Articles | Volume 25, issue 7
https://doi.org/10.5194/hess-25-4159-2021
© Author(s) 2021. 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-25-4159-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jul 2021
Research article |
| 22 Jul 2021
| 22 Jul 2021
Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times
Irish Climate Analysis and Research UnitS (ICARUS), Department of Geography, Maynooth University, Maynooth, Co. Kildare, Ireland
Conor Murphy
Irish Climate Analysis and Research UnitS (ICARUS), Department of Geography, Maynooth University, Maynooth, Co. Kildare, Ireland
Shaun Harrigan
Forecast Department, European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
Ciaran Broderick
Flood Forecasting Division, Met Éireann, Dublin 9, Ireland
Dáire Foran Quinn
Irish Climate Analysis and Research UnitS (ICARUS), Department of Geography, Maynooth University, Maynooth, Co. Kildare, Ireland
Saeed Golian
Irish Climate Analysis and Research UnitS (ICARUS), Department of Geography, Maynooth University, Maynooth, Co. Kildare, Ireland
Jeff Knight
Met Office Hadley Centre, Exeter, UK
Tom Matthews
Department of Geography and Environment, Loughborough University, Loughborough, UK
Christel Prudhomme
Forecast Department, European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
Department of Geography and Environment, Loughborough University, Loughborough, UK
UK Centre for Ecology & Hydrology (UKCEH), Wallingford, UK
Adam A. Scaife
Met Office Hadley Centre, Exeter, UK
College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Exeter, UK
Nicky Stringer
Met Office Hadley Centre, Exeter, UK
Robert L. Wilby
Department of Geography and Environment, Loughborough University, Loughborough, UK
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Larissa Nora van der Laan, Anouk Vlug, Adam A. Scaife, Fabien Maussion, and Kristian Förster
The Cryosphere, 19, 3879–3896, https://doi.org/10.5194/tc-19-3879-2025, https://doi.org/10.5194/tc-19-3879-2025, 2025
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Usually, glacier models are supplied with climate information from long (e.g., 100-year) simulations by global climate models. In this paper, we test the feasibility of supplying glacier models with shorter simulations to get more accurate information on 5–10-year timescales. Reliable information on these timescales is very important, especially for water management experts, to know how much meltwater to expect, affecting rivers, agriculture and drinking water.
Lisa Claire Orme, Francis Ludlow, Natasha Langton, Jenny Kristina Sjӧstrӧm, Malin Kylander, Conor Murphy, Sean Pyne-O'Donnell, Jonathan Turner, Nannan Li, Sarah Jessica Davies, Fraser Mitchell, and John Alphonsus Matthews
EGUsphere, https://doi.org/10.5194/egusphere-2025-3737, https://doi.org/10.5194/egusphere-2025-3737, 2025
This preprint is open for discussion and under review for Climate of the Past (CP).
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The link between storms and volcanic eruptions in northwest Europe is investigated. Past storminess was reconstructed using sand deposits in a coastal peatbog from western Ireland. Together with similar records from northwest Europe this shows six periods of high storminess in the last 2500 years, coinciding roughly with the largest eruptions of this time. A record of past windiness for Ireland (600–1616CE) created from the “Irish Annals” supports enhanced storminess for 3 years after eruptions.
Annie Y.-Y. Chang, Shaun Harrigan, Maria-Helena Ramos, Massimiliano Zappa, Christian M. Grams, Daniela I. V. Domeisen, and Konrad Bogner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3411, https://doi.org/10.5194/egusphere-2025-3411, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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This study presents a machine learning-aided hybrid forecasting framework to improve early warnings of low flows in the European Alps. It combines weather regime information, streamflow observations, and model simulations (EFAS). Even using only weather regime data improves predictions over climatology, while integrating different data sources yields the best result, emphasizing the value of integrating diverse data sources.
Blanca Ayarzagüena, Amy H. Butler, Peter Hitchcock, Chaim I. Garfinkel, Zac D. Lawrence, Wuhan Ning, Philip Rupp, Zheng Wu, Hilla Afargan-Gerstman, Natalia Calvo, Álvaro de la Cámara, Martin Jucker, Gerbrand Koren, Daniel De Maeseneire, Gloria L. Manney, Marisol Osman, Masakazu Taguchi, Cory Barton, Dong-Chang Hong, Yu-Kyung Hyun, Hera Kim, Jeff Knight, Piero Malguzzi, Daniele Mastrangelo, Jiyoung Oh, Inna Polichtchouk, Jadwiga H. Richter, Isla R. Simpson, Seok-Woo Son, Damien Specq, and Tim Stockdale
EGUsphere, https://doi.org/10.5194/egusphere-2025-3611, https://doi.org/10.5194/egusphere-2025-3611, 2025
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
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Sudden Stratospheric Warmings (SSWs) are known to follow a sustained wave dissipation in the stratosphere, which depends on both the tropospheric and stratospheric states. However, the relative role of each state is still unclear. Using a new set of subseasonal to seasonal forecasts, we show that the stratospheric state does not drastically affect the precursors of three recent SSWs, but modulates the stratospheric wave activity, with impacts depending on SSW features.
Mark D. Rhodes-Smith, Victoria A. Bell, Nicky Stringer, Helen Baron, Helen Davies, and Jeff Knight
EGUsphere, https://doi.org/10.5194/egusphere-2025-2506, https://doi.org/10.5194/egusphere-2025-2506, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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River flow forecasts up to three months ahead can allow early preparations for future floods and droughts. We test a new forecasting system using weather forecasts made by selecting historical weather patterns that match current conditions and running them through a simulation of Great Britain's rivers. Our tests show that this system performs particularly well in the winter and spring, in northern Scotland and in southern England. We now use this system to produce forecasts regularly.
Wilson Chan, Katie Facer-Childs, Maliko Tanguy, Eugene Magee, Burak Bulut, Nicky Stringer, Jeff Knight, and Jamie Hannaford
EGUsphere, https://doi.org/10.5194/egusphere-2025-2369, https://doi.org/10.5194/egusphere-2025-2369, 2025
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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.
Katherine Egan, Calum Baugh, Rebecca Emerton, Christel Prudhomme, Daniele Castellana, and Sebongile Hlubi
Abstr. Int. Cartogr. Assoc., 9, 8, https://doi.org/10.5194/ica-abs-9-8-2025, https://doi.org/10.5194/ica-abs-9-8-2025, 2025
Gwyneth Matthews, Hannah L. Cloke, Sarah L. Dance, and Christel Prudhomme
EGUsphere, https://doi.org/10.5194/hess-2024-3989, https://doi.org/10.5194/hess-2024-3989, 2025
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Forecasts provide information crucial for managing floods and for water resource planning, but they often have errors. “Post-processing” reduces these errors but is usually only applied at river gauges, leaving areas without gauges uncorrected. We developed a new method that uses spatial information contained within the forecast to spread information about the errors from gauged locations to ungauged areas. Our results show that the method successfully makes river forecasts more accurate.
Aleena M. Jaison, Lesley J. Gray, Scott M. Osprey, Jeff R. Knight, and Martin B. Andrews
Weather Clim. Dynam., 5, 1489–1504, https://doi.org/10.5194/wcd-5-1489-2024, https://doi.org/10.5194/wcd-5-1489-2024, 2024
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Models have biases in semi-annual oscillation (SAO) representation, mainly due to insufficient eastward wave forcing. We examined if the bias is from increased wave absorption due to circulation biases in the low–middle stratosphere. Alleviating biases at lower altitudes improves the SAO, but substantial bias remains. Alternative methods like gravity wave parameterization changes should be explored to enhance the modelled SAO, potentially improving sudden stratospheric warming predictability.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Margarita Choulga, Francesca Moschini, Cinzia Mazzetti, Stefania Grimaldi, Juliana Disperati, Hylke Beck, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 28, 2991–3036, https://doi.org/10.5194/hess-28-2991-2024, https://doi.org/10.5194/hess-28-2991-2024, 2024
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CEMS_SurfaceFields_2022 dataset is a new set of high-resolution maps for land type (e.g. lake, forest), soil properties and population water needs at approximately 2 and 6 km at the Equator, covering Europe and the globe (excluding Antarctica). We describe what and how new high-resolution information can be used to create the dataset. The paper suggests that the dataset can be used as input for river, weather or other models, as well as for statistical descriptions of the region of interest.
Lisa Degenhardt, Gregor C. Leckebusch, and Adam A. Scaife
Weather Clim. Dynam., 5, 587–607, https://doi.org/10.5194/wcd-5-587-2024, https://doi.org/10.5194/wcd-5-587-2024, 2024
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This study investigates how dynamical factors that are known to influence cyclone or windstorm development and strengthening also influence the seasonal forecast skill of severe winter windstorms. This study shows which factors are well represented in the seasonal forecast model, the Global Seasonal forecasting system version 5 (GloSea5), and which might need improvement to refine the forecast of severe winter windstorms.
Matthew D. K. Priestley, David B. Stephenson, Adam A. Scaife, Daniel Bannister, Christopher J. T. Allen, and David Wilkie
Nat. Hazards Earth Syst. Sci., 23, 3845–3861, https://doi.org/10.5194/nhess-23-3845-2023, https://doi.org/10.5194/nhess-23-3845-2023, 2023
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This research presents a model for estimating extreme gusts associated with European windstorms. Using observed storm footprints we are able to calculate the return level of events at the 200-year return period. The largest gusts are found across NW Europe, and these are larger when the North Atlantic Oscillation is positive. Using theoretical future climate states we find that return levels are likely to increase across NW Europe to levels that are unprecedented compared to historical storms.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
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Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Philip E. Bett, Adam A. Scaife, Steven C. Hardiman, Hazel E. Thornton, Xiaocen Shen, Lin Wang, and Bo Pang
Weather Clim. Dynam., 4, 213–228, https://doi.org/10.5194/wcd-4-213-2023, https://doi.org/10.5194/wcd-4-213-2023, 2023
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Sudden-stratospheric-warming (SSW) events can severely affect the subsequent weather at the surface. We use a large ensemble of climate model hindcasts to investigate features of the climate that make strong impacts more likely through negative NAO conditions. This allows a more robust assessment than using observations alone. Air pressure over the Arctic prior to an SSW and the zonal-mean zonal wind in the lower stratosphere have the strongest relationship with the subsequent NAO response.
Katherine Dooley, Ciaran Kelly, Natascha Seifert, Therese Myslinski, Sophie O'Kelly, Rushna Siraj, Ciara Crosby, Jack Kevin Dunne, Kate McCauley, James Donoghue, Eoin Gaddren, Daniel Conway, Jordan Cooney, Niamh McCarthy, Eoin Cullen, Simon Noone, Conor Murphy, and Peter Thorne
Clim. Past, 19, 1–22, https://doi.org/10.5194/cp-19-1-2023, https://doi.org/10.5194/cp-19-1-2023, 2023
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The highest currently recognised air temperature (33.3 °C) ever recorded in the Republic of Ireland was logged at Kilkenny Castle in 1887. This paper reassesses the plausibility of the record using various methods such as inter-station reassessment and 20CRv3 reanalysis. As a result, Boora 1976 at 32.5 °C is presented as a more reliable high-temperature record for the Republic of Ireland. The final decision however rests with the national meteorological service, Met Éireann.
Shaun Harrigan, Ervin Zsoter, Hannah Cloke, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 27, 1–19, https://doi.org/10.5194/hess-27-1-2023, https://doi.org/10.5194/hess-27-1-2023, 2023
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Real-time river discharge forecasts and reforecasts from the Global Flood Awareness System (GloFAS) have been made publicly available, together with an evaluation of forecast skill at the global scale. Results show that GloFAS is skillful in over 93 % of catchments in the short (1–3 d) and medium range (5–15 d) and skillful in over 80 % of catchments in the extended lead time (16–30 d). Skill is summarised in a new layer on the GloFAS Web Map Viewer to aid decision-making.
Kieran M. R. Hunt, Gwyneth R. Matthews, Florian Pappenberger, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 26, 5449–5472, https://doi.org/10.5194/hess-26-5449-2022, https://doi.org/10.5194/hess-26-5449-2022, 2022
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In this study, we use three models to forecast river streamflow operationally for 13 months (September 2020 to October 2021) at 10 gauges in the western US. The first model is a state-of-the-art physics-based streamflow model (GloFAS). The second applies a bias-correction technique to GloFAS. The third is a type of neural network (an LSTM). We find that all three are capable of producing skilful forecasts but that the LSTM performs the best, with skilful 5 d forecasts at nine stations.
Peter Hitchcock, Amy Butler, Andrew Charlton-Perez, Chaim I. Garfinkel, Tim Stockdale, James Anstey, Dann Mitchell, Daniela I. V. Domeisen, Tongwen Wu, Yixiong Lu, Daniele Mastrangelo, Piero Malguzzi, Hai Lin, Ryan Muncaster, Bill Merryfield, Michael Sigmond, Baoqiang Xiang, Liwei Jia, Yu-Kyung Hyun, Jiyoung Oh, Damien Specq, Isla R. Simpson, Jadwiga H. Richter, Cory Barton, Jeff Knight, Eun-Pa Lim, and Harry Hendon
Geosci. Model Dev., 15, 5073–5092, https://doi.org/10.5194/gmd-15-5073-2022, https://doi.org/10.5194/gmd-15-5073-2022, 2022
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This paper describes an experimental protocol focused on sudden stratospheric warmings to be carried out by subseasonal forecast modeling centers. These will allow for inter-model comparisons of these major disruptions to the stratospheric polar vortex and their impacts on the near-surface flow. The protocol will lead to new insights into the contribution of the stratosphere to subseasonal forecast skill and new approaches to the dynamical attribution of extreme events.
Gwyneth Matthews, Christopher Barnard, Hannah Cloke, Sarah L. Dance, Toni Jurlina, Cinzia Mazzetti, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 26, 2939–2968, https://doi.org/10.5194/hess-26-2939-2022, https://doi.org/10.5194/hess-26-2939-2022, 2022
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The European Flood Awareness System creates flood forecasts for up to 15 d in the future for the whole of Europe which are made available to local authorities. These forecasts can be erroneous because the weather forecasts include errors or because the hydrological model used does not represent the flow in the rivers correctly. We found that, by using recent observations and a model trained with past observations and forecasts, the real-time forecast can be corrected, thus becoming more useful.
Samuel O. Awe, Martin Mahony, Edley Michaud, Conor Murphy, Simon J. Noone, Victor K. C. Venema, Thomas G. Thorne, and Peter W. Thorne
Clim. Past, 18, 793–820, https://doi.org/10.5194/cp-18-793-2022, https://doi.org/10.5194/cp-18-793-2022, 2022
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We unearth and analyse 2 decades of highly valuable measurements made on Mauritius at the Royal Alfred Observatory, where several distinct thermometer combinations were in use and compared, at the turn of the 20th century. This series provides unique insights into biases in early instrumental temperature records. Differences are substantial and for some instruments exhibit strong seasonality. This reinforces the critical importance of understanding early instrumental series biases.
Andy Jones, Jim M. Haywood, Adam A. Scaife, Olivier Boucher, Matthew Henry, Ben Kravitz, Thibaut Lurton, Pierre Nabat, Ulrike Niemeier, Roland Séférian, Simone Tilmes, and Daniele Visioni
Atmos. Chem. Phys., 22, 2999–3016, https://doi.org/10.5194/acp-22-2999-2022, https://doi.org/10.5194/acp-22-2999-2022, 2022
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Simulations by six Earth-system models of geoengineering by introducing sulfuric acid aerosols into the tropical stratosphere are compared. A robust impact on the northern wintertime North Atlantic Oscillation is found, exacerbating precipitation reduction over parts of southern Europe. In contrast, the models show no consistency with regard to impacts on the Quasi-Biennial Oscillation, although results do indicate a risk that the oscillation could become locked into a permanent westerly phase.
Adam A. Scaife, Mark P. Baldwin, Amy H. Butler, Andrew J. Charlton-Perez, Daniela I. V. Domeisen, Chaim I. Garfinkel, Steven C. Hardiman, Peter Haynes, Alexey Yu Karpechko, Eun-Pa Lim, Shunsuke Noguchi, Judith Perlwitz, Lorenzo Polvani, Jadwiga H. Richter, John Scinocca, Michael Sigmond, Theodore G. Shepherd, Seok-Woo Son, and David W. J. Thompson
Atmos. Chem. Phys., 22, 2601–2623, https://doi.org/10.5194/acp-22-2601-2022, https://doi.org/10.5194/acp-22-2601-2022, 2022
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Great progress has been made in computer modelling and simulation of the whole climate system, including the stratosphere. Since the late 20th century we also gained a much clearer understanding of how the stratosphere interacts with the lower atmosphere. The latest generation of numerical prediction systems now explicitly represents the stratosphere and its interaction with surface climate, and here we review its role in long-range predictions and projections from weeks to decades ahead.
Hadush Meresa, Conor Murphy, Rowan Fealy, and Saeed Golian
Hydrol. Earth Syst. Sci., 25, 5237–5257, https://doi.org/10.5194/hess-25-5237-2021, https://doi.org/10.5194/hess-25-5237-2021, 2021
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The assessment of future impacts of climate change is associated with a cascade of uncertainty linked to the modelling chain employed in assessing local-scale changes. Understanding and quantifying this cascade is essential for developing effective adaptation actions. We find that not only do the contributions of different sources of uncertainty vary by catchment, but that the dominant sources of uncertainty can be very different on a catchment-by-catchment basis.
Joaquín Muñoz-Sabater, Emanuel Dutra, Anna Agustí-Panareda, Clément Albergel, Gabriele Arduini, Gianpaolo Balsamo, Souhail Boussetta, Margarita Choulga, Shaun Harrigan, Hans Hersbach, Brecht Martens, Diego G. Miralles, María Piles, Nemesio J. Rodríguez-Fernández, Ervin Zsoter, Carlo Buontempo, and Jean-Noël Thépaut
Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, https://doi.org/10.5194/essd-13-4349-2021, 2021
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The creation of ERA5-Land responds to a growing number of applications requiring global land datasets at a resolution higher than traditionally reached. ERA5-Land provides operational, global, and hourly key variables of the water and energy cycles over land surfaces, at 9 km resolution, from 1981 until the present. This work provides evidence of an overall improvement of the water cycle compared to previous reanalyses, whereas the energy cycle variables perform as well as those of ERA5.
Louise J. Slater, Bailey Anderson, Marcus Buechel, Simon Dadson, Shasha Han, Shaun Harrigan, Timo Kelder, Katie Kowal, Thomas Lees, Tom Matthews, Conor Murphy, and Robert L. Wilby
Hydrol. Earth Syst. Sci., 25, 3897–3935, https://doi.org/10.5194/hess-25-3897-2021, https://doi.org/10.5194/hess-25-3897-2021, 2021
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Weather and water extremes have devastating effects each year. One of the principal challenges for society is understanding how extremes are likely to evolve under the influence of changes in climate, land cover, and other human impacts. This paper provides a review of the methods and challenges associated with the detection, attribution, management, and projection of nonstationary weather and water extremes.
Cited articles
Amnatsan, S., Yoshikawa, S., and Kanae, S.: Improved Forecasting of Extreme
Monthly Reservoir Inflow Using an Analogue-Based Forecasting Method: A Case
Study of the Sirikit Dam in Thailand, Water, 10, 1614,
https://doi.org/10.3390/w10111614, 2018. a
Anghileri, D., Voisin, N., Castelletti, A., Pianosi, F., Nijssen, B., and
Lettenmaier, D. P.: Value of long-term streamflow forecasts to reservoir
operations for water supply in snow-dominated river catchments, Water Resour.
Res., 52, 4209–4225, https://doi.org/10.1002/2015WR017864, 2016. a
Arnal, L., Cloke, H. L., Stephens, E., Wetterhall, F., Prudhomme, C., Neumann, J., Krzeminski, B., and Pappenberger, F.: Skilful seasonal forecasts of streamflow over Europe?, Hydrol. Earth Syst. Sci., 22, 2057–2072, https://doi.org/10.5194/hess-22-2057-2018, 2018. a, b, c
Arsenault, R., Brissette, F., and Martel, J.-L.: The hazards of split-sample
validation in hydrological model calibration, J. Hydrol., 566, 346–362,
https://doi.org/10.1016/j.jhydrol.2018.09.027, 2018. a
Baker, D. B., Richards, R. P., Loftus, T. T., and Kramer, J. W.: A New
Flashiness Index: Characteristics And Applications To Midwestern Rivers And
Streams, J. Am. Water Resour. Assoc., 40, 503–522,
https://doi.org/10.1111/j.1752-1688.2004.tb01046.x, 2004. a
Baker, L. H., Shaffrey, L. C., and Scaife, A. A.: Improved seasonal prediction of UK regional precipitation using atmospheric circulation, Int. J. Climatol., 38, e437–e453, https://doi.org/10.1002/joc.5382, 2018. a
Bazile, R., Boucher, M.-A., Perreault, L., and Leconte, R.: Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate, Hydrol. Earth Syst. Sci., 21, 5747–5762, https://doi.org/10.5194/hess-21-5747-2017, 2017. a
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. a, b, c
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. a
Bennett, J. C., Wang, Q. J., Robertson, D. E., Schepen, A., Li, M., and Michael, K.: Assessment of an ensemble seasonal streamflow forecasting system for Australia, Hydrol. Earth Syst. Sci., 21, 6007–6030, https://doi.org/10.5194/hess-21-6007-2017, 2017. a
Berghuijs, W. R., Woods, R. A., and Hrachowitz, M.: A precipitation shift from snow towards rain leads to a decrease in streamflow, Nat. Clim. Chang., 4, 583–586, https://doi.org/10.1038/nclimate2246, 2014. a, b
Bergmeir, C., Molina, D., and Benítez, J. M.: Memetic Algorithms with Local
Search Chains in R: The Rmalschains Package, J. Stat. Softw., 75, 1–33,
https://doi.org/10.18637/jss.v075.i04, 2016. a, b
Bergmeir, C., Benítez, J. M., Molina, D., Davies, R., Eddelbuettel, D., and
Hansen, N.: Rmalschains: Continuous Optimization using Memetic Algorithms
with Local Search Chains (MA-LS-Chains) in R, R package version 0.2-6, available at: https://CRAN.R-project.org/package=Rmalschains (last access: 15 November 2020), 2019. a
Bierkens, M. F. P. and van Beek, L. P. H.: Seasonal Predictability of European Discharge: NAO and Hydrological Response Time, J. Hydrometeorol., 10, 953–968, https://doi.org/10.1175/2009JHM1034.1, 2009. a
Bradley, A. A., Habib, M., and Schwartz, S. S.: Climate index weighting of
ensemble streamflow forecasts using a simple Bayesian approach, Water Resour.
Res., 51, 7382–7400, https://doi.org/10.1002/2014WR016811, 2015. a
Broderick, C., Matthews, T., Wilby, R. L., Bastola, S., and Murphy, C.:
Transferability of hydrological models and ensemble averaging methods between
contrasting climatic periods, Water Resour. Res., 52, 8343–8373,
https://doi.org/10.1002/2016WR018850, 2016. a, b
Broderick, C., Murphy, C., Wilby, R. L., Matthews, T., Prudhomme, C., and
Adamson, M.: Using a Scenario-Neutral Framework to Avoid Potential
Maladaptation to Future Flood Risk, Water Resour. Res., 55, 1079–1104,
https://doi.org/10.1029/2018WR023623, 2019. a
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. a, b
Coron, L., Andréassian, V., Perrin, C., Lerat, J., Vaze, J., Bourqui, M., and
Hendrickx, F.: Crash testing hydrological models in contrasted climate
conditions: An experiment on 216 Australian catchments, Water Resour. Res.,
48, W05552, https://doi.org/10.1029/2011WR011721, 2012. a
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. a
Coron, L., Delaigue, O., Thirel, G., Perrin, C., and Michel, C.: airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling, R package version 1.4.3.65, https://doi.org/10.15454/EX11NA, 2020. a
Day, G. N.: Extended Streamflow Forecasting Using NWSRFS, J. Water Resour.
Plan. Manag., 111, 157–170, https://doi.org/10.1061/(ASCE)0733-9496(1985)111:2(157),
1985. a
Demargne, J., Brown, J., Liu, Y., Seo, D.-J., Wu, L., Toth, Z., and Zhu, Y.:
Diagnostic verification of hydrometeorological and hydrologic ensembles,
Atmos. Sci. Lett., 11, 114–122, https://doi.org/10.1002/asl.261, 2010. a
Demargne, J., Wu, L., Regonda, S. K., Brown, J. D., Lee, H., He, M., Seo,
D.-J., Hartman, R., Herr, H. D., Fresch, M., Schaake, J., and Zhu, Y.: The
Science of NOAA's Operational Hydrologic Ensemble Forecast Service, Bull. Am.
Meteorol. Soc., 95, 79–98, https://doi.org/10.1175/BAMS-D-12-00081.1, 2014. a, b
Dixon, S. G. and Wilby, R. L.: A seasonal forecasting procedure for reservoir
inflows in Central Asia, River Res. Appl., 35, 1141–1154,
https://doi.org/10.1002/rra.3506, 2019. a
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?, Geophys. Res. Lett., 41, 5620–5628,
https://doi.org/10.1002/2014GL061146, 2014. a
Environmental Protection Agency: HydroNet, available at: https://epawebapp.epa.ie/hydronet/, last access: 19 July 2021. a
Fan, F. M., Schwanenberg, D., Alvarado, R., Assis dos Reis, A., Collischonn, W., and Naumman, S.: Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir, Water Resour. Manag., 30, 3609–3625,
https://doi.org/10.1007/s11269-016-1377-8, 2016. a
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,
Meteorol. Appl., 15, 19–24, https://doi.org/10.1002/met.45, 2008. a
Ficchì, A., Raso, L., Dorchies, D., Pianosi, F., Malaterre, P.-O., Overloop,
P.-J. V., and Jay-Allemand, M.: Optimal Operation of the Multireservoir
System in the Seine River Basin Using Deterministic and Ensemble Forecasts,
J. Water Resour. Plan. Manag., 142, 05015005,
https://doi.org/10.1061/(ASCE)WR.1943-5452.0000571, 2016. a
Foran Quinn, D., 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, Hydrol. Sci. J., 66,
672–688, https://doi.org/10.1080/02626667.2021.1874612, 2021. a, b, c
Förster, K., Hanzer, F., Stoll, E., Scaife, A. A., MacLachlan, C., Schöber, J., Huttenlau, M., Achleitner, S., and Strasser, U.: Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps), Hydrol. Earth Syst. Sci., 22, 1157–1173, https://doi.org/10.5194/hess-22-1157-2018, 2018. a
Franz, K. J., Hartmann, H. C., Sorooshian, S., and Bales, R.: Verification of
National Weather Service Ensemble Streamflow Predictions for Water Supply
Forecasting in the Colorado River Basin, J. Hydrometeorol., 4, 1105–1118,
https://doi.org/10.1175/1525-7541(2003)004<1105:VONWSE>2.0.CO;2, 2003. a
Franz, K. J., Hogue, T. S., Barik, M., and He, M.: Assessment of SWE data
assimilation for ensemble streamflow predictions, J. Hydrol., 519,
2737–2746, https://doi.org/10.1016/j.jhydrol.2014.07.008, 2014. a
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. a
Ghannam, K., Nakai, T., Paschalis, A., Oishi, C. A., Kotani, A., Igarashi, Y., Kumagai, T., and Katul, G. G.: Persistence and memory timescales in root-zone soil moisture dynamics, Water Resour. Res., 52, 1427–1445,
https://doi.org/10.1002/2015WR017983, 2014. a
Gibbs, M. S., McInerney, D., Humphrey, G., Thyer, M. A., Maier, H. R., Dandy, G. C., and Kavetski, D.: State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application, Hydrol. Earth Syst. Sci., 22, 871–887, https://doi.org/10.5194/hess-22-871-2018, 2018. a
Gneiting, T., Balabdaoui, F., and Raftery, A. E.: Probabilistic forecasts,
calibration and sharpness, J. R. Stat. Soc. B, 69, 243–268,
https://doi.org/10.1111/j.1467-9868.2007.00587.x, 2007. a
Greuell, W., Franssen, W. H. P., and Hutjes, R. W. A.: Seasonal streamflow forecasts for Europe – Part 2: Sources of skill, Hydrol. Earth Syst. Sci., 23, 371–391, https://doi.org/10.5194/hess-23-371-2019, 2019. a
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration, J. Hydrol. Eng., 4, 135–143, https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135), 1999. a
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. a
Gustard, A., Bullock, A., and Dixon, J. M.: Low flow estimation in the United
Kingdom, Tech. Rep. 108, Institute of Hydrology, Wallingford, UK, available at: http://nora.nerc.ac.uk/id/eprint/6050/1/IH_108.pdf (last access: 15 November 2020), 1992. a
Hamlet, A. F. and Lettenmaier, D. P.: Columbia River Streamflow Forecasting
Based on ENSO and PDO Climate Signals, J. Water Resour. Plan. Manag., 125,
333–341, https://doi.org/10.1061/(ASCE)0733-9496(1999)125:6(333), 1999. a
Hamlet, A. F., Huppert, D., and Lettenmaier, D. P.: Economic Value of Long-Lead Streamflow Forecasts for Columbia River Hydropower, J. Water Resour. Plan. Manag., 128, 91–101, https://doi.org/10.1061/(ASCE)0733-9496(2002)128:2(91), 2002. a
Hansen, N. and Ostermeier, A.: Completely Derandomized Self-Adaptation in
Evolution Strategies, Evol. Comput., 9, 159–195,
https://doi.org/10.1162/106365601750190398, 2001. a
Hersbach, H.: Decomposition of the Continuous Ranked Probability Score for
Ensemble Prediction Systems, Weather Forecast., 15, 559–570,
https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000. a, b
Klemeš, V.: Operational testing of hydrological simulation models, Hydrol.
Sci. J., 31, 13–24, https://doi.org/10.1080/02626668609491024, 1986. a
Laio, F. and Tamea, S.: Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrol. Earth Syst. Sci., 11, 1267–1277, https://doi.org/10.5194/hess-11-1267-2007, 2007. a
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.-Atmos., 114, D04114, https://doi.org/10.1029/2008JD010969, 2009. a, b
Luo, L. and Wood, E. F.: Monitoring and predicting the 2007 U.S. drought,
Geophys. Res. Lett., 34, L22702, https://doi.org/10.1029/2007GL031673, 2007. a
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. R. Meteorol.
Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2015. a
Mason, S. J. and Graham, N. E.: Conditional Probabilities, Relative Operating
Characteristics, and Relative Operating Levels, Weather Forecast., 14,
713–725, https://doi.org/10.1175/1520-0434(1999)014<0713:CPROCA>2.0.CO;2, 1999. a
Meißner, D., Klein, B., and Ionita, M.: Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe, Hydrol. Earth Syst. Sci., 21, 6401–6423, https://doi.org/10.5194/hess-21-6401-2017, 2017. a, b
MeteoSwiss: easyVerification: Ensemble Forecast Verification for Large Data
Sets, R package version: 0.4.4, available at: https://CRAN.R-project.org/package=easyVerification (last access: 15 November 2020), 2017. a
Mills, P., Nicholson, O., and Reed, D.: Flood Studies Update Technical Research Report: Volume IV – Physical Catchment Descriptors, Tech. rep., Office of Public Works, Trim, Ireland, available at: https://opw.hydronet.com/data/files/Technical Research Report - Volume IV - Physical Catchment Descriptors.pdf (last access: 15 November 2020), 2014. a
Mo, K. C. and Lettenmaier, D. P.: Hydrologic Prediction over the Conterminous
United States Using the National Multi-Model Ensemble, J. Hydrometeorol., 15,
1457–1472, https://doi.org/10.1175/JHM-D-13-0197.1, 2014. a
Molina, D., Lozano, M., García-Martínez, C., and Herrera, F.: Memetic
Algorithms for Continuous Optimisation Based on Local Search Chains, Evol.
Comput., 18, 27–63, https://doi.org/10.1162/evco.2010.18.1.18102, 2010. a
Murphy, C., Harrigan, S., Hall, J., and Wilby, R. L.: Climate-driven trends in mean and high flows from a network of reference stations in Ireland, Hydrol. Sci. J., 58, 755–772, https://doi.org/10.1080/02626667.2013.782407, 2013. a, b, c
Mushtaq, S., Chen, C., Hafeez, M., Maroulis, J., and Gabriel, H.: The economic value of improved agrometeorological information to irrigators amid climate variability, Int. J. Climatol., 32, 567–581, https://doi.org/10.1002/joc.2015, 2012. a
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – A discussion of principles, J. Hydrol., 10, 282–290,
https://doi.org/10.1016/0022-1694(70)90255-6, 1970. a
Neumann, J. L., Arnal, L., Emerton, R. E., Griffith, H., Hyslop, S., Theofanidi, S., and Cloke, H. L.: Can seasonal hydrological forecasts inform local decisions and actions? A decision-making activity, Geosci. Commun., 1, 35–57, https://doi.org/10.5194/gc-1-35-2018, 2018. a
Office of Public Works: Hydro-Data, available at: https://waterlevel.ie/hydro-data/, last access: 19 July 2021. a
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andréassian, V., Anctil, F.,
and Loumagne, C.: Which potential evapotranspiration input for a lumped
rainfall–runoff model?: Part 2 – Towards a simple and efficient potential
evapotranspiration model for rainfall–runoff modelling, J. Hydrol., 303,
290–306, https://doi.org/10.1016/j.jhydrol.2004.08.026, 2005. a
Pagano, T., Hapuarachchi, P., and Wang, Q.: Continuous rainfall-runoff model
comparison and short-term daily streamflow forecast skill evaluation, Tech.
Rep. EP103545, CSIRO: Water for a Healthy Country National Research Flagship,
Canberra, Australia, https://doi.org/10.4225/08/58542c672dd2c, 2010. a, b
Paiva, R. C. D., Collischonn, W., Bonnet, M. P., and de Gonçalves, L. G. G.: On the sources of hydrological prediction uncertainty in the Amazon, Hydrol. Earth Syst. Sci., 16, 3127–3137, https://doi.org/10.5194/hess-16-3127-2012, 2012. a
Pappenberger, F., Cloke, H. L., Parker, D. J., Wetterhall, F., Richardson,
D. S., and Thielen, J.: The monetary benefit of early flood warnings in
Europe, Environ. Sci. Policy, 51, 278–291,
https://doi.org/10.1016/j.envsci.2015.04.016, 2015a. a, b
Pappenberger, F., Ramos, M., Cloke, H., 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, J. Hydrol., 522,
697–713, https://doi.org/10.1016/j.jhydrol.2015.01.024, 2015b. a, b
Pechlivanidis, I. G., Crochemore, L., Rosberg, J., and Bosshard, T.: What Are
the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts?,
Water Resour. Res., 56, e2019WR026987, https://doi.org/10.1029/2019WR026987, 2020. a, b, c, d
Perrin, C., Michel, C., and Andréassian, V.: Improvement of a parsimonious
model for streamflow simulation, J. Hydrol., 279, 275–289,
https://doi.org/10.1016/S0022-1694(03)00225-7, 2003. a, b
Pool, S., Vis, M., and Seibert, J.: Evaluating model performance: towards a
non-parametric variant of the Kling-Gupta efficiency, Hydrol. Sci. J., 63,
1941–1953, https://doi.org/10.1080/02626667.2018.1552002, 2018. a, b
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,
Hydrol. Sci. J., 62, 2753–2768, https://doi.org/10.1080/02626667.2017.1395032, 2017. a, b, c
Renard, B., Kavetski, D., Kuczera, G., Thyer, M., and Franks, S. W.:
Understanding predictive uncertainty in hydrologic modeling: The challenge of
identifying input and structural errors, Water Resour. Res., 46, W05521,
https://doi.org/10.1029/2009WR008328, 2010. a
Robertson, D., Bennett, J., and Schepen, A.: How good is my forecasting method? Some thoughts on forecast evaluation using cross-validation based on
Australian experiences, available at:
https://hepex.inrae.fr/how-good-is-my-forecasting-method- some-thoughts-on-forecast-evaluation-using-cross-validation-based-on-australian-experiences/,
(last access: 15 November 2020), 2016. a
Santos, L., Thirel, G., and Perrin, C.: Continuous state-space representation of a bucket-type rainfall-runoff model: a case study with the GR4 model using state-space GR4 (version 1.0), Geosci. Model Dev., 11, 1591–1605, https://doi.org/10.5194/gmd-11-1591-2018, 2018. a
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. a
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. a, b, c
Schepen, A. and Wang, Q. J.: Model averaging methods to merge operational
statistical and dynamic seasonal streamflow forecasts in Australia, Water
Resour. Res., 51, 1797–1812, https://doi.org/10.1002/2014WR016163, 2015. a
Sear, D. A., Armitage, P. D., and Dawson, F. H.: Groundwater dominated rivers, Hydrol. Process., 13, 255–276, https://doi.org/10.1002/(SICI)1099-1085(19990228)13:3<255::AID-HYP737>3.0.CO;2-Y, 1999. a
Sharma, S., Siddique, R., Reed, S., Ahnert, P., and Mejia, A.: Hydrological
Model Diversity Enhances Streamflow Forecast Skill at Short- to Medium-Range
Timescales, Water Resour. Res., 55, 1510–1530, https://doi.org/10.1029/2018WR023197,
2019. a
Shukla, S., Sheffield, J., Wood, E. F., and Lettenmaier, D. P.: On the sources of global land surface hydrologic predictability, Hydrol. Earth Syst. Sci., 17, 2781–2796, https://doi.org/10.5194/hess-17-2781-2013, 2013. a
Singh, S. K.: Long-term Streamflow Forecasting Based on Ensemble Streamflow
Prediction Technique: A Case Study in New Zealand, Water Resour. Manag., 30,
2295–2309, https://doi.org/10.1007/s11269-016-1289-7, 2016. a
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. a
Staudinger, M. and Seibert, J.: Predictability of low flow – An assessment
with simulation experiments, J. Hydrol., 519, 1383–1393,
https://doi.org/10.1016/j.jhydrol.2014.08.061, 2014. a
Steirou, E., Gerlitz, L., Apel, H., and Merz, B.: Links between large-scale
circulation patterns and streamflow in Central Europe: A review, J. Hydrol.,
549, 484–500, https://doi.org/10.1016/j.jhydrol.2017.04.003, 2017. a
Stringer, N., Knight, J., and Thornton, H.: Improving Meteorological Seasonal
Forecasts for Hydrological Modeling in European Winter, J. Appl. Meteorol.
Climatol., 59, 317–332, https://doi.org/10.1175/JAMC-D-19-0094.1, 2020. a, b, c, d
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. a, b
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. a
Tang, Q., Zhang, X., Duan, Q., Huang, S., Yuan, X., Cui, H., Li, Z., and Liu,
X.: Hydrological monitoring and seasonal forecasting: Progress and
perspectives, J. Geogr. Sci., 26, 904–920, https://doi.org/10.1007/s11442-016-1306-z,
2016. a
Thielen, J., Bartholmes, J., Ramos, M.-H., and de Roo, A.: The European Flood Alert System – Part 1: Concept and development, Hydrol. Earth Syst. Sci., 13, 125–140, https://doi.org/10.5194/hess-13-125-2009, 2009. a
Turner, S. W. D., Bennett, J. C., Robertson, D. E., and Galelli, S.: Complex relationship between seasonal streamflow forecast skill and value in reservoir operations, Hydrol. Earth Syst. Sci., 21, 4841–4859, https://doi.org/10.5194/hess-21-4841-2017, 2017. a
Twedt, T. M., Schaake Jr., J. C., and Peck, E. L.: National weather service extended streamflow prediction, in: Proceedings of the 45th Annual Western Snow Conference, Western Snow Conference, 18–21 April 1977, Albuquerque, New Mexico, USA, 52–57, available at: https://westernsnowconference.org/sites/westernsnowconference.org/PDFs/1977Twedt.pdf (last access: 15 November 2020), 1977. a
van Dijk, A. I. J. M., Peña-Arancibia, J. L., Wood, E. F., Sheffield, J.,
and Beck, H. E.: Global analysis of seasonal streamflow predictability using
an ensemble prediction system and observations from 6192 small catchments
worldwide, Water Resour. Res., 49, 2729–2746, https://doi.org/10.1002/wrcr.20251,
2013. a
Vaze, J., Chiew, F. H. S., Perraud, J. M., Viney, N., Post, D., Teng, J., Wang, B., Lerat, J., and Goswami, M.: Rainfall-Runoff Modelling Across Southeast Australia: Datasets, Models and Results, Australas. J. Water Resour., 14, 101–116, https://doi.org/10.1080/13241583.2011.11465379, 2011. a
Viel, C., Beaulant, A.-L., Soubeyroux, J.-M., and Céron, J.-P.: How seasonal forecast could help a decision maker: an example of climate service for water resource management, Adv. Sci. Res., 13, 51–55, https://doi.org/10.5194/asr-13-51-2016, 2016. a
Walsh, S.: New long-term rainfall averages for Ireland, in: Irish National
Hydrology Conference 2012, Hydrology Ireland, 13 November 2012, Tullamore, Ireland, 3–12, available at: http://hydrologyireland.ie/wp-content/uploads/2016/11/01-Walsh-New-Long-Term-Rainfall-Averages-for-Ireland-1.pdf (last access: 15 November 2020), 2012. a
Wanders, N., Thober, S., Kumar, R., Pan, M., Sheffield, J., Samaniego, L., and Wood, E. F.: Development and Evaluation of a Pan-European Multimodel Seasonal Hydrological Forecasting System, J. Hydrometeorol., 20, 99–115,
https://doi.org/10.1175/JHM-D-18-0040.1, 2019. a
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. a, b
Watts, G., von Christierson, B., Hannaford, J., and Lonsdale, K.: Testing the resilience of water supply systems to long droughts, J. Hydrol., 414–415, 255–267, https://doi.org/10.1016/j.jhydrol.2011.10.038, 2012. a
Wedgbrow, C. S., Wilby, R. L., Fox, H. R., and O'Hare, G.: Prospects for
seasonal forecasting of summer drought and low river flow anomalies in
England and Wales, Int. J. Climatol., 22, 219–236, https://doi.org/10.1002/joc.735,
2002. a
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. a
Werner, K., Brandon, D., Clark, M., and Gangopadhyay, S.: Climate Index
Weighting Schemes for NWS ESP-Based Seasonal Volume Forecasts, J.
Hydrometeorol., 5, 1076–1090, https://doi.org/10.1175/JHM-381.1, 2004.
a
Wetterhall, F. and Di Giuseppe, F.: The benefit of seamless forecasts for hydrological predictions over Europe, Hydrol. Earth Syst. Sci., 22, 3409–3420, https://doi.org/10.5194/hess-22-3409-2018, 2018. a
Wilby, R. L.: Seasonal Forecasting of River Flows in the British Isles Using
North Atlantic Pressure Patterns, Water Environ. J., 15, 56–63,
https://doi.org/10.1111/j.1747-6593.2001.tb00305.x, 2001. a, b
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, 4th edition,
Elsevier, Amsterdam, the Netherlands, 2019. a
Wood, A. W. and Lettenmaier, D. P.: A Test Bed for New Seasonal Hydrologic
Forecasting Approaches in the Western United States, Bull. Am. Meteorol.
Soc., 87, 1699–1712, https://doi.org/10.1175/BAMS-87-12-1699, 2006. a
Wood, A. W. and Lettenmaier, D. P.: An ensemble approach for attribution of
hydrologic prediction uncertainty, Geophys. Res. Lett., 35, L14401,
https://doi.org/10.1029/2008GL034648, 2008. a, b, c, d
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. a
Yang, L., Tian, F., Sun, Y., Yuan, X., and Hu, H.: Attribution of hydrologic forecast uncertainty within scalable forecast windows, Hydrol. Earth Syst. Sci., 18, 775–786, https://doi.org/10.5194/hess-18-775-2014, 2014. a
Yuan, X. and Zhu, E.: A First Look at Decadal Hydrological Predictability by
Land Surface Ensemble Simulations, Geophys. Res. Lett., 45, 2362–2369,
https://doi.org/10.1002/2018GL077211, 2018. a, b
Yuan, X., Wood, E. F., and Ma, Z.: A review on climate-model-based seasonal
hydrologic forecasting: physical understanding and system development, WIREs
Water, 2, 523–536, https://doi.org/10.1002/wat2.1088, 2015. a, b
Yuan, X., Ma, F., Wang, L., Zheng, Z., Ma, Z., Ye, A., and Peng, S.: An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 1: Understanding the role of initial hydrological conditions, Hydrol. Earth Syst. Sci., 20, 2437–2451, https://doi.org/10.5194/hess-20-2437-2016, 2016. a
Zhao, T. and Zhao, J.: Joint and respective effects of long- and short-term
forecast uncertainties on reservoir operations, J. Hydrol., 517, 83–94,
https://doi.org/10.1016/j.jhydrol.2014.04.063, 2014. a
Short summary
We benchmarked the skill of ensemble streamflow prediction (ESP) for a diverse sample of 46 Irish catchments. We found that ESP is skilful in the majority of catchments up to several months ahead. However, the level of skill was strongly dependent on lead time, initialisation month, and individual catchment location and storage properties. We also conditioned ESP with the winter North Atlantic Oscillation and show that improvements in forecast skill, reliability, and discrimination are possible.
We benchmarked the skill of ensemble streamflow prediction (ESP) for a diverse sample of 46...
