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| Titel |
A trading-space-for-time approach to probabilistic continuous streamflow predictions in a changing climate – accounting for changing watershed behavior |
| VerfasserIn |
R. Singh, T. Wagener, K. Werkhoven, M. E. Mann, R. Crane |
| 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 ; 15, no. 11 ; Nr. 15, no. 11 (2011-11-29), S.3591-3603 |
| Datensatznummer |
250013033
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| Publikation (Nr.) |
 copernicus.org/hess-15-3591-2011.pdf |
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| Zusammenfassung |
| Projecting how future climatic change might impact streamflow is an
important challenge for hydrologic science. The common approach to solve
this problem is by forcing a hydrologic model, calibrated on historical data
or using a priori parameter estimates, with future scenarios of
precipitation and temperature. However, several recent studies suggest that
the climatic regime of the calibration period is reflected in the resulting
parameter estimates and model performance can be negatively impacted if the
climate for which projections are made is significantly different from that
during calibration. So how can we calibrate a hydrologic model for
historically unobserved climatic conditions? To address this issue, we
propose a new trading-space-for-time framework that utilizes the similarity
between the predictions under change (PUC) and predictions in ungauged
basins (PUB) problems. In this new framework we first regionalize climate
dependent streamflow characteristics using 394 US watersheds. We then assume
that this spatial relationship between climate and streamflow
characteristics is similar to the one we would observe between climate and
streamflow over long time periods at a single location. This assumption is
what we refer to as trading-space-for-time. Therefore, we change the limits
for extrapolation to future climatic situations from the restricted locally
observed historical variability to the variability observed across all
watersheds used to derive the regression relationships. A typical watershed
model is subsequently calibrated (conditioned) on the predicted signatures
for any future climate scenario to account for the impact of climate on
model parameters within a Bayesian framework. As a result, we can obtain
ensemble predictions of continuous streamflow at both gauged and ungauged
locations. The new method is tested in five US watersheds located in
historically different climates using synthetic climate scenarios generated
by increasing mean temperature by up to 8 °C and changing mean
precipitation by −30% to +40% from their historical values.
Depending on the aridity of the watershed, streamflow projections using
adjusted parameters became significantly different from those using
historically calibrated parameters if precipitation change exceeded −10%
or +20%. In general, the trading-space-for-time approach resulted in a
stronger watershed response to climate change for both high and low flow
conditions. |
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