Preprints
https://doi.org/10.5194/hess-2020-604
https://doi.org/10.5194/hess-2020-604

  14 Dec 2020

14 Dec 2020

Review status: this preprint is currently under review for the journal HESS.

Conditioning Ensemble Streamflow Prediction with the North Atlantic Oscillation improves skill at longer lead times

Seán Donegan1, Conor Murphy1, Shaun Harrigan2, Ciaran Broderick3, Saeed Golian1, Jeff Knight4, Tom Matthews5, Christel Prudhomme2,5,6, Dáire Foran Quinn1, Adam A. Scaife4,7, Nicky Stringer4, and Robert L. Wilby5 Seán Donegan et al.
  • 1Irish Climate Analysis and Research UnitS (ICARUS), Department of Geography, Maynooth University, Co. Kildare, Ireland
  • 2Forecast Department, European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
  • 3Flood Forecasting Division, Met Éireann, Dublin 9, Ireland
  • 4Met Office Hadley Centre, Exeter, UK
  • 5Department of Geography and Environment, Loughborough University, Loughborough, UK
  • 6UK Centre for Ecology & Hydrology (UKCEH), Wallingford, UK
  • 7College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Exeter, UK

Abstract. 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 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. Results also highlight the potential utility of ESP for decision-making, as measured by its ability to forecast low and high flow events. In addition to our benchmarking experiment, we conditioned ESP on 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 of 7–18 % were possible over lead times of 1 to 3 months, and that NAO-conditioned ESP is 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.

Seán Donegan et al.

 
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Seán Donegan et al.

Seán Donegan et al.

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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 and discrimination are possible.