Articles | Volume 20, issue 6
https://doi.org/10.5194/os-20-1457-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-20-1457-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Nov 2024
Research article |
| 12 Nov 2024
| 12 Nov 2024
A three-quantile bias correction with spatial transfer for the correction of simulated European river runoff to force ocean models
Stefan Hagemann
CORRESPONDING AUTHOR
Institute of Coastal Systems – Analysis and Modelling, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502 Geesthacht, Germany
Thao Thi Nguyen
Institute of Coastal Systems – Analysis and Modelling, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502 Geesthacht, Germany
Ha Thi Minh Ho-Hagemann
Institute of Coastal Systems – Analysis and Modelling, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502 Geesthacht, Germany
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Cited articles
Arora, V. K., Seiler, C., Wang, L., and Kou-Giesbrecht, S.: Towards an ensemble-based evaluation of land surface models in light of uncertain forcings and observations, Biogeosciences, 20, 1313–1355, https://doi.org/10.5194/bg-20-1313-2023, 2023.
Becker, G. A., Dick, S., and Dippner, J. W.: Hydrography of the German Bight, Mar. Ecol. Prog. Ser., 91, 9–18, https://doi.org/10.3354/meps091009, 1992.
Becker, G. A., Giese, H., Isert, K., König, P., Langenberg, H., Pohlmann, T., and Schrum, C.: Mesoscale structures, fluxes and water mass variability in the German Bight as exemplified in the KUSTOS- experiments and numerical models, Deutsche Hydrographische Zeitschrift, 51, 155–179, https://doi.org/10.1007/bf02764173, 1999.
Borgvang, S. A., Skarbøvik, E., and Pengerud, A.: RID 2006 data report: Presentation and Assessment of the OSPAR Contracting Parties' RID 2006 Data, Norwegian Institute for Agricultural and Environmental Research, London, No. 376/2008, 373 pp., https://www.ospar.org/documents?v=7121 (last access: 7 November 2024) 2008.
Brown, J. D. and Seo, D. J.: A nonparametric postprocessor for bias correction of hydrometeorological and hydrologic ensemble forecasts, J. Hydrometeorol., 11, 642–665, https://doi.org/10.1175/2009jhm1188.1, 2010.
Brown, J. D. and Seo, D. J.: Evaluation of a nonparametric post-processor for bias correction and uncertainty estimation of hydrologic predictions, Hydrol. Process., 27, 83–105, https://doi.org/10.1002/hyp.9263, 2012.
Budhathoki, A., Tanaka, T., and Tachikawa, Y.: Correcting streamflow bias considering its spatial structure for impact assessment of climate change on floods using d4PDF in the Chao Phraya River Basin, Thailand, J. Hydrol.-Reg. Stud., 42, 101150, https://doi.org/10.1016/j.ejrh.2022.101150, 2022.
Cannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes?, J. Climate, 28, 6938–6959, https://doi.org/10.1175/Jcli-D-14-00754.1, 2015.
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P., Bronnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman, P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, O., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D., and Worley, S. J.: The Twentieth Century Reanalysis Project, Q. J. Roy. Meteor. Soc., 137, 1–28, https://doi.org/10.1002/qj.776, 2011.
Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H., and Buontempo, C.: WFDE5: bias-adjusted ERA5 reanalysis data for impact studies, Earth Syst. Sci. Data, 12, 2097–2120, https://doi.org/10.5194/essd-12-2097-2020, 2020 (data set is available at https://cds.climate.copernicus.eu, last access: 7 November 2024).
Daewel, U. and Schrum, C.: Low-frequency variability in North Sea and Baltic Sea identified through simulations with the 3-D coupled physical–biogeochemical model ECOSMO, Earth Syst. Dynam., 8, 801–815, https://doi.org/10.5194/esd-8-801-2017, 2017.
Daraio, J. A.: Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows, Water, 12, 2312, https://doi.org/10.3390/w12082312, 2020.
Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T., and Hanasaki, N.: GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land Surface, B. Am., Meteor. Soc., 87, 1381–1398, https://doi.org/10.1175/bams-87-10-1381, 2006.
Droghei, R., Buongiorno Nardelli, B., and Santoleri, R.: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016), Front. Mar. Sci., 5, 84, https://doi.org/10.3389/fmars.2018.00084, 2018.
Farkas, C. and Skarbøvik, E.: OSPAR Contracting Parties' RID 2019 Data Report, NIBIO – Norwegian Institute for Bioeconomy Research [data set], 57 pp., https://odims.ospar.org/en/submissions/ospar_rid_data_reports_2019_01/ (last access: 7 November 2024), 2021.
Farmer, W. H., Over, T. M., and Kiang, J. E.: Bias correction of simulated historical daily streamflow at ungauged locations by using independently estimated flow duration curves, Hydrol. Earth Syst. Sci., 22, 5741–5758, https://doi.org/10.5194/hess-22-5741-2018, 2018.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Haddeland, I., Clark, D. B., Franssen, W., Ludwig, F., Voß, F., Arnell, N. W., Bertrand, N., Best, M., Folwell, S., Gerten, D., Gomes, S., Gosling, S. N., Hagemann, S., Hanasaki, N., Harding, R., Heinke, J., Kabat, P., Koirala, S., Oki, T., Polcher, J., Stacke, T., Viterbo, P., Weedon, G. P., and Yeh, P.: Multimodel estimate of the global terrestrial water balance: setup and first results, J. Hydrometeorol., 12, 869–884, https://doi.org/10.1175/2011jhm1324.1, 2011.
Hagemann, S. and Stacke, T.: Complementing ERA5 and E-OBS with high-resolution river discharge over Europe, Oceanologia, 65, 230–248, https://doi.org/10.1016/j.oceano.2022.07.003, 2022.
Hagemann, S. and Stacke, T.: Bias corrected high resolution river runoff over Europe, World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/Biasc_hr_riverro_Eu, 2023.
Hagemann, S., Stacke, T., and Ho-Hagemann, H. T. M.: High Resolution Discharge Simulations Over Europe and the Baltic Sea Catchment, Front. Earth Sci., 8, 12, https://doi.org/10.3389/feart.2020.00012, 2020.
Hagemann, S., Ho-Hagemann, H. T. M., and Hanke, M.: The Hydrological Discharge Model – a river runoff component for offline and coupled model applications, Zenodo [code], https://doi.org/10.5281/zenodo.10405875, 2023.
Hassler, B. and Lauer, A.: Comparison of reanalysis and observational precipitation datasets including ERA5 and WFDE5, Atmosphere, 12, 1462, https://doi.org/10.3390/atmos12111462, 2021.
HELCOM: The Third Baltic Sea Pollution Load Compilation, Balt. Sea Environ. Proc., no 70, Baltic Marine Environment Protection Commission–Helsinki Commission, Helsinki, Finland, 134 pp., https://helcom.fi/wp-content/uploads/2019/10/BSEP70.pdf (last access: 7 November 2024), 1998.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J. n., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E. a., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Ho-Hagemann, H. T. M., Hagemann, S., Grayek, S., Petrik, R., Rockel, B., Staneva, J., Feser, F., and Schrum, C.: Internal Model Variability of the Regional Coupled System Model GCOAST-AHOI, Atmosphere, 11, 227–227, https://doi.org/10.3390/atmos11030227, 2020.
Hordoir, R. and Meier, H. E. M.: Freshwater fluxes in the Baltic Sea: A model study, J. Geophys. Res., 115, C08028, https://doi.org/10.1029/2009jc005604, 2010.
Hordoir, R., Polcher, J., Brun-Cottan, J. C., and Madec, G.: Towards a parametrization of river discharges into ocean general circulation models: a closure through energy conservation, Clim. Dynam., 31, 891–908, https://doi.org/10.1007/s00382-008-0416-4, 2008.
ISIMIP: ISIMIP2a Simulation protocol (extended version), https://www.isimip.org/documents/647/ISIMIP2a_protocol_230302.pdf (last access: 7 November 2024), 2023.
Kim, H.: Global Soil Wetness Project Phase 3 Atmospheric Boundary Conditions (Experiment 1), Data Integration and Analysis System (DIAS) [data set], https://doi.org/10.20783/DIAS.501, 2017.
Kim, K. B., Kwon, H. H., and Han, D. W.: Bias-correction schemes for calibrated flow in a conceptual hydrological model, Hydrol. Res., 52, 196–211, https://doi.org/10.2166/nh.2021.043, 2021.
Klein, H. and Frohse, A.: Oceanographic Processes in the German Bight, Heide, Holstein, Boyens, 60–76, https://hdl.handle.net/20.500.11970/101594 (last access: 7 November 2024), 2008.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012.
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019.
Krzysztofowicz, R. and Maranzano, C. J.: Hydrologic uncertainty processor for probabilistic stage transition forecasting, J. Hydrol., 293, 57–73, https://doi.org/10.1016/j.jhydrol.2004.01.003, 2004.
Lehmann, A. and Hinrichsen, H.-H.: On the thermohaline variability of the Baltic Sea, J. Mar. Syst., 25, 333–357, https://doi.org/10.1016/s0924-7963(00)00026-9, 2000.
Lenhart, H. J., Mills, D. K., Baretta-Bekker, H., van Leeuwen, S. M., van der Molen, J., Baretta, J. W., Blaas, M., Desmit, X., Kuhn, W., Lacroix, G., Los, H. J., Menesguen, A., Neves, R., Proctor, R., Ruardij, P., Skogen, M. D., Vanhoutte-Brunier, A., Villars, M. T., and Wakelin, S. L.: Predicting the consequences of nutrient reduction on the eutrophication status of the North Sea, J. Mar. Syst., 81, 148–170, https://doi.org/10.1016/j.jmarsys.2009.12.014, 2010.
Madadgar, S., Moradkhani, H., and Garen, D.: Towards improved post-processing of hydrologic forecast ensembles, Hydrol. Process., 28, 104–122, https://doi.org/10.1002/hyp.9562, 2014.
Madec, G., Bourdallé-Badie, R., Bouttier, P.-A., Bricaud, C., Bruciaferr, D., Calvert, D., Chanut, J., Clementi, E., Coward, A., Delrosso, D., Ethé, C., Flavoni, S., Graham, T., Harle, J., Iovino, D., Lea, D., Lévy, C., Lovato, T., Martin, N., and Vancoppenolle, M.: NEMO ocean engine, Notes du Pôle de modélisation de l'Institut Pierre-Simon Laplace (IPSL), 27, Zenodo, https://doi.org/10.5281/zenodo.3248739, 2017.
Malek, K., Reed, P., Zeff, H., Hamilton, A., Wrzesien, M., Holtzman, N., Steinschneider, S., Herman, J., and Pavelsky, T.: Bias correction of hydrologic projections strongly impacts inferred climate vulnerabilities in institutionally complex water systems, J. Water Res. Plan. Man., 148, 04021095, https://doi.org/10.1061/(Asce)Wr.1943-5452.0001493, 2022.
Maraun, D., Shepherd, T. G., Widmann, M., Zappa, G., Walton, D., Gutiérrez, J. M., Hagemann, S., Richter, I., Soares, P. M. M., Hall, A., and Mearns, L. O.: Towards process-informed bias correction of climate change simulations, Nat. Clim. Change, 7, 764–773, https://doi.org/10.1038/Nclimate3418, 2017.
Marzeion, B., Levermann, A., and Mignot, J.: The Role of Stratification-Dependent Mixing for the Stability of the Atlantic Overturning in a Global Climate Model∗, J. Phys. Oceanogr., 37, 2672–2681, https://doi.org/10.1175/2007jpo3641.1, 2007.
Mengel, M., Treu, S., Lange, S., and Frieler, K.: ATTRICI v1.1 – counterfactual climate for impact attribution, Geosci. Model Dev., 14, 5269–5284, https://doi.org/10.5194/gmd-14-5269-2021, 2021.
Merchán, D., Causapé, J., and Abrahao, R.: Impact of irrigation implementation on hydrology and water quality in a small agricultural basin in Spain, Hydrol. Sci. J., 58, 1400–1413-1400–1413, 2013.
Nguyen, T. T., Staneva, J., Grayek, S., Bonaduce, A., Hagemann, S., Pham, N. T., Kumar, R., and Rakovec, O.: Impacts of extreme river discharge on coastal dynamics and environment: Insights from high-resolution modeling in the German Bight, Reg. Stud. Mar. Sci., 73, 103476, https://doi.org/10.1016/j.rsma.2024.103476, 2024.
Piani, C., Weedon, G. P., Best, M., Gomes, S. M., Viterbo, P., Hagemann, S., and Haerter, J. O.: Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models, J. Hydrol., 395, 199–215, https://doi.org/10.1016/j.jhydrol.2010.10.024, 2010.
Shi, X. G., Wood, A. W., and Lettenmaier, D. P.: How Essential is Hydrologic Model Calibration to Seasonal Streamflow Forecasting?, J. Hydrometeorol., 9, 1350–1363, https://doi.org/10.1175/2008jhm1001.1, 2008.
Stacke, T. and Hagemann, S.: HydroPy (v1.0): a new global hydrology model written in Python, Geosci. Model Dev., 14, 7795–7816, https://doi.org/10.5194/gmd-14-7795-2021, 2021.
Svendsen, L. M. and Gustafsson, B.: Waterborne nitrogen and phosphorus inputs and water flow to the Baltic Sea 1995–2020, https://helcom.fi/wp-content/uploads/ (last access: 8 November 2024), 2022.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, https://doi.org/10.1029/2000jd900719, 2001.
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods, J. Hydrol., 456–457, 12–29, https://doi.org/10.1016/j.jhydrol.2012.05.052, 2012.
Väli, G., Meier, H. E. M., and Elken, J.: Simulated halocline variability in the Baltic Sea and its impact on hypoxia during 1961-2007, J. Geophys. Res.-Oceans, 118, 6982–7000, https://doi.org/10.1002/2013jc009192, 2013.
Van Leeuwen, S. and Lenhart, H. J.: OSPAR ICG-EMO riverine database 2020-05-01 used in 2020 workshop, NIOZ [data set], https://doi.org/10.25850/nioz/7b.b.vc, 2021.
Van Leeuwen, S. and Hagemann, S.: Mapping of IGC-EMO nutrient loads on the high resolution HD model grid (Version 1), World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/IGC-EMO_HD_v1, 2023.
Vinayachandran, P. N., Jahfer, S., and Nanjundiah, R. S.: Impact of river runoff into the ocean on Indian summer monsoon, Environ. Res. Lett., 10, 054008, https://doi.org/10.1088/1748-9326/10/5/054008, 2015.
Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O., and Schewe, J.: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP): Project framework, P. Natl. Acad. Sci. USA, 111, 3228–3232, https://doi.org/10.1073/pnas.1312330110, 2014.
Weedon, G. P., Gomes, S., Viterbo, P., Shuttleworth, W. J., Blyth, E., Österle, H., Adam, J. C., Bellouin, N., Boucher, O., and Best, M.: Creation of the WATCH Forcing Data and Its Use to Assess Global and Regional Reference Crop Evaporation over Land during the Twentieth Century, J. Hydrometeorol., 12, 823–848, https://doi.org/10.1175/2011JHM1369.1, 2011.
Yoshimura, K. and Kanamitsu, M.: Dynamical global downscaling of global reanalysis, Mon. Weather Rev., 136, 2983–2998, https://doi.org/10.1175/2008mwr2281.1, 2008.
Zhao, L., Duan, Q., Schaake, J., Ye, A., and Xia, J.: A hydrologic post-processor for ensemble streamflow predictions, Adv. Geosci., 29, 51–59, https://doi.org/10.5194/adgeo-29-51-2011, 2011.
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019.
Short summary
We have developed a methodology for the bias correction of simulated river runoff to force ocean models in which low, medium, and high discharges are corrected once separated at the coast. We show that the bias correction generally leads to an improved representation of river runoff in Europe. The methodology is suitable for model regions with a sufficiently high coverage of discharge observations, and it can be applied to river runoff based on climate hindcasts or climate change simulations.
We have developed a methodology for the bias correction of simulated river runoff to force ocean...
