 |
| Titel |
The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models |
| VerfasserIn |
M. C. Demirel, M. J. Booij, A. Y. Hoekstra |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 1 ; Nr. 19, no. 1 (2015-01-16), S.275-291 |
| Datensatznummer |
250120592
|
| Publikation (Nr.) |
 copernicus.org/hess-19-275-2015.pdf |
|
|
|
|
|
| Zusammenfassung |
| This paper investigates the skill of 90-day low-flow forecasts using two
conceptual hydrological models and one data-driven model based on Artificial
Neural Networks (ANNs) for the Moselle River. The three models, i.e. HBV,
GR4J and ANN-Ensemble (ANN-E), all use forecasted meteorological inputs
(precipitation P and potential evapotranspiration PET), whereby we employ
ensemble seasonal meteorological forecasts. We compared low-flow forecasts
for five different cases of seasonal meteorological forcing: (1) ensemble P
and PET forecasts; (2) ensemble P forecasts and observed climate mean PET;
(3) observed climate mean P and ensemble PET forecasts; (4) observed climate
mean P and PET and (5) zero P and ensemble PET forecasts as input for the
models. The ensemble P and PET forecasts, each consisting of 40 members,
reveal the forecast ranges due to the model inputs. The five cases are
compared for a lead time of 90 days based on model output ranges, whereas
the models are compared based on their skill of low-flow forecasts for
varying lead times up to 90 days. Before forecasting, the hydrological
models are calibrated and validated for a period of 30 and 20 years
respectively. The smallest difference between calibration and validation
performance is found for HBV, whereas the largest difference is found for
ANN-E. From the results, it appears that all models are prone to
over-predict runoff during low-flow periods using ensemble seasonal
meteorological forcing. The largest range for 90-day low-flow forecasts is
found for the GR4J model when using ensemble seasonal meteorological
forecasts as input. GR4J, HBV and ANN-E under-predicted 90-day-ahead low
flows in the very dry year 2003 without precipitation data. The results of
the comparison of forecast skills with varying lead times show that GR4J is
less skilful than ANN-E and HBV. Overall, the uncertainty from ensemble P
forecasts has a larger effect on seasonal low-flow forecasts than the
uncertainty from ensemble PET forecasts and initial model conditions. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|