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

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 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.

Opportunities for seasonal forecasting to support water management outside the tropics

“…This is the case in larger systems and for variables or ecological species which are less sensitive to seasonal climate. For streamflow forecasts, for example, initial conditions provide much of the forecasting skill, and predictability is highest, in slower-responding catchments with larger water storage and groundwater contributions (Pechlivanidis et al, 2020;Girons Lopez et al, 2021;Donegan et al, 2021;Harrigan et al, 2018). Many successful streamflow forecasting systems do not include seasonal climate model forecasts, showing that a great deal can be achieved using only historic information, in some cases/seasons outperforming predictions which use seasonal climate model forecasts (e.g.…”

“…Methods inspired by ensemble streamflow prediction are likely candidates, potentially made more nuanced by, for example, using the North Atlantic Oscillation (NAO) index or other climate signals to condition the forecast (e.g. Donegan et al, 2021;Najafi et al, 2012;Sabzipour et al, 2020), or longer-term climate projections (Gronewold et al, 2017).…”

Opportunities for seasonal forecasting to support water management outside the tropics

“…This is the case in larger systems and for variables or ecological species which are less sensitive to seasonal climate. For streamflow forecasts, for example, initial conditions provide much of the forecasting skill and predictability is highest in slower-responding catchments with larger water storage and groundwater contributions (Donegan et al, 2020;Girons Lopez et al, 2021;Harrigan et al, 2018;Pechlivanidis et al, 2020). Many successful streamflow forecasting systems do not include seasonal climate model forecasts, showing that a great deal can be achieved using only historic information, in some cases/seasons outperforming predictions which use seasonal climate model forecasts (e.g.…”

“…Day, 1985) are likely candidates, potentially made more nuanced by, for example, using North Atlantic Oscillation (NAO) index or other climate signals to condition the forecast (e.g. Donegan et al, 2020;Najafi et al, 2012;Sabzipour et al, 2020), or longer-term climate projections (Gronewold et al, 2017).…”

Spatio-Temporal Variability in North Atlantic Oscillation Monthly Rainfall Signatures in Great Britain

“…These NAO rainfall signatures propagate through the hydrological cycle, with NAO responses observed across Great Britain in catchment runoff [14][15][16], groundwater [17,18] and fluvial water temperatures [19], although this propagation can be moderated by catchment characteristics such as topography, landcover and geology [15,16]. Ongoing research is exploring whether this understanding of NAO-rainfall-flow propagation can be incorporated into seasonal streamflow modelling and forecasting [20,21].…”