GIS Assets

Calculations in GeoCARET rely on access to various GIS assets (layer) which are used for reservoir and catchment delineations and calculating reservoir and catchment characteristics, such as population density, surface runoff, mean monthly temperatures, etc.

The abbreviated table of GIS assets with data URLs in Google Earth Engine (GEE), Web links to the data and its documentations, and literature references, is provided below.

Note

[home-folder] in the GEE Data URL column refers to the location of private assets that we have uploaded to GEE and which would not have been accessible in GEE otherwise. The [home-folder] variable is projects/ee-future-dams.

Access to Private Assets

Important

The users need to request permission from us to use those private assets before they can make successful runs with GeoCARET. Please refer to the Installation Guidelines for further instructions. To request access to those assets please send email to: Tomasz Janus - Email 1 or Tomasz Janus - Email 2 with your email address registered with the Google Earth Engine.

Asset Table

GIS Assets used for calculation of GHG emission model inputs
No.	Asset Name	GEE Data URL	Website	References
1	WWF Hydrosheds	WWF/HydroSHEDS	GEE1a, GEE1b, PROVIDER1	Lehner et al. [1]
2	WWF Hydrosheds Flow Accumulation 15 Arc Seconds	WWF/HydroSHEDS/15ACC	GEE2, PROVIDER2	Lehner et al. [1]
3	WWF Hydrosheds Drainage Direction 15 Arc Seconds	WWF/HydroSHEDS/15DIR	GEE3, PROVIDER3	Lehner et al. [1]
4	Hydrobasins 12	WWF/HydroSHEDS/v1/Basins/hybas_12	GEE4 , PROVIDER4	Lehner et al. [1]
5	NASA SRTM Digital Elevation 30m	USGS/SRTMGL1_003	GEE5 , PROVIDER5	Farr et al. [2]
6	RESOLVE Ecoregions 2017	RESOLVE/ECOREGIONS/2017	GEE6 , PROVIDER6	Poggio et al. [3]
7	Soilgrids 250m OCS	projects/soilgrids-isric/ocs_mean	GEE7	Poggio et al. [3]
8	Soilgrids 250m SOC	projects/soilgrids-isric/soc_mean	GEE8	Poggio et al. [3]
9	Soilgrids 250m N	projects/soilgrids-isric/nitrogen_mean	GEE9	Poggio et al. [3]
10	Soilgrids 250m BDOD	projects/soilgrids-isric/bdod_mean	GEE10	Poggio et al. [3]
11	Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces, University of Idaho	IDAHO_EPSCOR/TERRACLIMATE	GEE11 , PROVIDER11	Abatzoglou et al. [4]
12	NASA-USDA Enhanced SMAP Global Soil Moisture Data	NASA_USDA/HSL/SMAP10KM_soil_moisture	GEE12 , PROVIDER12	Chan et al. [5]
13	GPWv411: Population Density (Gridded Population of the World Version 4.11)	CIESIN/GPWv411/GPW_Population_Density	GEE13 , PROVIDER13	Center for International Earth Science Information Network - CIESIN - Columbia University [6]
14	Average Monthly and Annual Direct Normal Irradiance Data, One-Degree Resolution of the World, NASA/SSE, 1983-2005	[home-folder]/XHEET_ASSETS/GHI_NASA_low	PROVIDER14	NASA Langley Atmospheric Sciences Data Center [7]
15	UNH-GRDC Composite Runoff Fields V1.0	[home-folder]/XHEET_ASSETS/cmp_ro_grdc	PROVIDER15	Fekete et al. [8]
16	WorldClim 2.1: new 1-km spatial resolution climate surfaces for global land areas (monthly mean temperature variables)	[home-folder]/XHEET_ASSETS/wc2-1_30s_tavg	PROVIDER16	Fick and Hijmans [9]
17	WorldClim 2.1: new 1-km spatial resolution climate surfaces for global land areas (bioclimactic variables)	[home-folder]/XHEET_ASSETS/wc2-1_30s_bio_12	PROVIDER17	Fick and Hijmans [9]
18	OlsenP kgha1 World	[home-folder]/XHEET_ASSETS/OlsenP_kgha1_World	–	–
19	Regridded Monthly Terrestrial Water Balance (Universoty of Delaware)	[home-folder]/XHEET_ASSETS/Eo150_clim_xyz_updated	PROVIDER19	Willmott et al. [10]
20	Koppen-Geiger Global 1-km climate classification maps	[home-folder]/XHEET_ASSETS/Beck_KG_V1_present_0p0083	PROVIDER20	Beck et al. [11]
21	HydroRIVERS v10	[home-folder]/HEET_ASSETS/HydroRIVERS_v10	PROVIDER21	Lehner and Grill [12]
22	CCI Land Cover Maps (Years 1992, 2000, 2010, 2020)	[home-folder]/XHEET_ASSETS/ESACCI-LC-L4-LCCS-Map-300m-P1Y	DOWNLOAD22 , VIEW22	Copernicus Climate Change Service [13]

GIS Assets with Metadata

A fuller datasets of GIS assets containing additional metadata can be downloaded in a CSV file format by clicking on the icon below.

The information about which GIS layer data source (layer and variable (band/feature)) is used for calculating each output parameter, is documented in Output Data Specification.

The legend to the metadata fields is provided below.

metadata_field	description
gis_layer_title	GIS layer title
gis_layer_location	GIS layer storage location on Google Earth Engine
ee_url	URL of GIS Layer Entry in Google Earth Engine
ee_asset_type	Google Earth Engine asset type
ee_asset_vars	Band/feature used from Google Earth Engine asset
tool_data_usage	Description of how the GIS layer is utilised by this tool
tool_data_year	Which years of data are utilised from the GIS layer
temporal_resolution	Temporal resolution of the data e.g. daily, monthly, yearly values etc
temporal_coverage	Temporal coverage of the GIS layer
dataset_provider	Dataset provider
dataset_provider_link	Dataset provider url
pixel_resolution	Pixel Resolution
terms_of_use	Terms of Use
citations	Citations
paper_url	Link to relevant publication
notes	Additional notes

Private Assets

GeoCARET relies on a number of data sources that were unavailable in Google Earth Engine catalog at the time of development. These assets are hosted on our Google Earth Engine account at projects/ee-future-dams/XHEET_ASSETS. In order to use GeoCARET the users need to obtain privileges to access (read) the asset files individually -see Access to Private Assets. Please check Installation as a Python Package for more details`.

Locations of Private Assets

Note

The data below duplicates the data provided in Asset Table and GIS Assets with Metadata. We have kept it in the documentation for now because we have not had time to check that there are no discrepancies between the two sources of the same data.

projects/ee-future-dams/XHEET_ASSETS/HydroRIVERS_v10 - shp file.
projects/ee-future-dams/XHEET_ASSETS/ESACCI-LC-L4-LCCS-Map-300m-P1Y-[Year]-v2-0-7cds [3] - nc file
projects/ee-future-dams/XHEET_ASSETS/GHI_NASA_low
projects/ee-future-dams/XHEET_ASSETS/cmp_ro_grdc - TiFF file
projects/ee-future-dams/XHEET_ASSETS/wc2-1_30s_bio_12
projects/ee-future-dams/XHEET_ASSETS/Beck_KG_V1_present_0p0083- TiFF file.
projects/ee-future-dams/XHEET_ASSETS/wc2-1_30s_tavg
projects/ee-future-dams/XHEET_ASSETS/OlsenP_kgha1_World [4]
projects/ee-future-dams/XHEET_ASSETS/Eo150_clim_xyz_updated
projects/ee-future-dams/XHEET_ASSETS/wc2-1_30s_prec
projects/ee-future-dams/XHEET_ASSETS/C3S-LC-L4-LCCS-Map-300m-P1Y-2020-v2.1.1.nc - nc file

Footnotes

Asset Sources

Asset	Website	Data url
6	weblink1	dataurl1
2	weblink2	dataurl2
11	weblink3	dataurl3
3	weblink4, weblink4b, weblink4c	dataurl4 dataurl4b
1	weblink5	dataurl5
4	weblink6	dataurl6
5	weblink7	dataurl7
10	weblink7	dataurl7
7	weblink7	dataurl7
9	weblink8	dataurl8

Preparation/Pre-processing

Some of the assets had to be pre-processed before uploading them to GEE, such that their file formats and data conform to GEE’s specifications. These pre-processing steps are listed below.

Asset	Name	Description	Pre-processing Notes
6	Köppen-Geiger climate classifications	Global maps of the Köppen-Geiger climate classification at an unprecedented 1‑km resolution for the present day (1980–2016)	No pre-processing
2, 11	Land Cover Maps - v2.0.7 (1992, 2000, 2010), 2.1.1 (2020)	Global land cover maps at 300 m spatial resolution [2]; The spatial coverage is latitude -90-90 degrees, longitude -180-180 degrees, and the coordinate system is the geographic coordinate WGS84 [1]	Data downloaded in netcdf format converted to GeoTiff using example code
3	NASA/SSE Irradiance Data 1983-2005	Solar: Average Monthly and Annual Direct Normal Irradiance Data, One-Degree Resolution of the World from NASA/SSE, 1983-2005. This polygon shapefile represents the 22 year average monthly and annual measurements (kWh/m\(^2\)/day) of global horizontal irradiance (GHI) for the entire world.	No pre-processing
1	HydroRIVERS v1.0	HydroRIVERS is a database aiming to provide the vectorized line network of all global rivers that have a catchment area of at least 10 km\(^2\) or an average river flow of 0.1 cubic meters per second, or both.	No pre-processing
4	UNH/GRDC Runoff Fields Data (composite monthly runoff fields and annual total runoff fields)	Three sets of annual and monthly climatological (1+12 layers per set) runoff fields…The sets are observed, WBM-simulated, and composite monthly runoff fields.	Pre-processing to add Projection information
5	WorldClim Historical Climate data	This is WorldClim version 2.1 climate data for 1970-2000. This version was released in January 2020. There are monthly climate data for minimum, mean, and maximum temperature, precipitation, solar radiation, wind speed, water vapor pressure, and for total precipitation. There are also 19 “bioclimatic” variables.	No pre-processing
10
7
9	Regridded Monthly Terrestial Water Balance Climatologies		Convert XYZ to tiff; Add projection; derive annual total evapo transpiration from monthly values

Footnotes

Literature

[1] (1,2,3,4)

Bernhard Lehner, Kristine Verdin, and Andy Jarvis. New global hydrography derived from spaceborne elevation data. Eos, Transactions American Geophysical Union, 89(10):93–94, 2008. URL: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2008EO100001, arXiv:https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2008EO100001, doi:https://doi.org/10.1029/2008EO100001.

[2]

Tom G. Farr, Paul A. Rosen, Edward Caro, Robert Crippen, Riley Duren, Scott Hensley, Michael Kobrick, Mimi Paller, Ernesto Rodriguez, Ladislav Roth, David Seal, Scott Shaffer, Joanne Shimada, Jeffrey Umland, Marian Werner, Michael Oskin, Douglas Burbank, and Douglas Alsdorf. The Shuttle Radar Topography Mission. Reviews of Geophysics, 2007. URL: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2005RG000183, arXiv:https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2005RG000183, doi:https://doi.org/10.1029/2005RG000183.

[3] (1,2,3,4,5)

L. Poggio, L. M. de Sousa, N. H. Batjes, G. B. M. Heuvelink, B. Kempen, E. Ribeiro, and D. Rossiter. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1):217–240, 2021. URL: https://soil.copernicus.org/articles/7/217/2021/, doi:10.5194/soil-7-217-2021.

[4]

John T. Abatzoglou, Solomon Z. Dobrowski, Sean A. Parks, and Katherine C. Hegewisch. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5(1):170191, Jan 2018. URL: https://doi.org/10.1038/sdata.2017.191, doi:10.1038/sdata.2017.191.

[5]

Steven K. Chan, Rajat Bindlish, Peggy E. O'Neill, Eni Njoku, Tom Jackson, Andreas Colliander, Fan Chen, Mariko Burgin, Scott Dunbar, Jeffrey Piepmeier, Simon Yueh, Dara Entekhabi, Michael H. Cosh, Todd Caldwell, Jeffrey Walker, Xiaoling Wu, Aaron Berg, Tracy Rowlandson, Anna Pacheco, Heather McNairn, Marc Thibeault, José Martínez-Fernández, Ángel González-Zamora, Mark Seyfried, David Bosch, Patrick Starks, David Goodrich, John Prueger, Michael Palecki, Eric E. Small, Marek Zreda, Jean-Christophe Calvet, Wade T. Crow, and Yann Kerr. Assessment of the SMAP Passive Soil Moisture Product. IEEE Transactions on Geoscience and Remote Sensing, 54(8):4994–5007, 2016. doi:10.1109/TGRS.2016.2561938.

[6]

Center for International Earth Science Information Network - CIESIN - Columbia University. Gridded Population of the World, Version 4 (GPWv4): Population Count, Revision 11. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). 2019. Accessed 01 APR 2020. doi:https://doi.org/10.7927/H4JW8BX5.

[7]

NASA Langley Atmospheric Sciences Data Center. Solar: Average Monthly and Annual Direct Normal Irradiance Data, One-Degree Resolution of the World from NASA/SSE, 1983-2005. 2008. [Shapefile]. Retrieved from https://earthworks.stanford.edu/catalog/stanford-fd535zg0917.

[8]

Balázs M. Fekete, Charles J. Vörösmarty, and Wolfgang Grabs. High-resolution fields of global runoff combining observed river discharge and simulated water balances. Global Biogeochemical Cycles, 16(3):15–1–15–10, 2002. URL: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/1999GB001254, arXiv:https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/1999GB001254, doi:https://doi.org/10.1029/1999GB001254.

[9] (1,2)

Stephen E. Fick and Robert J. Hijmans. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12):4302–4315, 2017. URL: https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.5086, arXiv:https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.5086, doi:https://doi.org/10.1002/joc.5086.

[10]

Cort J. Willmott, Clinton M. Rowe, and Yale Mintz. Climatology of the terrestrial seasonal water cycle. Journal of Climatology, 5(6):589–606, 1985. URL: https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.3370050602, arXiv:https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.3370050602, doi:https://doi.org/10.1002/joc.3370050602.

[11]

Hylke E. Beck, Niklaus E. Zimmermann, Tim R. McVicar, Noemi Vergopolan, Alexis Berg, and Eric F. Wood. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data, 5(1):180214, Oct 2018. URL: https://doi.org/10.1038/sdata.2018.214, doi:10.1038/sdata.2018.214.

[12]

Bernhard Lehner and Günther Grill. Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems. Hydrological Processes, 27(15):2171–2186, 2013. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/hyp.9740, arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.9740, doi:https://doi.org/10.1002/hyp.9740.

[13]

Copernicus Climate Change Service. Land cover classification gridded maps from 1992 to present derived from satellite observation. Climate Data Store, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), (Accessed on 01-APR-2020) 2019. URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=form, doi:10.24381/cds.006f2c9a.

Preparing Inputs

Output Data