Cite this dataset as
Hooftman, D.A.P., Bullock, J.M., Neugarten, R.A. , Chaplin-Kramer, R., Willcock, S. (2023). Global ensembles of Ecosystem Service map outputs modelled at 1km resolution for water supply, recreation, carbon storage, fuelwood and forage production. NERC EDS Environmental Information Data Centre. (Dataset). https://doi.org/10.5285/bd940dad-9bf4-40d9-891b-161f3dfe8e86
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This dataset is available under the terms of the Open Government Licence 
Global ensembles of Ecosystem Service map outputs modelled at 1km resolution for water supply, recreation, carbon storage, fuelwood and forage production
This data set contains Global maps of five ecosystem services using 6 different among-model ensemble approaches: the provisioning services of water supply, biomass for fuelwood and forage production, the regulating service Carbon Storage for CO2 retention and the cultural non-material service Recreation. For water, the data comes as one shapefile with polygons per watershed, each polygon containing seven ensemble estimates. The other services – recreation, carbon storage, biomass for fuelwood and forage production – come as seven tiff- maps at a 1-km2 resolution with associated world files for each tiff-map contains 43,200 x 18,600 pixels for one ensemble approach, with LZW compressed file sizes between 400MB and 950MB. For all maps, 600dpi jpg depictions are added to the supporting information with uniform colour scaling set for the median ensemble per service.
Ensemble output maps were calculated with different approaches following the supporting documentation and associated publication. Uncertainty estimates for these services are included as variation among contributing model outputs and among the employed ensemble approaches.
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, with additional funding from ES/R009279/1 (MobilES) & ES/T007877/1 (RUST).
Ensemble output maps were calculated with different approaches following the supporting documentation and associated publication. Uncertainty estimates for these services are included as variation among contributing model outputs and among the employed ensemble approaches.
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, with additional funding from ES/R009279/1 (MobilES) & ES/T007877/1 (RUST).
Publication date: 2023-01-23
View record as
Formats
TIFF
Shapefile
Spatial information
- Study area
-
- Spatial representation types
-
Raster
Vector - Spatial reference system
- WGS 84
Provenance & quality
The ensembles, their approach methodology, and their validations are currently in revision after review in Science Advances as Willcock et al. (2023): Model Ensembles of Ecosystem Services Fill Global Certainty and Capacity Gaps. Relevant Matlab and Python codes can be found at https://github.com/GlobalEnsembles
Global among model ensembles for recreation, carbon storage, and biomass for fuelwood and forage production are provided as 1-km2 gridcells; water supply ensembles are provided per catchment polygons associated to the 15,289 worldwide HydroSHEDS catchment definitions (https://www.hydrosheds.org/). Model data included outputs from among others: InVEST, ARIES, WaterWorld, Co$ting Nature, LPJ-GUESS, TEEB, Scholes, Aqueduct, FAO livestock distributions, and a wide variety of biomass models such as from ESA CCI Biomass Climate Change Initiative, GEOCARBON global forest biomass and Global Forest Watch. These data sets are not provided here, but a full list with links to these data sets or software, where applicable, can be found in the supporting documentation. Note that license restrictions could apply.
Ensembles approaches include: unweighted (mean and median) approaches and weighted averaging with weights determined following multiple methods according to Hooftman et al. (2022): the deterministic correlation coefficient among models, the first principal component among models and weights iterated as regression to the median and leave-one-out cross validation. Uncertainty is presented by the Standard Error of Mean among contributing model outputs and among ensemble approaches, calculated as the standard deviation corrected with the amount of contributing models/ensembles per cell.
Prior to ensemble calculations: all individual model outputs have been normalised against the lower 2.5% and upper 97.5% percentile. Afterwards, the resulting Ensembles have been identically re-normalised to ensure a 0-1 scale. For all details about the individual model approaches, their synchronisation, ensemble algorithms and their validation we refer to the supporting documentation and associated publication.
Global among model ensembles for recreation, carbon storage, and biomass for fuelwood and forage production are provided as 1-km2 gridcells; water supply ensembles are provided per catchment polygons associated to the 15,289 worldwide HydroSHEDS catchment definitions (https://www.hydrosheds.org/). Model data included outputs from among others: InVEST, ARIES, WaterWorld, Co$ting Nature, LPJ-GUESS, TEEB, Scholes, Aqueduct, FAO livestock distributions, and a wide variety of biomass models such as from ESA CCI Biomass Climate Change Initiative, GEOCARBON global forest biomass and Global Forest Watch. These data sets are not provided here, but a full list with links to these data sets or software, where applicable, can be found in the supporting documentation. Note that license restrictions could apply.
Ensembles approaches include: unweighted (mean and median) approaches and weighted averaging with weights determined following multiple methods according to Hooftman et al. (2022): the deterministic correlation coefficient among models, the first principal component among models and weights iterated as regression to the median and leave-one-out cross validation. Uncertainty is presented by the Standard Error of Mean among contributing model outputs and among ensemble approaches, calculated as the standard deviation corrected with the amount of contributing models/ensembles per cell.
Prior to ensemble calculations: all individual model outputs have been normalised against the lower 2.5% and upper 97.5% percentile. Afterwards, the resulting Ensembles have been identically re-normalised to ensure a 0-1 scale. For all details about the individual model approaches, their synchronisation, ensemble algorithms and their validation we refer to the supporting documentation and associated publication.
Related
Correspondence/contact details
Hooftman, D.A.P.
Lactuca: environmental data analyses and Modelling, The Netherlands
Danny.hooftman@lactuca.nl
Authors
Hooftman, D.A.P.
Lactuca: environmental data analyses and Modelling,
https://orcid.org/0000-0001-9835-6897 Other contacts
- Rights holder
-
Bangor University
- Custodian
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NERC EDS Environmental Information Data Centreinfo@eidc.ac.uk
- Publisher
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NERC EDS Environmental Information Data Centreinfo@eidc.ac.uk
Additional metadata
- Topic categories
- economy
environment - INSPIRE themes
- Environmental Monitoring Facilities
Land Use - Keywords
- Carbon stocks , ecosystem services , Ensemble modelling , Fuelwood , Global maps , livestock , natural capital , recreation , sustainable development , water supply , Weighted averaging
- Funding
- Natural Environment Research Council Award: NE/T00391X/1
Economic and Social Research Council Award: ES/R009279/1
Economic and Social Research Council Award: ES/T007877/1 - Last updated
- 27 February 2024 16:17