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  • Data comprise scenarios of how land use can be in the future and how will it affect ecosystem services in rural Mozambique. The scenarios were constructed from information gathered at five workshops held in Maputo, Xai Xai, Quelimane and Lichinga in 2014 and 2015. The objective of the workshops was to examine aspects that influence well-being (e.g. ecosystem services) and their causes (e.g. change in land use) in the Miombo woodland area of rural Mozambique and identify actions that could contribute to poverty alleviation and biodiversity conservation. The final objective was to construct scenarios of how the land use can be in Mozambique in the future (2035). The data were collected as part of the Abrupt Changes in Ecosystem Services and Wellbeing in Mozambican Woodlands (ACES) project and were funded by the Ecosystem Services for Poverty Alleviation (ESPA) programme, funded by NERC, the Economic & Social Research Council (ESRC) and the Department for International Development (DfID), the three are government organizations from UK. The project was led by the University of Edinburgh, with the collaboration of the Universidad Mondlane, the IIED, and other organizations. Full details about this dataset can be found at

  • Data comprise a set of broadleaf afforestation scenarios (provided as netCDF files) that may be run with the Joint UK Land Environment Simulator (JULES), a community land surface model. The scenarios are based on the CEH Land Cover 2000 classification. Afforestation takes place according to catchment structure and existing land cover. Scenarios cover twelve river catchments in Great Britain: Dee, Tay, Ouse, Ure, Derwent, Thames, Avon, Tamar, Severn at Bewdley , Severn at Haw Bridge, Ribble and Clyde. Afforestation scenarios relate to two catchment properties: - (1) River network structure and (2) Land use. By using these two catchment properties, in conjunction with different extents of afforestation, up to 288 afforestation scenarios per catchment are generated. This dataset was created as part of the NERC doctoral training partnerships (grant number NE/L002612/1). Full details about this dataset can be found at