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Integrating territorial pattern changes into the relationship between carbon sequestration and water yield in the Yangtze River Basin, China

Carbon Balance and Management volume 20, Article number: 1 (2025) Cite this article

Abstract

Territorial pattern plays an important role in regional ecosystem management and service provision. It is significant to demonstrate the coordination relationships between the territorial space evolutions and ecosystem services for sustainable regional development. This study focused on quantifying the impacts of production-living-ecological space change on carbon sequestration and water yield in the upper and middle-lower reaches of the Yangtze River Basin. Our results indicated that the production-living-ecological space variation trends are similar between the upper and middle-lower reaches during 2000–2020, while their impacts on ecosystem services are different in their respective regions. In the upper reaches, the changes in production and ecological space had a direct positive impact on NPP while the changes of living space had a negative impact on the NPP. However, the changes of production-living-ecological space had no significant effects on the water yield. In contrast, the changes of production and ecological space had no significant effect on the NPP in the middle-lower reaches, while the changes of ecological space had a positive effect on the water yield. Additionally, we also found that social-economic factors had no significant effects on the changes of ecological space in the middle-lower reaches of the Basin. We suggested that policy makers need to optimize the distribution of territorial space in order to maintain sustainable development.

Introduction

The territorial pattern supports many ecosystem services, including the various benefits that human acquires from ecosystems and provide a platform for linking human society and natural environment [6, 7]. Ecosystem services can be seen as the contribution of ecosystems to human well-being and play an important role in sustaining people’s survival and development [25, 24]. However, the Millennium Ecosystem Assessment [6} represented that approximately 60% of global ecosystem services have been degraded by human activities over the past half-century. Many previous studies reported that territorial pattern change was an important driving factor that led to ecosystem service changes [11, 13, 20, 27]. With the increasing population, urban expansion, and rapid economic growth in China, regional territorial patterns were changed dramatically [39]. This change has greatly increased the demand for natural resources and put unprecedented pressure on regional ecosystem services [8, 37]. Therefore, more and more scientists and policy makers are focusing on researching the relationship between territorial pattern changes and ecosystem services [46].

The territorial pattern is multifunctional, often containing production, ecological and living functions [1]. Currently, rational planning of the production-living-ecological space has become one of the core strategies on the sustainable development of China [49]. The spatial variations of production-living-ecological space will change the function and structure of the regional ecosystem directly, thus impacting the supplying of ecosystem services at different scales [10]. However, research on the relationships between production-living-ecological space and regional ecosystem service is still scarce. We should understand the variations of production-living-ecological space and their effects on regional supplying of ecosystem services, which is crucial for exploring regional sustainable development and ecological security [47].

Quantitative evaluation of net primary productivity (NPP) and water yield is valuable for understanding trend in the functions of carbon sequestration and water supply in a regional ecosystem [21, 41]. Because the water availability is a key factor impacting regional NPP and its spatial pattern while NPP is an important indicator of water yield function in basin area, these two ecosystem services are crucial for the sustainable development of region [51]. However, climate change and human activities are having multiple impacts on these two ecosystem services in global and regional watersheds, which brings many uncertainties to the assessment and prediction of regional sustainable development [14, 28, 42].

Simulation analysis is an efficient way that can help better quantify the impacts of land spatial change on ecosystem services and compare driving mechanisms between human activities and natural factors [26, 53]. Regional carbon dynamic assessment could be realized by a process-based and light-use efficiency model, which is the Carnegie-Ames-Stanford Approach (CASA) [44]. Meanwhile the water yield module in the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model [14, 38] is a regional scale model based on the water balance principles. Specifically, the CASA model and InVEST model have been widely applied to estimate carbon sequestration and water yield, respectively, in different regions of China [20, 21, 29, 50].

This study selected the Yangtze River Basin (YRB), a vital region in China, as the research area. With the rapid economic growth, the production-living-ecological space has changed over the past two decades in this region. We combined the data-modeling fusion method and Partial Least Squares Path Modeling analysis to reveal the relationships between the changes in production-living-ecological space and regional ecosystem services from 2000 to 2020 in the upper and middle-lower reaches of YRB. The objectives of our study include: (1) to evaluate the changes in production-living-ecological space during 2000–2020; (2) to simulate and analyze the spatial variations of carbon sequestration and water yield; (3) to reveal the effects of production-living-ecological space changes on carbon sequestration and water yield in the upper and middle-lower reaches of YRB. This research could provide a theoretical basis for elevating the coordinated development of production-living-ecological spaces in the Yangtze River Basin, and thereby contribute to the carbon and water balance for regional sustainment development in the future.

Methods

The study area

The Yangtze River Basin, which lies between 24°30′–35°45′N and 90°33′–122°25′E in China (Fig. 1) covering an area of 1.8 million km2, accounts for about 18.8% of the territory of China [52]. There are 19 provinces with a total population of 400 million in this study area. In the whole basin, the historical annual mean temperature is about 12–18℃ and tends to be higher in the east and lower in the west. The average annual precipitation ranges from 1000 to 1400 mm [43]. In this study, the river is segmented into the upper and middle-lower reaches with Yichang city as the boundary [48].

Fig. 1
figure 1

Location of the study area and Yangtze River Basin

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Data

We obtained the monthly mean temperature and precipitation data with a 1 km resolution from Resource and Environment Science and Data Center (https://www.resdc.cn/). The monthly total radiation maps in 2000, 2005, 2010, 2015, and 2020 were estimated by interpolating the observations of meteorological stations (http://data.cma.cn). The DEM data were derived from ASTER Global Digital Elevation Model V002 (http://www.gscloud.cn/). While soil maps with 30 arc-second resolution were acquired from the Cold and Arid Regions Sciences Data Center at Lanzhou (http://westdc.westgis.ac.cn/). The monthly NDVI data with 1 km resolution were obtained from MODIS-NDVI products (MOD13Q1) (http://modis.gsfc.nasa.gov/). Moreover, we also obtained the socio-economic data from China City Statistical Yearbooks, including total population (TP), the proportion of the urban population (PUP), gross regional product (GRP), and the number of industrial Enterprises above Designated Size (NIEDS).

Production-living-ecological space integrates economic activities (production), residential areas (living), and natural environments (ecological) into a sustainable and harmonious development model, and aims to balance the needs of economic growth, human habitation, and environmental sustainability. Production space is the areas designated for economic activities such as industrial zones, business districts, and agricultural lands. Living space pertains to residential areas where people live and spend their daily lives. Ecological space is the natural environment, including green spaces, water bodies, and other areas that support biodiversity and ecological balance. Because the concept of the production-living-ecological space originates from human-land couplings [49], production space, living space, and ecological space were classified based on the function of land use in this study [5, 22]. The land use maps with a resolution of 1 km in 2000, 2005, 2010, 2015, and 2020 were derived from Resource and Environment Science and Data Center (https://www.resdc.cn/) (Fig. 2), which contains 7 classes: Built-up land, Grassland, Cropland, Forestland, Water, Desert (Sand land), and Bare land (Table S1).

Fig. 2
figure 2

Spatially explicit land use maps of the Yangtze River Basin in (a) 2000, (b) 2005, (c) 2010, (d) 2015, and (e) 2020

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In this study, we focus on the influence of land spatial change on two ecosystem services (Fig. 3). Firstly, the obtained data were input into the ecological models for quantization and assessment of the carbon sequestration and water yield. Secondly, the spatial variation of the two services in the YRB was analyzed by the spatial analysis tool. Finally, we adopted a structural equation model to explore the influence of various factors on the relationships among landscape configuration, carbon sequestration service, and water yield service.

Fig. 3
figure 3

Data processing flowchart in this study

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Ecosystem service assessment

Carbon sequestration

In this study, the CASA model was applied to calculate the carbon dynamics at a regional scale [21, 29]. Absorbed photosynthetic active solar radiation (APAR) and light-use efficiency (ε) were two important parameters for the model to calculate NPP [30]. We calculated the APAR as follows Eq. (1):

$$\:\text{A}\text{P}\text{A}\text{R}(x,\:t)=\text{S}\text{O}\text{L}(x,\:t)\times\:\text{F}\text{P}\text{A}\text{R}(x,\:t)\times\:0.5$$
(1)

where SOL is the total solar radiation. t is time while x is the spatial location. FPAR (see Eq. (2)) is the fraction of the photosynthetically active radiation absorbed by vegetation canopy, which is calculated by FPAR(x, t)NDVI (see Eq. (3)) and FPAR(x, t)SR (see Eq. (4)):

$$\:\text{F}\text{P}\text{A}\text{R}\left(x,t\right)=\frac{{\text{F}\text{P}\text{A}\text{R}\left(x,t\right)}_{\text{N}\text{D}\text{V}\text{I}}+{\text{F}\text{P}\text{A}\text{R}\left(x,t\right)}_{\text{S}\text{R}}}{2}$$
(2)
$$\eqalign{& \:{\text{FPAR}}{(x,t)_{{\text{NDVI}}}} \cr & = \frac{{\left( {{\text{NDVI}}\left( {x,t} \right) - {\text{NDV}}{{\text{I}}_{i,min}}} \right)({\text{FPA}}{{\text{R}}_{{\text{max}}}} - {\text{FPA}}{{\text{R}}_{{\text{min}}}})}}{{({\text{NDV}}{{\text{I}}_{i,{\text{max}}}} - {\text{NDV}}{{\text{I}}_{{\text{i}},{\text{min}}}})}} \cr & + {\text{FPA}}{{\text{R}}_{{\text{min}}}} \cr}$$
(3)
$$\eqalign{& \:{\text{FPAR}}{(x,t)_{{\text{SR}}}} \cr & = \frac{{({\text{SR}}\left( {x,t} \right) - {\text{S}}{{\text{R}}_{i,{\text{min}}}})({\text{FPA}}{{\text{R}}_{{\text{max}}}} - {\text{FPA}}{{\text{R}}_{{\text{min}}}})}}{{{\text{S}}{{\text{R}}_{i,{\text{max}}}} - {\text{S}}{{\text{R}}_{i,{\text{min}}}}}} \cr & + {\text{FPA}}{{\text{R}}_{{\text{min}}}} \cr}$$
(4)

FPARmax and FPARmin are the maximum (i.e. 0.950) and minimum (i.e. 0.001) values of FPAR in this study, respectively. NDVIi,min and NDVIi,max are the minimum and maximum values of the NDVI for the land use type of i in the t month (Table S2).

SR(x, t) is the simple ratio of NDVI in x pixel and t month (see Eq. (5)).

$$\:\:\text{S}\text{R}\left(x,t\right)=\frac{1+\text{N}\text{D}\text{V}\text{I}(x,t)}{1-\text{N}\text{D}\text{V}\text{I}(x,t)}\:$$
(5)

SRi,max and SRi,min are the maximum and minimum values of the SR for the land use type of i in the t month, respectively (Table S2).

ε is the light use efficiency, which indicates the efficiency of plant to transfer absorbed photosynthetic effective solar radiation into organic carbon [21] (see Eq. (6)):

$$\eqalign{& \varepsilon \left( {x,t} \right) = {T_{\varepsilon 1}}\left( {x,t} \right) \times {T_{\varepsilon 2}}\left( {x,t} \right) \cr & \times {W_\varepsilon }\left( {x,t} \right) \times {\varepsilon _{\max }} \cr}$$
(6)

where Tɛ1(x, t) and Tɛ2(x, t) indicate the reduction in light-use efficiency caused by the temperature factor; Wɛ(x, t) reflects the reduction in light-use efficiency brought by the moisture factor. Meanwhile, εmax is the maximum light-use efficiency (Table S2) [44]. For more details about model parameters, see Liu et al., [21].

Water yield

The water yield was quantified using the InVEST model. Annual water yield was estimated by the Budko curve and annual average precipitation [35] (see Eq. (7)).

$$\:{Y}_{xj}=\left(1-\frac{{AET}_{xj}}{{P}_{x}}\right)\times\:{P}_{x}$$
(7)

where Yxj is the water yield in x pixel for the land-use type of j; AETxj refers to the actual evapotranspiration, and Px is the annual average precipitation in x pixel. The evapotranspiration partition\(\:\frac{{AET}_{xj}}{{P}_{x}}\) was calculated as following Eq. (8):

$$\:\frac{{AET}_{xj}}{{P}_{x}}=\frac{1+{\omega\:}_{x}\times\:{R}_{xj}}{1+{\omega\:}_{x}\times\:{R}_{xj}+\frac{1}{{R}_{xj}}}$$
(8)

where ωx (see Eq. (9)) is the plant available water coefficient in x pixel; Rxj (see Eq. (10)) is the dimensionless Budyko Dryness index of the cell.

$$\:{\omega\:}_{x}=\text{Z}\times\:\frac{{AWC}_{x}}{{P}_{x}}$$
(9)
$$\:{R}_{xj}=\frac{{k}_{xj}\times\:{ET}_{0x}}{{P}_{x}}$$
(10)

where Z is a hydro-geological parameter, which can characterize the seasonal rainfall distribution on basin, ranging from 1 to 30. AWCx is the plant-available water content. kxj refers to the plant evapotranspiration in x pixel for land-use type of j. ET0x is the reference evapotranspiration in x pixel (Hargreaves and Allen, 2003) (see Eq. (11)).

$$\eqalign{& \:E{T_0} = 0.0023 \times \:{R_a} \cr & \times \:\left[ {\left( {{T_{mzx}} + {T_{min}}} \right) \div 2 + 17.8} \right] \cr & \times \:{\left( {{T_{mzx}} - {T_{min}}} \right)^{0.5}} \cr}$$
(11)

where Ra represents the extraterrestrial radiation; Tmin and Tmax refer to the daily minimum and maximum temperatures, respectively.

Statistical analysis

Trend analysis

The least-square linear regression model is applied to analyze the temporal variation. The modeled slope represented the changing trend. While the t statistic is adopted to test the significance of the modelled slope, which is documented by the p value.

Spearman’s rank correlation

Spearman’s rank correlation method is utilized to measure the correlation between various indicators. Its characteristics rank the data and is non-parametric which does not depend on data belonging to any particular distribution. In this study, the Spearman’s rank correlation was used to identify the association among the various driving factors at the regional scale.

Partial least squares path modeling

Partial Least Squares Path Modeling (PLS-PM), a PLS approach to structural equation modeling [40], is an approach for analyzing systems of relationships between multiple sets of variables [34]. In this study, PLS-PM was used to explore the pathways of how production–living–ecological space, through affecting factors considered in the linear regressions, affected carbon sequestration service and water yield service. PLS-PM analysis is performed using “plspm” package (version 0.4.9) in R software (version 4.1.2).

Results

Changes of territorial space

From 2000 to 2020, ecological space was always the largest proportion of land-use type (i.e. about 70% of the study area) in the whole YRB (Table S3). Meanwhile, we found that the proportion of ecological space in the upper reaches (i.e. 73.1—73.26%) was higher than in the middle-lower reaches (i.e. 60.65—61.35%) of the study area (Table 1). In the past 20 years, ecological space experienced no changed in the upper reaches and slightly decreased by about 0.7% in the middle-lower reaches of the basin. Specifically, except that forest land had a slightly increasing during 2000–2020, other land use types had varying degrees of decreasing. Moreover, the production space accounted for about 26.84–28.07% of the total drainage area from 2000 to 2020 (Table S3). In contrast to ecological space, the proportion of production space in the upper reaches (i.e. 25.04—26%) was lower than in the middle-lower reaches (i.e. 32.92—35.44%) of the study area (Table 1). Our results indicated that the production space exhibited a continuous decreasing trend both in the upper and middle-lower reaches of study area. Additionally, the living space was the smallest territorial space in YRB (Table S3). However, whether in the upper or middle-lower reaches, it had significantly increased during 2000–2020 (Table 1). Compared with 2000, living space increased by 88.89% and 99.69% in 2020 for the upper and middle-lower regions respectively. Overall, the changes of production-living-ecological space had a similar trend in the both upper and middle-lower reaches of YRB.

Table 1 Land use area and their changes between 2000 and 2020 in the upper and middle-lower reaches of the Yangtze River Basin
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Spatial variations of carbon sequestration and water yield

In the whole YRB, the mean annual NPP was 299.6 \(gC \cdot {m^{ - 2}}\) and showed a distinct heterogeneity that ranged between 0 and 576.8 \(gC \cdot {m^{ - 2}}\) from west to east during 2000–2020 (Fig. 4a). Annual NPP in the upper reaches (i.e. 305.3 \(gC \cdot {m^{ - 2}}\)) is slightly higher than in the middle-lower reaches (i.e. 293.0 \(gC \cdot {m^{ - 2}}\)) of the basin. The peak values of NPP were mostly concentrated in the southwest Yunnan Province, while the lowest values were distributed in the northwest. Our results indicated that the average change rate of NPP was 2.6 \(gC \cdot {m^{ - 2}}y{r^{ - 1}}\) in the whole YRB (Fig. 4b). 38.7% of the YRB was decreasing in NPP from 2000 to 2020, which was mainly distributed in the northwest and northeast. The lowest change rate of NPP was − 24.8 \(gC \cdot {m^{ - 2}}y{r^{ - 1}}\)in Dali city. Meanwhile, there were 55.2% of the study area that witnessed a significant increase in NPP, which was scattered in the southwest and southeast of the study area. There were about 6.1% of regions without obvious NPP changes in the YRB.

Fig. 4
figure 4

Spatial variations of (a) net primary productivity (NPP) and (b) change rate of NPP from 2000 to 2020 in the Yangtze River Basin

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The mean value of water yield in the study area was 814.2 \(kg \cdot {m^{ - 2}}\) and decreased from southeast to northwest during 2000–2020 (Fig. 5a). While the highest values (i.e. 1596.7 \(kg \cdot {m^{ - 2}}\)) occurred in the southeast of YRB. Due to the impacts of water from upstream, the water yield in the middle-lower reaches (i.e. 1068.2 \(kg \cdot {m^{ - 2}}\)) was significantly higher than in the upper reaches (i.e. 760.4 \(kg \cdot {m^{ - 2}}\)) of the YRB. In addition, during 2000–2020, the water yield had an obvious downward trend (i.e. -6.2 kg•m− 2 yr− 1) in the upper reaches but an upward trend (i.e. 8.6 \(kg \cdot {m^{ - 2}}y{r^{ - 1}}\)) in the middle-lower reaches of the study area (Fig. 5b). The largest reduction in water yield occurred in Henan, Chongqing, Sichuan, and Yunnan Provinces.

Fig. 5
figure 5

Spatial variations of (a) water yield and (b) change rate of water yield from 2000 to 2020 in the Yangtze River Basin

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The lower correlation coefficients between NPP and water yield were mainly distributed in the upper reaches of the study area (Fig. 6a). However, a higher correlation coefficient was in the middle-lower reaches of the YRB. The highest value of the correlation coefficient was exhibited in the south of Hunan and Jiangxi Provinces in the middle-lower reaches (Fig. 6a). Our results also demonstrated that the areas with increased both NPP and water yield were mainly located in the middle-lower reaches of the YRB (Fig. 6b). While the area with decreased of both NPP and water yield could be dispersedly observed in the upper reaches but sparsely distributed in the middle-lower reaches of the study area (Fig. 6b). In addition, the areas with decreased NPP and increased water yield were mainly located in the central and east parts of the middle-lower reaches region. Overall, both NPP and water yield had different trends of changes in the upper and middle-lower reaches of YRB.

Fig. 6
figure 6

Spatial relationship between NPP and water yield. (a) exhibited Spearman’s rank correlation between NPP and water yield at the pixel scale. (b) exhibited the changes in NPP and water yield

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Relationship between territorial space and ecosystem service

Through spearman’s rank correlation analysis, there was a significant negatively correlation between production and living space with a spearman’s correlation coefficient of -0.89 in the upstream of YRB, (Fig S1a). Among the socio-economic factors, the gross regional product (GRP) and the number of industrial enterprises above the designated size (NIEDS) were significantly correlated with a correlation coefficient of 0.90. Both GRP and NIEDS had a significant correlation (i.e. 0.91) with living space (Fig S1a). There was also a strong negative covariance between production space and the GRP as well as between production space and the NIEDS, with correlation coefficients of -0.81 and − 0.85, respectively (Fig S1a). However, there was no significant correlation between the production-living-ecological space and two ecosystem services in the upstream of YRB. Different from the upstream of YRB, there was a positive correlation between the NPP and production space with a spearman’s correlation coefficient of 0.43 in the middle-lower reaches (Fig S1b). The NPP also had a negative correlation with both ecological (i.e. -0.42) and living space (i.e. -0.36). Consistent with the upper reaches, between production and living space had a significant negative correlation by -0.96 (Fig S1b). While the GRP still had a significant positive correlation with living space (i.e. 0.89) and a significant negative correlation with production space (-0.87).

Based on the PLS-PM, we constructed a conceptual model with a goodness of fit by 0.61 and 0.64 in the upper and middle-lower reaches of YRB, respectively. The PLS-PM analysis indicated that social-economic had a positive impact on the changes of living space, but a negative impact on the changes of production and the ecological space in upstream of YRB (Fig. 7a). We also found that the changes of production and ecological space had a direct positive impact on NPP, while the changes of living space had a negative impact on the NPP. However, the changes of production-living-ecological space had no significantly effects on the water yield. Different from the upstream of YRB, social-economic had no effect on the changes of ecological space in the middle-lower reaches of YRB (Fig. 7b). Meanwhile the changes of ecological space had a positive impact on the water yield.

Fig. 7
figure 7

The conceptual model was determined by partial least squares path modeling (PLS-PM) in the (a) upper and (b) middle-lower reaches of Yangtze River Basin. Note: NIEDS—number of industrial Enterprises above Designated Size. The solid and dotted lines respectively document the active and inactive factor pathways. The blue line documents a positive effect, while the red line documents a negative effect

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Discussions

Validation

It is challenging to obtain measured NPP data at the regional scale. Even if these data could be acquired, it seems impossible to ensure that the field data and the remote-sensing images are obtained in the same period. Due to the difficulty in verifying the accuracy of simulation results on a regional scale [31]; Liu et al. [23], in this study, we compared and verified our simulation results of NPP and water yield with relevant research in the YRB. Our simulated mean annual NPP (299.6 gC•m− 2) was lower than the simulation results (397.63 gC•m− 2) reported by Fang et al., [8], but this result was higher than the mean value of NPP (227.66–257.6 gC•m− 2) in the literature of Yang et al., [47]. The underestimated NPP may be related to the model parameter setting and the simulation period. The most recent simulation of the previous study is only up to 2018, while our research interval is 2000–2020. Meanwhile our spatial distribution characteristics of NPP were almost consistent with them. Similar results also appear in water yield, our result of mean water yield (814.2 kg•m− 2) was higher than the results by Fang et al., [8], which reported water yield was 735.22 kg•m− 2.While this result was litter lower than the mean value of water yield (840.15 kg•m− 2) by Zhang et al., [52]. From the spatial distribution, we had consistent results that the water yield is significantly higher in the middle-lower than in the upper reaches of YRB. There are two possible reasons for this. One is precipitation, which is regarded as one of the major factors influence water yield [12, 19]. Based on the precipitation data, we know that the average annual precipitation is higher in the middle-lower (i.e. 1480 mm) than in the upper reaches (i.e. 1049 mm) of YRB from 2000 to 2020. Another reason may be caused by the confluence of rivers, with more water from upstream and tributary in the middle-lower reaches considered by the InVEST model for simulating of the water yield [32]. Overall, the similar change trend indicated that the model’s estimation accuracy was satisfactory.

Driving mechanisms of territorial space and ecosystem service

The spatial variations of production-living-ecological space are directly related to social-economic factors [47]. Population growth and economic development inevitably require an increase in living space [39]. While the expansion of urbanization is dominated by the occupation of cropland at past 20 years in YRB (Table S1). Therefore, in this study, human active factors (i.e. total population, proportion of urban population, gross regional product and NIEDS) had strong negative effects on the production space and positive effects on the living space both in the upper and middle-lower reaches of YRB (Fig S1 and 7). Moreover, vigorous afforestation and environmental protection projects are significant reasons for maintaining the stability of ecological space in study area. However, the NPP of newly afforestation is often lower, which leads to a decrease in the average NPP of the region. This may explain why there is a negative correlation between NPP and the changes of ecological spatial in our research results.

Land use change may have an impact on the local climate, such as the “heat island” effect caused by urbanization (Inostroza et al., [16]; Filho et al., [9]. However, our analysis results indicated that the changes of production-living-ecological space had no significant influence on regional climate changes (Fig S1). We infer that the possible reason is the change in the intensity of anthropological activities under the spatial variation of production-living-ecological space, such as the increase of carbon dioxide emissions, is the main reason for climate change at regional and large scale [3]. Unfortunately, this study did not consider more human activity intensity factors, such as industrial gas emissions and automobile exhaust emissions.

It is undeniable that climate change is still an essential factor affecting regional ecosystem services [21, 33]. Both spearman’s rank correlation and PLS-PM analysis indicated that climate was the main driver factor on NPP and water yield in the upper reaches of YRB (Fig S1a and 7a). However, in the middle-lower reaches, the influence of climate on NPP and water yield had been weakened and had a negative effect on water yield (Fig S1b and 7b). In addition, our results also indicated that variation of annual precipitation had a correlation coefficient of 0.84 with the water yield and a correlation coefficient of 0.56 with the NPP in the upper reaches (Fig S1a). Meanwhile, variation of annual precipitation had a correlation coefficient of 0.73 with the water yield, while variation of monthly average temperature had a correlation coefficient of 0.71 with the NPP in the middle-lower reaches (Fig S1b).

Limitations and future works

Although some papers have analyze the carbon or water of the Yangtze River Basin, we have following two innovations in this study. On the one hand, we constructed a framework to link the carbon and water of the Yangtze River Basin. The net primary productivity (NPP) and water yield were assessed over a 20-year period (2000–2020), and two ecosystem services are valuable for understanding trend in the functions of carbon sequestration and water supply. On the other hand, we tied the land use structure and two ecosystem services by this framework, providing a comprehensive understanding of the impacts of production, living, and ecological spaces on two ecosystem services.

This study still has some limitations, which can be further improved in the future. Firstly, the identification and classification of territorial space based on remote sensing data cannot achieve sufficient confidence in accuracy and reliability [15]; Zhang et al., 2019). For example, if all forest is considered as the same land use category, the effects of forest age, species composition, canopy coverage and other potentially important information will be ignored. Therefore, it is necessary to reduce the negative effects on the image classification results and improve the reliability and accuracy of identification and classification for land use images [17]. Secondly, the parameters of model (i.e. both CASA and InVEST models) were difficult to verify, so we input parameters based on previous studies, which impact the accuracy of the simulation results [36, 45]. Thirdly, a wider range of socio-economic factors (e.g. industrial water consumption, natural growth rate of population, proportion of the primary industry, etc.) should be considered for understanding the potential influence of human actives on ecosystem services. Moreover, the uncertainty from natural disturbances (e.g. insects, drought, fire, etc.) were not considered, which, if ignored, may cause an overestimation of ecosystem services at the regional scale [4, 18]. Overall, the influence of the above limitations on the simulation of ecosystem services and land use changes needs to be further considered and addressed in future studies.

Conclusion

Understanding the driving factors of the changes of production-living-ecological space is conducive to better understanding the relationship between land spatial and regional ecosystem services. In this study, we discovered that (1) the changes of production-living-ecological space had a similar trend in the both upper and middle-lower reaches of YRB from 2000 to 2020, while both NPP and water yield had decreasing trends in the upper reaches but increasing trend in the middle-lower reaches; (2) the changes of production-living-ecological space had no significant effect on the water yield in the upper reaches, but the changes of ecological space had a positive effect on the water yield in the middle-lower reaches, while the changes of living space had a negative effect on NPP both in the upper and middle-lower reaches; (3) social-economic factors had a positive impact on the living space but the negative impact on the production space in the study area. The difference in the allocation ratio of production-living-ecological space may account for the differences between the upper and middle-lower reaches in the impact of spatial variation on ecosystem services. Our findings can benefit regional forest and water resource management in the context of climate change and human actives. Based on this study, a reasonable spatial allocation will facilitate the balance between social-economic development and ecosystem service supply in the future, as well as is an effective way to maintain the regional of the Yangtze River Basin sustainable development.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

This work support by the National Natural Science Foundation of China (grant no. 42201114, 42371484, U22A20570, and 42001218), the Natural Science Foundation of Hunan Province, China (grant no. 2021JJ40338), the General Project of Hubei Social Science Fund (Grant number 2021211, HBSK2022YB357) and the national college students research learning and innovative experiment project (S202310542036).

Funding

This work support by the National Natural Science Foundation of China (grant no. 42201114, 42371484, and U22A20570), the Natural Science Foundation of Hunan Province, China (grant no. 2024JJ5265, 2021JJ40338), and the national college students research learning and innovative experiment project (S202310542036).

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Authors and Affiliations

  1. College of Geographic Science, Hunan Normal University, Changsha, 410081, China

    Zelin Liu, Xiaoting Yu, Cong Liu, Ziying Zou, Changhui Peng, Peng Li, Jiayi Tang, Haoyun Liu & Yihang Zhu

  2. Department of Biological Sciences, University of Québec at Montreal, Montreal, QC, H3C 3P8, Canada

    Changhui Peng & Chunbo Huang

  3. Research Center for Spatial Planning and Human-Environment System Simulation, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430078, China

    Chunbo Huang

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  1. Zelin Liu
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Contributions

Z.L, C.H and C.L conceived of the study idea; Z.L and C.H led the writing of the manuscript. C.P, Z.Z, H.L, and P.L. reviewed and edited the manuscript; Y.Z. and J.T. helped perform the analysis with constructive discussions. All authors contributed critically to the drafts and gave final approval for publication.

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Correspondence to Cong Liu or Chunbo Huang.

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Liu, Z., Yu, X., Liu, C. et al. Integrating territorial pattern changes into the relationship between carbon sequestration and water yield in the Yangtze River Basin, China. Carbon Balance Manage 20, 1 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13021-024-00289-7

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Carbon Balance and Management

ISSN: 1750-0680

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