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Uncertainty analysis of multi-model flood forecasts

Plate, Erich J.; Shahzad, Khurram M.

This paper demonstrates, by means of a systematic uncertainty analysis, that the use of outputs from more than one model can significantly improve conditional forecasts of discharges or water stages, provided the models are structurally different. Discharge forecasts from two models and the actual forecasted discharge are assumed to form a three-dimensional joint probability density distribution (jpdf), calibrated on long time series of data. The jpdf is decomposed into conditional probability density distributions (cpdf) by means of Bayes formula, as suggested and explored by Krzysztofowicz in a series of papers. In this paper his approach is simplified to optimize conditional forecasts for any set of two forecast models. Its application is demonstrated by means of models developed in a study of flood forecasting for station Stung Treng on the middle reach of the Mekong River in South-East Asia. Four different forecast models were used and pairwise combined: forecast with no model, with persistence model, with a regression model, and with a rainfall-runoff model. Working with cpdfs requires determination of dependency among variabl ... mehr

Zugehörige Institution(en) am KIT Institut für Wasser und Gewässerentwicklung (IWG)
Publikationstyp Zeitschriftenaufsatz
Jahr 2015
Sprache Englisch
Identifikator DOI: 10.3390/w7126654
ISSN: 2073-4441
URN: urn:nbn:de:swb:90-575819
KITopen ID: 1000057581
Erschienen in Water
Band 7
Heft 12
Seiten 6788-6809
Lizenz CC BY 4.0: Creative Commons Namensnennung 4.0 International
Schlagworte forecast uncertainty, Bayesian uncertainty analysis, conditional flood forecasting, data based models, Mekong flood
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