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| Titel |
Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming |
| VerfasserIn |
L. Mediero, L. Garrote, A. Chavez-Jimenez |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 12, no. 12 ; Nr. 12, no. 12 (2012-12-19), S.3719-3732 |
| Datensatznummer |
250011262
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| Publikation (Nr.) |
 copernicus.org/nhess-12-3719-2012.pdf |
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| Zusammenfassung |
| Opportunities offered by high performance computing provide a significant
degree of promise in the enhancement of the performance of real-time flood
forecasting systems. In this paper, a real-time framework for probabilistic
flood forecasting through data assimilation is presented. The distributed
rainfall-runoff real-time interactive basin simulator (RIBS) model is
selected to simulate the hydrological process in the basin. Although the
RIBS model is deterministic, it is run in a probabilistic way through the
results of calibration developed in a previous work performed by the
authors that identifies the probability distribution functions that
best characterise the most relevant model parameters. Adaptive techniques
improve the result of flood forecasts because the model can be adapted to
observations in real time as new information is available. The new adaptive
forecast model based on genetic programming as a data assimilation technique
is compared with the previously developed flood forecast model based on the
calibration results. Both models are probabilistic as they generate an
ensemble of hydrographs, taking the different uncertainties inherent in any
forecast process into account. The Manzanares River basin was selected as a
case study, with the process being computationally intensive as it requires
simulation of many replicas of the ensemble in real time. |
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