Spatiotemporal simulation of sustainable development based on ecosystem services under climate change

2.2. Data sources and processing 1. Land use data and normalized difference vegetation index (NDVI): These data were obtained from the Resource and Environment Data Center of the Chinese Academy of Sci- ences (https://www.resdc.cn/), with a spatial resolution of 30m for the years 2000, 2005, 2010, 2015, and 2020. (2) The 30m resolution digital elevation model (DEM) data was sourced from Geospatial Data Cloud (http://www.gscloud.cn). Slope data were calculated from the DEM using ArcGIS 10.8, also at a spatial resolution of 30m. Data on soil erodibil- ity, root depth, and plant water capacity were obtained from the International Soil Refer- ence and Information Centre (https://data.isric.org), with a resolution of 1km. The temporal resolution of these data is static, as they do not change frequently over short peri- ods. (3) Social data: Population and gross domestic product (GDP) data were sourced from the Resource and Environment Science Data Center (https://www.resdc.cn/), with the year being 2020. The distance to railways, water sources, and roads was determined using spatial analysis tools in ArcGIS 10.8. (4) Meteorological data: These were obtained from the National Earth System Science Data Center (http://www.geodata.cn) and include precipita- tion, temperature, and evapotranspiration, with a monthly temporal resolution. The rainfall erosion factor was calculated using daily rainfall data. Future climate scenarios were down- loaded from the Intergovernmental Panel on Climate Change (IPCC) (https://esgf-node. llnl.gov).

To ensure data quality, measures such as outlier correction and consistency checks were implemented. Specifically, the meteorological data underwent downscaling correction. For all raster data, resampling was performed using ArcGIS 10.8 to standardize the resolution to 30 meters. To meet the input requirements of the models, all data were projected onto the Kra- sovsky_1940_Albers coordinate system. The patch-generating land use simulation (PLUS) model was used to simulate future LUCs, while the integrated valuation of ecosystem services and tradeoffs (InVEST) model was utilized to assess ESs under different scenarios. The selec- tion of these methods provided robust technical support for the research. 2.3. Land use and climate change scenario modeling settings Based on the research objectives, future climate data under the shared socioeconomic path- ways (SSPs) combined with representative concentration pathways (RCPs) scenarios were integrated with future land use scenarios [26]. Three scenarios were established: SSP126 Sus- tainable Development, SSP245 Natural Growth, and SSP585 Urban Expansion, to simulate LUC distribution characteristics by 2035 under different scenarios [27]. These three scenarios encompass a broad range of climate change pathways, from low emissions to high emissions, and are representative of various conditions. Additionally, these scenarios reflect different socioeconomic development patterns, thereby providing a comprehensive analytical perspec- tive for the research.

1. SSP126 sustainable development scenario [28]. Under the SSP126 (low forcing, low emis- sions for sustainable development pathways) climate change model, the focus is on high ecological benefits, restricting the conversion of high-grade ecological land to lower grades, and controlling the transformation of ecological land into production and living areas. The probability of conversion from other lands to forests, grasslands, and water bodies is increased, promoting an overall improvement in the ecological landscape. This scenario represents an ideal sustainable development pathway that emphasizes ecological protection and the use of renewable energy. PLOS ONE Sustainable development simulation under climate change: A spatiotemporal ecosystem services approach PLOS ONE | https://doi.org/10.1371/journal.pone.0316605 February 4, 2025 4 / 19