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3D Global Water-Cycle Model / External data sources
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External Data Sources Used

This page lists external data sources used in the global model, including dataset names, summaries, licenses, and source links. General values, estimated values, and pre-generated derivative datasets prepared for model construction are not included in this list.

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External Sources

External Data-Source List

This list summarizes external data sources used in the global model, their licenses, and their source links. Details on how reclassified, corrected, combined, or pre-generated inputs are used in the model are provided in the input-data table.

External data sourceSummaryLicenseSource
Köppen-Geiger Global 1-km climate maps Global climate-classification data used to organize surface conditions and regional characteristics according to terrain and climate. CC-BY 4.0 gloh2o.org
CHELSA-monthly Monthly climate data from CHELSA V2.1. Precipitation and potential evapotranspiration are used as steady forcing. CC0 1.0 CHELSA-monthly
ERA5 post-processed daily statistics on single levels Global reanalysis data used for daily precipitation, temperature, wind speed, surface pressure, relative humidity, and radiation in transient analysis. CC-BY Copernicus CDS
ISIMIP / CMIP6 GCM scenario inputs Climate-scenario inputs used to compare future climate conditions in the global model. ISIMIP Terms ISIMIP
ALOS World 3D - 30m (AW3D30) Global land-elevation data used as base information for the ground surface, terrain relief, and weathering-surface generation. Commercial use allowed JAXA EORC
GEBCO_2026 Grid Global bathymetry grid used to define marine cells, water depths, and boundary conditions. Public Domain GEBCO
HydroSHEDS v1 ACC / ELV Global river and hydro-terrain data. Accumulation area and elevation are used to organize river locations and bed elevations. Commercial use allowed HydroSHEDS
HydroLAKES Global lake data used to organize lake extent and lake-surface information. CC-BY 4.0 HydroLAKES
GLOBathy Global lake-bathymetry data used to organize lake-bed elevation and lake topography. CC0 1.0 GLOBathy
SoilGrids Global soil data. Sand, silt and clay content and bulk density are connected to hydraulic-property classes for topsoil and cover layers. CC-BY 4.0 SoilGrids FAQ
USGS World Geologic Maps World geologic maps reclassified into model geologic codes and used as bedrock conditions. Public Domain USGS
Global Land Cover and Land Use 2019 (GLCLUC2019) Global land-use and land-cover data used for surface conditions such as forest, cropland, built-up areas, and water bodies. CC-BY 4.0 GLAD UMD
ESA CCI PFT 2020 Global plant-functional-type data used to classify forest types and connect vegetation conditions to forest evapotranspiration. No use restriction ESA CCI
ETH Global Canopy Height 2020 Global canopy-height data used for forest evapotranspiration and vegetation-parameter settings. CC-BY 4.0 ETH Research Collection
Crowther Global Tree Density Global tree-density data used to set forest structure. CC BY-ND 4.0 Yale EliScholar
ISIMIP3 crop calendar Crop calendar used to prepare crop coefficients and soil evaporation coefficients for FAO-56. ISIMIP Terms ISIMIP
GRDC-Caravan River-discharge observation time series included in GRDC-Caravan. The model uses the Caravan dataset, not direct GRDC distribution. CC-BY 4.0 Caravan GitHub

Note: License conditions follow the original data providers.

References

  1. Köppen-Geiger climate maps: Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data, 5, 180214. https://doi.org/10.1038/sdata.2018.214
  2. CHELSA-monthly: Karger, D. N., Brun, P., & Zilker, F. (2025). CHELSA-monthly climate data at high resolution. EnviDat. https://www.doi.org/10.16904/envidat.686
  3. CHELSA V2.1 model: Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the earth's land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122
  4. ERA5 daily statistics: Copernicus Climate Change Service. ERA5 post-processed daily statistics on single levels from 1940 to present. Copernicus Climate Data Store. https://doi.org/10.24381/cds.4991cf48
  5. WFDE5: Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H., & Buontempo, C. (2020). WFDE5: bias-adjusted ERA5 reanalysis data for impact studies. Earth System Science Data, 12, 2097-2120. https://doi.org/10.5194/essd-12-2097-2020
  6. ISIMIP3BASD: Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). Geoscientific Model Development, 12, 3055-3070. https://doi.org/10.5194/gmd-12-3055-2019
  7. W5E5: Lange, S. (2019). WFDE5 over land merged with ERA5 over the ocean (W5E5). Version 1.0. GFZ Data Services. https://doi.org/10.5880/pik.2019.023
  8. ISIMIP3BASD software: Lange, S. (2020). ISIMIP3BASD v2.4.1. Zenodo. https://doi.org/10.5281/zenodo.3898426
  9. HydroSHEDS: Lehner, B., Verdin, K., & Jarvis, A. (2008). New global hydrography derived from spaceborne elevation data. Eos, Transactions American Geophysical Union, 89(10), 93-94. https://doi.org/10.1029/2008EO100001
  10. HydroLAKES: Messager, M. L., Lehner, B., Grill, G., Nedeva, I., & Schmitt, O. (2016). Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications, 7, 13603. https://doi.org/10.1038/ncomms13603
  11. GLOBathy: Khazaei, B., Read, L. K., Casali, M., Sampson, K. R., & Yates, D. N. (2022). GLOBathy, the global lakes bathymetry dataset. Scientific Data, 9, 36. https://doi.org/10.1038/s41597-022-01132-9
  12. SoilGrids: Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. SOIL, 7, 217-240. https://doi.org/10.5194/soil-7-217-2021
  13. GRDC-Caravan: Kratzert, F., Nearing, G., Addor, N., Erickson, T., Gauch, M., Gilon, O., Gudmundsson, L., Hassidim, A., Klotz, D., Nevo, S., Shalev, G., & Matias, Y. (2023). Caravan - A global community dataset for large-sample hydrology. Scientific Data, 10, 61. https://doi.org/10.1038/s41597-023-01975-w
  14. Crowther Global Tree Density: Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., et al. (2015). Mapping tree density at a global scale. Nature, 525, 201-205. https://doi.org/10.1038/nature14967
  15. Forest evapotranspiration model: Inokoshi, S., Gomi, T., Chiu, C.-W., Onda, Y., Hashimoto, A., Zhang, Y., & Saitoh, T. M. (2023). A watershed-scale evapotranspiration model considering forest type, stand parameters, and climate factors. Forest Ecology and Management, 547, 121387. https://doi.org/10.1016/j.foreco.2023.121387
  16. Forest-floor evaporation model: Chiu, C.-W., Hashimoto, A., Inokoshi, S., Gomi, T., Onda, Y., & Sun, X. (2025). Developing a Structural Framework to Estimate Forest Floor Evapotranspiration With the Relative Yield Index (Ry). Ecohydrology, 18(7), e70125. https://doi.org/10.1002/eco.70125
  17. Snow and snowmelt analysis: 深沢壮騎・多田和広（2024）水循環モデルを用いた降雪地域における積雪・融雪期の再現性向上手法の検討．日本地下水学会2024年秋季講演会講演予稿集，講演番号07．
  18. FAO-56: Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, FAO, Rome.
  19. Soil-water retention properties: Green, T. R., Constantz, J. E., & Freyberg, D. L. (1996). Upscaled soil-water retention using van Genuchten's function. Journal of Hydrologic Engineering, 1(3), 123-130.
  20. Mualem model: Mualem, Y. (1976). A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resources Research, 12(3), 513-522.
  21. van Genuchten model: van Genuchten, M. T. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44(5), 892-898.
  22. Rosetta3: Zhang, Y., & Schaap, M. G. (2017). Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3). Journal of Hydrology, 547, 39-53. https://doi.org/10.1016/j.jhydrol.2017.01.004
  23. unsatfit: Seki, K. (2022). unsatfit. https://sekika.github.io/unsatfit/
  24. SWRC Fit / unsatfit: Seki, K. (2024). SWRC Fit and unsatfit for parameter determination of unsaturated soil properties. 東洋大学紀要自然科学篇，68，57-79．
  25. Root water-uptake model: Feddes, R. A., Kowalik, P. J., & Zaradny, H. (1978). Simulation of field water use and crop yield. Simulation Monographs, Pudoc.
  26. Evaporation efficiency: Lehmann, P., Merlin, O., Gentine, P., & Or, D. (2018). Soil Texture Effects on Surface Resistance to Bare-Soil Evaporation. Geophysical Research Letters, 45(19), 10398-10405. https://doi.org/10.1029/2018GL078803
  27. GETFLOWS: Tosaka, H., Itoh, K., & Furuno, T. (2000). Fully coupled formulation of surface flow with 2-phase subsurface flow for hydrological simulation. Hydrological Processes, 14(3), 449-464.
  28. Model evaluation metric: Pool, S., Vis, M. J. P., & Seibert, J. (2018). Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency. Hydrological Sciences Journal, 63(13-14), 1941-1953. https://doi.org/10.1080/02626667.2018.1552002

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See the input-data section of the technical overview page for how these sources are used as model inputs.

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