Spatio-temporal variation and simulation prediction of carbon storage in Lijiang River Basin based on PLUS- InVEST model

doi:10.21203/rs.3.rs-5178295/v1

Quantitative assessment and simulation of terrestrial ecosystem carbon storage are of significant importance for future climate regulation and ecosystem management. In this paper, focusing on the Lijiang River Basin, we utilized the PLUS model and the InVEST model to evaluate the dynamic changes in land use and carbon storage from 2001 to 2041 under different development scenarios. The results indicate: (1) From 2001 to 2021, the areas of forest, shrub, grassland, and water bodies decreased, while the areas of cropland and impervious land increased. (2) Under the three scenarios, the changes in land use areas exhibited distinct characteristics. (3) From 2001 to 2021, the carbon storage in the Basin exhibited an overall declining trend. Under the scenarios of inertial development, ecological priority, and urban development, the projected carbon storage in Basin for 2041 will be 144.27×10⁶t, 145.72×10⁶t, and 143.8×10⁶t, respectively. (4) The carbon storage in the karst landform area decreased by 3.85%, and the carbon storage in the non-karst landform area decreased by 2.57%. Those results suggest that implementing reasonable planning and restrictions in construction areas, as well as controlling the conversion of high carbon density land to low carbon density land, can contribute to increasing regional carbon storage. Therefore, the results obtained can provide scientific references for optimizing regional land use structure, improving regional ecosystem carbon storage, and serving the construction of Guilin National sustainable development agenda innovation demonstration zone.

Land use change

Terrestrial ecosystems

Carbon density

Markov-PLUS model

Multi-scenario simulation

The global climate problem has brought great challenges to the survival and development of human beings. Climate warming will increase the global social cost, and it is necessary to find countermeasures to stop this warming trend. In November 2021, the United Nations Climate Conference[1] further emphasized the urgency of mitigating carbon emissions in the Glasgow Climate Convention[2] reached by the 26th Conference of the Parties(COP26), Here are two main ways to reduce carbon in the atmosphere, one is to reduce carbon emissions, and the other is to increase carbon storage in terrestrial ecosystems. The terrestrial ecological carbon storage system regulates the climate by absorbing and emitting greenhouse gases in the atmosphere through plants and soils. The conversion of different land cover types will cause the fluctuation of terrestrial ecosystem carbon storage[3–5]. The land use/cover change has a crucial impact on climate warming. Therefore, a comprehensive assessment and prediction of carbon storage in land use is of great significance for the overall sustainable development.

In the past few decades, most of research on the impact of land use change on terrestrial ecosystem carbon storage has been investigated, mainly adopting field survey method[6] and model simulation method[7]. The model simulation method is to visualize and evaluate carbon stocks at different scales through mathematical models. Among them, the InVEST model developed by Stanford University is the most mature. The carbon module has been widely used since in 2007[8], and shows a trend of diversification in application fields. The PLUS model [9], FLUS model[10], CBM-CFS3 model[11] and CASA model [12] are mainly used in this model. The study area involves countries[13], provinces and cities [14], forests [15], nature reserves [16] and watersheds[17, 18]. The research above shows that most of the research on ecosystem carbon storage through future scenario simulation fails to provide an optimal land use pattern. A multi-scenario approach at the basin scale is being considered to simulate future land use patterns and evaluate changes in carbon storage. This method holds greater practical significance and reference value for achieving sustainable development and the goal of carbon neutrality, thereby contributing to the ecological health of the basin. Based on the land use data of the Lijiang River Basin from 2001 to 2021, combined with the modified PLUS model, this paper simulated and predicted the landscape pattern in 2031 and 2041 under different scenarios. The InVEST model is also used to evaluate the ecosystem carbon storage under different scenarios. Those results can provide a scientific basis for the allocation of land resources and the sustainable development of natural resources in the Lijiang River Basin.

1.1 study area

The Lijiang River Basin is located at 110°5'-110°44'E and 24°38'-25°56'N, with an area of about 5768.29km², which is shown in Fig. 1. It originates from the south of the old boundary in the northeast of Maoer Mountain in Xing'an County, Guilin, and flows from north to south into Pingle Town and Gongcheng River in Pingle County, with a total length of 214 km [19]. The basin has a subtropical monsoon climate, with an annual average temperature of around 20°C and an annual average rainfall of 1949.5 mm. The four seasons are distinct, characterized by hot and rainy summers, and mild and rainy winters[20–22].

1.2 Methods

1.2.1 Land use simulation based on Markov-PLUS model

Compared with the traditional CA model, the PLUS model is based on the mining framework of land expansion analysis strategy (LEAS) while the CA model is based on multi-type random seeds (CARS). This model can mine the driving factors of land expansion and landscape change, and obtain higher simulation accuracy and more similar landscapes [23, 24] .

(1) land expansion analysis strategy

The random forest algorithm is used to mine the growth probability and driving factors to obtain the contribution of the development probability and driving factors of various types of land.

\(P_{{(i,k(x))}}^{d}= - P_{{(i,k(x))}}^{d}=\frac{{\sum\nolimits_{{n=1}}^{M} {I({h_n}(x)=d)} }}{M}\)

where \(P_{{(i,k(x))}}^{d}\)is the development probability of -type land in unit , and \(d=1\), if other land use types converted toland use types, \(d=0\)otherwise. is a vector composed of multiple driving factors; is the indicator function of the decision tree; is the total number of decision trees; \({h_n}(x)\) is the prediction type of the nth decision tree of vector .

(2) CA model based on multi-type random seeds

Under the constraints of development probability, combined with random seed generation and threshold decreasing mechanism, the PLUS model can automatically generate spatio-temporal dynamic simulation patches. Using the Monte Carlo method, when the land neighborhood effect is 0, the probability surface of each land \(OP_{{i,k}}^{{1,t}}\) as follows

\(OP_{{i,k}}^{{1,t}}=\left\{ \begin{gathered} P_{{i,k}}^{1} \times (r \times {\mu _k}) \times D_{k}^{t}\;,\;if\;\Omega _{{i,k}}^{t}=0\;and\;r<P_{{i,k}}^{1}, \hfill \\ P_{{i,k}}^{1} \times \;\Omega _{{i,k}}^{t} \times D_{k}^{t},\;all\;others. \hfill \\ \end{gathered} \right.\)

\(\Omega _{{i,k}}^{t}=\frac{{con(c_{i}^{{t - 1}}=k)}}{{n \times n - 1}} \times {w_k}\)

\(D_{k}^{t}=\left\{ \begin{gathered} D_{k}^{{t{\text{-1}}}},\;if\;\left| {G_{k}^{{t{\text{-1}}}}} \right| \leqslant \left| {G_{k}^{{t{\text{-2}}}}} \right|, \hfill \\ D_{k}^{{t{\text{-1}}}} \times \frac{{G_{k}^{{t{\text{-2}}}}}}{{G_{k}^{{t{\text{-1}}}}}},\;if\;0>G_{k}^{{t{\text{-2}}}}>G_{k}^{{t{\text{-1}}}},\; \hfill \\ D_{k}^{{t{\text{-1}}}} \times \frac{{G_{k}^{{t{\text{-2}}}}}}{{G_{k}^{{t{\text{-1}}}}}},\;if\;G_{k}^{{t{\text{-1}}}}>G_{k}^{{t{\text{-2}}}}>0. \hfill \\ \end{gathered} \right.\)

where is random values in the range of 0–1, \({\mu _k}\) is the threshold value of new land use patches generated by -type land use type, \(D_{k}^{t}\) is an adaptive drive coefficient and it depends on the gap between the current number of land use type and the target demand, \(\Omega _{{i,k}}^{t}\) is the neighborhood effect of the unit which means that \(\Omega _{{i,k}}^{t}\) is the component of the neighborhood under the proportion of land cover.is the type of land use.

1.2.2 Carbon storage calculation based on InVEST model

Based on the landscape type data and carbon density table (see Table 1) of the Lijiang River Basin, the carbon storage and sequestration module in the InVEST model [25,26] was used to evaluate the change of carbon storage in the Lijiang River Basin, the function are as follows

C_{i_tot}=C_{i_above}+C_{i_below}+C_{i_soil}+C_{i_dead} (1)

where is the land use type, \({C_{i\_tot}}\) is the total carbon storage in the ecosystem; \({C_{i\_above}}\) is the aboveground carbon storage of vegetation(such as bark, trunk, leaves, etc.); \({C_{i\_below}}\) is the underground carbon storage of vegetation (vegetation roots); \({C_{i\_soil}}\) is soil carbon storage (including mineral soil and organic soil); C_{i _ dead} is the carbon stock of dead organic matter (including dead trees and litter). Multiplying the biomass by carbon content of plant and combining with the vegetation biomass distribution map of China (spatial resolution of 500m), the carbon storage of plant is obtained. The carbon content of plant and soil was determined by concentrated sulfuric acid-potassium dichromate hydration heating method. Soil carbon storage (\({C_{soil}}\)) is calculated according to Teng et al[27]. Other parts of carbon storage refer to the relevant literature [28–30].

Table 1

Carbon density of land use landscape types in Lijiang River(t/hm²)
types	\({C_{i\_above}}\)	\({C_{i\_below}}\)	\({C_{i\_soil}}\)	\({C_{i\_dead}}\)
cropland	13.50	2.70	96.59	1.00
forest	73.59	25.20	207.33	3.50
shrub	18.96	5.69	9.4	2.47
grassland	5.01	13.53	117.06	1.00
waters	0.21	0.00	0.00	0.00
Barren land	19.52	3.9	0.86	0.00
impervious land	1.20	0.93	12.48	0.00

1.2.3 Simulation of future scenario

In order to ensure the sustainability of carbon storage resources in the Lijiang River Basin. Inertial development model, ecological priority model and urban development model are considered.

(1) Inertial scenario. Given the assumption that the evolution of landscape patterns in the Basin is unaffected by the new policy, Besides, the parameters for neighborhood factors and transfer cost matrices of each land use type remain unchanged.

(2) Ecological protection scenario. Emphasis has been placed on strengthening the management of ecological protection areas such as forests, grasslands, and water bodies. Measures have been implemented to curb the transformation of park green spaces and land within ecological protection areas into impervious land. There is a strict control in place to counter the disorderly expansion trend of impervious land, with the aim of mitigating the decline in forest and arable land. Therefore, in this scenario, decrease the conversion rate of forest, shrub, and grassland by approximately 50% to impervious land and increase the conversion rate of arable land to forest by 30% based on the inertial scenario and the land-use transfer matrix for the period 2021–2031.

(3) Urban development scenario. " The New Urbanization Plan for Guangxi (2021–2035)" indicates that the level of urbanization development in Guangxi is currently below the national average. There is a need for further improvement in urban and rural infrastructure to enhance the overall urbanization process. In this scenario, raise the conversion rate of cropland, forest, shrub, and grassland to impervious land by 20%. Besides, reduce the conversion rate of impervious land to arable land, forest, shrub, and grassland by 20%.

1.2.4 Data source

The land cover dataset for the Lijiang River Basin is derived from the annual land cover dataset published by Huang[31]. The factors influencing changes in land types mainly include natural and socio-economic factors, with specific indicators and sources outlined in Table 2.

Table 2

Introduction to the data
Data type	Name	Years	Source	Resolution
Topography and Landforms	DEM	2006	ALOS	12.5m
	Slope		From DEM
	Aspect
	Curvature
Climate	Surface runoff	2010	ERA5	11132m
	Temperature
	Precipitation
	ET		MOD16A2	500m
Distance	Distance to river	2015	National Geomatics Center of China	30m
	Distance to artificial	2015
	Distance to town	2010
	Distance to road
	Distance to rural residential area
Vegetation	LAI	2010	MODIS Landsat8 OLI/TIRS	500m
	GPP			500m
	VCF			30m
Population Income	population density	2010	WoridPop	100m
	Rural per capita net income		Social and Economic Statistical Yearbook	1000m
	Fiscal Revenue			1000m
Agricultural development	Grain yield per unit area			\
Agricultural development	Meat production per unit area			\
Tourism	Tourist point density	2020	Qunar combines Python and ArcGIS for kernel density calculation.	\
Tourism	Hotel density	2020		\
Else	LISA-I	2010	Land use type data calculation	\
	Power plant density	2018	WRI kernel density calculation	\
	Rocky desertification	2010	Combined with slope climate vegetation and other factors to calculate	30m

2.1 Model accuracy validation

The simulated results were validated using the FOM statistical tool, revealing a \(\:Kappa\:\)coefficient of 0.82 and an overall accuracy of 0.92. This indicates that the Markov-PLUS model demonstrates high simulation accuracy and is well-suited for application in the Basin.

2.2 The transfer of land use change in Lijiang River Basin from 2001 to 2021

The total forest area in 2021 reached 4,135.63 km², accounting for 70.94% of the study area. According to the Table .3, which is shown below, between 2001 and 2021, all types of land used in the Lijiang River Basin underwent transitions. The total area of transitioning land uses was 608.62 km², which makes up 10.44% of the total area of the study region. A total of 211.29 km² of farmland was converted, with the main reductions being transformed into forest land and construction land, at 139.22 km² and 67.49 km² respectively. Forest land saw a cumulative conversion of 354.51 km², which is 2.25 times the amount converted into forest land. The converted forest land primarily went to farmland, construction land, and shrubland, with areas of 340.058 km², 6.421 km², and 5.14 km² respectively, accounting for 95.92%, 1.81%, and 1.45% of the total converted forest land. All types of land use transformed about 67.49 km² into construction land.

According to Fig. 2, the land use transition diagram for the Lijiang River Basin from 2001 to 2021, the primary conversions were among forest land, farmland, and construction land. During the periods of 2001–2006 and 2016–2021, more forest land was converted to farmland than vice versa. However, during 2006–2016, influenced by the policy of converting farmland back to forest (grain-for-green program), the growth in farmland was restrained, and more farmland was converted to forest land than forest land to farmland. Construction land primarily served as an area for conversion into, with only a small amount being converted out. The main source of land converted into construction land was farmland.

Table 3

Land Use Transition Matrix of the Lijiang River Basin from 2001 to 2021(km²)
2001	2021
2001	cropland	forest	shrub	grassland	water	barren land	impervious land	total	total of transferred out
cropland	1115.16	340.06	4.65	2.03	9.86	0.00	2.16	1473.93	358.77
forest	139.22	3977.39	16.63	0.630	1.200	0.00	0.21	4135.29	157.90
shrub	0.07	5.141	2.31	0.039	0.000	0.00	0.00	7.56	5.25
grassland	0.44	0.82	0.30	0.416	0.028	0.00	0.00	2.01	1.59
waters	5.07	2.07	0.00	0.256	43.05	0.00	1.41	51.85	8.80
barren land	0.01	0.01	0.00	0.00	0.00	0.00	0.00	0.02	0.02
impervious land	67.49	6.42	0.01	0.586	1.80	0.00	82.66	158.96	76.29
total	1327.46	4331.90	23.91	3.96	55.94	0.00	86.44	5829.61	——
total of transferred out	212.30	354.51	21.60	3.55	12.89	0.00	3.78	——	608.62

2.3 Changes of carbon storage in Lijiang River Basin

2.3.1 Estimation of total carbon storage from 2001 to 2021

The total carbon storage of Lijiang River Basin in 2001,2006,2011,2016 and 2021 was 149.39 × 10⁶ t, 147.97 × 10⁶ t, 146.38 × 10⁶ t, 144.28 × 10⁶ t and 144.87 × 10⁶ t, respectively. According to Fig. 3, the decrease of carbon storage in the Basin continued to increase during 2001 to 2016. The carbon storage was stable and there was a small increase during 2016 to 2021. Overall, the total carbon storage in the Basin showed a trend of decreasing, then increasing slowly, and the land use/cover pattern developed steadily.

2.3.2 The karst topography in the Lijiang River Basin and its impact on carbon storage

The Lijiang River Basin is primarily composed of karst and non-karst topographies, with the non-karst area covering approximately 3,432.46 km² and the karst area covering about 2,393.66 km², making up 41% of the entire basin, which are shown in Figs. 5 and 6. There are significant differences in carbon storage distribution based on topography. The mean carbon storage density in non-karst areas is much higher than in karst areas, and the carbon storage in non-karst areas accounts for about two-thirds of the total carbon storage in the Lijiang River Basin.

Over the past 20 years, both the mean carbon storage density and total carbon storage in both karst and non-karst areas showed a decreasing trend. The carbon storage in karst areas decreased by 3.85%, while in non-karst areas it decreased by 2.57%. Between 2016 and 2021, there was a slight increase in both carbon storage and mean carbon density in both karst and non-karst areas. The increases in carbon storage were 0.51 × 10⁶ t and 0.08 × 10⁶ t respectively, and the increases in mean carbon density were 0.19 t/hm² and 0.02 t/hm². Through integrated management and restoration projects focusing on the protection of vulnerable ecological zones within the karst areas, the carbon storage in karst areas has been effectively enhanced.

2.3.3 The impact of land use changes on carbon storage in the Lijiang River Basin

The changes in carbon storage in the Lijiang River Basin show a high degree of consistency with changes in land use types. According to Table 4, variations in carbon storage are closely related to transformations in land use categories. Transfers between different land types can lead to fluctuations in ecosystem carbon storage. The increase in forest, grassland, and shrubland areas is the primary reason for the increase in carbon storage.Converting from land types with high carbon density to those with low carbon density can result in the release of previously stored carbon dioxide into the atmosphere.

The conversion of farmland to forest land resulted in an increase in carbon storage of 27.26 ×10⁶ t, and the substantial conversion of farmland to forest land is the main reason for the increase in carbon storage, contributing 32.35 ×10⁶ t to the increase. However, the increase brought by the conversion of farmland to forest land is far less than the loss caused by the conversion of forest land to farmland. The loss of carbon storage due to the degradation of forest land to farmland is 66.59 ×10⁶ t, accounting for 94.23% of the total loss from conversions. The Lijiang River Basin has undergone rapid urbanization, and the conversion of other land uses to construction land resulted in a total loss of carbon storage of 0.86 ×10⁶ t.

Table 4

Carbon Storage Transition Matrix of the Lijiang River Basin
2021
2001	cropland	forest	shrub	grassland	waters	barren land	impervious land	total	total of transferred out
cropland	12.69	-6.66	0.04	0.00	0.11	0.00	0.02	6.19	-6.49
forest	2.73	123.15	0.45	0.01	0.04	0.00	0.01	126.38	3.23
shrub	0.00	-0.14	0.01	0.00	0.00	0.00	0.00	-0.13	-0.14
grassland	0.00	-0.01	0.00	0.01	0.00	0.00	0.00	0.00	-0.01
waters	-0.06	-0.06	0.00	0.00	0.00	0.00	0.00	-0.13	-0.13
barren land	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
impervious land	-0.67	-0.19	0.00	-0.01	0.00	0.00	0.12	-0.74	-0.86
total	14.69	116.08	0.50	0.00	0.15	0.00	0.15	131.57
total of transferred out	2.00	-7.07	0.49	0.00	0.15		0.03		-4.40

2.4 Analysis of the predicted land use in the Lijiang River basin

2.4.1 Prediction of land use

According to Table 5, it can be found that from 2021 to 2041, the changes in various land use types in the Lijiang River Basin differ significantly under different scenarios. Under the ecological protection scenario, the areas of cropland, shrubland, and grassland show slight decreases, whereas the forestland area increases substantially. Additionally, there is a slight increase in the areas designated for construction and water .

Table 5

Changes in Land Use in the Lijiang River Basin
Types of land use	Inertial scenario		Urban development scenario		Ecological protection scenario
Types of land use	2021–2031	2031–2041	2021–2031	2031–2041	2021–2031	2031–2041
cropland	19.79	13.19	13.01	44.10	-21.88	-21.37
forest	-25.97	-13.06	-18.54	-44.74	18.04	17.49
shrub	-0.55	-0.27	-0.55	-1.07	-0.32	-0.33
grassland	-0.10	-0.20	-0.07	-0.18	-0.07	-0.08
waters	4.26	0.50	3.59	1.08	2.16	2.14
barren land	0.00	0.00	0.00	0.00	0.00	0.00
impervious land	2.57	-0.16	2.56	0.78	2.11	2.16

2.4.2 Scenario prediction of carbon storage

Based on the result shown in Figs. 6 and 7, the carbon density in the Lijiang River Basin ranges from 0.0189 to 27.8658 t/hm², and the spatial pattern of carbon storage generally shows higher levels in the northeast and southeast, with lower levels in the central and western parts. Under the inertial scenario, the projected carbon storage for 2031 and 2041 is 144.53 ×10⁶ t and 144.27 ×10⁶ t, respectively. From 2021 to 2041, the cumulative loss of carbon storage is estimated to be 0.84 ×10⁶ t, indicating that the basin will function as a carbon source in the future. This suggests that the overall trend of decreasing carbon storage continues, but the rate of decline slows down over time. Under the urban development scenario, the Lijiang River Basin is projected to lose a cumulative 1.31 ×10⁶ t of carbon storage from 2021 to 2041. Moreover, the rate of decline accelerates over time, with a significant decrease in carbon storage occurring in the latter ten years of the simulation. Under the ecological protection scenario, the declining trend of carbon storage in the Lijiang River Basin has changed, with a cumulative carbon sequestration of 0.61 ×10⁶ t from 2021 to 2041. This indicates a transition from a carbon source to a carbon sink. Areas with increased carbon density are distributed relatively loosely, mainly located in the mid-to-lower reaches of the Lijiang River Basin. This indicates that implementing ecological protection strategies contributes positively to the sustainable development of the Lijiang River Basin's ecology.

3. 1 Impact of watershed land use changes on carbon storage

Changes in carbon storage are closely related to the type of regional land. In the past two decades, land use types have shifted to varying degrees. Among them, Yangshuo County and other places in the middle and lower reaches of the basin are the main areas of reduction in carbon storage. As urbanization progresses, impervious land continues to encroach on both cropland and forest, leading to the conversion of high carbon density forest and cropland into low carbon density impervious land. With the development of tourism, the natural scenery in the middle and lower reaches of the Lijiang River has been vigorously developed, which has resulted in a large loss of forest. In the early stages of development, the Lijiang River basin experienced uncontrolled and coarse expansion, leading to chaotic construction, excavation, farming, and business operations. These issues have caused severe damage to the ecosystem. Faced with this situation, the Guilin municipal government implemented strict law enforcement measures and invested substantial financial resources, laying a solid foundation for the ecological recovery of the Lijiang River basin. The recovery of ecosystems has a certain time lag, which is one of the key reasons for the increase in carbon storage in the basin over the past five years. The results forecasted for carbon reserves in 2031 and 2041 show that different development scenarios will lead to two completely different future development patterns. The analysis of carbon storage prediction results for the year 2031 and 2041 indicates that under different development scenarios, the Lijiang River Basin will experience significantly different development patterns. Under the scenarios of inertial development and urban development, the carbon storage exhibits similar trends, with a further acceleration of the declining trend in carbon storage. However, under the ecological protection development scenario, there will be a noticeable upward trend in carbon storage.

3.2 Impact of karst topography on carbon storage

The Lijiang River basin is a crucial part of the southern ecological barrier in China and is also home to a Karst World Heritage site. Karst landforms form a complex multi-phase, multi-layered system with ecological vulnerabilities such as high ecological sensitivity, low self-regulation, restoration capabilities against external disturbances and poor stability in landscape types. As human activities expand, there is inevitable damage to the Karst landforms in the Lijiang River basin. The cost of remediation after damage in Karst regions tends to be higher than in non-Karst areas. Therefore, it is necessary to establish a rational ecological monitoring system for Karst areas, strengthen research on ecological restoration, and collaborate with multilateral institutions to build long-term, institutional partnerships for ecological restoration. This includes sharing international management experiences with the goal of preserving the green mountains and clear waters of the Lijiang River region.

3.3 Countermeasures to alleviate the declining trend of carbon stocks

The main land use type in the Lijiang River Basin is woodland. Combining with the carbon storage changes under different scenarios in the Lijiang River Basin, it can be seen that in the future, the region should focus on protecting forestland resources, monitor the distribution and changes of land use types. Besides, in order to increase the ability of adaption to climate change, the local should strengthen the supervision of ecological protection in key areas such as ecological protection red lines, carry out monitoring and evaluation of the effectiveness of ecosystem protection, and enhance the carbon sequestration function of ecosystems. This will consolidate the results of ecological construction and at the same time point out the direction of ecological and environmental governance for the realization of the carbon neutrality goal. Furthermore, it is essential to moderate the development intensity in the middle and lower reaches of tourist cities and counties. This approach ensures the preservation of the crucial status of the northern ecological barrier. Additionally, efforts should be made to promote the construction of national reserve forests, thereby safeguarding the structural integrity of the forest ecosystem. Moreover, in order to understand the carbon cycle of terrestrial ecosystems, we should make full use of the news media to carry out publicity and education on climate change and ecological environment protection.

In order to improve capacity of regional carbon sink, the ecological priority strategy is also an effective method. For example, Deng Yuanjie et al. [32] (2020) showed that the carbon storage services of Chang County in the Loess Plateau were improved with the implementation of the project of returning farmland to forest and grassland. Yao Ping et al [33] (2014) showed that the capacity of forest carbon sink in southwest China has improved under the influence of the farmland return to forest project, further confirming that the ecological priority strategy is conducive to increasing regional carbon reserves.

Acknowledgments

We gratefully acknowledge the financial support provided by the Guangxi Science and Technology Major Program (Grant Nos. 2021AB33019) and the National Natural Science Foundation of China (Grant Nos. 52478053 and Grant Nos. 52078222).

The experiment conforms to the current law of China where the experiment is located.

• Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

• Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Xinran Zhou], [Jinye Wang] , [Wen He], [Liang Tang] and [Hui Li]. The first draft of the manuscript was written by [Xinran Zhou] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

• Data Availability

The datasets analysed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

Spatio-temporal variation and simulation prediction of carbon storage in Lijiang River Basin based on PLUS- InVEST model

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Abstract

Figures

Introduction

1. Materials and methods