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| Titel |
Stochastic spatial disaggregation of extreme precipitation to validate a regional climate model and to evaluate climate change impacts over a small watershed |
| VerfasserIn |
P. Gagnon, A. N. Rousseau |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 18, no. 5 ; Nr. 18, no. 5 (2014-05-09), S.1695-1704 |
| Datensatznummer |
250120352
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| Publikation (Nr.) |
 copernicus.org/hess-18-1695-2014.pdf |
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| Zusammenfassung |
Regional climate models (RCMs) are valuable tools to evaluate impacts of
climate change (CC) at regional scale. However, as the size of the area of
interest decreases, the ability of a RCM to simulate extreme precipitation
events decreases due to the spatial resolution. Thus, it is difficult to
evaluate whether a RCM bias on localized extreme precipitation is caused by
the spatial resolution or by a misrepresentation of the physical processes
in the model. Thereby, it is difficult to trust the CC impact projections
for localized extreme precipitation. Stochastic spatial disaggregation
models can bring the RCM precipitation data at a finer scale and reduce the
bias caused by spatial resolution. In addition, disaggregation models can
generate an ensemble of outputs, producing an interval of possible values
instead of a unique discrete value.
The objective of this work is to evaluate whether a stochastic spatial
disaggregation model applied on annual maximum daily precipitation (i)
enables the validation of a RCM for a period of reference, and (ii) modifies
the evaluation of CC impacts over a small area. Three simulations of the
Canadian RCM (CRCM) covering the period 1961–2099 are used over a small
watershed (130 km2) located in southern Québec, Canada. The
disaggregation model applied is based on Gibbs sampling and accounts for
physical properties of the event (wind speed, wind direction, and convective
available potential energy – CAPE), leading to realistic spatial
distributions of precipitation. The results indicate that disaggregation has
a significant impact on the validation. However, it does not provide a
precise estimate of the simulation bias because of the difference in
resolution between disaggregated values (4 km) and observations, and because
of the underestimation of the spatial variability by the disaggregation
model for the most convective events. Nevertheless, disaggregation
illustrates that the simulations used mostly overestimated annual maximum
precipitation depth in the study area during the reference period. Also,
disaggregation slightly increases the signal of CC compared to the RCM raw
simulations, highlighting the importance of spatial resolution in CC impact
evaluation of extreme events. |
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