Forcing for HD Model from HydroPy and subsequent HD Model river runoff over Europe based on EOBS22 and ERA5 data


Hagemann, Stefan; Stacke, Tobias

This experiment comprises data that have been used in Hagemann et al. (submitted). It comprises daily data of surface runoff and subsurface runoff from HydroPy and simulated daily discharges (river runoff) of the HD model. The discharge data close the water cycle at the land-ocean interface so that the discharges can be used as lateral freshwater input for ocean models applied in the European region.

a) HD5-ERA5
ERA5 is the fifth generation of atmospheric reanalysis (Hersbach et al., 2020) produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). It provides hourly data on many atmospheric, land-surface, and sea-state parameters at about 31 km resolution.
The global hydrology model HydroPy (Stacke and Hagemann, 2021) was driven by daily ERA5 forcing data from 1979-2018 to generate daily input fields of surface and subsurface runoff at the ERA5 resolution. It uses precipitation and 2m temperature directly from the ERA5 dataset. Furthermore, potential evapotranspiration (PET) was calculated from ERA5 data in a pre-processing step and used as an additional forcing for HydroPy. Here, we applied the Penman-Monteith equation to calculate a reference evapotranspiration following (Allen et al., 1998) that was improved by replacing the constant value for albedo with a distributed field from the LSP2 dataset (Hagemann, 2002). In order to initialize the storages in the HydroPy model and to avoid any drift during the actual simulation period, we conducted a 50-years spin-up simulation by repeatedly using year 1979 of the ERA5 dataset as forcing.
To generate river runoff, the Hydrological discharge (HD) model (Hagemann et al., 2020; Hagemann and Ho-Hagemann, 2021) was used that was operated at 5 arc minutes horizontal resolution. The HD model was set up over the European domain covering the land areas between -11°W to 69°E and 27°N to 72°N. First, the forcing data of surface and sub-surface runoff simulated by HydroPy were interpolated to the HD model grid. Then, daily discharges were simulated with the HD model.

The E-OBS dataset (Cornes et al., 2018) comprises several daily gridded surface variables at 0.1° and 0.25° resolution over Europe covering the area 25°N-71.5°N x 25°W-45°E. The dataset has been derived from station data collated by the ECA&D (European Climate Assessment & Dataset) initiative (Klein Tank et al., 2002; Klok and Klein Tank, 2009). In the present study, we use the best-guess fields of precipitation and 2m temperature of vs. 22 (EOBS22) at 0.1° resolution for the years 1950-2018.
HydroPy was driven by daily EOBS22 data of temperature and precipitation at 0.1° resolution from 1950-2019. The potential evapotranspiration (PET) was calculated following the approach proposed by (Thornthwaite, 1948) including an average day length at a given location.
As for HD5-ERA5, the forcing data of surface and sub-surface runoff simulated by HydroPy were first interpolated to the HD model grid. Then, daily discharges were simulated with the HD model.

Main reference:
Hagemann, S., Stacke, T. Complementing ERA5 and E-OBS with high-resolution river discharge over Europe. Oceanologia. Submitted.
coastDat-Land-Ocean-Fluxes (coastDat - Regional Water and Matter Fluxes at the Land-Ocean Interface)
Dr. Stefan Hagemann (
Spatial Coverage
Longitude -11 to 69 Latitude 27 to 72
Temporal Coverage
1950-01-01 to 2019-12-31 (calendrical)
Use constraints
Creative Commons Attribution 4.0 International (CC BY 4.0) (
Data Catalog
World Data Center for Climate
51.19 GiB (54960476309 Byte)
completely archived
Creation Date
Future Review Date
Cite as
Hagemann, Stefan; Stacke, Tobias (2021). Forcing for HD Model from HydroPy and subsequent HD Model river runoff over Europe based on EOBS22 and ERA5 data. World Data Center for Climate (WDCC) at DKRZ.

[Entry acronym: EOBS_ERA5-River_Runoff] [Entry id: 3889897]
Result Date
1. Number of data sets is correct and > 0: passed;
2. Size of every data set is > 0: passed;
3. The data sets and corresponding metadata are accessible: passed;
4. The data sizes are controlled and correct: passed;
5. The temporal coverage description (metadata) is consistent to the data: passed;
6. The format is correct: passed;
7. Variable description and data are consistent: passed
WDCC-TQA checklist
Method Description
Checks performed by WDCC. The list of TQA metrics are documented in the 'WDCC User Guide for Data Publication' Chapter 8.1.1
Method Url
Result Date
Contact typePersonORCIDInstitute
Contact Dr. Stefan Hagemann-Helmholtz-Zentrum Hereon
Author Dr. Stefan Hagemann-Helmholtz-Zentrum Hereon
Author Dr. Tobias Stacke 0000-0003-4637-5337Helmholtz-Zentrum Hereon
Investigator Dr. Stefan Hagemann-Helmholtz-Zentrum Hereon
Metadata Dr. Stefan Hagemann-Helmholtz-Zentrum Hereon


[1] DOI Hagemann, Stefan; Stacke, Tobias; Ho-Hagemann, Ha T. M. (2020). High Resolution Discharge Simulations Over Europe and the Baltic Sea Catchment. doi:10.3389/feart.2020.00012
[2] DOI Cornes, Richard C.; van der Schrier, Gerard; van den Besselaar, Else J. M.; Jones, Philip D. (2018). An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets. doi:10.1029/2017jd028200
[3] DOI Hersbach, Hans; Bell, Bill; Berrisford, Paul; Hirahara, Shoji; Horányi, András; Muñoz‐Sabater, Joaquín; Nicolas, Julien; Peubey, Carole; Radu, Raluca; Schepers, Dinand; Simmons, Adrian; Soci, Cornel; Abdalla, Saleh; Abellan, Xavier; Balsamo, Gianpaolo; Bechtold, Peter; Biavati, Gionata; Bidlot, Jean; Bonavita, Massimo; Chiara, Giovanna; Dahlgren, Per; Dee, Dick; Diamantakis, Michail; Dragani, Rossana; Flemming, Johannes; Forbes, Richard; Fuentes, Manuel; Geer, Alan; Haimberger, Leo; Healy, Sean; Hogan, Robin J.; Hólm, Elías; Janisková, Marta; Keeley, Sarah; Laloyaux, Patrick; Lopez, Philippe; Lupu, Cristina; Radnoti, Gabor; Rosnay, Patricia; Rozum, Iryna; Vamborg, Freja; Villaume, Sebastien; Thépaut, Jean‐Noël. (2020). The ERA5 global reanalysis. doi:10.1002/qj.3803
[4] DOI Klein Tank, A. M. G.; Wijngaard, J. B.; Können, G. P.; Böhm, R.; Demarée, G.; Gocheva, A.; Mileta, M.; Pashiardis, S.; Hejkrlik, L.; Kern-Hansen, C.; Heino, R.; Bessemoulin, P.; Müller-Westermeier, G.; Tzanakou, M.; Szalai, S.; Pálsdóttir, T.; Fitzgerald, D.; Rubin, S.; Capaldo, M.; Maugeri, M.; Leitass, A.; Bukantis, A.; Aberfeld, R.; van Engelen, A. F. V.; Forland, E.; Mietus, M.; Coelho, F.; Mares, C.; Razuvaev, V.; Nieplova, E.; Cegnar, T.; Antonio López, J.; Dahlström, B.; Moberg, A.; Kirchhofer, W.; Ceylan, A.; Pachaliuk, O.; Alexander, L. V.; Petrovic, P. (2002). Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. doi:10.1002/joc.773
[5] DOI Klok, E. J.; Klein Tank, A. M. G. (2009). Updated and extended European dataset of daily climate observations. doi:10.1002/joc.1779
[6] DOI THORNTHWAITE, C. W. (1948). An Approach Toward a Rational Classification of Climate. doi:10.1097/00010694-194807000-00007
[7] Allen, Richard G.; Pereira, Luis S.; Raes, Dirk; Smith, Martin. (1998). Crop evapotranspiration: guidelines for computing crop water requirements.
[8] Hagemann, Stefan. (2002). An improved land surface parameter dataset for global and regional climate models. ISSN 0937-1060

Is compiled by

[1] DOI Hagemann, Stefan; Ho-Hagemann, Ha Thi Minh. (2021). The hydrological discharge model - a river runoff component for offline and coupled model applications. doi:10.5281/zenodo.4893099
[2] DOI Stacke, Tobias; Hagemann, Stefan. (2021). HydroPy (v1.0): A new global hydrology model written in Python. doi:10.5194/gmd-2021-53

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