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3D National Water-Cycle Model / External data sources
National Water-Cycle Model

External Data Sources Used

This page lists external data sources used in the national 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 national 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
JMA Mesh Climatology 2020 (precipitation) Gridded normal precipitation data for Japan, used as precipitation input for steady analysis. PDL1.0 Japan Meteorological Agency
CHELSA-monthly Monthly climate data from CHELSA V2.1. Potential evapotranspiration is converted to annual mean daily values and used in steady analysis. CC0 1.0 CHELSA-monthly
JMA analyzed precipitation Spatially analyzed precipitation data for Japan, used as precipitation time series for transient analysis. PDL1.0 JMA analyzed precipitation
NARO Agro-Meteorological Grid Square Data Daily gridded meteorological data for Japan, including temperature, wind speed, humidity, sunshine duration, and radiation. Used for GSI, leaf-on and leaf-off dates, crop coefficients, and soil evaporation coefficients. Permission required NARO
ERA5 post-processed daily statistics on single levels ERA5 single-level data processed into daily statistics. Used for surface pressure and related daily meteorological conditions. CC-BY Copernicus CDS
ISIMIP / CMIP6 GCM scenario inputs Climate-scenario inputs used to compare future climate conditions in the national model after separate preprocessing. ISIMIP Terms ISIMIP
Fundamental Geospatial Data DEM5A / DEM10B Land-elevation data for Japan, used as base information for the ground surface, terrain relief, and the upper surface of subsurface structure. Survey-result use conditions Fundamental Geospatial Data
Use conditions
GEBCO_2026 Grid Global bathymetry grid used to define marine cells, water depths, and boundary conditions. Public Domain GEBCO
Japan Surface Flow Direction Map (J-FlwDir) Data containing surface-flow direction, hydrologically corrected elevation, and upstream catchment area across Japan. Used to organize river locations and bed elevations. CC-BY 4.0 JapanDir
Yamazaki Lab
National Land Numerical Information Lake Data (W09) Japanese lake extent and attribute data, used to organize lake extent, lake-surface elevation, and lake-bed elevation. PDL1.0 NLNI W09
Terms
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
Japan Soil Inventory physical-property maps / 1:200,000 soil maps Japanese soil physical-property and soil-classification data, used to organize hydraulic properties for topsoil and cover layers. CC-BY 4.0 Japan Soil Inventory
Japanese forest soil physical-property data (Yamashita et al. 2021) Existing dataset of forest-soil physical properties, used to set topsoil properties in forest areas. CC-BY 4.0 Yamashita et al. 2021
AIST 1:200,000 Seamless Digital Geological Map of Japan V2 Geologic map of Japan, reclassified into model geologic codes and used as bedrock conditions. Government Standard Terms of Use 2.0 Seamless Geological Map
JAXA High-Resolution Land Use and Land Cover Map (Japan) Japanese land-use and land-cover data, used to organize land use, forest areas, and vegetation types from ALOS forest classes. JAXA research-data terms JAXA EORC
JAXA data policy
National Land Numerical Information Land Use Mesh (L03-b) Japanese mesh land-use data, used as auxiliary information for land-use classification. PDL1.0 NLNI L03-b
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
MLIT Water Information System Time-series data at Japanese river observation sites, used to diagnose model reproducibility by comparing with simulated discharge. PDL1.0 Water Information System
Dam Quantity Database Dam inflow time series, used to diagnose model reproducibility by comparing with simulated discharge. Government Standard Terms of Use 2.0 / CC BY compatible Dam Quantity DB
Copyright and disclaimer

Note: License conditions follow the original data providers.

References

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. ISIMIP3BASD software: Lange, S. (2020). ISIMIP3BASD v2.4.1. Zenodo. https://doi.org/10.5281/zenodo.3898426
  8. 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
  9. 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
  10. 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
  11. 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
  12. Soil classification: 小原洋・大倉利明・高田裕介・神山和則・前島勇治・浜崎忠雄（2011）包括的土壌分類 第1次試案．農業環境技術研究所報告，29，1-73．
  13. Seamless Digital Geological Map of Japan: 産総研地質調査総合センター（2023）20万分の1日本シームレス地質図V2．https://gbank.gsj.jp/seamless/
  14. Soil physical-environment database: 滝本貴弘・高田裕介・桑形恒男（2017）土壌温度・水分変動を予測するための都道府県別土壌物理環境データベースの作成．日本土壌肥料学雑誌，88(4)，309-317．
  15. Japan Soil Inventory soil map: 日本土壌インベントリー（2026a）縮尺20万分の1土壌図（Shapeファイル県別・全国一括）．https://soil-inventory.rad.naro.go.jp/download20.html
  16. Japan Soil Inventory soil physical properties: 日本土壌インベントリー（2026b）土壌物理特性値マップ作成用データ（全国一括ダウンロード）．https://soil-inventory.rad.naro.go.jp/download/
  17. Forest soil classification: 林業試験場土じょう部（1976）林野土壌の分類（1975）．林業試験場研究報告，280，1-28．
  18. Carbon dynamics in Japanese cedar plantations: Toriyama, J., Hashimoto, S., Osone, Y., Yamashita, N., Tsurita, T., Shimizu, T., Saitoh, T. M., Sawano, S., Lehtonen, A., & Ishizuka, S. (2021). Estimating spatial variation in the effects of climate change on the net primary production of Japanese cedar plantations based on modeled carbon dynamics. PLoS ONE, 16(2), e0247165.
  19. Japanese forest soil physical-property data: Yamashita, N., Tsurita, T., Toriyama, J., & Hashimoto, S. (2021). A spatial dataset of soil physical properties in Japanese forest [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4505671
  20. Optuna: Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2623-2631. https://doi.org/10.1145/3292500.3330701
  21. 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．
  22. 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.
  23. 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.
  24. Mualem model: Mualem, Y. (1976). A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resources Research, 12(3), 513-522.
  25. 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.
  26. 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
  27. unsatfit: Seki, K. (2022). unsatfit. https://sekika.github.io/unsatfit/
  28. 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．
  29. Root water-uptake model: Feddes, R. A., Kowalik, P. J., & Zaradny, H. (1978). Simulation of field water use and crop yield. Simulation Monographs, Pudoc.
  30. 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
  31. 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.
  32. 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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