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. b) HD5-EOBS 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.

The data of this experiment have been used in (Hagemann et al., 2020). It comprise daily data of surface runoff and subsurface runoff (drainage) from JSBACH and MPI-HM and simulated daily discharges (river runoff). 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. Different to the published version of HD model parameters (5.0) on Zenodo, an earlier version (4.0) of flow directions and model parameters has been used that is provided as an auxiliary data file. 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 respective forcing data of surface and sub-surface runoff were interpolated to the HD model domain using conservative remapping. Then, daily discharges were simulated with the HD model for the period 1979-2009 (1999-2009 for HD5-MESCAN). In addition, daily discharges were analogously simulated using only JSBACH forcing with the global 0.5° version 1.10 of the HD model. The associated flow directions and model parameters of vs. 1.10 are provided as an auxiliary data file. The HD forcing data are: a) HD5-JSBACH In order to generate daily input fields of surface runoff and drainage, the land surface scheme JSBACH (vs. 3 + frozen soil physics; (Ekici et al., 2014)) was forced globally at 0.5° with daily atmospheric forcing data based on the Interim Re-Analysis of the European Centre for Medium-Range Weather Forecast (ERA-Interim; (Dee et al., 2011)). These forcing data are bias-corrected (see (Beer et al., 2014)) towards the so-called WATCH forcing data (WFD; (Weedon et al., 2011)) that have been generated in the EU project WATCH. b) HD5-MPIHM The MPI-M hydrology model MPI-HM (Stacke and Hagemann, 2012) was driven by daily WATCH forcing data based on ERA-Interim (WFDEI; (Weedon et al., 2014)) from 1979-2009 to generate daily input fields of surface runoff and drainage at global 0.5° resolution. c) HD5-MESCAN Six hourly data of surface runoff and drainage (variable name: percolation) were retrieved from the MESCAN-SURFEX regional surface reanalysis (Bazile et al., 2017) created in the EU project UERRA (Uncertainties in Ensembles of Regional ReAnalysis; www.uerra.eu). SURFEX (Masson et al., 2013) is a land surface platform that was driven by atmospheric forcing at 5.5 km. The forcing comprises 24h-precipitation, near-surface temperature and relative humidity analyzed by the MESCAN surface analysis system as well as radiative fluxes and wind downscaled at 5.5 km from the 3DVar re-analysis conducted with the HARMONIE system at 11 km (Ridal et al., 2017). The latter has been generated using six-hourly fields of the ERA-Interim reanalysis as boundary conditions and covers a domain comprising Europe and parts of the Atlantic, which is similar to the European domain of the Coordinated Downscaling Experiment (CORDEX) at 11 km.

Model runs over Europe were conducted within the ESM project (www.esm-project.net/) for the Frontier Simulations supporting the water and matter fluxes from the European landmass to receiving water bodies (Baltic Sea, Atlantic Ocean and the Mediterranean Sea). Daily discharge from the mesoscale Hydrologic Model (mHM; Samaniego et al., 2010; Kumar et al., 2013; Code version: git.ufz.de/mhm/mhm git version: 35b5cb1) operated at the spatial resolution of 1/16deg for the simulation period from 1.1.1960-31.12.2019 across the European domain (Longitude -11 to 41 Latitude 35 to 72). Model runs were conducted within the ESM project (www.esm-project.net/) for the Frontier Simulations supporting the water and matter fluxes from the European landmass to receiving water bodies (Baltic Sea, Atlantic Ocean and Mediterranian Sea). Special consideration was given to the coastal cells by filtering out those (bordering) grid cells that do not have 100% landmass (i.e., cells with a significant proportion of water bodies/sea/ocean coverage). Meteorological forcing data are based on the E-OBS v21e (daily precipitation, temperature, Hofstra et al. 2009), potential evapotranspiration is based on the Hargreaves-Samani method. Soil characteristics are obtained from the global SoilGrids database (Hengtl et al. 2014; the land cover is derived from the Globcover_V2 (http://due.esrin.esa.int/page_globcover.php); geomorphological features are based on the GMTED2010 (Danielson et al., 2011). Model parameterization was constrained using the observed discharge time series from the GRDC stations (https://portal.grdc.bafg.de/), satisfying the following three conditions: gauge LAT>48degN, area> 5000km2, area <170000km2. Multi-basin calibration and validation were employed to check the consistency of model simulations following Rakovec et al., 2016 and Samaniego et al. 2019, as follows. Calibration objective function using KGE, DDS algorithm with 500 iterations, to account for uncertainty in the calibration process and the basin selections, 50 random initial conditions were randomly drawn sub-set of basins (N=6basins). The best parameter set in the cross-validations across 1201 basins was selected for the final run (ID: 542). A static 2D file of flow direction over Europe at the routing resolution 1/16deg. Internal upscaling to 1/16deg from the higher resolution (1/512deg) done within mHM (Code version: mesoscale Hydrologic Model (git.ufz.de/mhm/mhm git version: 35b5cb1). Special consideration was given to the coastal cells by filtering out those (bordering) grid cells that do not have 100% landmass (i.e., cells with a significant proportion of water bodies/sea/ocean coverage). Flow direction network (lat,lon) and routed runoff (time,lat,lon) at 1/16deg are provided as separate datasets.

The data of this experiment have been used in (Hagemann et al., 2020). It comprise daily data of surface runoff and subsurface runoff (drainage) from JSBACH and MPI-HM and simulated daily discharges (river runoff). 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. Different to the published version of HD model parameters (5.0) on Zenodo, an earlier version (4.0) of flow directions and model parameters has been used that is provided as an auxiliary data file. 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 respective forcing data of surface and sub-surface runoff were interpolated to the HD model domain using conservative remapping. Then, daily discharges were simulated with the HD model for the period 1979-2009 (1999-2009 for HD5-MESCAN). In addition, daily discharges were analogously simulated using only JSBACH forcing with the global 0.5° version 1.10 of the HD model. The associated flow directions and model parameters of vs. 1.10 are provided as an auxiliary data file. The HD forcing data are: a) HD5-JSBACH In order to generate daily input fields of surface runoff and drainage, the land surface scheme JSBACH (vs. 3 + frozen soil physics; (Ekici et al., 2014)) was forced globally at 0.5° with daily atmospheric forcing data based on the Interim Re-Analysis of the European Centre for Medium-Range Weather Forecast (ERA-Interim; (Dee et al., 2011)). These forcing data are bias-corrected (see (Beer et al., 2014)) towards the so-called WATCH forcing data (WFD; (Weedon et al., 2011)) that have been generated in the EU project WATCH. b) HD5-MPIHM The MPI-M hydrology model MPI-HM (Stacke and Hagemann, 2012) was driven by daily WATCH forcing data based on ERA-Interim (WFDEI; (Weedon et al., 2014)) from 1979-2009 to generate daily input fields of surface runoff and drainage at global 0.5° resolution. c) HD5-MESCAN Six hourly data of surface runoff and drainage (variable name: percolation) were retrieved from the MESCAN-SURFEX regional surface reanalysis (Bazile et al., 2017) created in the EU project UERRA (Uncertainties in Ensembles of Regional ReAnalysis; www.uerra.eu). SURFEX (Masson et al., 2013) is a land surface platform that was driven by atmospheric forcing at 5.5 km. The forcing comprises 24h-precipitation, near-surface temperature and relative humidity analyzed by the MESCAN surface analysis system as well as radiative fluxes and wind downscaled at 5.5 km from the 3DVar re-analysis conducted with the HARMONIE system at 11 km (Ridal et al., 2017). The latter has been generated using six-hourly fields of the ERA-Interim reanalysis as boundary conditions and covers a domain comprising Europe and parts of the Atlantic, which is similar to the European domain of the Coordinated Downscaling Experiment (CORDEX) at 11 km.

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. b) HD5-EOBS 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. (2022) Complementing ERA5 and E-OBS with high-resolution river discharge over Europe. Oceanologia 65: 230-248, doi:10.1016/j.oceano.2022.07.003

Model runs over Europe were conducted within the ESM project (www.esm-project.net/) for the Frontier Simulations supporting the water and matter fluxes from the European landmass to receiving water bodies (Baltic Sea, Atlantic Ocean and the Mediterranean Sea). Daily discharge from the mesoscale Hydrologic Model (mHM; Samaniego et al., 2010; Kumar et al., 2013; Code version: git.ufz.de/mhm/mhm git version: 35b5cb1) operated at the spatial resolution of 1/16deg for the simulation period from 1.1.1960-31.12.2022 across the European domain (Longitude -11 to 41 Latitude 35 to 72). Model runs were conducted within the ESM project (www.esm-project.net/) for the Frontier Simulations supporting the water and matter fluxes from the European landmass to receiving water bodies (Baltic Sea, Atlantic Ocean and Mediterranian Sea). Special consideration was given to the coastal cells by filtering out those (bordering) grid cells that do not have 100% landmass (i.e., cells with a significant proportion of water bodies/sea/ocean coverage). Meteorological forcing data are based on the E-OBS v21e (daily precipitation, temperature, Hofstra et al. 2009), potential evapotranspiration is based on the Hargreaves-Samani method. Soil characteristics are obtained from the global SoilGrids database (Hengtl et al. 2014; the land cover is derived from the Globcover_V2 (http://due.esrin.esa.int/page_globcover.php); geomorphological features are based on the GMTED2010 (Danielson et al., 2011). Model parameterization was constrained using the observed discharge time series from the GRDC stations (https://portal.grdc.bafg.de/), satisfying the following three conditions: gauge LAT>48degN, area> 5000km2, area <170000km2. Multi-basin calibration and validation were employed to check the consistency of model simulations following Rakovec et al., 2016 and Samaniego et al. 2019, as follows. Calibration objective function using KGE, DDS algorithm with 500 iterations, to account for uncertainty in the calibration process and the basin selections, 50 random initial conditions were randomly drawn sub-set of basins (N=6basins). The best parameter set in the cross-validations across 1201 basins was selected for the final run (ID: 542). A static 2D file of flow direction over Europe at the routing resolution 1/16deg. Internal upscaling to 1/16deg from the higher resolution (1/512deg) done within mHM (Code version: mesoscale Hydrologic Model (git.ufz.de/mhm/mhm git version: 35b5cb1). Special consideration was given to the coastal cells by filtering out those (bordering) grid cells that do not have 100% landmass (i.e., cells with a significant proportion of water bodies/sea/ocean coverage). Flow direction network (lat,lon) and routed runoff (time,lat,lon) at 1/16deg are provided as separate datasets. Meteorological forcing data of the mHM model from 1.1.2020 to 31.12.2022 are based on the E-OBS v26e.

1 Dataset description In ocean model or Earth System model applications, the riverine freshwater inflow is an important flux affecting salinity and marine stratification in coastal areas. However, in climate change studies, the river runoff based on climate model output often has large biases on local, regional or even basin wide scales. If these biases are too large, the ocean model forced by the runoff will drift into a different climate state compared to the observed state, which is especially relevant for semi-enclosed seas like the Baltic Sea. In order to fulfil the demands for low biases in river runoff, a three-part bias correction was developed by Hagemann et al. (in prep.) that comprises different correction factors for low, medium and high percentile ranges of river runoff over Europe. First, we utilized the global hydrology model HydroPy (Stacke and Hagemann 2021) and the Hydrological Discharge (HD) model (Hagemann et al. 2020) to simulate daily discharge time series over the European domain at 1/12° horizontal resolution Sect. 1.1) from 1901-2019. Then, we bias-corrected these time series as described in Sect. 1.2 to generate bias-corrected discharges at coastal ocean boxes of the European HD model domain from 1901-2019. 1.1 Century-long high-resolution discharge simulation over Europe Analogous to Hagemann and Stacke (2022), the global hydrology model HydroPy (Vs. 1.0.2 Stacke and Hagemann 2021) and the Hydrological Discharge (HD) model (Vs. 5.2.0, Hagemann et al. 2023) were used to simulate daily discharge time series over the European domain at 1/12° horizontal resolution. Daily data of two atmospheric datasets were utilized to force HydroPy that provided the input to the HD model. The Global Soil Wetness Project Phase 3 (GWSP3; Dirmeyer et al. 2006; Kim 2017) dataset is available at 0.5° resolution from 1901-2014. Here, we used the data from 1901-1978, and then the simulated time series were continued by using the WFDE5 dataset (Cucchi et al. 2020; 0.5° resolution) from 1979-2019. 1.2 Generation of bias corrected HD discharge data In order to apply the bias correction of Hagemann et al. (in prep.) to the simulated time series of daily discharge from 1901-2019, two sets of bias correction factors were derived. The first set uses the WFDE5-based discharges and discharge station observations for the period 1979-2014. This set was used to bias-correct the simulated discharge at HD river mouths from 1979-2019. The second set uses a further discharge simulation where we continued the GSWP3-based simulation with GSWP3 forcing until 2014. Again, the set of bias-correction factors was derived for the period 1979-2014 using discharge station observations. Then, this set was applied to bias-correct the simulated discharge at HD river mouths from 1901-1978. Detailed information you can find in the specified sections of the attached PDF https://www.wdc-climate.de/ui/entry?acronym=Biasc_hr_riverro_Eu_AdI_v1_0 Recently, a bug has been discovered in the part of the bias correction procedure, which transfers the bias correction factors from the station locations to the river mouths. Here, accidentally the bias correction factors from a previous simulation, which had utilized GSWP3 data, HydroPy and the HD model, were transferred to the river mouths for the whole considered period from 1901-2019. It can be noted that these factors still have improved the simulated inflows for most of the basins compared to the uncorrected HD model discharges. However, fixing this bug (see Version 1.1: https://www.wdc-climate.de/ui/entry?acronym=Biasc_hr_riverro_Eu_v1_1) has led to general improvement for most of the basins. Note that the other datasets of this Version 1.0 did not change. Acknowledgments This dataset was generated within the CoastalFutures project that was funded by the German Federal Ministry of Education and Research under grant number 03F0911A-K.

1 Dataset description In ocean model or Earth System model applications, the riverine freshwater inflow is an important flux affecting salinity and marine stratification in coastal areas. However, in climate change studies, the river runoff based on climate model output often has large biases on local, regional or even basin wide scales. If these biases are too large, the ocean model forced by the runoff will drift into a different climate state compared to the observed state, which is especially relevant for semi-enclosed seas like the Baltic Sea. In order to fulfil the demands for low biases in river runoff, a three-part bias correction was developed by Hagemann et al. (in prep.) that comprises different correction factors for low, medium and high percentile ranges of river runoff over Europe. First, we utilized the global hydrology model HydroPy (Stacke and Hagemann 2021) and the Hydrological Discharge (HD) model (Hagemann et al. 2020) to simulate daily discharge time series over the European domain at 1/12° horizontal resolution Sect. 1.1) from 1901-2019. Then, we bias-corrected these time series as described in Sect. 1.2 to generate bias-corrected discharges at coastal ocean boxes of the European HD model domain from 1901-2019. 1.1 Century-long high-resolution discharge simulation over Europe Analogous to Hagemann and Stacke (2022), the global hydrology model HydroPy (Vs. 1.0.2 Stacke and Hagemann 2021) and the Hydrological Discharge (HD) model (Vs. 5.2.0, Hagemann et al. 2023) were used to simulate daily discharge time series over the European domain at 1/12° horizontal resolution. Daily data of two atmospheric datasets were utilized to force HydroPy that provided the input to the HD model. The Global Soil Wetness Project Phase 3 (GWSP3; Dirmeyer et al. 2006; Kim 2017) dataset is available at 0.5° resolution from 1901-2014. Here, we used the data from 1901-1978, and then the simulated time series were continued by using the WFDE5 dataset (Cucchi et al. 2020; 0.5° resolution) from 1979-2019. 1.2 Generation of bias corrected HD discharge data In order to apply the bias correction of Hagemann et al. (in prep.) to the simulated time series of daily discharge from 1901-2019, two sets of bias correction factors were derived. The first set uses the WFDE5-based discharges and discharge station observations for the period 1979-2014. This set was used to bias-correct the simulated discharge at HD river mouths from 1979-2019. The second set uses a further discharge simulation where we continued the GSWP3-based simulation with GSWP3 forcing until 2014. Again, the set of bias-correction factors was derived for the period 1979-2014 using discharge station observations. Then, this set was applied to bias-correct the simulated discharge at HD river mouths from 1901-1978. Detailed information you can find in the specified sections of the attached PDF (https://www.wdc-climate.de/ui/entry?acronym=Biasc_hr_riverro_Eu_AdI_v1_1). Recently, a bug has been discovered in the part of the bias correction procedure, which transfers the bias correction factors from the station locations to the river mouths. Here, accidentally the bias correction factors from a previous simulation, which had utilized GSWP3 data, HydroPy and the HD model, were transferred to the river mouths for the whole considered period from 1901-2019. It can be noted that these factors still have improved the simulated inflows for most of the basins compared to the uncorrected HD model discharges. However, fixing this bug has led to general improvement for most of the basins. Fig. 1 in the attached PDF (https://www.wdc-climate.de/ui/entry?acronym=Biasc_hr_riverro_Eu_AdI_v1_1) provides an example for the major Baltic Sea sub-basins and shows the inflow biases compared to HELCOM observational estimates. Note that the other datasets of Version 1.0 (https://doi.org/10.26050/WDCC/Biasc_hr_riverro_Eu) did not change.

Under the heading of the OSPAR convention (Sect. 1.1), the IGC-EMO database of daily freshwater inflows and nutrient loads was compiled by van Leeuwen and Lenhart (2021), which covers the major rivers discharging into the Baltic Sea, North Sea and Northeast Atlantic (Sect. 1.2). In this database, the data are distributed in separate ASCII files according to the respective country and river. In order to allow an easier utilization within a regional Earth System or ocean modelling framework, we mapped the IGC-EMO data onto the flow grid of the European 1/12° domain of the Hydrological Discharge (HD) model (Sect. 1.3). This mapping was done for daily time series of discharge, total nitrogen (N), total phosphorus (P) and Silicate for the period 1940-2022 following the procedure described in Sect. 1.4. Detailed information you can find in the specified sections of the attached PDF https://www.wdc-climate.de/ui/entry?acronym=IGC-EMO_HD_info