averages of the CIC, LWHC, SSC and WRC values were marked with different letters; otherwise, the averages of CIC, LWHC, SSC or WRC values were expressed by the same letter. In our research, statistical analysis was performed using the software package SPSS 14.0, and a p-

2.3. Methods 2.3.1. Empirical model The maximum capacity of precipitation intercepted and stored by the forest can be simulated by the empirical model of WRC as the sum of CIC, LWHC and SSC (Wen and Liu, 1995). Thus, the WRC was calculated according to equation (1): (1)

where WRCi is the WRC in the ith forest sublot (mm), CICi is the CIC in the ith forest sublot (mm), LWHCi is the LWHC in the ith forest sublot (mm), and SSCi is the SSC in the ith forest sublot (mm). The maximum capacity of precipitation intercepted by the canopy depends on the climate conditions and the canopy interception rate; therefore, the CIC value is determined by the highest daily precipitation and canopy interception rate (Wen and Liu, 1995; Zhang et al., 2010). The CIC was calculated according to equation (2): (2)

where CICi is the CIC in the ith forest sublot (mm), Pi is the highest daily precipitation in the ith forest sublot (mm), and Ci is the canopy interception rate of the ith forest sublot (%). The LWHC value is largely dependent upon litter storage and its maximum water-holding rate (Wen and Liu, 1995; Zhang et al., 2010). The LWHC was calculated according to equation (3):

LWHCi = Lsi × Li /10

(4)

where SSCi is the SSC in the ith forest sublot (mm), Sdi is the soil depth in the ith forest sublot (mm), and Si is the soil noncapillary porosity in the ith forest sublot (%).

2.2.2. Spatial data The highest daily precipitation data were used to calculate the CIC values. The LAI (Xiao et al., 2014; Xiao et al., 2016), fractional vegetation cover (FVC) and digital elevation model (DEM) parameters were selected as factors potentially affecting the CIC, LWHC, SSC and WRC (Table 1). The highest daily precipitation value was determined for each station and interpolated by ANUSPLIN software (Fig. 1d). Then, the highest daily precipitation value for each observation site was obtained based on the longitudes and latitudes of the sites. The normalized difference vegetation index (NDVI) (Xu, 2018) from 2000 to 2015 were chosen to compute the FVC using a dimidiate pixel model (Zeng et al., 2003; Jiang et al., 2016) because the 95% of 1045 observation sites were investigated in this period.

CICi = Pi × Ci

The Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn).

according to equation (4):

(Fig. 1c). The timing of investigations of these 1045 observation sites ranged from 1987 to 2017, but 95% of the observation sites were investigated between 2000 and 2015.

WRCi = CICi + LWHCi + SSCi

The National Earth System Science Data Sharing Infrastructure, National Science & Technology Infrastructure of China (http://www.geodata.cn). The Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn). FVC was computed by NDVI using a dimidiate pixel model.

(3)

where LWHCi is the LWHC in the ith forest sublot (mm), Lsi is the litter storage in the ith forest sublot (t/ha), and Li is the litter maximum water-holding rate of the ith forest sublot (%). The capacity of water stored in forest soil has a close correlation with the noncapillary porosity and the depth of the soil layer; thus, the SSC value can be estimated by the soil noncapillary porosity and soil depth (Wen and Liu, 1995; Zhang et al., 2010). The SSC was calculated 5

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Fig. 3. Distributions of the CIC, LWHC, SSC and WRC values in different basins in China. The error bars represent the standard deviations of the CIC, LWHC, SSC and WRC values. Different letters indicate significant differences in the CIC, LWHC, SSC and WRC values between basins (p < 0.05).

CIC values of the CTMNF (20.77 ± 9.76 mm), TNF (21.72 ± 2.69 mm) and TDBF (24.17 ± 11.75 mm) in the YTRB were higher than those in the SHRB, LRB, NWRB, HRB and YRB. For subtropical and tropical forests, the CIC values of SNF, STMNF, SEBF and TMR were ranged from 21.81 ± 7.07 mm to 43.48 ± 8.25 mm among the basins. However, the CIC value of SDBF in the HRB was 10.11 ± 2.33 mm. The CIC value of STBF, which was primarily distributed in the YTRB, was 17.12 ± 10.47 mm. The LWHC values in cold and temperate forests were generally higher than those in subtropical and tropical forests (p < 0.05) (Table 2). Among the cold and temperate forests, CTMNF had the highest LWHC value, and the LWHC values of CTMNF in the YRB (5.52 ± 1.98 mm) and YTRB (2.38 ± 0.40 mm) were significantly (p < 0.05) lower than those in the northern basins of China (SHRB, 9.06 ± 3.09 mm; NWRB, 12.01 ± 0.73 mm; HRB, 10.01 ± 7.11 mm) (Fig. 4a). Notably, the LWHC value for TNF was significantly lower than those for CTMNF and TDBF (p < 0.05). For subtropical and tropical forests, TMR and SDBF had lower LWHC values than those for STMNF, SEBF and SNF (Table 2). The LWHC values of SNF (2.04 ± 0.53 mm) and SEBF (1.62 ± 0.93 mm) in the PRB were much lower than those in the other basins (Fig. 4a). The maximum SSC value was observed in SEBF, followed by SL and STMNF, and the SSC values of the other forest types were between 52.19 ± 44.10 mm and 59.12 ± 43.98 mm (Table 2). We compared the SSC value in each forest type among basins (Fig. 4a). For cold and temperate forests, the SSC values of CTMNF, TNF and TDBF in the HRB were the highest among the basins, followed by those in the YRB and YTRB. For subtropical and tropical forests, the SSC value of SNF in the PRB (108.65 ± 31.47 mm) was approximately twice as high as that in the YTRB (50.59 ± 12.67 mm) and SERB (46.94 ± 24.87 mm), and the SSC value of SDBF in the HRB (120.28 ± 40.19 mm) was approximately three times larger than that in the YTRB (45.76 ± 28.31 mm).

value < 0.05 was used as the standard of significance. 3. Results 3.1. Spatial distributions of the CIC, LWHC, SSC and WRC values 3.1.1. The CIC, LWHC, SSC and WRC values in different basins The spatial distributions of the CIC, LWHC, SSC and WRC values were significantly different in the different basins in China. The CIC values were significantly higher in the basins of southern China (YTRB, 22.04 ± 11.31 mm; SERB, 21.94 ± 6.62 mm; SWRB, 25.66 ± 4.83 mm; PRB, 27.68 ± 13.55 mm) than those in the basins of northern China (SHRB, LRB, NWRB, HRB, YRB and HuRB) (p < 0.05) (Fig. 3). The LWHC values gradually decreased from north (SHRB, 7.88 ± 6.36 mm; LRB, 7.90 ± 3.22 mm; NWRB, 8.42 ± 6.89 mm) to south (PRB, 1.92 ± 1.19 mm; SWRB, 1.37 ± 1.19 mm; SERB, 2.00 ± 1.31 mm). The SSC values in the SWRB (106.25 ± 38.44 mm) and PRB (86.63 ± 64.53 mm) were significantly higher than those in the SHRB (35.19 ± 19.42 mm), LRB (40.72 ± 16.72 mm) and HuRB (31.99 ± 20.30 mm) (p < 0.05). The variations in the WRC across basins were mostly explained by the SSC (62.48–80.41%), followed by the CIC (12.12–28.28%) and LWHC (1.03–13.99%). The WRC values in the SWRB (133.28 ± 31.83 mm) and PRB (116.23 ± 43.33 mm), which had high SSC and CIC values, were significantly higher than those in the LRB (62.64 ± 15.43 mm), SHRB (56.32 ± 23.17 mm) and HuRB (46.22 ± 23.52 mm), in which the SSC values were much lower (p < 0.05). 3.1.2. The CIC, LWHC, SSC and WRC values in different forest types The CIC values in cold and temperate forests (CTMNF, TNF and TDBF) were significantly lower than those in subtropical and tropical forests (SNF, STMNF, SDBF, SEBF and TMR) (Table 2 and Fig. 4a). The 6

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Table 2 The CIC, LWHC, SSC and WRC values in different forest types. Forest types

CIC (mm) (%)

LWHC (mm) (%)

SSC (mm) (%)

WRC (mm)

CTMNF (n = 154) TNF (n = 85) TDBF (n = 235) SNF (n = 199) STMNF (n = 50) SDBF (n = 50) SEBF (n = 66) TMR (n = 27) STBF (n = 51) SL (n = 78)

14.22 16.55 15.91 22.14 17.64 21.21 22.71 31.50 18.56 12.43

7.27 2.90 5.80 3.80 5.27 2.90 4.65 1.88 5.25 3.80

53.01 59.12 56.25 55.16 60.42 52.19 77.18 58.03 53.42 64.08

74.50 ± 44.50a 78.57 ± 45.00ab 77.96 ± 44.26ab 81.10 ± 44.47ab 83.33 ± 44.17ab 76.29 ± 43.97a 104.54 ± 44.53b 91.41 ± 44.61b 77.23 ± 43.71ab 80.31 ± 43.92ab

± ± ± ± ± ± ± ± ± ±

10.31ab (19.09) 10.24b (21.06) 10.32b (20.41) 10.32c (27.30) 9.98b (21.17) 10.41bc (27.80) 10.33 cd (21.72) 9.16d (34.36) 10.38bc (24.03) 10.35a (15.48)

± ± ± ± ± ± ± ± ± ±

1.09c (9.76) 1.31a (3.69) 1.09b (7.44) 1.12a (4.69) 1.43b (6.32) 1.17a (3.80) 1.13ab (4.45) 1.50a (2.06) 1.14b (6.80) 1.30ab (4.73)

± ± ± ± ± ± ± ± ± ±

43.89a (71.15) 43.98b (75.25) 43.75b (72.15) 44.00ab (68.01) 43.28bc (72.51) 44.10a (68.41) 44.01c (73.83) 43.41b (63.48) 43.79a (69.17) 43.20c (79.79)

Note: Different letters indicate significant differences (p < 0.05). The numbers in brackets indicate the CIC, LWHC or SSC as a percentage of WRC in each forest type.

SEBF (121.92 ± 74.96 mm) in the PRB, STMNF (108.38 ± 2.21 mm) in the YTRB and SDBF (134.35 ± 33.41 mm) in the HRB were higher than those in the other basins. The CIC, LWHC, SSC and WRC values of SL in the YTRB were higher than those in the HRB and YRB.

The contribution of SSC values to the WRC values in different forest types ranged from 62.24% to 89.65%. The WRC values in cold and temperate forests (CTMNF, TDBF and TNF) were lower than those in subtropical and tropical forests (SNF, STMNF, TMR and SEBF), except for SDBF (Table 2). The WRC values of CTMNF, TNF and TDBF in the HRB were the highest among the basins, followed by those in the YRB and YTRB (Fig. 4a). The WRC values of SNF (140.34 ± 39.89 mm) and

3.1.3. The CIC, LWHC, SSC and WRC values in different soil types The average of CIC value for ferralsol and anthrosol in southern

Fig. 4. The CIC, LWHC and SSC values in the different forest types (a) and soil types (b) among basins. 7

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Table 3 The CIC, LWHC, SSC and WRC values in different soil types. Soil types

CIC (mm) (%)

LWHC (mm) (%)

SSC (mm) (%)

WRC (mm)

Alfisol (n = 210) Semi-alfisol (n = 172) Pedocal (n = 45) Primarosols (n = 205) Semi-hydromorphic soil (n = 39) Anthrosol (n = 87) Alpine soil (n = 42) Ferralsol (n = 207)

14.88 ± 7.07b (20.72) 12.34 ± 4.50ab (12.09) 7.68 ± 3.08a (14.28) 20.43 ± 9.41c (29.41) 11.67 ± 3.54ab (25.01) 21.25 ± 10.88c (27.44) 11.33 ± 4.40ab (12.55) 25.12 ± 12.53d (24.65)

5.13 3.60 6.57 3.01 6.52 2.07 7.09 2.19

51.83 86.17 39.55 46.04 28.47 46.80 71.85 74.59

71.83 ± 45.50a 102.11 ± 37.87b 53.80 ± 44.47a 69.47 ± 44.54a 46.66 ± 59.83a 77.44 ± 35.97ab 90.27 ± 37.34ab 101.90 ± 49.97b

± ± ± ± ± ± ± ±

6.03c (7.14) 3.70b (3.53) 4.66 cd (12.21) 2.77ab (4.33) 4.58 cd (13.97) 1.30a (2.67) 4.98d (7.85) 1.51a (2.15)

± ± ± ± ± ± ± ±

38.75a (72.16) 50.98b (84.39) 22.29a (73.51) 34.71a (66.27) 25.18a (61.02) 36.88a (60.43) 18.52b (79.59) 49.69b (73.20)

Note: Different letters indicate significant differences (p < 0.05). The numbers in brackets indicate the CIC, LWHC or SSC as a percentage of WRC in each soil type.

factors, the CIC values increased with increases in the highest daily precipitation, but the increase slowed when the precipitation is higher than 130 mm (Fig. 6a). The CIC values increased with increasing mean annual temperature (Fig. 6b) and forest structure, including increases in FVC for values ranging from 10 to 80% (Fig. 6f), LAI for values from 0 to 20 (Fig. 6g), canopy density (Fig. 6h) and crown width (Fig. 6i). The CIC values changed with longitude, as demonstrated by a unimodal curve with peak values at a longitude of 110° (Fig. 6c). Latitude (Fig. 6d) and elevation (Fig. 6e) were negatively correlated with the CIC values.

China were significantly higher than those for semi-alfisol, pedocal and semi-hydromorphic soil in northern China (Table 3). The average CIC value for ferralsol was the highest among the different soil types, and the lowest average CIC value occurred in pedocal in China (Fig. 4b). For primarosols, the average CIC value (17.17 ± 3.20 mm) was the highest in the YTRB, followed by that in the YRB and HRB, and the lowest average CIC value was observed in the LRB (Fig. 4b). The ferralsol and anthrosol soil types in southern China had significantly lower average LWHC values than the alpine soil, pedocal, semi-hydromorphic soil types in northern China (Table 3). For anthrosol, the LWHC value in the YTRB (13.98 ± 8.92 mm) was far higher than that in the SERB (1.81 ± 0.14 mm) (Fig. 4b). The LWHC value (6.53 ± 0.80 mm) for primarosols was the highest in the LRB, followed by the HRB and YRB, while the lowest one was in the YTRB (Fig. 4b). The LWHC value of alpine soil in the NWRB (8.99 ± 1.61 mm) was approximately four times higher than that in the YTRB (2.79 ± 0.62 mm) (Fig. 4b). The maximum SSC value was observed in semi-alfisol, followed by ferralsol and alpine soil, which were significantly higher than those in the other soil types. The lowest average SSC value in China was observed in semi-hydromorphic soil (Table 3). The SSC value of pedocal in the NWRB (69.93 ± 10.54 mm) was approximately two times higher than that in the YRB (33.61 ± 8.51 mm) (Fig. 4b). Similar to SSC, the highest average WRC value was observed in semi-alfisol, followed by ferralsol, and lower WRC values were observed in pedocal and semi-hydromorphic soil in China because the SSC values accounted for 61.02–84.30% of the WRC values (Table 3). For alfisol and primarosols, the SSC and WRC values were the highest in the YTRB, followed by the HRB, and the lowest values were observed in the LRB (Fig. 4b).

3.2.2. Factors influencing the LWHC values The path model for the LWHC values exhibited a good fit (df = 11, F = 76.12, RMSE = 0.058) (Fig. 7 and Table 5) and explained 97% of the variation in the LWHC values. Although latitude had a negative direct effect on the LWHC values, it had a positive indirect effect that was mediated by temperature and litter storage. The negative effects of precipitation, temperature, elevation and aspect on the LWHC values were combinations of direct and indirect effects. The direct effects of litter storage, litter thickness and forest age on the LWHC values led to positive effects on the LWHC values. According to the path analysis of the LWHC values in various basins (Table 5), litter storage was the most significant factor affecting the LWHC values in the different basins. Among the climatic factors, precipitation had a significant impact on the LWHC values in the PRB. The positive effect of temperature on the LWHC values in the YRB was opposite the pattern observed in the HRB, YTRB and PRB. Among the location and terrain factors, longitude and terrain factors played significant roles in northern China (SHRB + LRB, HRB and YTRB). The effect of aspect on the LWHC values in the YRB was positive, which was different from the effects in the SHRB + LRB and PRB. Among the canopy structure factors, LAI in the HRB and canopy density in the NWRB and YTRB positively affected the LWHC values. According to the scatterplots between the LWHC values and significant factors, the LWHC values were negatively correlated with the highest daily precipitation (Fig. 8a) and mean annual temperature (Fig. 8b), and positively correlated with latitude (Fig. 8c). The LWHC values changed with elevation, as demonstrated by a unimodal curve with a peak value at the elevation of 1000 m (Fig. 8d). As shown in Fig. 8e, a higher aspect value represented sunnier conditions, and the LWHC values tended to be high in locations with a north-facing aspect. There were significant positive correlations between the LWHC values and litter storage (Fig. 8g) and litter thickness (Fig. 8h).

3.2. Factors influencing the CIC, LWHC, SSC and WRC values 3.2.1. Factors influencing the CIC values The path model for the CIC values explained 97% of their variance, and the model exhibited a good fit (df = 9, F = 18.57, RMSE = 0.054) (Fig. 5 and Table 4). The positive effects of precipitation, temperature and longitude on the CIC values included combinations of direct and indirect effects. The indirect effects of latitude and elevation on the CIC values were mediated by precipitation, which led to negative effects of latitude and elevation on the CIC values. Among the different basins, precipitation was the dominant climatic factor affecting the CIC values in the HRB, YRB and YTRB, and terrain factors were the most significant factors affecting the CIC values in the SHRB + LRB (Table 4). The effect of slope on the CIC values was positive in the YRB, which was opposite to the effect observed in the SHRB + LRB. The effect of aspect on the CIC values was positive in the HRB, which was also opposite to the effect observed in the SHRB + LRB. The canopy structure factors, such as the FVC, LAI and canopy density, had positive impacts on the CIC values across China, and in SHRB + LRB, HRB, YRB and YTRB. According to the scatterplots between the CIC values and significant

3.2.3. Factors influencing the SSC values The path model for the SSC values exhibited a good fit (df = 9, F = 34.47, RMSE = 0.052) (Fig. 9 and Table 6) and explained 95% of the variation in the SSC values. Soil depth and total porosity had significant positive effects on the SSC values, and capillary porosity had a negative effect on the SSC values. The positive indirect effect of temperature and the negative direct effect of latitude on the SSC values caused the total effects of these factors on the SSC values to be 0.04 and 8

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Fig. 5. Conceptual model on the CIC values that was used for the path analysis. (Boxes represent the different variables. Black line arrows indicate direct effects, and values associated with lines represent direct path coefficients. Colored arrows indicate the relationships between variables, and values associated with lines represent the correlation coefficient between variables to calculate the indirect path coefficients of one variable on CIC values. Each sub-path in the entire path model is significant.)

−0.16, respectively. The direct and total effects of litter storage on the SSC values were positive. The indirect effect of canopy density through capillary porosity was negative, causing the total effects of canopy density to be −0.03. According to the path analysis of the SSC values in various basins (Table 6), soil depth, capillary porosity and total porosity strongly influenced the SSC values in different basins. The total porosity had indirect and total negative effects on the SSC values in the SHRB + LRB, which differed from the effects of total porosity in other basins. Among the climatic factors, precipitation had a significant positive impact on the SSC values in the SHRB + LRB, and temperature had significant positive impacts on the SSC values in the YTRB and PRB. The effect of longitude on the SSC values in the SERB was opposite that in the YRB and YTRB. Terrain factors (elevation and aspect) had significant

impacts on the SSC values in the different basins (except the PRB). FVC and LAI in the YRB, SERB and PRB and litter storage in the HRB, YRB and YTRB had significant positive effects on the SSC values. According to the scatterplots between the SSC values and significant factors, the SSC values increased with increasing temperature for temperatures of 15 °C and above (Fig. 10a). There were negative correlations between the SSC values and latitude (Fig. 10b), capillary porosity (Fig. 10g) and bulk density (Fig. 10i), but there was a positive correlation between the SSC values and total porosity (Fig. 10h). High SSC values tended to be found in locations with a north-facing aspect (Fig. 10c). The SSC values exhibited a positive linear correlation with soil depth (Fig. 10f). Additionally, there were positive correlations between the SSC values and canopy density (Fig. 10d) and litter storage (Fig. 10e).

Table 4 Total (direct) path coefficients of path analysis on the CIC values in China and various basins. Factors

China

Precipitation Temperature Longitude Latitude Elevation Aspect Slope FVC LAI Forest density Canopy density Crown width R2

0.79 (4.78) 0.73 (-1.28) 0.34 (-1.23) −0.62 (2.26) −0.46 (0.80)

0.19 (0.75) 0.20 (0.91)

SHRB + LRB

HRB

YRB

YTRB

0.57 (1.25)

0.90 (0.96)

0.62 (0.48)

−0.12 (1.21) 0.43 (0.14) 0.13 (0.67) 0.21 (-0.34)

−0.12 (−1.53) −0.38 (−1.40) 0.15 (0.92) 0.16 (0.70)

0.55 (0.27) 0.35 (0.19) 0.19 (0.27)

0.20 (0.21) 0.27 (0.50) 0.97

0.15 (0.22) 0.90

0.97

0.03 (0.30) 0.95

0.90

Note: Direct path coefficients indicate direct effects of factors, and indirect path coefficients indicate indirect effects. The total path coefficients of the factors equal the sum of the direct and indirect effects. The absolute values of the decision coefficients indicate the magnitude of the factor effects. R2 values close to 1 indicate that much of the total variance is explained in our path analysis. 9

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Fig. 6. Scatterplots between the CIC values and significant factors, including (a) the highest daily precipitation, (b) mean annual temperature, (c) longitude, (d) latitude, (e) elevation, (f) FVC, (g) LAI, (h) canopy density and (i) crown width. (The two dashed red lines indicate the 95% confidence interval of the regressions; R2 is the coefficient of determination after fitting).

Fig. 7. Conceptual model on the LWHC values that was used for the path analysis. (Boxes represent the different variables. Black line arrows indicate direct effects, and values associated with lines represent direct path coefficients. Colored arrows indicate the relationships between variables, and values associated with lines represent the correlation coefficient between variables to calculate the indirect path coefficients of one variable on LWHC values. Each sub-path in the entire path model is significant.). 10

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Table 5 Total (direct) path coefficients of path analysis on the LWHC values in China and various basins. Factors

China

Precipitation Temperature Longitude Latitude Elevation Aspect Slope LAI Canopy density Forest age Litter storage Litter thickness R2

−0.34 (0.20) −0.61 (−0.63)

SHRB + LRB

−0.33 (−0.12) 0.39 (−0.58) −0.01 (−0.34) −0.34 (−0.15)

NWRB

HRB

YRB

YTRB

PRB

0.08 (−0.15) −0.39 (−0.35)

−0.41 (−0.54)

−0.31 (0.18) −0.74 (−0.43)

−0.25 (0.14)

−0.88 (−0.15) 0.33 (−0.22)

0.16 (−0.34) 0.23 (0.37) −0.27 (−0.15)

−0.41 (−0.11) −0.13 (0.16)

0.17 (0.28) −0.10 (−0.37) 0.11 (0.01) 0.77 (0.19)

0.21 (0.18) 0.83 (0.84) 0.02 (0.18) 0.97

0.73 (0.91) −0.15 (0.17) 0.90

0.59 (0.32)

0.94 (0.88)

0.97 (0.96)

0.85 (0.83)

0.76 (0.69)

0.98

0.91

0.90

0.90

0.93 (0.55) 0.80 (0.19) 0.99

Note: Direct path coefficients indicate direct effects of factors, and indirect path coefficients indicate indirect effects. The total path coefficients of the factors equal the sum of the direct and indirect effects. The absolute values of the decision coefficients indicate the magnitude of the factor effects. R2 values close to 1 indicate that much of the total variance was explained in our path analysis.

differed greatly between the SHRB + LRB and the PRB. Elevation, canopy structure and litter thickness had significant positive impacts on the WRC values in the SHRB + LRB. However, precipitation and latitude significantly affected the WRC values in the PRB. In the YRB and YTRB, precipitation, aspect, LAI and canopy density significantly affected the WRC values. According to the scatterplots between the WRC values and significant factors, the WRC values dramatically increased as the highest daily precipitation increased from 100 mm to 150 mm (Fig. 12a). The WRC values decreased slightly from west to east (Fig. 12b). High WRC values tended to be found in locations with north-facing or southwestfacing aspects (Fig. 12d). The WRC values were positively correlated with the soil depth from 30 cm to 100 cm (Fig. 12g) and total porosity (when total porosity was greater than 40%) (Fig. 12i). In contrast, the correlations of the WRC values with capillary porosity (Fig. 12h) and bulk density (Fig. 12j) were negative. The WRC values were high when LAI (Fig. 12e) and litter storage (Fig. 12f) were high in our research.

3.2.4. Factors influencing the WRC values The path model for the WRC values exhibited a good fit (df = 10, F = 66.57, RMSE = 0.045) (Fig. 11 and Table 7) and explained 96% of the variation in the WRC values. Soil depth, capillary porosity and total porosity had the greatest influence on the WRC values, and their effects on the WRC values were similar to those on the SSC values. The effects of litter storage and LAI on the WRC values involved combinations of positive direct effects and negative indirect effects. The significant effect of precipitation on the WRC values was positive. The direct and total effects of longitude, aspect and slope on the WRC values were negative. According to the path analysis on the WRC values in various basins (Table 7), soil depth, capillary porosity and total porosity were the dominant factors affecting the WRC values in the different basins. Moreover, the bulk density and litter storage also had significant negative and positive impacts on the WRC values in different basins, respectively. In addition to the factors mentioned above, other factors

Fig. 8. Scatterplots between the LWHC values and significant factors, including (a) the highest daily precipitation, (b) mean annual temperature, (c) latitude, (d) elevation, (e) aspect, (f) forest age, (g) litter storage and (h) litter thickness. (The two dashed red lines indicate the 95% confidence interval of the regressions; R2 is the coefficient of determination after fitting).

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Fig. 9. Conceptual model on the SSC values that was used for the path analysis. (Boxes represent the different variables. Black line arrows indicate direct effects, and values associated with lines represent direct path coefficients. Colored arrows indicate the relationships between variables, and values associated with lines represent the correlation coefficient between variables to calculate the indirect path coefficients of one variable on SSC values. Each sub-path in the entire path model is significant.)

4. Discussion

et al. (2003) (0–8 mm). In northern China, the LWHC values of Platycladus orientalis, Pinus tabulaeformis, Quercus variabilis and Acer truncatum were between 2.36 mm and 5.38 mm (Li et al., 2017). We also found that the ranges of SSC values were similar to the results of Liu et al. (2003), who found that the SSC values ranged from 36 to 142 mm, with an average of 89 mm. The total watershed SSC values increased from 30 to 57 mm in the Yanduhe Basin, which is located in the YTRB (Guo et al., 2015), and the ranges of SSC values were consistent with those found in our study in the YTRB. We found that the WRC values followed a decreasing trend from south to north in China, which was consistent with the trends estimated by an empirical model (Wen and Liu, 1995) and the InVEST model (Ouyang et al., 2016; Gong et al., 2017). The ranges of WRC values throughout China in our study were

4.1. Spatial trends of the CIC, LWHC, SSC and WRC values The results showed that the CIC decreased from south to north in China. This result was consistent with the spatial trends of the CIC values identified in Liu et al. (2003), which were based on 20 forest ecological stations in different bioregions of China. At the same time, the CIC values in cold and temperate forests were lower than those in subtropical and tropical forests, and the LWHC values in cold and temperate forests were higher than those in subtropical and tropical forests (Liu et al., 2003), which was consistent with our results. The ranges of LWHC values in our study were consistent with those in Liu

Table 6 Total (direct) path coefficients of path analysis on the SSC values in China and various basins. Factors Precipitation Temperature Longitude Latitude Elevation Aspect FVC LAI Canopy density Litter storage Soil depth Capillary porosity Total porosity Bulk density R2

China

SHRB + LRB

HRB

YRB

YTRB

SERB

PRB

0.69 (0.03) 0.04 (−0.25) −0.16 (−0.12) −0.16 (−0.18) 0.58 (0.06)

0.83 (0.14)

−0.31 (−0.10)

−0.03 (0.09) 0.20 (0.07) 0.64 (0.67) −0.46 (−1.19) 0.48 (1.10) −0.02 (−0.12) 0.95

0.06 (0.27) −0.07 (−0.03) −0.22 (0.10) 0.23 (0.28)

−0.14 (−0.03) 0.12 (0.05)

0.55 (−0.17) 0.19 (0.15)

0.45 (0.06)

0.18 (0.51) −0.52 (−3.55) −0.33 (3.03)

0.23 (0.42) 0.64 (0.94) −0.63 (−0.44) 0.12 (0.63)

0.01 (0.06) 0.64 (0.52) −0.36 (−1.34) 0.12 (1.19)

0.31 (0.09) 0.78 (0.63) −0.50 (−0.68) 0.13 (0.91)

0.99

0.98

0.98

0.90

0.19 (0.10)

0.25 (0.40) 0.53 (0.33)

0.82 (0.47) −0.01 (−0.65) 0.58 (0.99) −0.47 (−0.08) 0.99

0.85 (1.03) −0.22 (0.51) 0.22 (0.63) −0.76 (−1.77) 0.96

Note: Direct path coefficients indicate direct effects of factors, and indirect path coefficients indicate indirect effects. The total path coefficients of the factors equal the sum of the direct and indirect effects. The absolute values of the decision coefficients indicate the magnitude of the factor effects. R2 values close to 1 indicate that much of the total variance was explained in our path analysis. 12

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Fig. 10. Scatterplots between the SSC values and significant factors, including (a) mean annual temperature, (b) latitude, (c) aspect, (d) canopy density, (e) litter storage, (f) soil depth, (g) capillary porosity, (h) total porosity and (i) soil bulk density. (The two dashed red lines indicate the 95% confidence interval of the regressions; R2 is the coefficient of determination after fitting).

were consistent with those found in Sun et al. (2013). The gradual saturation of the CIC values with an increase in precipitation may explain this trend (Schellekens et al., 2000; Pypker et al., 2006; Sun et al., 2013). The increase in temperature increased the ET rate and delayed the saturation of canopy interception (Xu et al., 2006; Klamerus-Iwan and Błońska, 2018). Latitude and elevation influenced the CIC values via precipitation distributions in China (Liu et al., 2003). Forest structure positively influenced the CIC to some extent (Wang et al., 2012;

also similar to those estimated in Wen and Liu (1995) (from 40 mm to 170 mm). The results of the present study may be used as a benchmark for remote sensing or ecosystem model-based studies.

4.2. Influence mechanism of factors affecting the CIC, LWHC, SSC and WRC values The trends of the CIC values with the highest daily precipitation

Fig. 11. Conceptual model on the WRC values that was used for the path analysis. (Boxes represent the different variables. Black line arrows indicate direct effects, and values associated with lines represent direct path coefficients. Colored arrows indicate the relationships between variables, and values associated with lines represent the correlation coefficient between variables to calculate the indirect path coefficients of one variable on WRC values. Each sub-path in the entire path model is significant.) 13

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Table 7 Total (direct) path coefficients of path analysis on the WRC values in China and various basins. Factors

China

Precipitation Longitude Latitude Elevation Aspect Slope LAI Forest age Canopy density Litter storage Litter thickness Soil depth Capillary porosity Total porosity Bulk density R2

0.11 (0.16) −0.15 (−0.10)

SHRB + LRB

YRB

YTRB

PRB

0.33 (0.10)

0.50 (0.09)

0.37 (0.08) −0.10 (0.14)

0.66 (0.76) −0.33 (−0.10) −0.10 (−0.11) 0.07 (0.13)

0.01 (0.21) 0.65 (0.66) −0.21 (−1.18) 0.14 (1.15) −0.04 (−0.10) 0.96

0.24 (0.23) 0.17 (0.17) 0.56 (0.58) 0.20 (0.11) 0.38 (0.45) −0.53 (−1.12) −0.53 (1.18) −0.38 (−0.19) 0.99

−0.50 (−0.10)

−0.30 (−0.17)

0.50 (0.23)

0.31 (0.11)

0.05 (0.04) 0.06 (0.17)

0.04 (0.23)

0.30 (0.09)

0.69 (0.76) −0.72 (−0.98) 0.24 (0.82) −0.45 (−0.17) 0.98

0.73 (0.53) −0.73 (−1.29) 0.47 (0.94) −0.31 (−0.30) 0.99

0.49 (0.60) −0.37 (−0.96) 0.37 (0.67) −0.20 (−0.30) 0.96

Note: Direct path coefficients indicate direct effects of factors, and indirect path coefficients indicate indirect effects. The total path coefficients of the factors equal the sum of the direct and indirect effects. The absolute values of the decision coefficients indicate the magnitude of the factor effects. R2 values close to 1 indicate that much of the total variance was explained in our path analysis.

8.22, 7.61, and 10.78 mm, respectively, which is consistent with our results for the trend of LWHC values with forest age. The soil bulk density increased with increasing soil depth, while the SSC values and total porosity gradually decreased with increasing soil bulk density (Li et al., 2015). Aspect was the important factor that controlled the SSC values at shallow soil depths (Mei et al., 2018). Soil depth was a dominant factor that governed the changes in the SSC values (Ilek et al., 2015), as thick soil layers can store rainwater temporarily and drain it gradually (Ohnuki et al., 2008). Increased canopy density and litter storage promote soil structural changes and improve soil nutrient contents (Xu et al., 2013; Ilek et al., 2015). The correlation between WRC values and precipitation in our study

Caldwell et al., 2016; Sun et al., 2018). The distributions of forest structure characteristics, such as the increasing LAI (Tang et al., 2007) and NDVI (Guo et al., 2017) from northwestern to southeastern China, also positively influenced the CIC values. The increases in precipitation and temperature increased the litter decomposition rate (Peng and Liu, 2002; Cornwell et al., 2008), and latitude affected the LWHC values via precipitation and temperature. South-facing aspects are usually sunny and promote the decomposition of litter, which may explain the trend of the LWHC values with aspect. LWHC values have a strong linear relationship with litter storage regardless of litter type (Sato et al., 2004). Sun et al. (2013) found that the LWHC values of young, middle-aged, and mature forest stands were

Fig. 12. Scatterplots between the WRC values and significant factors, including (a) the highest daily precipitation, (b) longitude, (c) slope, (d) aspect, (e) LAI, (f) litter storage, (g) soil depth, (h) capillary porosity, (i) total porosity and (j) bulk density. (The two dashed red lines indicate the 95% confidence interval of the regressions; R2 is the coefficient of determination after fitting).

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Appendix A. Supplementary data

was consistent with the results of Sun et al. (2013) and Su and Fu (2013). Slope was the key factor affecting WRC, and the flat slope had the highest WRC value (Zhang et al., 2011), which is consistent with our result for the trend of WRC values with slope. The soil layer was the major contributor to the WRC service (Zhang et al., 2010). Forest structure, including LAI and litter storage, can result in changes in WRC values (Caldwell et al., 2016).

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5. Conclusions First, the average CIC (LWHC) values of the different forest types (TMR, SNF and SDBF) and soil types (ferralsol, anthrosol and primarosols) distributed in southern China (PRB, SERB, SWRB and YTRB) were approximately two (1/2) times higher (lower) than those of the forest types (CTMNF and TDBF) and soil types (semi-alfisol, pedocal and semi-hydromorphic soil) in northern China (SHRB, LRB and NWRB). Second, the average SSC values for the SWRB and PRB and the semi-alfisol and ferralsol soil types were 2.5 times higher than those for the SHRB, LRB and HuRB and the semi-hydromorphic soil and pedocal soil types. Finally, the SSC values contributed approximately 60–85% to the WRC values in the different basins, soil types and forest types in China. The SSC values accounted for high percentages (79–85%) of the WRC values in the HRB and SWRB, in the SL and TDBF forest types, and in the alpine soil and semi-alfisol soil types. Most of the factors had consistent effects on the CIC, LWHC, SSC and WRC values at both national and basin scales in China. However, the effects of some locations, terrains and canopy structures on the CIC (including factors related to aspect, slope and forest density), LWHC (including factors related to longitude, slope, LAI and canopy density), SSC (including factors related to longitude, elevation, FVC and LAI) and WRC (including factors related to latitude, elevation, forest age and canopy density) values were significant in only certain basins and not throughout China. In addition, the direct and total effects of most of the factors on the CIC, LWHC, SSC and WRC values were consistent. In contrast, the indirect effects of some factors (temperature on CIC, LWHC and SSC; latitude on CIC, LWHC, SSC and WRC; elevation, aspect and slope on CIC and LWHC) determined the total effects because the climate, location and terrain factors affected these values through changes in hydrothermal conditions. This study highlights that there are large spatial variations in the distributions of and influencing factors of CIC, LWHC, SSC and WRC at different scales in China. These results, combined with some social factors affecting the WRC in different areas, such as human population density, the demands of food and fresh water, social economy and urban and rural built-up land, can provide a scientific basis for making decisions regarding forest protection and forest ecosystem services. Author Contributions X.W. outlined the paper, collected and analysed the data and wrote the paper. W.S. proposed the scientific hypothesis, conceived and designed the study and wrote the paper. B.G. reviewed the manuscript, gave some comments and helped writing. F.T. proposed the scientific hypothesis, provided suggestions and helped writing. Acknowledgments This study was supported by the National Key Research and Development Program of China (2017YFA0604703), Fund for National Natural Science Foundation of China (41771111), Excellent Young Talents in Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (2016RC201), Youth Innovation Promotion Association (2018071), the Natural Science Foundation of Hebei Province (No. D2019205123) and Chinese land surveying and Planning Institute Outsourcing Project (2018121101356). 15

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