Abstract
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 (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 (ρ=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.
Highlights
Skilful hydrological forecasts at lead times of weeks to months can benefit water resources management (Anghileri et al, 2016; Dixon and Wilby, 2019; Viel et al, 2016; Wetterhall and Di Giuseppe, 2018) and help mitigate extreme events by enhancing preparedness and improving operational decisions
Ensemble streamflow prediction is a popular approach to seasonal hydrological forecasting that is still used some 40 years after its initial development
We benchmarked ensemble streamflow prediction (ESP) skill for a diverse sample of Irish catchments and conclude that it is skilful against streamflow climatology but that the level of skill is strongly dependent on lead time, initialisation month, and individual catchment location and storage properties
Summary
Skilful hydrological forecasts at lead times of weeks to months can benefit water resources management (Anghileri et al, 2016; Dixon and Wilby, 2019; Viel et al, 2016; Wetterhall and Di Giuseppe, 2018) and help mitigate extreme events by enhancing preparedness and improving operational decisions Hydrological forecasts have been used to modify reservoir operations for hydropower production (Fan et al, 2016), storage and supply (Turner et al, 2017), and the management of flood and drought conditions (Amnatsan et al, 2018; Ficchì et al, 2016; Watts et al, 2012). They have been shown to benefit sectors such as agriculture (Mushtaq et al, 2012), tourism (Fundel et al, 2013), and navigation (Meißner et al, 2017). Pappenberger et al (2015a) claim that the European Flood Awareness System (EFAS; Thielen et al, 2009) saves around EUR 400 for every EUR 1 invested
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