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| Titel |
Calibration approaches for distributed hydrologic models in poorly gaged basins: implication for streamflow projections under climate change |
| VerfasserIn |
S. Wi, Y. C. E. Yang, S. Steinschneider, A. Khalil, C. M. Brown |
| 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 ; 19, no. 2 ; Nr. 19, no. 2 (2015-02-10), S.857-876 |
| Datensatznummer |
250120627
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| Publikation (Nr.) |
 copernicus.org/hess-19-857-2015.pdf |
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| Zusammenfassung |
| This study tests the performance and uncertainty of calibration strategies
for a spatially distributed hydrologic model in order to improve model
simulation accuracy and understand prediction uncertainty at interior
ungaged sites of a sparsely gaged watershed. The study is conducted using a
distributed version of the HYMOD hydrologic model (HYMOD_DS)
applied to the Kabul River basin. Several calibration experiments are
conducted to understand the benefits and costs associated with different
calibration choices, including (1) whether multisite gaged data should be
used simultaneously or in a stepwise manner during model fitting, (2) the
effects of increasing parameter complexity, and (3) the potential to estimate
interior watershed flows using only gaged data at the basin outlet. The
implications of the different calibration strategies are considered in the
context of hydrologic projections under climate change. To address the
research questions, high-performance computing is utilized to manage the
computational burden that results from high-dimensional optimization
problems. Several interesting results emerge from the study. The
simultaneous use of multisite data is shown to improve the calibration over
a stepwise approach, and both multisite approaches far exceed a calibration
based on only the basin outlet. The basin outlet calibration can lead to
projections of mid-21st century streamflow that deviate substantially
from projections under multisite calibration strategies, supporting the use
of caution when using distributed models in data-scarce regions for climate
change impact assessments. Surprisingly, increased parameter complexity does
not substantially increase the uncertainty in streamflow projections, even
though parameter equifinality does emerge. The results suggest that
increased (excessive) parameter complexity does not always lead to increased
predictive uncertainty if structural uncertainties are present. The largest
uncertainty in future streamflow results from variations in projected
climate between climate models, which substantially outweighs the
calibration uncertainty. |
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