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| Titel |
Evaluation of Regional Climate Models: Extremes important for Hydrological Projections |
| VerfasserIn |
S. Thober, L. Samaniego, R. Kumar |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250064317
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| Zusammenfassung |
| Well parameterized precipitation-runoff models are often able to reproduce past hydrographs
and other state variables quite satisfactorily. In general, extreme events like floods are often
linked with heavy rainfall events, persistently long wet spells, rapid temperature increases in
winter, among other extreme or rare situations. Getting information to force hydrologic
models in future times is not a trivial task. A common approach is to downscale
meteorological variables from Regional Climate Models (RCMs) to the desired spatial and
temporal resolution required for these models. Therefore, it is crucial that extreme events are
well represented in the RCM’s outputs.
A significant effort on the evaluation of RCMs has been focused during the past decades
to the mean behavior of RCM trajectories and foremost to the identification and correction of
model biases. To compare RCMs with each other, it was common to rank them according to
their performance. In more recent literature this procedure was enhanced by taking
Multi-Model approaches into consideration, where the mean of an ensemble of RCMs is in
general better than a single RCM output. Furthermore it has been of interest to evaluate
extremes and to correct them. Most studies evaluating RCMs over Europe use either the EObs
data set or reanalysis data as reference.
In this study, we also investigate the performance of RCMs with respect to extreme
statistics relevant for hydrological projections. In addition to quantifying biases of the RCMs,
we focus on the evaluation of the spatio-temporal structure of observed extreme
statistics given below. Principal Component Analysis (PCA) was used to evaluate the
dimensionality of the selected variables and a bootstrapping technique to test the
null hypothesis that observed and simulated extreme values come from the same
population. To the best of our knowledge, these techniques have not been used in this
context. In addition to that, temporal trends and Taylor diagram for all statistics we
considered.
Reanalysis data from thirteen different RCMs from the ENSEMBLES project covering
Germany were chosen for the period from 1961 to 2000. A high resolution data set derived
by the interpolation of a dense station network operated by the German Weather
Service (DWD) was used as a reference (over 5500 rainfall gauges and 1100 weather
stations). This study focuses on extreme variables derived from daily precipitation and
temperature, such as 95 percentiles of annual and seasonal total precipitation, 95
percentile of daily temperature, frequency of heavy rainfall, cold and hot days per
year.
Preliminary results showed that it is very unlikely that the RCM derived statistics and
the respective observations are coming from the same populations with a p-values
of 1%. PCA, on the contrary, showed that some RCMs are able to represent the
overall variability of the observed field in a satisfactory manner (for example for
summer and winter total precipitation). A preliminary conclusion of this study
indicated that RCM outputs did not preserve the spatio-temporal structure of observed
extremes to the level required for operational purposes in hydrological predictions. |
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