Abstract. Skilful hydrological forecasts at sub-seasonal to seasonal lead times would
be extremely beneficial for decision-making in water resources management,
hydropower operations, and agriculture, especially during drought conditions.
Ensemble streamflow prediction (ESP) is a well-established method for
generating an ensemble of streamflow forecasts in the absence of skilful
future meteorological predictions, instead using initial hydrologic
conditions (IHCs), such as soil moisture, groundwater, and snow, as the
source of skill. We benchmark when and where the ESP method is skilful across
a diverse sample of 314 catchments in the UK and explore the relationship
between catchment storage and ESP skill. The GR4J hydrological model was
forced with historic climate sequences to produce a 51-member ensemble of
streamflow hindcasts. We evaluated forecast skill seamlessly from lead times
of 1 day to 12 months initialized at the first of each month over a 50-year
hindcast period from 1965 to 2015. Results showed ESP was skilful against a
climatology benchmark forecast in the majority of catchments across all lead
times up to a year ahead, but the degree of skill was strongly conditional on
lead time, forecast initialization month, and individual catchment location
and storage properties. UK-wide mean ESP skill decayed exponentially as a
function of lead time with continuous ranked probability skill scores across
the year of 0.75, 0.20, and 0.11 for 1-day, 1-month, and 3-month lead times,
respectively. However, skill was not uniform across all initialization
months. For lead times up to 1 month, ESP skill was higher than average when
initialized in summer and lower in winter months, whereas for longer seasonal
and annual lead times skill was higher when initialized in autumn and winter
months and lowest in spring. ESP was most skilful in the south and east of
the UK, where slower responding catchments with higher soil moisture and
groundwater storage are mainly located; correlation between catchment base
flow index (BFI) and ESP skill was very strong (Spearman's rank correlation
coefficient =0.90 at 1-month lead time). This was in contrast to the more
highly responsive catchments in the north and west which were generally not
skilful at seasonal lead times. Overall, this work provides scientific
justification for when and where use of such a relatively simple forecasting
approach is appropriate in the UK. This study, furthermore, creates a low
cost benchmark against which potential skill improvements from more
sophisticated hydro-meteorological ensemble prediction systems can be judged.