skilful seasonal forecasts of streamflow over europe?
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ID: 209568
2018
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Abstract
This paper considers whether there is any added value in using seasonal
climate forecasts instead of historical meteorological observations for
forecasting streamflow on seasonal timescales over Europe. A Europe-wide
analysis of the skill of the newly operational EFAS (European Flood Awareness
System) seasonal streamflow forecasts (produced by forcing the Lisflood model
with the ECMWF System 4 seasonal climate forecasts), benchmarked against the
ensemble streamflow prediction (ESP) forecasting approach (produced by
forcing the Lisflood model with historical meteorological observations), is
undertaken. The results suggest that, on average, the System 4 seasonal
climate forecasts improve the streamflow predictability over historical
meteorological observations for the first month of lead time only (in terms
of hindcast accuracy, sharpness and overall performance). However, the
predictability varies in space and time and is greater in winter and autumn.
Parts of Europe additionally exhibit a longer predictability, up to 7 months
of lead time, for certain months within a season. In terms of hindcast
reliability, the EFAS seasonal streamflow hindcasts are on average less
skilful than the ESP for all lead times. The results also highlight the
potential usefulness of the EFAS seasonal streamflow forecasts for
decision-making (measured in terms of the hindcast discrimination for the
lower and upper terciles of the simulated streamflow). Although the ESP is
the most potentially useful forecasting approach in Europe, the EFAS seasonal
streamflow forecasts appear more potentially useful than the ESP in some
regions and for certain seasons, especially in winter for almost 40 % of
Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however
not mirrored in the System 4 seasonal climate hindcasts, hinting at the need
for a better understanding of the link between hydrological and
meteorological variables on seasonal timescales, with the aim of improving
climate-model-based seasonal streamflow forecasting.
| Reference Key |
arnal2018hydrologyskilful
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|---|---|
| Authors | ;L. Arnal;L. Arnal;H. L. Cloke;H. L. Cloke;H. L. Cloke;H. L. Cloke;E. Stephens;F. Wetterhall;C. Prudhomme;C. Prudhomme;C. Prudhomme;J. Neumann;B. Krzeminski;F. Pappenberger |
| Journal | materials research bulletin |
| Year | 2018 |
| DOI |
10.5194/hess-22-2057-2018
|
| URL | |
| Keywords |
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