 |
| Titel |
Monthly streamflow forecasting in the Rhine basin |
| VerfasserIn |
Simon Schick, Ole Rössler, Rolf Weingartner |
| Konferenz |
EGU General Assembly 2017
|
| Medientyp |
Artikel
|
| Sprache |
en
|
| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250148419
|
| Publikation (Nr.) |
 EGU/EGU2017-12675.pdf |
|
|
|
|
|
| Zusammenfassung |
| Forecasting seasonal streamflow of the Rhine river is of societal relevance as the Rhine is an
important water way and water resource in Western Europe. The present study investigates
the predictability of monthly mean streamflow at lead times of zero, one, and two months
with the focus on potential benefits by the integration of seasonal climate predictions.
Specifically, we use seasonal predictions of precipitation and surface air temperature released
by the European Centre for Medium-Range Weather Forecasts (ECMWF) for a regression
analysis.
In order to disentangle forecast uncertainty, the ’Reverse Ensemble Streamflow
Prediction’ framework is adapted here to the context of regression: By using appropriate
subsets of predictors the regression model is constrained to either the initial conditions,
the meteorological forcing, or both. An operational application is mimicked by
equipping the model with the seasonal climate predictions provided by ECMWF.
Finally, to mitigate the spatial aggregation of the meteorological fields the model is
also applied at the subcatchment scale, and the resulting predictions are combined
afterwards.
The hindcast experiment is carried out for the period 1982-2011 in cross validation mode
at two gauging stations, namely the Rhine at Lobith and Basel. The results show that monthly
forecasts are skillful with respect to climatology only at zero lead time. In addition, at zero
lead time the integration of seasonal climate predictions decreases the mean absolute error by
5 to 10 percentage compared to forecasts which are solely based on initial conditions. This
reduction most likely is induced by the seasonal prediction of precipitation and not air
temperature.
The study is completed by bench marking the regression model with runoff simulations
from ECMWFs seasonal forecast system. By simply using basin averages followed by a
linear bias correction, these runoff simulations translate well to monthly streamflow.
Though the regression model is only slightly outperformed, we argue that runoff
out of the land surface component of seasonal climate forecasting systems is an
interesting option when it comes to seasonal streamflow forecasting in large river basins. |
|
|
|
|
|
|