Dataset List

Vol.|Area

Data Details

Predicting Land-Ecological-Economic System and Carbon Storage Dataset of Sichuan Province of China in 2030


GAO Yifan1SONG Changqing2HUANG Jiarui2WANG Yuanhui2YE Sijing2GAO Peichao*1,2
1 State Key Laboratory of Earth Surface Process and Resource Ecology,Beijing Normal University,Beijing 100875,China2 Center for GeoData and Analysis,Beijing Normal University,Beijing 100875,China

DOI:10.3974/geodb.2024.11.04.V1

Published:Nov. 2024

Visitors:13       Data Files Downloaded:1      
Data Downloaded:1.84 MB      Citations:

Key Words:

land system,CLUMondo,carbon storage assessment,Sichuan

Abstract:

The authors utilized the CLUMondo model to predict land system changes and estimated the carbon storage in Sichuan Province in 2030 by considering land use intensity and ecological-economic trade-offs. The dataset includes: (1) raster data of land system in Sichuan Province for the years 2010, 2020, and predicted raster data of land system data in 2030 under nine scenarios; (2) estimated carbon storage of Sichuan Province in 2030 under nine scenarios; (3) carbon density. The spatial resolution of the land system raster data is 1 km. The dataset is archived in .tif and .xlsx data formats, and consists of 18 data files with data size of 51.4 MB (Compressed into one file with 1.84 MB). The analysis paper based on the dataset was published in Acta Ecologica Sinica, Vol. 44, No. 9, 2024.

Foundation Item:

National Natural Science Foundation of China (42230106, 42271418); State Key Laboratory of Earth Surface Processes and Resource Ecology (2022-ZD-04, 2023-WT-02)

Data Citation:

GAO Yifan, SONG Changqing, HUANG Jiarui, WANG Yuanhui, YE Sijing, GAO Peichao*.Predicting Land-Ecological-Economic System and Carbon Storage Dataset of Sichuan Province of China in 2030[J/DB/OL]. Digital Journal of Global Change Data Repository, 2024. https://doi.org/10.3974/geodb.2024.11.04.V1.

References:


     [1] Schleussner, C. F., Rogelj, J., Schaeffer, M., et al. Science and policy characteristics of the Paris Agreement temperature goal [J]. Nature Climate Change, 2016, 6(9): 827-835. DOI: 10.1038/nclimate3096.
     [2] Meinshausen, M., Lewis, J., McGlade, C., et al. Realization of Paris Agreement pledges may limit warming just below 2 °C [J]. Nature, 2022, 604(7905): 304-309. DOI: 10.1038/s41586-022-04553-z. [3] Gao, P. C., Song, C. Q. Review of Climate Economics and the Future of Humanity [J]. Economic Geography, 2021, 41(10): 41. [4] Mallapaty, S.How China could be carbon neutral by mid - century[J].Nature, 2020, 586(7830): 482 - 484.DOI: 10.1038 / d41586 - 020 - 02927 - 9 [5] Brovkin, V., Sitch S., Von Bloh, W., et al. Role of land cover changes for atmospheric CO2 increase and climate change during the last 150 years[J].Global Change Biology, 2004, 10(8): 1253 - 1266.DOI: 10.1111 / j.1365 - 2486.2004.00812.x. [6] Hu, J.X., Huang, F., Tie, L.H., et al.Economic value dynamics of carbon sequestration in forest vegetation of Sichuan Province[J].Acta Ecologica Sinica, 2019, 39(1): 158 - 163.DOI: 10.5846 / stxb201809292123. [7] Huang, C.D., Zhang, J., Yang, W.Q., et al.Spatial differentiation characteristics of forest vegetation carbon stock in Sichuan Province[J].Acta Ecologica Sinica, 2009, 29(9): 5115 - 5121. [8] Chen, J., Ban, Y.F., Li, S.N.Open access to Earth land - cover map[J].Nature, 2014, 514(7523): 434 - 434.DOI: 10.1038 / 514434c. [9] Chen, J., Chen J., Liao, A.P., et al.Global land cover mapping at 30 m resolution: A POK - based operational approach[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 103: 7 - 27.DOI: 10.1016 / j.isprsjprs.2014.09.002.
     [10] National Bureau of Statistics of the People's Republic of China. China Statistical Yearbook [M]. Beijing: China Statistics Press, 2020.
     [11] Poggio, L., de Sousa, L.M., Batjes, N.H., et al.SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty[J].Soil, 2021, 7(1): 217 - 240.DOI: 10.5194 / soil - 7 - 217 - 2021.
     [12] Spawn, S.A., Sullivan, C.C., Lark, T.J., et al.Harmonized global maps of above and belowground biomass carbon density in the year 2010[J].Scientific Data, 2020, 7(1): 1 - 22.DOI: 10.1038 / s41597 - 020 - 0444 - 4.
     [13] van Asselen, S., Verburg, P.H.A land system representation for global assessments and land‐use modeling[J].Global Change Biology, 2012, 18(10): 3125 - 3148.DOI: 10.1111 / j.1365 - 2486.2012.02759.x.
     [14] Gao, P.C., Gao, Y.F., Ou, Y., et al.Fulfilling global climate pledges can lead to major increase in forest land on Tibetan Plateau[J].iScience, 2023, 26(4).DOI: 10.1016 / j.isci.2023.106364.
     [15] Jin, X.L., Jiang, P.H., Ma, D.X., et al.Land system evolution of Qinghai - Tibetan Plateau under various development strategies[J].Applied Geography, 2019, 104: 1 - 9.DOI: 10.1016 / j.apgeog.2019.01.007.
     [16] Xie, G.D., Zhang, C.X., Zhang, L.M., et al.Improvement of the evaluation method for ecosystem service value based on per unit area[J].Journal of Natural Resources, 2015, 30(8): 1243 - 1254.DOI: 10.11849 / zrzyxb.2015.08.001.
     [17] van Asselen, S., Verburg, P.H.Land cover change or land‐use intensification: simulating land system change with a global‐scale land change model[J].Global change biology, 2013, 19(12): 3648 - 3667.DOI: 10.1111 / gcb.12331.
     [18] Domingo, D., Palka, G., Hersperger, A.M.Effect of zoning plans on urban land - use change: A multi - scenario simulation for supporting sustainable urban growth[J].Sustainable Cities and Society, 2021, 69: 102833.DOI: 10.1016 / j.scs.2021.102833.
     [19] Malek, Ž., Verburg, P.H., Geijzendorffer, I.R., et al.Global change effects on land management in the Mediterranean region[J].Global Environmental Change, 2018, 50: 238 - 254.DOI: 10.1016 / j.gloenvcha.2018.04.007.
     [20] Wang, Y., van Vliet, J., Pu, L.J., et al.Modeling different urban change trajectories and their trade - offs with food production in Jiangsu Province, China[J].Computers, Environment and Urban Systems, 2019, 77: 101355.DOI: 10.1016 / j.compenvurbsys.2019.101355.
     [21] van Vliet, J., Verburg, P.H.A short presentation of CLUMondo[M] // Geomatic approaches for modeling land change scenarios. Springer. 2018: 485-492.
     [22] Gao, P.C., Gao, Y.F., Zhang, X.D., et al.CLUMondo - BNU for simulating land system changes based on many - to - many demand–supply relationships with adaptive conversion orders[J].Scientific Reports, 2023, 13(1): 5559.DOI: 10.1038 / s41598 - 023 - 31001 - 3.
     [23] Gao, Y.F., Song, C.Q., Wang, Y.H., et al.Carbon storage prediction of terrestrial ecosystems and hotspot analysis in Sichuan Province by considering land use intensity and eco - economic trade - offs[J].Acta Ecologica Sinica, 2024, 44(9): 1 - 12.DOI: 10.20103 / j.stxb.202211113250.
     [24] Shao, Z., Chen, R., Zhao, J., et al.Spatio - temporal evolution and prediction of carbon storage in Beijing's ecosystem based on FLUS and InVEST models [J]. Acta Ecologica Sinica, 2022, 42(23): 1-14. DOI: 10.5846/stxb202201100094.
     [25] Van Vliet, J., Bregt, A.K., Hagen - Zanker, A.Revisiting Kappa to account for change in the accuracy assessment of land - use change models[J].Ecological modelling, 2011, 222(8): 1367 - 1375.DOI: 10.1016 / j.ecolmodel.2011.01.017.
     [26] Zhang, T.Y., Cheng, C.X., Wu, X.D.Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution[J].Scientific Data, 2023, 10(1): 748.DOI: 10.1038 / s41597 - 023 - 02637 - 7.
     [27] Zhang, J.D., Mei, Z.X., Lv, J.H., et al.Simulating multiple land use scenarios based on the FLUS model considering spatial autocorrelation[J].Journal of Geo - information Science, 2020, 22(3): 531 - 542.DOI: 10.12082 / dqxxkx.2020.190359.
     [28] Wang, H., Zeng, Y.N.Urban expansion model based on extreme learning machine[J].Acta Geodaetica et Cartographica Sinica, 2018, 47(12): 1680 - 1690.DOI: 10.11947 / j.AGCS.2018.20170586.
     

Data Product:

ID Data Name Data Size Operation
1 LandSystem&CarbonStorage.rar 1887.40KB
Co-Sponsors

Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences

The Geographical Society of China

Parteners

Committee on Data for Science and Technology (CODATA) Task Group on Preservation of and Access to Scientific and Technical Data in/for/with Developing Countries (PASTD)

Jomo Kenyatta University of Agriculture and Technology

Digital Linchao GeoMuseum