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Uncertainty Analysis of Multi-Model Flood Forecasts

Department of Water and River Basin Management Karlsruhe, Karlsruhe Institute of Technology, Karlsruhe 76133, Germany
Department of Civil Engineering, Institute of Southern Punjab, Multan 60000, Pakistan
Author to whom correspondence should be addressed.
Academic Editors: Paolo Reggiani and Ezio Todini
Water 2015, 7(12), 6788-6809;
Received: 3 September 2015 / Revised: 31 October 2015 / Accepted: 17 November 2015 / Published: 1 December 2015
(This article belongs to the Special Issue Uncertainty Analysis and Modeling in Hydrological Forecasting)
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 variables, for which linear regressions are required, as was done by Krzysztofowicz. His Bayesian approach based on transforming observed probability distributions of discharges and forecasts into normal distributions is also explored. Results obtained with his method for normal prior and likelihood distributions are identical to results from direct multiple regressions. Furthermore, it is shown that in the present case forecast accuracy is only marginally improved, if Weibull distributed basic data were converted into normally distributed variables. View Full-Text
Keywords: forecast uncertainty; Bayesian uncertainty analysis; conditional flood forecasting; data based models; Mekong flood forecast uncertainty; Bayesian uncertainty analysis; conditional flood forecasting; data based models; Mekong flood
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Plate, E.J.; Shahzad, K.M. Uncertainty Analysis of Multi-Model Flood Forecasts. Water 2015, 7, 6788-6809.

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