Integration of Remotely Sensed Data into Hydrological Models to Improve for Water Quality Model in Data-Poor Region

Toyin Omotoso, Gregory Lane-Serff, Robert Young

Thursday 2 july 2015

16:45 - 17:00h at Central America (level 0)

Themes: (T) Water resources and hydro informatics (WRHI), (ST) Catchment hydrology

Parallel session: 13H. Water resources - Catchment

The accuracy and 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 Keywords: Satellite-based rainfall estimate; data-poor environment; performance rating; reliability.