Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times
Skilful hydrological forecasts can benefit decision-making in water resources management and other water-related sectors that require long-term planning. In Ireland, no such service exists to deliver forecasts at the catchment scale. In order to understand the potential for hydrological forecasting...
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ftloughboroughun:oai:figshare.com:article/14737875 2023-05-15T17:32:02+02:00 Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times Seán Donegan Conor Murphy Shaun Harrigan Ciaran Broderick Dáire Foran Quinn Saeed Golian Jeff Knight Tom Matthews Christel Prudhomme Adam Scaife Nicky Stringer Robert Wilby 2021-07-22T00:00:00Z https://figshare.com/articles/journal_contribution/Conditioning_ensemble_streamflow_prediction_with_the_North_Atlantic_Oscillation_improves_skill_at_longer_lead_times/14737875 unknown 2134/14737875.v1 https://figshare.com/articles/journal_contribution/Conditioning_ensemble_streamflow_prediction_with_the_North_Atlantic_Oscillation_improves_skill_at_longer_lead_times/14737875 CC BY 4.0 CC-BY Uncategorized Environmental Engineering Physical Geography and Environmental Geoscience Civil Engineering Text Journal contribution 2021 ftloughboroughun 2022-01-01T19:08:29Z Skilful hydrological forecasts can benefit decision-making in water resources management and other water-related sectors that require long-term planning. In Ireland, no such service exists to deliver forecasts at the catchment scale. In order to understand the potential for hydrological forecasting in Ireland, we benchmark the skill of Ensemble Streamflow Prediction (ESP) for a diverse sample of 46 catchments using the GR4J hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 day to 12 months for each of 12 initialisation months, January to December. Our results show that ESP is skilful against a probabilistic climatology benchmark 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. Mean ESP skill was found to decay rapidly as a function of lead time, with continuous ranked probability skill scores (CRPSS) of 0.8 (1 day), 0.32 (2-week), 0.18 (1-month), 0.05 (3-month), and 0.01 (12-month). Forecasts were generally more skilful when initialised in summer than other seasons. A strong correlation (ρ = 0.94) was observed between forecast skill and catchment storage capacity (baseflow index), with the most skilful regions, the Midlands and East, being those where slowly responding, high storage catchments are located. Forecast reliability and discrimination were also assessed with respect to low and high flow events. In addition to our benchmarking experiment, we conditioned ESP with the winter North Atlantic Oscillation (NAO) using adjusted hindcasts from the Met Office’s Global Seasonal Forecasting System version 5. We found gains in winter forecast skill (CRPSS) of 7–18% were possible over lead times of 1 to 3 months, and that improved reliability and discrimination make NAO-conditioned ESP particularly effective at forecasting dry winters, a critical season for water resources management. We conclude that ESP is skilful ... Other Non-Article Part of Journal/Newspaper North Atlantic North Atlantic oscillation Loughborough University: Figshare |
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Open Polar |
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Loughborough University: Figshare |
op_collection_id |
ftloughboroughun |
language |
unknown |
topic |
Uncategorized Environmental Engineering Physical Geography and Environmental Geoscience Civil Engineering |
spellingShingle |
Uncategorized Environmental Engineering Physical Geography and Environmental Geoscience Civil Engineering Seán Donegan Conor Murphy Shaun Harrigan Ciaran Broderick Dáire Foran Quinn Saeed Golian Jeff Knight Tom Matthews Christel Prudhomme Adam Scaife Nicky Stringer Robert Wilby Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times |
topic_facet |
Uncategorized Environmental Engineering Physical Geography and Environmental Geoscience Civil Engineering |
description |
Skilful hydrological forecasts can benefit decision-making in water resources management and other water-related sectors that require long-term planning. In Ireland, no such service exists to deliver forecasts at the catchment scale. In order to understand the potential for hydrological forecasting in Ireland, we benchmark the skill of Ensemble Streamflow Prediction (ESP) for a diverse sample of 46 catchments using the GR4J hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 day to 12 months for each of 12 initialisation months, January to December. Our results show that ESP is skilful against a probabilistic climatology benchmark 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. Mean ESP skill was found to decay rapidly as a function of lead time, with continuous ranked probability skill scores (CRPSS) of 0.8 (1 day), 0.32 (2-week), 0.18 (1-month), 0.05 (3-month), and 0.01 (12-month). Forecasts were generally more skilful when initialised in summer than other seasons. A strong correlation (ρ = 0.94) was observed between forecast skill and catchment storage capacity (baseflow index), with the most skilful regions, the Midlands and East, being those where slowly responding, high storage catchments are located. Forecast reliability and discrimination were also assessed with respect to low and high flow events. In addition to our benchmarking experiment, we conditioned ESP with the winter North Atlantic Oscillation (NAO) using adjusted hindcasts from the Met Office’s Global Seasonal Forecasting System version 5. We found gains in winter forecast skill (CRPSS) of 7–18% were possible over lead times of 1 to 3 months, and that improved reliability and discrimination make NAO-conditioned ESP particularly effective at forecasting dry winters, a critical season for water resources management. We conclude that ESP is skilful ... |
format |
Other Non-Article Part of Journal/Newspaper |
author |
Seán Donegan Conor Murphy Shaun Harrigan Ciaran Broderick Dáire Foran Quinn Saeed Golian Jeff Knight Tom Matthews Christel Prudhomme Adam Scaife Nicky Stringer Robert Wilby |
author_facet |
Seán Donegan Conor Murphy Shaun Harrigan Ciaran Broderick Dáire Foran Quinn Saeed Golian Jeff Knight Tom Matthews Christel Prudhomme Adam Scaife Nicky Stringer Robert Wilby |
author_sort |
Seán Donegan |
title |
Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times |
title_short |
Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times |
title_full |
Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times |
title_fullStr |
Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times |
title_full_unstemmed |
Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times |
title_sort |
conditioning ensemble streamflow prediction with the north atlantic oscillation improves skill at longer lead times |
publishDate |
2021 |
url |
https://figshare.com/articles/journal_contribution/Conditioning_ensemble_streamflow_prediction_with_the_North_Atlantic_Oscillation_improves_skill_at_longer_lead_times/14737875 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
2134/14737875.v1 https://figshare.com/articles/journal_contribution/Conditioning_ensemble_streamflow_prediction_with_the_North_Atlantic_Oscillation_improves_skill_at_longer_lead_times/14737875 |
op_rights |
CC BY 4.0 |
op_rightsnorm |
CC-BY |
_version_ |
1766129957728157696 |