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| Titel |
The importance of hydrological uncertainty assessment methods in climate change impact studies |
| VerfasserIn |
M. Honti, A. Scheidegger, C. Stamm |
| 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. 8 ; Nr. 18, no. 8 (2014-08-29), S.3301-3317 |
| Datensatznummer |
250120452
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| Publikation (Nr.) |
 copernicus.org/hess-18-3301-2014.pdf |
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| Zusammenfassung |
| Climate change impact assessments have become more and more popular in
hydrology since the middle 1980s with a recent boost after the publication
of the IPCC AR4 report. From hundreds of impact studies a quasi-standard
methodology has emerged, to a large extent shaped by the growing public demand for
predicting how water resources management or flood protection should change
in the coming decades. The "standard" workflow relies on a model cascade
from global circulation model (GCM) predictions for selected IPCC scenarios
to future catchment hydrology. Uncertainty is present at each level and
propagates through the model cascade. There is an emerging consensus between
many studies on the relative importance of the different uncertainty sources.
The prevailing perception is that GCM uncertainty dominates hydrological
impact studies. Our hypothesis was that the relative importance of climatic
and hydrologic uncertainty is (among other factors) heavily influenced by the
uncertainty assessment method. To test this we carried out a climate change
impact assessment and estimated the relative importance of the uncertainty
sources. The study was performed on two small catchments in the Swiss Plateau
with a lumped conceptual rainfall runoff model. In the climatic part we
applied the standard ensemble approach to quantify uncertainty but in
hydrology we used formal Bayesian uncertainty assessment with two different
likelihood functions. One was a time series error model that was able to deal
with the complicated statistical properties of hydrological model residuals.
The second was an approximate likelihood function for the flow quantiles. The
results showed that the expected climatic impact on flow quantiles was small
compared to prediction uncertainty. The choice of uncertainty assessment
method actually determined what sources of uncertainty could be identified at
all. This demonstrated that one could arrive at rather different conclusions
about the causes behind predictive uncertainty for the same hydrological
model and calibration data when considering different objective functions for
calibration. |
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