Articles | Volume 30, issue 4
https://doi.org/10.5194/hess-30-905-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-905-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Feb 2026
Research article |
| 17 Feb 2026
| 17 Feb 2026
UK Hydrological Outlook using Historic Weather Analogues
UK Centre for Ecology & Hydrology (UKCEH), Wallingford, OX10 8BB, UK
Katie A. Facer-Childs
UK Centre for Ecology & Hydrology (UKCEH), Wallingford, OX10 8BB, UK
Maliko Tanguy
UK Centre for Ecology & Hydrology (UKCEH), Wallingford, OX10 8BB, UK
European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK
Eugene Magee
UK Centre for Ecology & Hydrology (UKCEH), Wallingford, OX10 8BB, UK
Burak Bulut
UK Centre for Ecology & Hydrology (UKCEH), Wallingford, OX10 8BB, UK
Nicky Stringer
Met Office, Exeter, EX1 3PB, UK
Jeff Knight
Met Office, Exeter, EX1 3PB, UK
Jamie Hannaford
UK Centre for Ecology & Hydrology (UKCEH), Wallingford, OX10 8BB, UK
Irish Climate Analysis and Research UnitS (ICARUS), Maynooth University, Maynooth, Co. Kildare, UK
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Cited articles
Akbarian, M., Saghafian, B., and Golian, S.: Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran, Journal of Hydrology, 620, 129480, https://doi.org/10.1016/j.jhydrol.2023.129480, 2023.
Arsenault, R., Gatien, P., Renaud, B., Brissette, F., and Martel, J.-L.: A comparative analysis of 9 multi-model averaging approaches in hydrological continuous streamflow simulation, Journal of Hydrology, 529, 754–767, https://doi.org/10.1016/j.jhydrol.2015.09.001, 2015.
Baker, L. H., Shaffrey, L. C., Sutton, R. T., Weisheimer, A., and Scaife, A. A.: An Intercomparison of Skill and Overconfidence/Underconfidence of the Wintertime North Atlantic Oscillation in Multimodel Seasonal Forecasts, Geophysical Research Letters, 45, 7808–7817, https://doi.org/10.1029/2018GL078838, 2018.
Baker, L. H., Shaffrey, L. C., Johnson, S. J., and Weisheimer, A.: Understanding the Intermittency of the Wintertime North Atlantic Oscillation and East Atlantic Pattern Seasonal Forecast Skill in the Copernicus C3S Multi-Model Ensemble, Geophysical Research Letters, 51, e2024GL108472, https://doi.org/10.1029/2024GL108472, 2024.
Baker, S. A., Rajagopalan, B., and Wood, A. W.: Enhancing Ensemble Seasonal Streamflow Forecasts in the Upper Colorado River Basin Using Multi-Model Climate Forecasts, J. American Water Resour. Assoc., 57, 906–922, https://doi.org/10.1111/1752-1688.12960, 2021.
Beckers, J. V. L., Weerts, A. H., Tijdeman, E., and Welles, E.: ENSO-conditioned weather resampling method for seasonal ensemble streamflow prediction, Hydrol. Earth Syst. Sci., 20, 3277–3287, https://doi.org/10.5194/hess-20-3277-2016, 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.
Chan, W. C. H., Arnell, N. W., Darch, G., Facer-Childs, K., Shepherd, T. G., and Tanguy, M.: Added value of seasonal hindcasts to create UK hydrological drought storylines, Nat. Hazards Earth Syst. Sci., 24, 1065–1078, https://doi.org/10.5194/nhess-24-1065-2024, 2024.
Chan, W. C. H., Barker, L. J., Faranda, D., and Hannaford, J.: River flow amplification under climate change: attribution and climate-driven storylines of the winter 2023/24 UK floods, Environ. Res. Lett., 20, 104035, https://doi.org/10.1088/1748-9326/adfdfe, 2025.
Chevuturi, A., Tanguy, M., Facer-Childs, K., Martínez-de La Torre, A., Sarkar, S., Thober, S., Samaniego, L., Rakovec, O., Kelbling, M., Sutanudjaja, E. H., Wanders, N., and Blyth, E.: Improving global hydrological simulations through bias-correction and multi-model blending, Journal of Hydrology, 621, 129607, https://doi.org/10.1016/j.jhydrol.2023.129607, 2023.
Chevuturi, A., Oltmanns, M., Tanguy, M., Harvey, B., Svensson, C., and Hannaford, J.: Oceanic drivers of UK summer droughts, Commun. Earth Environ., 6, 437, https://doi.org/10.1038/s43247-025-02367-1, 2025.
Chiverton, A., Hannaford, J., Holman, I., Corstanje, R., Prudhomme, C., Bloomfield, J., and Hess, T. M.: Which catchment characteristics control the temporal dependence structure of daily river flows?, Hydrol. Process., 29, 1353–1369, https://doi.org/10.1002/hyp.10252, 2015.
Coron, L., Thirel, G., Delaigue, O., Perrin, C., and Andréassian, V.: The Suite of Lumped GR Hydrological Models in an R package, Environ. Model. Softw., 94, 166–171, https://doi.org/10.1016/j.envsoft.2017.05.002, 2017.
Coron, L., Delaigue, O., Thirel, G., Dorchies, D., Perrin, C., and Michel, C.: airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling, R package version 1.7.6, Recherche Data Gouv, V1 [code], https://doi.org/10.15454/EX11NA, 2023.
Cosgrove, B., Gochis, D., Flowers, T., Dugger, A., Ogden, F., Graziano, T., Clark, E., Cabell, R., Casiday, N., Cui, Z., Eicher, K., Fall, G., Feng, X., Fitzgerald, K., Frazier, N., George, C., Gibbs, R., Hernandez, L., Johnson, D., Jones, R., Karsten, L., Kefelegn, H., Kitzmiller, D., Lee, H., Liu, Y., Mashriqui, H., Mattern, D., McCluskey, A., McCreight, J. L., McDaniel, R., Midekisa, A., Newman, A., Pan, L., Pham, C., RafieeiNasab, A., Rasmussen, R., Read, L., Rezaeianzadeh, M., Salas, F., Sang, D., Sampson, K., Schneider, T., Shi, Q., Sood, G., Wood, A., Wu, W., Yates, D., Yu, W., and Zhang, Y.: NOAA's National Water Model: Advancing operational hydrology through continental-scale modeling, J. American Water Resour. Assoc., 60, 247–272, https://doi.org/10.1111/1752-1688.13184, 2024.
Day, G. N.: Extended Streamflow Forecasting Using NWSRFS, J. Water Resour. Plann. Manage., 111, 157–170, https://doi.org/10.1061/(ASCE)0733-9496(1985)111:2(157), 1985.
Donegan, S., Murphy, C., Harrigan, S., Broderick, C., Foran Quinn, D., Golian, S., Knight, J., Matthews, T., Prudhomme, C., Scaife, A. A., Stringer, N., and Wilby, R. L.: Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times, Hydrol. Earth Syst. Sci., 25, 4159–4183, https://doi.org/10.5194/hess-25-4159-2021, 2021.
Dunstone, N., Smith, D., Scaife, A., Hermanson, L., Fereday, D., O'Reilly, C., Stirling, A., Eade, R., Gordon, M., MacLachlan, C., Woollings, T., Sheen, K., and Belcher, S.: Skilful Seasonal Predictions of Summer European Rainfall, Geophysical Research Letters, 45, 3246–3254, https://doi.org/10.1002/2017GL076337, 2018.
Eade, R., Smith, D., Scaife, A., Wallace, E., Dunstone, N., Hermanson, L., and Robinson, N.: Do seasonal-to-decadal climate predictions underestimate the predictability of the real world?, Geophysical Research Letters, 41, 5620–5628, https://doi.org/10.1002/2014GL061146, 2014.
Environment Agency: Monthly Water Situation Report: November 2022, Environment Agency, https://www.gov.uk/government/publications/water-situation-national-monthly-reports-for-england-2025/water-situation-november-2025-summary (last access: 12 February 2026), 2022.
Ferro, C. A. T., Richardson, D. S., and Weigel, A. P.: On the effect of ensemble size on the discrete and continuous ranked probability scores, Meteorological Applications, 15, 19–24, https://doi.org/10.1002/met.45, 2008.
Folland, C. K., Knight, J., Linderholm, H. W., Fereday, D., Ineson, S., and Hurrell, J. W.: The Summer North Atlantic Oscillation: Past, Present, and Future, J. Climate, 22, 1082–1103, https://doi.org/10.1175/2008JCLI2459.1, 2009.
Golian, S., Murphy, C., Wilby, R. L., Matthews, T., Donegan, S., Quinn, D. F., and Harrigan, S.: Dynamical–statistical seasonal forecasts of winter and summer precipitation for the Island of Ireland, Intl. Journal of Climatology, 42, 5714–5731, https://doi.org/10.1002/joc.7557, 2022.
Harrigan, S., Prudhomme, C., Parry, S., Smith, K., and Tanguy, M.: Benchmarking ensemble streamflow prediction skill in the UK, Hydrol. Earth Syst. Sci., 22, 2023–2039, https://doi.org/10.5194/hess-22-2023-2018, 2018.
Harrigan, S., Zsoter, E., Cloke, H., Salamon, P., and Prudhomme, C.: Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System, Hydrol. Earth Syst. Sci., 27, 1–19, https://doi.org/10.5194/hess-27-1-2023, 2023.
Hollis, D., McCarthy, M., Kendon, M., Legg, T., and Simpson, I.: HadUK-Grid – A new UK dataset of gridded climate observations, Geosci. Data J., 6, 151–159, https://doi.org/10.1002/gdj3.78, 2019.
Hulme, M. and Barrow, E. (Eds.): Climates of the British Isles: present, past and future, Psychology Press, ISBN-10 0415130174, 1997.
Huntingford, C., Marsh, T., Scaife, A. A., Kendon, E. J., Hannaford, J., Kay, A. L., Lockwood, M., Prudhomme, C., Reynard, N. S., Parry, S., Lowe, J. A., Screen, J. A., Ward, H. C., Roberts, M., Stott, P. A., Bell, V. A., Bailey, M., Jenkins, A., Legg, T., Otto, F. E. L., Massey, N., Schaller, N., Slingo, J., and Allen, M. R.: Potential influences on the United Kingdom's floods of winter 2013/14, Nat. Clim. Change, 4, 769–777, https://doi.org/10.1038/nclimate2314, 2014.
Institute of Hydrology and British Geological Survey: Hydrological data United Kingdom 1995 Yearbook: an account of rainfall, river flows, groundwater levels and river water quality January to December 1995, NERC Institute of Hydrology, Wallingford, https://nora.nerc.ac.uk/id/eprint/6946 (last access: 12 February 2026), 1996.
Kay, A. L., Dunstone, N., Kay, G., Bell, V. A., and Hannaford, J.: Demonstrating the use of UNSEEN climate data for hydrological applications: case studies for extreme floods and droughts in England, Nat. Hazards Earth Syst. Sci., 24, 2953–2970, https://doi.org/10.5194/nhess-24-2953-2024, 2024.
Keel, T., Brierley, C., and Edwards, T.: jsmetrics v0.2.0: a Python package for metrics and algorithms used to identify or characterise atmospheric jet streams, Geosci. Model Dev., 17, 1229–1247, https://doi.org/10.5194/gmd-17-1229-2024, 2024.
Kendon, M., Marsh, T., and Parry, S.: The 2010–2012 drought in England and Wales, Weather, 68, 88–95, https://doi.org/10.1002/wea.2101, 2013.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, Journal of Hydrology, 424/425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012.
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019.
Lane, R. A., Coxon, G., Freer, J. E., Wagener, T., Johnes, P. J., Bloomfield, J. P., Greene, S., Macleod, C. J. A., and Reaney, S. M.: Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain, Hydrol. Earth Syst. Sci., 23, 4011–4032, https://doi.org/10.5194/hess-23-4011-2019, 2019.
Lees, T., Buechel, M., Anderson, B., Slater, L., Reece, S., Coxon, G., and Dadson, S. J.: Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models, Hydrol. Earth Syst. Sci., 25, 5517–5534, https://doi.org/10.5194/hess-25-5517-2021, 2021.
Leutbecher, M. and Haiden, T.: Understanding changes of the continuous ranked probability score using a homogeneous Gaussian approximation, Q. J. Roy. Meteor. Soc., 147, 425–442, https://doi.org/10.1002/qj.3926, 2021.
Li, H., Luo, L., Wood, E. F., and Schaake, J.: The role of initial conditions and forcing uncertainties in seasonal hydrologic forecasting, J. Geophys. Res., 114, 2008JD010969, https://doi.org/10.1029/2008JD010969, 2009.
MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. J. Roy. Meteor. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2015.
Marsh, T., Parry, S., Kendon, M., and Hannaford, J.: The 2010-12 drought and subsequent extensive flooding: a remarkable hydrological transformation, NERC/Centre for Ecology & Hydrology, Wallingford, https://nora.nerc.ac.uk/id/eprint/503643 (last access: 12 February 2026), 2013.
Matthews, G., Barnard, C., Cloke, H., Dance, S. L., Jurlina, T., Mazzetti, C., and Prudhomme, C.: Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System, Hydrol. Earth Syst. Sci., 26, 2939–2968, https://doi.org/10.5194/hess-26-2939-2022, 2022.
Mendoza, P. A., Wood, A. W., Clark, E., Rothwell, E., Clark, M. P., Nijssen, B., Brekke, L. D., and Arnold, J. R.: An intercomparison of approaches for improving operational seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 21, 3915–3935, https://doi.org/10.5194/hess-21-3915-2017, 2017.
MeteoSwiss: _easyVerification: Ensemble Forecast Verification for Large Data Sets_, R package version 0.4.5, https://CRAN.R-project.org/package=easyVerification (last access: 12 February 2026), 2023.
Moulds, S., Slater, L. J., Dunstone, N. J., and Smith, D. M.: Skillful Decadal Flood Prediction, Geophysical Research Letters, 50, e2022GL100650, https://doi.org/10.1029/2022GL100650, 2023.
National River Flow Archive: Integrated Hydrological Units of the United Kingdom: Hydrometric Areas with Coastline, UKCEH, https://doi.org/10.5285/1957166d-7523-44f4-b279-aa5314163237, 2014.
Pappenberger, F., Ramos, M. H., Cloke, H. L., Wetterhall, F., Alfieri, L., Bogner, K., Mueller, A., and Salamon, P.: How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction, Journal of Hydrology, 522, 697–713, https://doi.org/10.1016/j.jhydrol.2015.01.024, 2015.
Prior, J. and Kendon, M.: The UK winter of 2009/2010 compared with severe winters of the last 100 years, Weather, 66, 4–10, https://doi.org/10.1002/wea.735, 2011.
Prudhomme, C., Hannaford, J., Harrigan, S., Boorman, D., Knight, J., Bell, V., Jackson, C., Svensson, C., Parry, S., Bachiller-Jareno, N., Davies, H., Davis, R., Mackay, J., McKenzie, A., Rudd, A., Smith, K., Bloomfield, J., Ward, R., and Jenkins, A.: Hydrological Outlook UK: an operational streamflow and groundwater level forecasting system at monthly to seasonal time scales, Hydrological Sciences Journal, 62, 2753–2768, https://doi.org/10.1080/02626667.2017.1395032, 2017.
Pushpalatha, R., Perrin, C., Le Moine, N., Mathevet, T., and Andréassian, V.: A downward structural sensitivity analysis of hydrological models to improve low-flow simulation, Journal of Hydrology, 411, 66–76, https://doi.org/10.1016/j.jhydrol.2011.09.034, 2011.
Quinn, D. F., Murphy, C., Wilby, R. L., Matthews, T., Broderick, C., Golian, S., Donegan, S., and Harrigan, S.: Benchmarking seasonal forecasting skill using river flow persistence in Irish catchments, Hydrological Sciences Journal, 66, 672–688, https://doi.org/10.1080/02626667.2021.1874612, 2021.
Rhodes-Smith, M. D., Bell, V. A., Stringer, N., Baron, H., Davies, H., and Knight, J.: Improved seasonal hydrological forecasting for Great Britain, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-2506, 2025.
Richardson, D., Fowler, H. J., Kilsby, C. G., and Neal, R.: A new precipitation and drought climatology based on weather patterns, International Journal of Climatology, 38, 630–648, https://doi.org/10.1002/joc.5199, 2018.
Scaife, A. A. and Smith, D.: A signal-to-noise paradox in climate science, npj Clim. Atmos. Sci., 1, 28, https://doi.org/10.1038/s41612-018-0038-4, 2018.
Scaife, A. A., Arribas, A., Blockley, E., Brookshaw, A., Clark, R. T., Dunstone, N., Eade, R., Fereday, D., Folland, C. K., Gordon, M., Hermanson, L., Knight, J. R., Lea, D. J., MacLachlan, C., Maidens, A., Martin, M., Peterson, A. K., Smith, D., Vellinga, M., Wallace, E., Waters, J., and Williams, A.: Skillful long-range prediction of European and North American winters, Geophys. Res. Lett., 41, 2514–2519, https://doi.org/10.1002/2014GL059637, 2014.
Scaife, A. A., Dunstone, N., Hardiman, S., Ineson, S., Li, C., Lu, R., Pang, B., Klein-Tank, A., Smith, D., Van Niekerk, A., Renwick, J., and Williams, N.: ENSO affects the North Atlantic Oscillation 1 year later, Science, 386, 82–86, https://doi.org/10.1126/science.adk4671, 2024.
Seager, R., Liu, H., Kushnir, Y., Osborn, T. J., Simpson, I. R., Kelley, C. R., and Nakamura, J.: Mechanisms of Winter Precipitation Variability in the European–Mediterranean Region Associated with the North Atlantic Oscillation, J. Climate, 33, 7179–7196, https://doi.org/10.1175/JCLI-D-20-0011.1, 2020.
Slater, L. J. and Villarini, G.: Enhancing the Predictability of Seasonal Streamflow With a Statistical-Dynamical Approach, Geophysical Research Letters, 45, 6504–6513, https://doi.org/10.1029/2018GL077945, 2018.
Slater, L. J., Arnal, L., Boucher, M.-A., Chang, A. Y.-Y., Moulds, S., Murphy, C., Nearing, G., Shalev, G., Shen, C., Speight, L., Villarini, G., Wilby, R. L., Wood, A., and Zappa, M.: Hybrid forecasting: blending climate predictions with AI models, Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, 2023.
Smith, D. M., Scaife, A. A., Eade, R., Athanasiadis, P., Bellucci, A., Bethke, I., Bilbao, R., Borchert, L. F., Caron, L.-P., Counillon, F., Danabasoglu, G., Delworth, T., Doblas-Reyes, F. J., Dunstone, N. J., Estella-Perez, V., Flavoni, S., Hermanson, L., Keenlyside, N., Kharin, V., Kimoto, M., Merryfield, W. J., Mignot, J., Mochizuki, T., Modali, K., Monerie, P.-A., Müller, W. A., Nicolí, D., Ortega, P., Pankatz, K., Pohlmann, H., Robson, J., Ruggieri, P., Sospedra-Alfonso, R., Swingedouw, D., Wang, Y., Wild, S., Yeager, S., Yang, X., and Zhang, L.: North Atlantic climate far more predictable than models imply, Nature, 583, 796–800, https://doi.org/10.1038/s41586-020-2525-0, 2020.
Stringer, N., Knight, J., and Thornton, H.: Improving Meteorological Seasonal Forecasts for Hydrological Modeling in European Winter, Journal of Applied Meteorology and Climatology, 59, 317–332, https://doi.org/10.1175/JAMC-D-19-0094.1, 2020.
Svensson, C.: Seasonal river flow forecasts for the United Kingdom using persistence and historical analogues, Hydrological Sciences Journal, 61, 19–35, https://doi.org/10.1080/02626667.2014.992788, 2016.
Svensson, C. and Jones, D. A.: Dependence between extreme sea surge, river flow and precipitation in eastern Britain, Intl. Journal of Climatology, 22, 1149–1168, https://doi.org/10.1002/joc.794, 2002.
Svensson, C. and Prudhomme, C.: Prediction of British summer river flows using winter predictors, Theor. Appl. Climatol., 82, 1–15, https://doi.org/10.1007/s00704-005-0124-5, 2005.
Svensson, C., Brookshaw, A., Scaife, A. A., Bell, V. A., Mackay, J. D., Jackson, C. R., Hannaford, J., Davies, H. N., Arribas, A., and Stanley, S.: Long-range forecasts of UK winter hydrology, Environ. Res. Lett., 10, 064006, https://doi.org/10.1088/1748-9326/10/6/064006, 2015.
Tanguy, M., Prudhomme, C., Smith, K., and Hannaford, J.: Historical gridded reconstruction of potential evapotranspiration for the UK, Earth Syst. Sci. Data, 10, 951–968, https://doi.org/10.5194/essd-10-951-2018, 2018.
Tanguy, M., Eastman, M., Chevuturi, A., Magee, E., Cooper, E., Johnson, R. H. B., Facer-Childs, K., and Hannaford, J.: Optimising ensemble streamflow predictions with bias correction and data assimilation techniques, Hydrol. Earth Syst. Sci., 29, 1587–1614, https://doi.org/10.5194/hess-29-1587-2025, 2025.
Thompson, V., Dunstone, N. J., Scaife, A. A., Smith, D. M., Slingo, J. M., Brown, S., and Belcher, S. E.: High risk of unprecedented UK rainfall in the current climate, Nature Communications, 8, 107, https://doi.org/10.1038/s41467-017-00275-3, 2017.
Troin, M., Arsenault, R., Wood, A. W., Brissette, F., and Martel, J.: Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years, Water Resources Research, 57, e2020WR028392, https://doi.org/10.1029/2020WR028392, 2021.
Vitolo, C., Fry, M., and Buytaert, W.: rnrfa: an R package to retrieve, filter and visualize data from the UK National River Flow Archive, R Journal, 8, 102–116, 2016.
Weisheimer, A. and Palmer, T. N.: On the reliability of seasonal climate forecasts, J. R. Soc. Interface., 11, 20131162, https://doi.org/10.1098/rsif.2013.1162, 2014.
West, H., Quinn, N., and Horswell, M.: Regional rainfall response to the North Atlantic Oscillation (NAO) across Great Britain, Hydrol. Res., 50, 1549–1563, https://doi.org/10.2166/nh.2019.015, 2019.
West, H., Quinn, N., and Horswell, M.: Monthly Rainfall Signatures of the North Atlantic Oscillation and East Atlantic Pattern in Great Britain, Atmosphere, 12, 1533, https://doi.org/10.3390/atmos12111533, 2021.
West, H., Quinn, N., and Horswell, M.: Spatio-temporal propagation of North Atlantic Oscillation (NAO) rainfall deviations to streamflow in British catchments, Hydrological Sciences Journal, 67, 676–688, https://doi.org/10.1080/02626667.2022.2038791, 2022.
Wilby, R. L., Wedgbrow, C. S., and Fox, H. R.: Seasonal predictability of the summer hydrometeorology of the River Thames, UK, Journal of Hydrology, 295, 1–16, https://doi.org/10.1016/j.jhydrol.2004.02.015, 2004.
Wood, A., Sankarasubramanian, A., and Mendoza, P.: Seasonal Ensemble Forecast Post-processing, in: Hydrometeorological Ensemble Forecasting, edited by: Duan, Q., Pappenberger, F., Wood, A., Cloke, H., and Schaake, J., Springer Berlin Heidelberg, Berlin, Heidelberg, 819–845, ISBN 978-3-642-39924-4 2019.
Wood, A. W. and Lettenmaier, D. P.: A Test Bed for New Seasonal Hydrologic Forecasting Approaches in the Western United States, Bull. Amer. Meteor. Soc., 87, 1699–1712, https://doi.org/10.1175/BAMS-87-12-1699, 2006.
Wood, A. W. and Lettenmaier, D. P.: An ensemble approach for attribution of hydrologic prediction uncertainty, Geophysical Research Letters, 35, 2008GL034648, 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, Journal of Hydrometeorology, 17, 651–668, https://doi.org/10.1175/JHM-D-14-0213.1, 2016.
Woollings, T., Hannachi, A., and Hoskins, B.: Variability of the North Atlantic eddy-driven jet stream, Q. J. Roy. Meteor. Soc., 136, 856–868, https://doi.org/10.1002/qj.625, 2010.
Short summary
The UK Hydrological Outlook river flow forecasting system recently implemented the Historic Weather Analogues method. The method improves winter river flow forecast skill across the UK, especially in upland, fast-responding catchments with low catchment storage. Forecast skill is highest in winter due to accurate prediction of atmospheric circulation patterns like the North Atlantic Oscillation. The Ensemble Streamflow prediction method remains a robust benchmark, especially for other seasons.
The UK Hydrological Outlook river flow forecasting system recently implemented the Historic...
