Accounting for three sources of uncertainty in ensemble hydrological forecasting
Seeking more accuracy and reliability, the hydrometeorological community has developed several tools to decipher the different sources of uncertainty in relevant modeling processes. Among them, the ensemble Kalman filter (EnKF), multimodel approaches and meteorological ensemble forecasting proved to...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-05-01
|
| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/20/1809/2016/hess-20-1809-2016.pdf |
| Summary: | Seeking more accuracy and reliability, the hydrometeorological community
has developed several tools to decipher the different sources of uncertainty
in relevant modeling processes. Among them, the ensemble Kalman filter (EnKF),
multimodel approaches and meteorological ensemble forecasting proved to have
the capability to improve upon deterministic hydrological forecast. This
study aims to untangle the sources of uncertainty by studying the
combination of these tools and assessing their respective contribution to the
overall forecast quality. Each of these components is able to capture a
certain aspect of the total uncertainty and improve the forecast at different
stages in the forecasting process by using different means. Their combination
outperforms any of the tools used solely. The EnKF is shown to contribute
largely to the ensemble accuracy and dispersion, indicating that the initial
conditions uncertainty is dominant. However, it fails to maintain the
required dispersion throughout the entire forecast horizon and needs to be
supported by a multimodel approach to take into account structural
uncertainty. Moreover, the multimodel approach contributes to improving the
general forecasting performance and prevents this performance from falling into the model
selection pitfall since models differ strongly in their ability. Finally, the
use of probabilistic meteorological forcing was found to contribute mostly to
long lead time reliability. Particular attention needs to be paid to the
combination of the tools, especially in the EnKF tuning to
avoid overlapping in error deciphering. |
|---|---|
| ISSN: | 1027-5606 1607-7938 |