Enhanced InVEST modeling reveals divergent trajectories of carbon storage in the eastern Qinghai-Xizang Plateau during 1990-2100
HUANG Kepan1, XU Haojie1,2,*(), LIU Zhifei1, WANG Dawei1
1State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems/Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs/Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China 2Center for Remote Sensing of Ecological Environments in Cold and Arid Regions, Lanzhou University, Lanzhou 730000, China
As a critical ecological barrier and carbon reservoir on the eastern Qinghai-Xizang Plateau, the Qilian Mountains (QLMs) play a vital role in maintaining regional ecological security. This study employed an enhanced Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model with dynamic interannual carbon density parameterization to evaluate carbon storage (CS) variations in the QLMs during the historical period of 1990-2020 and future period of 2030-2100 under climate scenarios (SSP126, SSP245, and SSP585, where SSP is the Shared Socio-economic Pathway). By integrating GeoDetector analysis, random forest modeling, and scenario simulations, we further assessed climate-human interactions and their impacts on CS. The QLMs had a multi-year average CS of 1.49×109 t during 1990-2020, with higher values in the southeast and lower values in the northwest. The spatial heterogeneity of CS was primarily driven by warming-wetting climatic gradients. Multi-scenario projections revealed divergent trajectories: under the SSP126 scenario, ecological restoration measures (e.g., mountain closure and afforestation) increased CS, whereas CS under SSP245 and SSP585 scenarios exhibited inverted U-shaped patterns. Notably, SSP585 resulted in 8.00×107 t losses due to extensive conversions from grassland to urban area during 2050-2100. Grassland and cropland CS, together with precipitation, emerged as the key determinants of regional CS dynamics, with a synergistic interaction between precipitation and temperature. This study introduced a century-scale, multi-scenario framework that captures temporal interactions and dynamically parameterizes carbon density coefficients for era-specific accuracy. The findings suggest optimizing vegetation coverage, managing urban expansion, and implementing climate adaptation strategies to enhance the total CS of this ecologically sensitive region.
Received: 16 October 2025
Published: 31 August 2026
HUANG Kepan, XU Haojie, LIU Zhifei, WANG Dawei. Enhanced InVEST modeling reveals divergent trajectories of carbon storage in the eastern Qinghai-Xizang Plateau during 1990-2100. Journal of Arid Land, 2026, 18(8): 1283-1303.
Fig. 1Elevation distribution of the Qilian Mountains (QLMs). The elevation data are sourced from the Resource and Environmental Science Data Platform (https://www.resdc.cn/data.aspx?DATAID=123).
Data type
Unit
Spatial resolution
Time period
Data source
Land use and land cover (LULC)
-
300 m
1990-2020
ESA CCI Land Cover (http://www.esa-landcover-cci.org/)
1 km
2030-2100
Zhang et al. (2023) (https://doi.org/10.6084/m9.figshare.23542860)
Net primary productivity (NPP)
g C/m2
5 km
1982-2018
National Earth System Science Data Center (https://www.glass.hku.hk/download.html)
Forest aboveground biomass (AGB)
Mg/hm2
30 m
1985-2023
Cai et al. (2025) (https://code.earthengine.google.com/4f8ad8d32ddb84e826e941a95f31f9be)
Soil organic carbon density (SOCD)
kg C/m2
250 m
2010-2018
National Tibetan Plateau Scientific Data Center (https://cstr.cn/18406.11.Terre.tpdc.272482)
Precipitation (PRE)
mm
1 km
1990-2020
Resource and Environmental Science Data Platform (https://www.resdc.cn/data.aspx?DATAID=379)
1 km
2030-2100
National Tibetan Plateau Scientific Data Center (https://data.tpdc.ac.cn/zh-hans/data/90a7b4b6-4009-463a-b34f-e8ebd0238617)
Mean annual temperature (MAT)
°C
1 km
1990-2020
Resource and Environmental Science Data Platform (https://www.resdc.cn/data.aspx?DATAID=379)
1 km
2030-2100
National Tibetan Plateau Scientific Data Center (https://data.tpdc.ac.cn/zh-hans/data/169d91c0-c38f-4253-84ae-03f3e18ba643)
Sunshine duration (SSD)
h
1 km
1990-2020
Resource and Environmental Science Data Platform (https://www.resdc.cn/data.aspx?DATAID=379)
Population density (PD)
persons/km2
1 km
1990-2020
Resource and Environmental Science Data Platform (https://www.resdc.cn/DOI/DOI.aspx?DDOII=32)
Gross domestic product (GDP)
104 CNY/km2
1 km
1990-2020
Resource and Environmental Science Data Platform (https://www.resdc.cn/DOI/DOI.aspx?DOIID=33)
Grazing intensity (GI)
SU/hm2
250 m
1990-2020
National Ecosystem Science Data Center (https://www.nesdc.org.cn/sdo/detail?id=672aea607e28174998e63234)
Table 1 Description of the data used in this study
Fig. 2Spatial distribution of land use and land cover (LULC) types during the historical period of 1990-2020 (a1-a4) and future period of 2030-2100 under SSP126 (b1-b4), SSP245 (c1-c4), and SSP585 (d1-d4) scenarios. SSP, Shared Socio-economic Pathway.
Fig. 3String plot of LULC types transfer during the historical period of 1990-2020 (a) and future period of 2030-2100 under SSP126 (b), SSP245 (c), and SSP585 (d) scenarios
Fig. 4Spatial distribution of carbon density (CD) during the historical period of 1990-2020 (a1-a4) and future period of 2030-2100 under SSP126 (b1-b4), SSP245 (c1-c4), and SSP585 (d1-d4) scenarios
Fig. 5Spatial variations and transfer matrices of terrestrial carbon storage (CS) during the historical period of 1990-2020 (a1-a4) and future period of 2030-2100 under SSP126 (b1-b4), SSP245 (c1-c4), and SSP585 (d1-d4) scenarios. The historical period is subdivided into three phases: 1990-2000, 2000-2010, and 2010-2020. The future period is subdivided into three phases: 2030-2050, 2050-2070, and 2070-2100.
Fig. 6Landscape indices of the Aggregation Index (AI), Mean Shape Index (SHAPE_MN), and Interspersion and Juxtaposition Index (IJI) for areas showing decreased CS (a-c) and increased CS (d-f) during the historical period of 1990-2020 and future period of 2030-2100 under SSP126, SSP245, and SSP585 scenarios. The future period is subdivided into three phases: 2030-2050, 2050-2070, and 2070-2100.
Fig. 7Results of factor detection (a) and interaction detection (b) of factors influencing terrestrial CS. PRE, precipitation; SSD, sunshine duration; PD, population density; GI, grazing intensity; GDP, gross domestic product; MAT, mean annual temperature. The q value indicates the factor's explanatory power for terrestrial CS. * and ** denote the statistical significance at the 95.00% and 99.00% confidence levels, respectively.
Fig. 8Multi-year average values of climatic (a-c) and anthropogenic (d-f) factors during 1990-2020 under different LULC types
Fig. 9Temporal variations in terrestrial CS (a), area proportions of different LULC types (b-f), MAT (g), and PRE (h) from 1990 to 2100
Fig. 10Importance of seven driving factors for explaining the temporal dynamics of terrestrial CS (a) in the study area and dependence of the interannual variation in terrestrial CS on grassland and cropland components (b and c), MAT (d), and PRE (e)
Fig. 11Uncertainty analysis of the CS modelling result. Error bars represent standard deviations.
Scenario
Period
LULC type
Cabove (t/hm2)
Cbelow (t/hm2)
Csoil (t/hm2)
Cdead (t/hm2)
History
1990-2020
Cropland
2.28
0.46
109.74
2.84
Woodland
99.14
25.71
142.64
2.00
Grassland
0.55
0.99
79.93
0.95
Urban area
0.00
0.00
36.01
0.00
Bareland
0.10
0.21
23.23
0.10
Water body
0.00
0.00
0.00
0.00
SSP126
2030
Cropland
2.46
0.49
118.58
3.07
Woodland
100.40
26.04
144.45
2.03
Grassland
0.60
1.10
88.23
1.05
Urban area
0.00
0.00
36.01
0.00
Bareland
0.12
0.23
26.23
0.11
Water body
0.00
0.00
0.00
0.00
2050
Cropland
2.54
0.51
122.60
3.17
Woodland
101.51
26.33
146.05
2.05
Grassland
0.62
1.13
90.83
1.08
Urban area
0.00
0.00
36.01
0.00
Bareland
0.13
0.26
29.22
0.13
Water body
0.00
0.00
0.00
0.00
2070
Cropland
2.51
0.50
121.00
3.13
Woodland
101.05
26.21
145.39
2.04
Grassland
0.62
1.14
91.35
1.09
Urban area
0.00
0.00
36.01
0.00
Bareland
0.13
0.26
29.22
0.13
Water body
0.00
0.00
0.00
0.00
2100
Cropland
2.53
0.51
121.80
3.15
Woodland
101.38
26.29
145.86
2.05
Grassland
0.62
1.14
91.35
1.09
Urban area
0.00
0.00
36.01
0.00
Bareland
0.13
0.27
29.97
0.13
Water body
0.00
0.00
0.00
0.00
SSP245
2030
Cropland
2.38
0.48
114.96
2.98
Woodland
99.42
25.78
143.04
2.01
Grassland
0.60
1.09
87.72
1.04
Urban area
0.00
0.00
36.01
0.00
Bareland
0.12
0.25
27.73
0.12
Water body
0.00
0.00
0.00
0.00
2050
Cropland
2.45
0.49
118.18
3.06
Woodland
100.41
26.04
144.47
2.03
Grassland
0.61
1.12
89.79
1.07
Urban area
0.00
0.00
36.01
0.00
Bareland
0.13
0.25
28.48
0.12
Water body
0.00
0.00
0.00
0.00
2070
Cropland
2.52
0.50
121.40
3.14
Woodland
101.22
26.25
145.63
2.04
Grassland
0.62
1.14
91.35
1.09
Urban area
0.00
0.00
36.01
0.00
Bareland
0.13
0.27
29.97
0.13
Water body
0.00
0.00
0.00
0.00
SSP245
2100
Cropland
2.53
0.51
122.20
3.16
Woodland
101.58
26.34
146.15
2.05
Grassland
0.62
1.14
91.35
1.09
Urban area
0.00
0.00
36.01
0.00
Bareland
0.13
0.27
29.97
0.13
Water body
0.00
0.00
0.00
0.00
SSP585
2030
Cropland
2.51
0.50
123.41
3.13
Woodland
100.90
26.17
145.17
2.04
Grassland
0.60
1.10
88.23
1.05
Urban area
0.00
0.00
36.01
0.00
Bareland
0.12
0.23
26.23
0.11
Water body
0.00
0.00
0.00
0.00
2050
Cropland
2.49
0.50
120.19
3.11
Woodland
100.91
26.17
145.19
2.04
Grassland
0.62
1.14
91.35
1.09
Urban area
0.00
0.00
36.01
0.00
Bareland
0.13
0.27
29.97
0.13
Water body
0.00
0.00
0.00
0.00
2070
Cropland
2.52
0.50
121.40
3.14
Woodland
101.44
26.31
145.95
2.05
Grassland
0.62
1.14
91.35
1.09
Urban area
0.00
0.00
36.01
0.00
Bareland
0.13
0.27
29.97
0.13
Water body
0.00
0.00
0.00
0.00
2100
Cropland
2.56
0.51
123.41
3.19
Woodland
101.69
26.37
146.31
2.05
Grassland
0.62
1.14
91.35
1.09
Urban area
0.00
0.00
36.01
0.00
Bareland
0.13
0.27
29.97
0.13
Water body
0.00
0.00
0.00
0.00
Table S1 Carbon density of different land use and land cover (LULC) types in the Qilian Mountains
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