MURAL - Maynooth University Research Archive Library

    Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times

    Donegan, Seán and Murphy, Conor and Harrigan, Shaun and Broderick, Ciaran and Foran Quinn, Dáire and Golian, Saeed and Knight, Jeff and Matthews, Tom and Prudhomme, Christel and Scaife, Adam A. and Stringer, Nicky and Wilby, Robert L. (2021) Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times. Hydrology and Earth System Sciences, 25 (7). pp. 4159-4183. ISSN 1607-7938

    Download (5MB) | Preview

    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...

    Add this article to your Mendeley library


    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 (Génie Rural à 4 paramètres Journalier) hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 d to 12 months for each of the 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 a continuous ranked probability skill score (CRPSS) of 0.8 (1 d), 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 (Ï=0.94) was observed between forecast skill and catchment storage capacity (baseflow index), with the most skilful regions, the Midlands and the 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 in a number of different contexts and thus should be operationalised in Ireland given its potential benefits for water managers and other stakeholders.

    Item Type: Article
    Additional Information: Cite as: 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. (2021) 'Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times', Hydrology and Earth System Sciences, 25(7), 4159, available: [accessed 14 Apr 2022].
    Keywords: hydrological forecasts; water resources management; ensemble streamflow prediction;
    Academic Unit: Faculty of Social Sciences > Geography
    Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
    Item ID: 15846
    Identification Number:
    Depositing User: Conor Murphy
    Date Deposited: 14 Apr 2022 11:11
    Journal or Publication Title: Hydrology and Earth System Sciences
    Publisher: Gale Academic Onefile
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page


    Downloads per month over past year

    Origin of downloads