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Ensemble outputs from Ecosystem Service models for water supply, aboveground carbon storage and use of water, grazing, charcoal and firewood by beneficiaries in sub-Saharan Africa

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www.data.gov.uk2024-06-29 更新2025-03-26 收录
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https://www.data.gov.uk/dataset/2dd44301-c183-4dfe-990f-c6d81d26c60a/ensemble-outputs-from-ecosystem-service-models-for-water-supply-aboveground-carbon-storage-and-use-of-water-grazing-charcoal-and-firewood-by-beneficiaries-in-sub-saharan-africa
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资源简介:
This dataset contains the gridded estimates per 1 km2 for mean and median ensemble outputs from 4-6 individual ecosystem service models for Sub-Saharan Africa, for above ground Carbon stock, firewood use, charcoal use and grazing use. Water use and supply are identically supplied as polygons. Individual model outputs are taken from previously published research. Making ensembles results in a smoothing effect whereby the individual model uncertainties are cancelled out and a signal of interest is more likely to emerge. Included ecosystem service models were: InVEST, Co$ting Nature, WaterWorld, Monetary value benefits transfer, LPJ-GUESS and Scholes models. Ensemble outputs have been normalised, therefore these ensembles project relative levels of service across the full area and can be used, for example, for optimisation or assignment of most important or sensitive areas. The work was completed under the "EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models" project (NE/T00391X/1) funded by the UKRI Landscape Decisions programme. Full details about this dataset can be found at https://doi.org/10.5285/11689000-f791-4fdb-8e12-08a7d87ad75f

本数据集汇聚了撒哈拉以南非洲地区针对地面碳储量、柴火消耗、木炭消耗以及放牧利用等生态系统服务的4-6个独立模型在每平方公里1平方公里的格网尺度上的均值与中位数集成输出。水资源的使用与供应以多边形形式提供。各个模型的输出数据均源自先前已发表的科研论文。通过集成分析,实现了模型不确定性的平滑化处理,从而消弭个体模型的不确定性,更易凸显感兴趣的信号。所包含的生态系统服务模型包括:InVEST、Co$ting Nature、WaterWorld、货币价值利益转移、LPJ-GUESS以及Scholes模型。集成输出结果已进行标准化处理,因此这些集成数据能够反映整个研究区域的相对服务水平,并可应用于诸如优化或分配关键或敏感区域等目的。该研究工作是在“EnsemblES - 利用集成技术捕捉生态系统服务模型精度与敏感性的项目”(NE/T00391X/1)的资助下完成的,该项目由英国研究与创新局(UKRI)景观决策项目资助。有关该数据集的详细信息,请参阅https://doi.org/10.5285/11689000-f791-4fdb-8e12-08a7d87ad75f。
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