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| Titel |
Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities |
| VerfasserIn |
Y. Liu, A. H. Weerts, M. Clark, H.-J. Hendricks Franssen, S. Kumar, H. Moradkhani, D.-J. Seo, D. Schwanenberg, P. Smith, A. I. J. M. Dijk, N. Velzen, M. He, H. Lee, S. J. Noh, O. Rakovec, P. Restrepo |
| 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 ; 16, no. 10 ; Nr. 16, no. 10 (2012-10-29), S.3863-3887 |
| Datensatznummer |
250013534
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| Publikation (Nr.) |
 copernicus.org/hess-16-3863-2012.pdf |
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| Zusammenfassung |
Data assimilation (DA) holds considerable potential for improving hydrologic
predictions as demonstrated in numerous research studies. However, advances
in hydrologic DA research have not been adequately or timely implemented in
operational forecast systems to improve the skill of forecasts for better
informed real-world decision making. This is due in part to a lack of
mechanisms to properly quantify the uncertainty in observations and forecast
models in real-time forecasting situations and to conduct the merging of
data and models in a way that is adequately efficient and transparent to
operational forecasters.
The need for effective DA of useful hydrologic data into the forecast
process has become increasingly recognized in recent years. This motivated a
hydrologic DA workshop in Delft, the Netherlands in November 2010, which
focused on advancing DA in operational hydrologic forecasting and water
resources management. As an outcome of the workshop, this paper reviews, in
relevant detail, the current status of DA applications in both hydrologic
research and operational practices, and discusses the existing or potential
hurdles and challenges in transitioning hydrologic DA research into
cost-effective operational forecasting tools, as well as the potential
pathways and newly emerging opportunities for overcoming these challenges.
Several related aspects are discussed, including (1) theoretical or
mathematical aspects in DA algorithms, (2) the estimation of different types
of uncertainty, (3) new observations and their objective use in hydrologic
DA, (4) the use of DA for real-time control of water resources systems, and
(5) the development of community-based, generic DA tools for hydrologic
applications. It is recommended that cost-effective transition of hydrologic
DA from research to operations should be helped by developing
community-based, generic modeling and DA tools or frameworks, and through
fostering collaborative efforts among hydrologic modellers, DA developers,
and operational forecasters. |
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