Downscaled land cover for SSP IAM "marker" scenarios, 2010-2100

This dataset is comprised of data from the Shared Socio-economic Pathways (SSP) database, downscaled using the Downscalr package (available here). The data comprises of global projections for land use modelled by GLOBIOM, covering the period from 2000 to 2100. The projections are based on combinations of three different scenario models – Representative Concentration Pathways (RCP), Shared climate Policy Assumptions (SPA), and Shared Socio-economic Pathways (SSP). The dataset consists of 18 NetCDF files, each representing a different combination of RCP, SPA, and SSP pathways. Each file contains projected data for the entire 100-year period. Each file has the following structure:1. One raster file containing data on land use types (GlOBIOM land use projections)2. One raster file containing information on pixel size (redundant) (pixel_area). This raster file is redundant for analysis purposes. Within the first layer (GLOBIOM land use projections), each raster has 80 bands. Each of these bands represents one type of land use in a given decade. The key for both of these is listed below. Land Classes lc_class ID Land Use Class name Definition Dynamics Initialisation 1 cropland_other cropland area, excluding 2nd. generation bioenergy plantations (but includes 1st generation bioenergy crops); both n-fixing and not; both perennial (e.g., oil palm) and annual can increase from deforestation or conversion of grassland or other natural land; can decrease if not used anymore initialized with GLC2000, further harmonized with SPAM and FAO (crop specific areas); 2 cropland_other cropland dedicated to 2nd generation bioenergy short rotation plantations; perennial cropland can increase from deforestation or conversion of other natural land or grassland; can decrease if not used anymore initialized with GLC2000, further harmonized with SPAM and FAO (crop specific areas) 3 grassland grassland used for feeding livestock, can be both rangeland or pasture, both temporary or permanent grassland can increase from deforestation or conversion of other natural land; can decresae if not used anymore or converted to cropland initialized in year 2000 with GLC2000, but a large share of grassland is not used (and rebalanced to 'other') 4 forest_unmanaged forests areas not managed, can be both primary or secondary (e.g., if used before year 2000), was present in year 2000 and excludes new forest (afforestation) can decrease from conversion to managed forest (as simulated by the G4M model), cropland or grassland initialized in year 2000 with GLC2000, further hamonized with SPAM, FAO and information from G4M model 5 forest_managed forests areas managed (for extractive use or carbon sequestration), includes both forest present in year 2000 and new forest (e.g., afforestation) can decrease from conversion to cropland or grassland, can increase as a result of afforestation of other natural land (only in suitable pixels, as simulated by the G4M model) initialized in year 2000 with GLC2000, further hamonized with SPAM, FAO and information from G4M model 6 restored land that was used as grassland or cropland and set aside for restoration (only from 2020 onwards) only in BIOD scenarios, and cannot decrease (i.e., in each pixel, any increase at any time step is new restoration land) none 7 other other vegetated (primary or secondary non-forest and non-agricultural vegetation, including schrubland, tundra, wetlands), and non-vegetated (bare land, deserts, water, ice or permanent snow) areas can increase as a result cropland or grassland abandonment (in all time steps for NOBIOD scenarios, before 2020 in BIOD scenarios) initialized with GLC2000, further harmonized with SPAM and FAO (crop specific areas); 8 built_up_areas built-up areas static value (does not change over time or scenarios) initialized with GLC2000, further harmonized with SPAM and FAO (crop specific areas); Years Time value Year 0 2010 10 2020 20 2030 30 2040 40 2050 50 2060 60 2070 70 2080 80 2090 90 2100 The spatial dimension is divided into pixels, according to the GLOBIOM's SimU classification system. Within each pixel, a share of each land cover class is given. These pixels are sometimes accumulated into larger combined pixels, where data for land cover is homogenous across a wider area. Some pixels are not simulated as they contain no crop land cover (e.g., glaciers, lakes). The data is spatially explicit at SimU Level, with cell size between 5 arc minutes and 0.5 degrees. The dataset was created as a Fast Track Analysis for the Bending the Curve of Biodiversity trends exercise, on the 11th of February 2018. An overview of the Bending the curve exercises is linked here. The data is intended for research and analysis purposes, under a Creative Commons license.本数据集源自共享社会经济路径（Shared Socio-economic Pathways, SSP）数据库，经Downscalr工具包（获取链接见此处）降尺度处理。数据集包含由GLOBIOM模型模拟的全球土地利用预测数据，时间跨度为2000年至2100年。该预测基于三种情景模型的组合——典型浓度路径（Representative Concentration Pathways, RCP）、共享气候政策假设（Shared Climate Policy Assumptions, SPA）以及共享社会经济路径（SSP）。 本数据集包含18个NetCDF文件，每个文件对应RCP、SPA与SSP路径的一种不同组合。每个文件涵盖完整的100年预测周期。 每个文件包含以下结构： 1. 一个栅格文件，存储土地利用类型数据（GLOBIOM土地利用预测结果） 2. 一个栅格文件，包含像元尺寸信息（pixel_area），该文件在分析中无实用价值，属于冗余数据。 在第一个图层（GLOBIOM土地利用预测结果）中，每个栅格包含80个波段，每个波段对应一个十年期的一种土地利用类型，二者的对应键值如下。 土地类别 | 土地类别ID | 土地利用类别名称 | 定义 | 动态变化 | 初始化 | | --- | --- | --- | --- | --- | | 1 | cropland_other | 耕地面积，排除第二代生物能源种植园（但包含第一代生物能源作物；涵盖固氮与非固氮作物、多年生作物如油棕与一年生作物） | 可通过砍伐森林、转换草地或其他自然用地增加；若不再使用则可减少 | 以GLC2000数据为基础，经SPAM与FAO（作物特定面积）进一步协调统一 | | 2 | cropland_other | 专用于第二代生物能源短轮作种植园的耕地，属于多年生耕地 | 可通过砍伐森林、转换其他自然用地或草地增加；若不再使用则可减少 | 以GLC2000数据为基础，经SPAM与FAO（作物特定面积）进一步协调统一 | | 3 | grassland | 用于饲喂牲畜的草地，可分为牧场或牧草地，涵盖临时或永久性草地 | 可通过砍伐森林、转换其他自然用地增加；若不再使用或被转换为耕地则可减少 | 2000年以GLC2000数据为基础，但大部分草地未被利用（并被重新平衡至“其他”类别） | | 4 | forest_unmanaged | 未进行经营管理的森林区域，可分为原始林或次生林（如2000年前曾被使用的区域），2000年时存在且不包含新增森林（人工造林） | 可因被转换为管理林、耕地或草地而减少 | 2000年以GLC2000数据为基础，经SPAM、FAO与G4M模型信息进一步协调统一 | | 5 | forest_managed | 进行经营管理的森林区域（用于采伐或固碳），包含2000年已存在的森林与新增森林（如人工造林） | 可因被转换为耕地或草地而减少，也可因其他自然用地的人工造林（仅在G4M模型模拟的适宜像元中）而增加 | 2000年以GLC2000数据为基础，经SPAM、FAO与G4M模型信息进一步协调统一 | | 6 | restored | 曾被用作草地或耕地、并被预留用于生态恢复的土地（仅2020年起适用） | 仅适用于BIOD情景，且不会减少（即每个像元中，任意时间步的新增区域均为新恢复用地） | 无 | | 7 | other | 其他植被覆盖区域（原始或次生非森林、非农业植被，包括灌丛、苔原、湿地）以及无植被覆盖区域（裸地、沙漠、水体、冰川或永久性积雪） | 可因耕地或草地弃用而增加（NOBIOD情景下所有时间步均适用，BIOD情景下仅2020年前适用） | 以GLC2000数据为基础，经SPAM与FAO（作物特定面积）进一步协调统一 | | 8 | built_up_areas | 建成区域 | 静态值（不随时间或情景变化） | 以GLC2000数据为基础，经SPAM与FAO（作物特定面积）进一步协调统一 | 年份对应表 | 时间值 | 年份 | | --- | --- | | 0 | 2010 | | 10 | 2020 | | 20 | 2030 | | 30 | 2040 | | 40 | 2050 | | 50 | 2060 | | 60 | 2070 | | 70 | 2080 | | 80 | 2090 | | 90 | 2100 | 空间维度按照GLOBIOM的SimU分类系统划分为像元。每个像元中给出各土地覆盖类别的占比。部分像元会被合并为更大的组合像元，此类像元内的土地覆盖数据在更广范围内均一。部分像元因无耕地覆盖（如冰川、湖泊）未被模拟。本数据为SimU层级的空间显式数据，像元尺寸介于5弧分至0.5度之间。 本数据集于2018年2月11日为《扭转生物多样性趋势》快速追踪分析项目创建。《扭转曲线》系列项目的概述可在此处查阅。本数据集遵循知识共享许可协议，仅用于研究与分析用途。