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You are here : eLibrary : IAHR World Congress Proceedings : 36th Congress - The Hague (2015) ALL CONTENT : Water resources and hydroinformatics : Merging rainfall from diverse sources to improve hydrological prediction
Merging rainfall from diverse sources to improve hydrological prediction
Author : BISWA BHATTACHARYA(1) & TEGEGNE MEKONNEN TAREKEGN(2)
ABSTRACT
Hydrological modelling depends heavily on rainfall data from raingauges. Raingauges measure rainfall in a fairly
straightforward manner. However, they measure what is falling on a tiny funnel, which is used to infer rainfall over a large
area. Weather radars and satellite based rainfall estimates such as from Tropical Rainfall Measuring Mission (TRMM)
provide new ways of complementing rainfall data from raingauges. These estimates of rainfall may differ a lot compared
to the values measured at raingauges. Combining the diverse rainfall sources is not easy as they employ different
measurement techniques and space-time scales. A method of merging these two rainfall products is explored in this study
under the framework of Bayesian Data Fusion (BDF) principle. The usefulness of the approach has been explored in a
case study on Lake Tana Basin of Upper Blue Nile Basin in Ethiopia. The merged data, along with the data from TRMM
and rain gauges, were used in a lumped conceptual model of the study area. Visual inspection of the simulated and
observed flow plots and statistical indices for goodness of fit were used to evaluate the predictive capability of the rainfall
sources. The model results with the BDF rainfall showed improved prediction of the observed discharge. The results
showed the capability of the proposed merging technique in estimating rainfall from diverse sources.
File Size : 788,357 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 36th Congress - The Hague (2015) ALL CONTENT
Article : Water resources and hydroinformatics
Date Published : 19/08/2015
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