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.
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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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.
Larissa van der Laan, Anouk Vlug, Adam A. Scaife, Fabien Maussion, and Kristian Förster
EGUsphere, https://doi.org/10.5194/egusphere-2024-387, https://doi.org/10.5194/egusphere-2024-387, 2024
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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 time scales. Reliable information on these time scales is very important, especially for water management experts to know how much meltwater to expect, for rivers, agriculture and drinking water.
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.
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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...