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| Titel |
The elasticity of hydrological forecast skill with respect to initial conditions and meteorological forcing for two major flood events in Germany |
| VerfasserIn |
Stephan Thober, Andy Wood, Luis Samaniego, Martyn Clark, Rohini Kumar, Matthias Zink |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250088912
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| Publikation (Nr.) |
 EGU/EGU2014-3089.pdf |
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| Zusammenfassung |
| Major flood events are causing severe socio-economic damages. In Germany alone, the havoc
wreaked by the 2002 and 2013 floods along the Elbe and Danube river amounted to more
than 11 bn EUR. Highly skilled hydrological forecasts can help to mitigate such damages.
Among several factors, these hydrological forecasts are strongly dependent on the initial
conditions of the land surface at the beginning of the forecast period and the forecast skill of
the meteorological forcing.
Prior research has investigated how uncertainties of the initial conditions and
meteorological forcing impact hydrological forecasts. In these studies, uncertainty is
investigated by coupling an ensemble of basin initial conditions (e.g., snow, soil moisture)
with an ensemble of meteorological forecasts (e.g., precipitation). However, most previous
hydrological predictability studies focus on seasonal forecasts (e.g., forecasts of
June-July-August flow volume, initialized on April 1st), and neglect the errors in
meteorological forecasts at lead times from 1-14 days.
In this study, an error growth model is proposed to investigate hydrological predictability
at lead times of 1-14 days. This error growth model calculates a time-dependent
weighted average between the perfect forecast and a stochastic perturbation of this. The
time-dependent weights are derived from a logistic function. This error growth model thus
attributes high weights to the perfect forecast for short lead times (e.g., less than
five days) and low weights for longer lead times (e.g., more than five days). For
longer lead times, more weight is given to the stochastic perturbation of the forecast
and, hence, the ensemble spread is larger for these lead times resembling a higher
uncertainty. Analogous to the error growth model, the initial conditions are calculated as a
weighted average between the perfect condition and a historic condition of the land
surface.
The proposed framework is tested in Germany for the 2002 and 2013 flood events along
the Elbe and Danube river. The mesoscale Hydrologic Model - mHM is used to
evaluate the impact of varying initial conditions and meteorological forcing. The
original meteorological data used to generate ensemble forcing is provided by the
German Weather Service (DWD). Common metrics such as mean absolute error
(MAE) and continuous ranked probability skill scores (CRPSS) are employed to
evaluate the forecast skill. Moreover, the elasticity is quantified which is defined
as the change in runoff skill per unit change either in forcing or initial condition
skill.
The analysis helps to understand the relative importance of basin initial conditions
and meteorological forecasts for extreme floods in Germany. Results indicate that
initial land surface conditions have great impact in hydrological forecast skill for
short lead times (e.g., 16.9% chance of reaching actual peak discharge with historic
land surface condition). For longer lead times, however, the hydrological forecast
skill becomes more dependent on the forecast skill in the meteorological forcing. |
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