IAHR, founded in 1935, is a worldwide independent member-based organisation of engineers and water specialists working in fields related to the hydro-environmental sciences and their practical application. Activities range from river and maritime hydraulics to water resources development and eco-hydraulics, through to ice engineering, hydroinformatics, and hydraulic machinery.
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You are here : eLibrary : IAHR World Congress Proceedings : 36th Congress - The Hague (2015) ALL CONTENT : Water resources and hydroinformatics : Integration of remotely sensed data into hydrological models to improve for water quality model in d...
Integration of remotely sensed data into hydrological models to improve for water quality model in data-poor region
reliability of the satellite-based rainfall estimates (SBRE) to correctly represent real time precipitation
in a data-poor region is the focus of investigation in this paper. The results, coupled with other environmental
parameters are intended to feed a simple rainfall–runoff model for river flow forecasting and to evaluate the
improvements brought by the assimilation of the information into the hydrological modeling a the data-poor environment.
A careful attempt is made here to separate the assessment of the SBRE and the hydrological modeling processes
because of the added structure and data requirements involved in the hydrological model which can better be handled by
such separation. SBRE data was evaluated against the available ground-gauged data of Ogbese River catchment in
South-West Nigeria as a case study. The ground data covered the year 2007 and 2008 against which the SBRE was
validated using some recommended model evaluation statistical techniques to review and examine ranges of values and
corresponding performance rating for the SBRE data. The rating determines its reliability within the context of temporal
and spatial accuracy for use in hydrologic modeling. The results revealed that the monthly series determined by the
satellite data and the ground measurement are in good agreement with the regression analysis for the mean monthly
rainfall showing a coefficient of determination, R2 of 0.76, significant at 95% confidence level. This suggests a kind of
satisfactory relief not only to problems associated with lack of good data (it that has been a serious burden to hydrologic
modeling that could furnish effective catchment management processes in most data-poor regions of the developing
countries) but also curtails the limitations of effective spatial coverage inherent in the ground gauge stations.Form Required :
File Size : 512,834 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 : 20/08/2015
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