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EM: Simulating the Land Use and Carbon Storage for Nature-Based Solutions (NbS) under Multi-Scenarios in the Three Gorges Reservoir Area: Integration of Remote Sensing Data and the RF–Markov–CA–InVEST Model (EM-1025)
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Expand AllEM Identity and Description
EM Identification
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EM ID
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EM-1025 |
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EM Short Name
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RF–Markov–CA–InVEST model, China |
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EM Full Name
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Simulating the Land Use and Carbon Storage for Nature-Based Solutions (NbS) under Multi-Scenarios in the Three Gorges Reservoir Area: Integration of Remote Sensing Data and the RF–Markov–CA–InVEST Model |
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EM Source or Collection
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InVEST |
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EM Source Document ID
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485 |
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Document Author
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Li, G., Cheng, G., Liu, G., Chen, C. and He, Y. |
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Document Year
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2023 |
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Document Title
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Simulating the land use and carbon storage for nature-based solutions (NbS) under multi-scenarios in the Three Gorges Reservoir area: Integration of remote sensing data and the rf–Markov–ca–InVEST model |
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Document Status
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Peer reviewed and published |
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Comments on Status
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Published journal manuscript |
Software and Access
| Not applicable | |
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Contact Name
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Guiyuan Li |
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Contact Address
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School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China |
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Contact Email
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lgy@hbut.edu.cn |
EM Description
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Summary Description
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Rapid industrialisation and urbanisation have moved contemporary civilization ahead but also deepened clashes with nature. Human society’s long-term evolution faces a number of serious problems, including the climate issue and frequent natural disasters. This research analyses the spatiotemporal evolution features of land use remote sensing data from 2005, 2010, 2015, and 2020. Under the Nature-based Solutions (NbS) idea, four scenarios are established: Business as Usual (BAU), Woodland Conservation (WLC), Arable Land Conservation (ALC), and Urban Transformation and Development (UTD). The RF–Markov–CA model is used to simulate the spatiotemporal patterns of land use for the years 2025 and 2030. Furthermore, the InVEST model is utilised to assess and forecast the spatiotemporal evolution features of carbon storage. The findings show that (1) the primary land use categories in the Three Gorges Reservoir Area (TGRA) from 2005 to 2020 are arable land and woodland. Arable land has a declining tendency, whereas woodland has an increasing–decreasing trend. (2) The WLC scenario exhibits the greatest growth in woodland and the lowest drop in grassland from 2020 to 2030, indicating a more stable ecosystem. (3) The TGRA demonstrates substantial geographic variation in carbon storage from 2005 to 2030, with a broad distribution pattern of “higher in the north, lower in the south, higher in the east, lower in the west, with the reservoir head > reservoir centre > reservoir tail”. (4) In comparison to the other three scenarios, the WLC scenario sees a slower development of construction and arable land from 2020 to 2030, whereas the ecological land area rises the highest and carbon storage increases. As a result, the WLC scenario is the TGRA’s recommended development choice. The study’s findings have substantial implications for the TGRA’s ecological preservation and management, as well as for the optimisation of ecosystem carbon cycling and the promotion of regional sustainable development. |
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Specific Policy or Decision Context Cited
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None provided |
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Biophysical Context
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Three Gorges Resevoir Area, Yangtze River |
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EM Scenario Drivers
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Arable Land Conservation (ALC) |
EM Relationship to Other EMs or Applications
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Method Only, Application of Method or Model Run
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Model Run Associated with a Specific EM Application |
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New or Pre-existing EM?
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Application of existing model ?Comment:Although this model does use InVEST, which has been incorporated into the ESML, the combination of a Random Forest (RF), Cellular Automata (CA), Markov model, and InVEST is considered a new overall model in the context of this publication. |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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Document ID for related EM
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None |
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EM ID for related EM
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EM-1026 | EM-1024 | EM-1023 |
EM Modeling Approach
EM Relationship to Time
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EM Temporal Extent
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2005-2030 |
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EM Time Dependence
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time-dependent |
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EM Time Reference (Future/Past)
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both |
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EM Time Continuity
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discrete |
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EM Temporal Grain Size Value
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5 |
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EM Temporal Grain Size Unit
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Year |
EM Spatial Extent
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Bounding Type
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Watershed/Catchment/HUC |
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Spatial Extent Name
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Three Gorges Resevoir |
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Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 |
Spatial Distribution of Computations
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EM Spatial Distribution
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spatially lumped (in all cases) |
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Spatial Grain Type
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Not applicable |
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Spatial Grain Size
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Not applicable |
EM Structure and Computation Approach
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EM Computational Approach
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Analytic |
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EM Determinism
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deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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Model Calibration Reported?
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Unclear |
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Model Goodness of Fit Reported?
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Yes |
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Goodness of Fit (metric| value | unit)
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Model Operational Validation Reported?
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Unclear ?Comment:Model validation was performed on the intermediate variables in this study using past data, however the response variables, which are future projections, do not have a validation associated with them. |
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Model Uncertainty Analysis Reported?
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Unclear |
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Model Sensitivity Analysis Reported?
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Unclear |
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Model Sensitivity Analysis Include Interactions?
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Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| None |
Centroid Lat/Long (Decimal Degree)
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Centroid Latitude
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30 |
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Centroid Longitude
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109 |
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Centroid Datum
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WGS84 |
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Centroid Coordinates Status
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Estimated |
Environments and Scales Modeled
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EM Environmental Sub-Class
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Rivers and Streams | Forests | Agroecosystems | Grasslands |
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Specific Environment Type
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Watershed with urban development |
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EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM Organismal Scale
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Not applicable |
Taxonomic level and name of organisms or groups identified
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| None Available |
EnviroAtlas URL
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| Dasymetric Allocation of Population, Average Annual Precipitation, Ecosystem Markets: Imperiled Species and Habitats |
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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EM Variable Names (and Units)
Predictor
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Intermediate
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Intermediate (Computed) Variables (and Units)
view details (4 variables)
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Response
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