Research article
15 Dec 2017
Research article | 15 Dec 2017
Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe
Dennis Meißner et al.
Related authors
The European 2015 drought from a hydrological perspective
Gregor Laaha, Tobias Gauster, Lena M. Tallaksen, Jean-Philippe Vidal, Kerstin Stahl, Christel Prudhomme, Benedikt Heudorfer, Radek Vlnas, Monica Ionita, Henny A. J. Van Lanen, Mary-Jeanne Adler, Laurie Caillouet, Claire Delus, Miriam Fendekova, Sebastien Gailliez, Jamie Hannaford, Daniel Kingston, Anne F. Van Loon, Luis Mediero, Marzena Osuch, Renata Romanowicz, Eric Sauquet, James H. Stagge, and Wai K. Wong
Hydrol. Earth Syst. Sci., 21, 3001–3024, https://doi.org/10.5194/hess-21-3001-2017,https://doi.org/10.5194/hess-21-3001-2017, 2017
Short summary
The European 2015 drought from a climatological perspective
Monica Ionita, Lena M. Tallaksen, Daniel G. Kingston, James H. Stagge, Gregor Laaha, Henny A. J. Van Lanen, Patrick Scholz, Silvia M. Chelcea, and Klaus Haslinger
Hydrol. Earth Syst. Sci., 21, 1397–1419, https://doi.org/10.5194/hess-21-1397-2017,https://doi.org/10.5194/hess-21-1397-2017, 2017
Short summary
Related subject area
Global sinusoidal seasonality in precipitation isotopes
Scott T. Allen, Scott Jasechko, Wouter R. Berghuijs, Jeffrey M. Welker, Gregory R. Goldsmith, and James W. Kirchner
Hydrol. Earth Syst. Sci., 23, 3423–3436, https://doi.org/10.5194/hess-23-3423-2019,https://doi.org/10.5194/hess-23-3423-2019, 2019
Short summary
Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework
Zhengke Pan, Pan Liu, Shida Gao, Jun Xia, Jie Chen, and Lei Cheng
Hydrol. Earth Syst. Sci., 23, 3405–3421, https://doi.org/10.5194/hess-23-3405-2019,https://doi.org/10.5194/hess-23-3405-2019, 2019
Short summary
A multi-objective ensemble approach to hydrological modelling in the UK: an application to historic drought reconstruction
Katie A. Smith, Lucy J. Barker, Maliko Tanguy, Simon Parry, Shaun Harrigan, Tim P. Legg, Christel Prudhomme, and Jamie Hannaford
Hydrol. Earth Syst. Sci., 23, 3247–3268, https://doi.org/10.5194/hess-23-3247-2019,https://doi.org/10.5194/hess-23-3247-2019, 2019
Short summary
Assessing the added value of the Intermediate Complexity Atmospheric Research (ICAR) model for precipitation in complex topography
Johannes Horak, Marlis Hofer, Fabien Maussion, Ethan Gutmann, Alexander Gohm, and Mathias W. Rotach
Hydrol. Earth Syst. Sci., 23, 2715–2734, https://doi.org/10.5194/hess-23-2715-2019,https://doi.org/10.5194/hess-23-2715-2019, 2019
Short summary
Distributive rainfall–runoff modelling to understand runoff-to-baseflow proportioning and its impact on the determination of reserve requirements of the Verlorenvlei estuarine lake, west coast, South Africa
Andrew Watson, Jodie Miller, Manfred Fink, Sven Kralisch, Melanie Fleischer, and Willem de Clercq
Hydrol. Earth Syst. Sci., 23, 2679–2697, https://doi.org/10.5194/hess-23-2679-2019,https://doi.org/10.5194/hess-23-2679-2019, 2019
Short summary
Mapping soil hydraulic properties using random-forest-based pedotransfer functions and geostatistics
Brigitta Szabó, Gábor Szatmári, Katalin Takács, Annamária Laborczi, András Makó, Kálmán Rajkai, and László Pásztor
Hydrol. Earth Syst. Sci., 23, 2615–2635, https://doi.org/10.5194/hess-23-2615-2019,https://doi.org/10.5194/hess-23-2615-2019, 2019
Short summary
On the choice of calibration metrics for “high-flow” estimation using hydrologic models
Naoki Mizukami, Oldrich Rakovec, Andrew J. Newman, Martyn P. Clark, Andrew W. Wood, Hoshin V. Gupta, and Rohini Kumar
Hydrol. Earth Syst. Sci., 23, 2601–2614, https://doi.org/10.5194/hess-23-2601-2019,https://doi.org/10.5194/hess-23-2601-2019, 2019
Short summary
Spatially distributed tracer-aided runoff modelling and dynamics of storage and water ages in a permafrost-influenced catchment
Thea I. Piovano, Doerthe Tetzlaff, Sean K. Carey, Nadine J. Shatilla, Aaron Smith, and Chris Soulsby
Hydrol. Earth Syst. Sci., 23, 2507–2523, https://doi.org/10.5194/hess-23-2507-2019,https://doi.org/10.5194/hess-23-2507-2019, 2019
Short summary
Spatial and temporal variability of groundwater recharge in a sandstone aquifer in a semiarid region
Ferdinando Manna, Steven Murray, Daron Abbey, Paul Martin, John Cherry, and Beth Parker
Hydrol. Earth Syst. Sci., 23, 2187–2205, https://doi.org/10.5194/hess-23-2187-2019,https://doi.org/10.5194/hess-23-2187-2019, 2019
Short summary
Future shifts in extreme flow regimes in Alpine regions
Manuela I. Brunner, Daniel Farinotti, Harry Zekollari, Matthias Huss, and Massimiliano Zappa
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-144,https://doi.org/10.5194/hess-2019-144, 2019
Revised manuscript accepted for HESS
Short summary
Twenty-first-century glacio-hydrological changes in the Himalayan headwater Beas River basin
Lu Li, Mingxi Shen, Yukun Hou, Chong-Yu Xu, Arthur F. Lutz, Jie Chen, Sharad K. Jain, Jingjing Li, and Hua Chen
Hydrol. Earth Syst. Sci., 23, 1483–1503, https://doi.org/10.5194/hess-23-1483-2019,https://doi.org/10.5194/hess-23-1483-2019, 2019
Short summary
Effects of univariate and multivariate bias correction on hydrological impact projections in alpine catchments
Judith Meyer, Irene Kohn, Kerstin Stahl, Kirsti Hakala, Jan Seibert, and Alex J. Cannon
Hydrol. Earth Syst. Sci., 23, 1339–1354, https://doi.org/10.5194/hess-23-1339-2019,https://doi.org/10.5194/hess-23-1339-2019, 2019
Short summary
Multi-site calibration and validation of SWAT with satellite-based evapotranspiration in a data-sparse catchment in southwestern Nigeria
Abolanle E. Odusanya, Bano Mehdi, Christoph Schürz, Adebayo O. Oke, Olufiropo S. Awokola, Julius A. Awomeso, Joseph O. Adejuwon, and Karsten Schulz
Hydrol. Earth Syst. Sci., 23, 1113–1144, https://doi.org/10.5194/hess-23-1113-2019,https://doi.org/10.5194/hess-23-1113-2019, 2019
Short summary
A simple topography-driven and calibration-free runoff generation module
Hongkai Gao, Christian Birkel, Markus Hrachowitz, Doerthe Tetzlaff, Chris Soulsby, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 23, 787–809, https://doi.org/10.5194/hess-23-787-2019,https://doi.org/10.5194/hess-23-787-2019, 2019
Short summary
Multi-scale temporal variability in meltwater contributions in a tropical glacierized watershed
Leila Saberi, Rachel T. McLaughlin, G.-H. Crystal Ng, Jeff La Frenierre, Andrew D. Wickert, Michel Baraer, Wei Zhi, Li Li, and Bryan G. Mark
Hydrol. Earth Syst. Sci., 23, 405–425, https://doi.org/10.5194/hess-23-405-2019,https://doi.org/10.5194/hess-23-405-2019, 2019
Short summary
Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across a large-sample of catchments in Great Britain
Rosanna A. Lane, Gemma Coxon, Jim E. Freer, Thorsten Wagener, Penny J. Johnes, John P. Bloomfield, Sheila Greene, Christopher J. A. Macleod, and Sim M. Reaney
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-635,https://doi.org/10.5194/hess-2018-635, 2019
Revised manuscript accepted for HESS
Short summary
Redressing the balance: quantifying net intercatchment groundwater flows
Laurène Bouaziz, Albrecht Weerts, Jaap Schellekens, Eric Sprokkereef, Jasper Stam, Hubert Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 22, 6415–6434, https://doi.org/10.5194/hess-22-6415-2018,https://doi.org/10.5194/hess-22-6415-2018, 2018
Short summary
Evaluating post-processing approaches for monthly and seasonal streamflow forecasts
Fitsum Woldemeskel, David McInerney, Julien Lerat, Mark Thyer, Dmitri Kavetski, Daehyok Shin, Narendra Tuteja, and George Kuczera
Hydrol. Earth Syst. Sci., 22, 6257–6278, https://doi.org/10.5194/hess-22-6257-2018,https://doi.org/10.5194/hess-22-6257-2018, 2018
Short summary
A propensity index for surface runoff on a karst plateau
Christian Reszler, Jürgen Komma, Hermann Stadler, Elmar Strobl, and Günter Blöschl
Hydrol. Earth Syst. Sci., 22, 6147–6161, https://doi.org/10.5194/hess-22-6147-2018,https://doi.org/10.5194/hess-22-6147-2018, 2018
Short summary
HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
Chaopeng Shen, Eric Laloy, Amin Elshorbagy, Adrian Albert, Jerad Bales, Fi-John Chang, Sangram Ganguly, Kuo-Lin Hsu, Daniel Kifer, Zheng Fang, Kuai Fang, Dongfeng Li, Xiaodong Li, and Wen-Ping Tsai
Hydrol. Earth Syst. Sci., 22, 5639–5656, https://doi.org/10.5194/hess-22-5639-2018,https://doi.org/10.5194/hess-22-5639-2018, 2018
Short summary
Improvement of the SWAT model for event-based flood simulation on a sub-daily timescale
Dan Yu, Ping Xie, Xiaohua Dong, Xiaonong Hu, Ji Liu, Yinghai Li, Tao Peng, Haibo Ma, Kai Wang, and Shijin Xu
Hydrol. Earth Syst. Sci., 22, 5001–5019, https://doi.org/10.5194/hess-22-5001-2018,https://doi.org/10.5194/hess-22-5001-2018, 2018
Assessment of hydrological pathways in East African montane catchments under different land use
Suzanne R. Jacobs, Edison Timbe, Björn Weeser, Mariana C. Rufino, Klaus Butterbach-Bahl, and Lutz Breuer
Hydrol. Earth Syst. Sci., 22, 4981–5000, https://doi.org/10.5194/hess-22-4981-2018,https://doi.org/10.5194/hess-22-4981-2018, 2018
Short summary
Cited articles
Alverado Montero, R., Schwanenberg, D., Krahe, P., Lisniak, D., Sensoy, A., Sorman, A. A., and Akkol, B.: Moving horizon estimation for assimilating H-SAF remote sensing data into the HBV hydrological model, Adv. Water Resour., 92, 248–257, https://doi.org/10.1016/j.advwatres.2016.04.011, 2016.
Bell, V. A., Davies, H. N., Kay, A. L., Brookshaw, A., and Scaife, A. A.: A national-scale seasonal hydrological forecast system: development and evaluation over Britain, Hydrol. Earth Syst. Sci., 21, 4681–4691, https://doi.org/10.5194/hess-21-4681-2017, 2017.
Beven, K. J.: Equifinality and Uncertainty in Geomorphological Modelling, in: The Scientific Nature of Geomorphology, edited by: Rhoads, B. L. and Thorn, C. E., Wiley, Chichester, UK, 289–313, 1996.
CCNR: Annual Report 2016 – Inland Navigation in Europe – Market Observation, Central Commission for the Navigation of the Rhine, Strasbourg, France, 2016.
Crochemore, L., Ramos, M.-H., and Pappenberger, F.: Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 20, 3601–3618, https://doi.org/10.5194/hess-20-3601-2016, 2016.
Day, G. N.: Extended streamflow forecasting using NWSRFS, J. Water Resour. Plan. Man., 111, 157–170, 1985.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, 2011.
Demirel, M. C., Booij, M. J., and Hoekstra, A. Y.: Identification of appropriate lags and temporal resolutions for low flow indicators in the River Rhine to forecast low flows with different lead times, Hydrol. Proc., 27, 2742–2758, https://doi.org/10.1002/hyp.9402, 2012.
Demirel, M. C., Booij, M. J., and Hoekstra, A. Y.: Effect of different uncertainty sources on the skill of 10 day ensemble low flow forecasts for two hydrological models, Water Resour. Res., 49, 4035–4053, https://doi.org/10.1002/wrcr.20294, 2013.
Demirel, M. C., Booij, M. J., and Hoekstra, A. Y.: The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models, Hydrol. Earth Syst. Sci., 19, 275–291, https://doi.org/10.5194/hess-19-275-2015, 2015.
Domeisen, D. I. V., Butler, A. H., Fröhlich, K., Bittner, M., Müller, W. A., and Baehr, J.: Seasonal Predictability over Europe Arising from El Niño and Stratospheric Variability in the MPI-ESM Seasonal Prediction System, J. Climate, 28, 256–271, https://doi.org/10.1175/JCLI-D-14-00207.1, 2015.
DWD: Deliverable D.1.7 Gridded 20 yr dataset of surface solar radiation, cloud albedo, cloud fraction and surface albedo derived from MVIRI (SEVIRI) using the Heliosat method (0.05°), EU funded project EURO4M, EURO4M project report, available at http://www.euro4m.eu/downloads/D1.7_Gridded-20yr-dataset-of-SIS-SAL-CFC-CAL-derived-from-MVIRI-SEVIRI.pdf (last access: 11 December 2017), 2013.
European Commission: White paper – European transport policy for 2010: time to decide, COM(2001)370, Brussels, available at: https://ec.europa.eu/transport/sites/transport/files/themes/strategies/doc/2001_white_paper/lb_com_2001_0370_en.pdf (last access: 11 December 2017), 2001.
Fundel, F. and Zappa, M.: Hydrological Ensemble Forecasting in Mesoscale Catchments: Sensitivity to Initial Conditions and Value of Reforecasts, Water Resour. Res., 47, W09520, https://doi.org/10.1029/2010WR009996, 2011.
Fundel, F., Jörg-Hess, S., and Zappa, M.: Monthly hydrometeorological ensemble prediction of streamflow droughts and corresponding drought indices, Hydrol. Earth Syst. Sci., 17, 395–407, https://doi.org/10.5194/hess-17-395-2013, 2013.
Funk, D., Pouget, L., Dubus, L., Falloon, P., Meißner, D., Klein, B., Palin, E., Viel, C., Foster, K., Lootvoet, M., Bosi, L., Creswick, J., Davis, M., and Jimenez, I.: White paper on sector specific vulnerabilities, Deliverable 11.2, EUPORIAS – European Provision Of Regional Impacts Assessments on Seasonal and Decadal Timescales, Grant Agreement 308291, EUPORIAS project report, available at: http://www.euporias.eu/system/files/D11.2_Final.pdf (last access: 11 December 2017), 2015.
Gámiz-Fortis, S., Pozo-Vázquez, D., Trigo, R. M., and Castro-Díez, Y.: Quantifying the predictability of winter river flow in Iberia, Part II: seasonal predictability, J. Climate, 21, 2503–2518, 2008.
Gelfan, A. N., Motovilov, Yu. G., and Moreido, V. M.: Ensemble seasonal forecast of extreme water inflow into a large reservoir, Proc. IAHS, 369, 115–120, https://doi.org/10.5194/piahs-369-115-2015, 2015.
Haag, I., Johst, M., Sieber, A., and Bremicker, M.: Guideline for the Calibration of LARSIM Water Balance Models for operational Application in Flood Forecasting–Calibration Guide, available at: http://hmdblog.rlp.de/luwg/larsim/, last access: 3 March 2016.
Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., and New, M. A.: European daily high-resolution gridded dataset of surface temperature and precipitation, J. Geophys. Res.-Atmos., 113, D20119, https://doi.org/10.1029/2008JD010201, 2008.
Hersbach, H.: Decomposition of the continuous ranked probability score for ensemble prediction systems, Weather Forecast., 15, 559–570, 2000.
Huang, B., Banzon, V. F., Freeman, E., Lawrimore, J., Liu, W., Peterson, T. C., Smith, T. M., Thorne, P. W., Woodruff, S. D., and Zhang, H.-M.: Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4): Part I, Upgrades and intercomparisons, J. Climate, 28, 911–930, https://doi.org/10.1175/JCLI-D-14-00006.1, 2014.
Leander, R. and Buishand, T.: Resampling of regional climate model output forthe simulation of extreme river flows, J. Hydrol., 332, 487–496, 2007.
Ionita, M., Lohmann, G., Rimbu, N., and Chelcea, S.: Interannual Variability of Rhine River Streamflow and Its Relationship with Large-Scale Anomaly Patterns in Spring and Autumn, J. Hydrometeorol., 13, 172–188, 2012.
Ionita, M., Dima, M., Lohmann, G., Scholz, P., and Rimbu, N.: Predicting the June 2013 European Flooding based on Precipitation, Soil Moisture and Sea Level Pressure, J. Hydrometeorol., 16, 598–614, https://doi.org/10.1175/JHM-D-14-0156.1, 2014.
Ionita, M., Tallaksen, L. M., Kingston, D. G., Stagge, J. H., Laaha, G., Van Lanen, H. A. J., Scholz, P., Chelcea, S. M., and Haslinger, K.: The European 2015 drought from a climatological perspective, Hydrol. Earth Syst. Sci., 21, 1397–1419, https://doi.org/10.5194/hess-21-1397-2017, 2017.
Jörg-Hess, S., Griessinger, N., and Zappa, M.: Probabilistic Forecasts of Snow Water Equivalent and Runoff in Mountainous Areas, J. Hydrometeorol., 16, 2169–2186, https://doi.org/10.1175/JHM-D-14-0193.1, 2015.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A., Reynolds, R., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-year reanalysis project, B. Am. Meteorol. Soc., 77, 437–470, 1996.
Kistler, R., Collins, W., Saha, S., White, G., Woollen, J., Kalnay, E., Chelliah, M., Ebisuzaki, W., Kanamitsu, M., Kousky, V., van den Dool, H., Jenne, R., and Fiorino, M.: The NCEP-NCAR 50-Year Reanalysis: Monthly Means CD-ROM and Documentation, B. Am. Meteorol. Soc., 82, 247–267, https://doi.org/10.1175/1520-0477(2001)082<0247:TNNYRM>2.3.CO;2, 2001.
Klein, B. and Meißner, D.: Vulnerability of Inland Waterway Transport and Waterway Management on Hydro-meteorological Extremes, Deliverable 9.1, IMPREX (Improving Predictions of Hydrological Extremes), Grant Agreement Number 641811, IMPREX project report, available at: http://www.imprex.eu/system/files/generated/files/resource/d9-1-imprex-v2-0.pdf (last access: 11 December 2017), 2016.
Laaha, G., Gauster, T., Tallaksen, L. M., Vidal, J.-P., Stahl, K., Prudhomme, C., Heudorfer, B., Vlnas, R., Ionita, M., Van Lanen, H. A. J., Adler, M.-J., Caillouet, L., Delus, C., Fendekova, M., Gailliez, S., Hannaford, J., Kingston, D., Van Loon, A. F., Mediero, L., Osuch, M., Romanowicz, R., Sauquet, E., Stagge, J. H., and Wong, W. K.: The European 2015 drought from a hydrological perspective, Hydrol. Earth Syst. Sci., 21, 3001–3024, https://doi.org/10.5194/hess-21-3001-2017, 2017.
Lenderink, G., Buishand, A., and van Deursen, W.: Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach, Hydrol. Earth Syst. Sci., 11, 1145–1159, https://doi.org/10.5194/hess-11-1145-2007, 2007.
Lohmann, G., Rimbu, N., and Dima, M.: Where can the Arctic oscillation be reconstructed? Towards a reconstruction of climate modes based on stable teleconnections, Clim. Past Discuss., https://doi.org/10.5194/cpd-1-17-2005, 2005.
Ludwig, K. and Bremicker, M.: The Water Balance Model LARSIM, Design, Content and Applications, edited by: Leibundgut, C., Demuth, S., and Lange, J., Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp., 2006.
Marke, T.: Development and Application of a Model Interface to couple Land Surface Models with Regional Climate Models for Climate Change Risk Assessment in the Upper Danube Watershed, Dissertation, LMU München, Fakultät für Geowissenschaften, 2008.
Meißner, D. and Klein, B.: Probabilistic Shipping Forecast, in: “Handbook of Hydrometeorological Ensemble Forecasting”, edited by: Duan, Q., Pappenberger, F., Thielen, J., Wood, A., Cloke, H. L., and Schaake, J. C., Springer, https://doi.org/10.1007/978-3-642-40457-3_58-1, 2016.
Molteni, F., Stockdale, T., Balmaseda, M., Balsamo, G., Buizza, R., Ferranti, L., Magnusson, L., Mogensen, K., Palmer, T., and Vitart, F.: The new ECMWF seasonal forecast system (System 4), ECMWF Tech. Memo., 656, 49 pp., available at: http://www.ecmwf.int/sites/default/files/elibrary/2011/11209-new-ecmwf-seasonal-forecast-system-system-4.pdf (last access: 29 August 2016), 2011.
Murphy, A. H.: What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting, Weather Forecast., 8, 281–293, 1993.
Nilson, E., Lingemann, I., Klein, B., and Krahe, P.: Impact of hydrological change on navigation conditions, Deliverable 1.4 ECCONET – Effects of climate change on the inland waterway transport network, ECCONET project report, available at: http://www.tmleuven.be/project/ecconet/ECCONET_D1.4_final.pdf (last access: 11 December 2017), 2012.
Olsson, J., Uvo, C. B., Foster, K., and Yang, W.: Technical Note: Initial assessment of a multi-method approach to spring-flood forecasting in Sweden, Hydrol. Earth Syst. Sci., 20, 659–667, https://doi.org/10.5194/hess-20-659-2016, 2016.
Piani, C., Haerter, J., and Coppola, E.: Statistical bias correction for daily precipitation in regional climate models over Europe, Theor. Appl. Climatol., 2010, 187–192, https://doi.org/10.1007/s00704-009-0134-9, 2010.
Rauthe, M., Steiner, H., Riediger, U., Mazurkiewicz, A., and Gratzki, A.: A Central European precipitation climatology – Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS), Meteorol. Z., 22, 235–256, 2013.
Richardson, D. S.: Skill and relative economic value of ECMWF ensemble prediction system, Q. J. Roy. Meteorol. Soc., 126, 649–667, 2000.
Richardson, D. S.: Economic Value and Skill, in: Forecast Verification: A Practitioner's Guide in Atmospheric Science, edited by: Joliffe, S. B., 2nd edn., John Wiley & Sons, Ltd., 167–184, 2012.
Richter, B. D., Baumgartner, J. V., Braun, D. P., and Powell, J.: A spatial assessment of hydrologic alteration within a river network, River Res. Appl., 14, 329–340, 1998.
Rimbu, N., Dima, M., Lohmann, G., and Stefan, S.: Impacts on the North Atlantic Oscillation and the El Niño–Southern Oscillation on Danube river flow variability, Geophys. Res. Lett., 31, L23203, https://doi.org/10.1029/2004GL020559, 2004.
Rimbu, N., Dima, M., Lohmann, G., and Musat, I.: Seasonal prediction of Danube flow variability based on stable teleconnection with sea surface temperature, Geophys. Res. Lett., 32, L21704, https://doi.org/10.1029/2005GL024241, 2005.
Robertson, D. E., Pokhrel, P., and Wang, Q. J.: Improving statistical forecasts of seasonal streamflows using hydrological model output, Hydrol. Earth Syst. Sci., 17, 579–593, https://doi.org/10.5194/hess-17-579-2013, 2013.
Roulin, E.: Skill and relative economic value of medium-range hydrological ensemble predictions, Hydrol. Earth Syst. Sci., 11, 725–737, https://doi.org/10.5194/hess-11-725-2007, 2007.
Shukla, S. and Lettenmaier, D. P.: Seasonal hydrologic prediction in the United States: understanding the role of initial hydrologic conditions and seasonal climate forecast skill, Hydrol. Earth Syst. Sci., 15, 3529–3538, https://doi.org/10.5194/hess-15-3529-2011, 2011.
Svensson, C.: Seasonal river flow forecasts for the United Kingdom using persistence and historical analogues, Hydrol. Sci. J., 61, 19–35, https://doi.org/10.1080/02626667.2014.992788, 2016.
Trigo, R. M., Pozo-Vázquez, D., Osborn, T. J., Castro-Diez, Y., Gamiz-Fortis, S., and Esteban-Parra, M. J.: North Atlantic Oscillation influence on precipitation, river flow and water resources in the Iberian Peninsula, Int. J. Climatol., 24, 925–944, 2004.
Tucci, C. E. M., Clarke, R. T., Collischonn, W., da Silva Dias, P. L., and de Oliveira, G. S.: Long-term flow forecasts based on climate and hydrologic modeling: Uruguay River basin, Water Resour. Res., 39, 1181, https://doi.org/10.1029/2003WR002074, 2003.
Van Lanen, H. A. J., Laaha, G., Kingston, D. G., Gauster, T., Ionita, M., Vidal, J.-P., Vlnas, R., Tallaksen, L. M., Stahl, K., Hannaford, J., Delus, C., Fendekova, M., Mediero, L., Prudhomme, C., Rets, E., Romanowicz, R. J., Gailliez, S., Wong, W. K., Adler, M.-J., Blauhut, V., Caillouet, L., Chelcea, S., Frolova, N., Gudmundsson, L., Hanel, M., Haslinger, K., Kireeva, M., Osuch, M., Sauquet, E., Stagge, J. H., and Van Loon, A. F.: Hydrology needed to manage droughts: the 2015 European case, Hydrol. Process., 16, 1373–1381, https://doi.org/10.1002/hyp.10838, 2016.
Wang, E., Zhang, Y., Luo, J., Chiew, F. H. S., and Wang, Q. J.: Monthly and Seasonal Streamflow Forecasts Using Rainfall-Runoff Modeling and Historical Weather Data, Water Resour. Res., 47, W05516, https://doi.org/10.1029/2010WR009922, 2011.
Wilby, R. L., Wedgbrow, C. S., and Fox, H. R.: Seasonal predictability of the summer hydrometeorology of the River Thames, UK, J. Hydrol., 295, 1–16, 2004.
Wilks, D. S.: A skill score based on economic value for probability forecasts, Meteorol. Appl., 8, 209–219, 2001.
Wood, A. W., Maurer, E. P., Kumar, A., and Lettenmaier, D. P.: Long-range experimental hydrologic forecasting for the eastern United States, J. Geophys. Res.-Atmos., 107, 4429, https://doi.org/10.1029/2001JD000659, 2002.
Wood, A. W. and Lettenmaier, D. P.: An ensemble approach for attribution of hydro-logic prediction uncertainty, Geophys. Res. Lett., 35, L14401, https://doi.org/10.1029/2008GL034648, 2008.
Wood, A. W., Hopson, T., Newman, A., Brekke, L., Arnold, J., and Clark, M.: Quantifying Streamflow Forecast Skill Elasticity to Initial Condition and Climate Prediction Skill, J. Hydrometeorol., 17, 651–668, https://doi.org/10.1175/JHM-D-14-0213.1, 2016.
Yossef, N. C., Winsemius, H., Weerts, A., van Beek, R., and Bierkens, M. F. P.: Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing, Water Resour. Res., 49, 4687–4699, https://doi.org/10.1002/wrcr.20350, 2013.
Zappa, M., Bernhard, L., Spirig, C., Pfaundler, M., Stahl, K., Kruse, S., Seidl, I., and Stähli, M.: A prototype platform for water resources monitoring and early recognition of critical droughts in Switzerland, Proc. IAHS, 364, 492–498, https://doi.org/10.5194/piahs-364-492-2014, 2014.
Zhao, T., Bennett, J. C., Wang, Q. J., Schepen, A., Wood, A. W., Robertson, D. E., and Ramos, M. H.: How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts? J. Climate, 30, 3185–3196, 2017.
Zink, M., Samaniego, L., Kumar, R., Thober, S., Mai, J., Scäfer, D., and Marx, A.: The German drought monitor, Environ. Res. Lett., 11, 074002, https://doi.org/10.1088/1748-9326/11/7/074002, 2016.