Scenario-based Simulation of Land Use/Cover Change and Carbon Storage Evaluation in Zhangye City Using the PLUS-InVEST Model

Yingying Zhou; Dong Yang; Weihuan Li; Lan Yang; Zemeng Ma; Longyan Wang; Qianyu Liu

doi:10.54097/19vfen85

Authors

Yingying Zhou
Dong Yang
Weihuan Li
Lan Yang
Zemeng Ma
Longyan Wang
Qianyu Liu

DOI:

https://doi.org/10.54097/19vfen85

Keywords:

Carbon Storage (CS), Land Use Change, Ecological Protection, InVEST Model, PLUS Model

Abstract

Carbon storage (CS) and its cycling processes in terrestrial ecosystems constitute a fundamental component of the global carbon cycle. Ecologically fragile regions, characterized by low ecosystem stability and high sensitivity to external disturbances, play a critical role in understanding regional and global carbon dynamics. Land Use/Land Cover Change (LUCC), as a major manifestation of human activities, is widely recognized as a key driver influencing CS and its spatiotemporal variations. However, for a typical ecologically fragile area such as Zhangye City in Gansu Province, there remains a lack of multi-scenario quantitative assessments regarding the impacts of LUCC on CS. In this study, the InVEST model and the PLUS model were integrated to analyze the evolution of land use patterns and CS in Zhangye City from 1990 to 2020. Furthermore, land use configurations and corresponding CS distributions under four ecological protection scenarios for 2030 were simulated. The results indicate that: (1) from 1990 to 2020, grassland and unused land decreased by 0.69% and 1.35%, respectively, while cultivated land expanded by 1.82% (approximately 698.86 km²), reflecting a notable shift in land use structure; (2) LUCC contributed to a net increase of 0.49×10⁷ t in total CS, exhibiting a spatial pattern of gradual increase from north to south; (3) by 2030, compared with the natural development scenario, CS under low-, medium-, and high-level ecological protection scenarios increased to 38.05×10⁷ t, 38.06×10⁷ t (38.05546×10⁷ t), and 38.06×10⁷ t (38.05544×10⁷t), respectively, with the medium-level ecological protection scenario yielding the most optimal land use configuration. By coupling the InVEST and PLUS models, this study effectively characterizes the impacts of land use change on CS, and provides methodological support for land use optimization and CS assessment in ecologically fragile regions. Based on these findings, it is recommended to strengthen the protection of ecological land, appropriately regulate the expansion of built-up areas, and promote a coordinated balance between economic development and ecological conservation, thereby contributing to the achievement of carbon peaking and carbon neutrality goals.

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References

[1] Cai W, Ng B, Wang G, et al. Increased ENSO sea surface temperature variability under four IPCC emission scenarios. Nature Climate Change, 2022, 12: 228-231.

[2] Chu L, Oloo F, Bergstedt H, Blaschke T. Assessing the link between human modification and changes in land surface temperature in Hainan, China using image archives from google earth engine. Remote Sensing, 2020, 12(5), 888.

[3] Karl T, Trenberth K. Modern Global Climate Change. Science, 2003, 302(5651): 1719-1723.

[4] Lashof D, Ahuja D. Relative contributions of greenhouse gas emissions to global warming. Nature, 1990, 344: 529-531.

[5] Seddon N, Smith A, Smith P, et al. Getting the message right on nature-based solutions to climate change. Global Change Biology, 2021, 27(8): 1518-1546.

[6] Gu S, Li S, Santos I. Anthropogenic land use substantially increases riverine CO2 emissions. Journal of Environmental Sciences, 2022, 118: 158-170.

[7] Singh P, Kikon N, Verma P. Impact of land use change and urbanization on urban heat island in Lucknow city, Central India. A remote sensing based estimate. Sustainable Cities and Society, 2017, 32: 100-114.

[8] Fu C, Xu M. Achieving carbon neutrality through ecological carbon sinks: A systems perspective. Green Carbon, 2023, 1(1): 43-46.

[9] Ren Y, Zhang L, li X, et al. Spatiotemporal variations and driving mechanisms of carbon storage in Central Asia: Insights from the PLUS-InVEST models and machine learning. Journal of Environmental Management, 2025, 389, 126123.

[10] Hanson E, Nwakile C, Hammed V. Carbon capture, utilization, and storage (CCUS) technologies: Evaluating the effectiveness of advanced CCUS solutions for reducing CO2 emissions. Results in Surfaces and Interfaces, 2025, 18, 100381.

[11] Chang X, Xing Y, Wang J, et al. Effects of land use and cover change (LUCC) on terrestrial carbon storage in China between 2000 and 2018. Resources, Conservation and Recycling, 2022, 182, 106333.

[12] Wang K, Li X, Yu X, et al. Optimizing the Land Use and Land Cover Pattern to Increase Its Contribution to Carbon Neutrality. Remote Sensing, 2022, 14(19), 4751.

[13] Qiao X, Li Z, Lin J, et al. Assessing current and future soil erosion under changing land use based on InVEST and FLUS models in the Yihe River Basin, North China. International Soil and Water Conservation Research, 2024, 12(2): 298-312.

[14] Anthony M, Tedersoo L, De Vos B. Fungal community composition predicts forest carbon storage at a continental scale. nature communications, 2024, 15, 2385.

[15] Yang Q, Huang Z, Wu L, et al. Resilience changes of carbon stocks to quantify the long-term effects of ecological engineering projects in subtropical forests of China based on satellite-derived net ecosystem production time series and inventory data. Land Degradation & Development, 2024, 35(7): 2329-2344.

[16] Verburg P, Soepboer W, Veldkamp A, et al. Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model. Environmental Management, 2002, 30(3): 391-405.

[17] Subedi P, Subedi K, Thapa B. Application of a Hybrid Cellular Automaton-Markov (CA-Markov) Model in Land-Use Change Prediction: A Case Study of Saddle Creek Drainage Basin, Florida. Applied Ecology and Environmental Sciences, 2013, 1(6): 126-132.

[18] Liu X, Liang X, Li X, et al. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning, 2017, 168: 94-116.

[19] Liang X, Guan Q, Clarke K, et al. Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China. Computers, Environment and Urban Systems, 2021, 85, 101569.

[20] Li C, Wu Y, Gao B, et al. Multi-scenario simulation of ecosystem service value for optimization of land use in the Sichuan-Yunnan ecological barrier, China. Ecological Indicators, 2021, 132, 108328.

[21] Houghton R. Why are estimates of the terrestrial carbon balance so different?. Global Change Biology, 2003, 9(4): 500-509.

[22] Sun W, Liu X. Review on carbon storage estimation of forest ecosystem and applications in China. Forest Ecosystems, 2020, 7, 4.

[23] Potter C, Randerson J, Field C, et al. Terrestrial ecosystem production: A process model based on global satellite and surface data. Global Biogeochemical Cycles, 1993, 7(4): 811-841.

[24] Prince S, Goward S. Global Primary Production: A Remote Sensing Approach. Journal of Biogeography, 1995, 22(4/5): 815-835.

[25] Wang Z, Zeng J, Chen W. Impact of urban expansion on carbon storage under multi-scenario simulations in Wuhan, China. Environmental Science and Pollution Research, 2022, 29: 45507-45526.

[26] Yao L, Zhang X, Zhou L, et al. Ecosystem service tradeoffs and synergies effects of land use change in Mountain-Oasis-Desert complex system: A case study of Zhangye City. Acta Ecologica Sinica, 2022, 42(20): 8138-8151.

[27] Jin Z, Xiong C, Luan Q, Wang F. Dynamic evolutionary analysis of land use/cover and ecosystem service values on Hainan Island. International Journal of Environmental Research and Public Health, 2023, 20(1), 776.

[28] Gong W, Duan X, Sun Y, et al. Multi-scenario simulation of land use/cover change and carbon storage assessment in Hainan coastal zone from perspective of free trade port construction. Journal of Cleaner Production, 2023, 385, 135630.

[29] Zhou J, Zhao Y, Huang P, et al. Impacts of ecological restoration projects on the ecosystem carbon storage of inland river basin in arid area, China. Ecological Indicators, 2020, 118, 106803.

[30] Huang D, Huang J, Liu T. Delimiting urban growth boundaries using the CLUE-S model with village administrative boundaries. Land Use Policy, 2019, 82: 422-435.

[31] Anindita S, Sleutel S, Vandenberghe D, et al. Land use impacts on weathering, soil properties, and carbon storage in wet Andosols, Indonesia. Geoderma, 2022, 423, 115963.

[32] Li Y, Liu Z, Li S, Li X. Multi-scenario simulation analysis of land use and carbon storage changes in Changchun city based on FLUS and InVEST model. Land, 2022b, 11(5), 647.

[33] Lai J, Qi S, Chen J, et al. Exploring the spatiotemporal variation of carbon storage on Hainan Island and its driving factors: Insights from InVEST, FLUS models, and machine learning. Ecological Indicators, 2025, 172, 113236.

[34] Liu Mengyuan. Assessment of carbon storage and habitat quality in Hexi region based on InVEST model. Lanzhou University, 2023.

[35] Zaher H, Sabir M, Benjelloun H, Paul-Igor H. Effect of forest land use change on carbohydrates, physical soil quality and carbon stocks in Moroccan cedar area. Journal of Environmental Management, 2020, 254, 109544.

[36] Wang J, Zhou W, Pickett S, et al. A multiscale analysis of urbanization effects on ecosystem services supply in an urban megaregion. Science of the Total Environment, 2019, 662: 824-833.

[37] Wang G, Cheng G, Shen Y. Features of eco-environmental changes in Hexi Corridor Region in the last 50 years and comprehensive control strategies. Journal of Natural Resources, 2002, 17(01): 78-86.