Poster template by ResearchPosters.co.za Effect of Topography in Satellite Rainfall Estimation Errors: Observational Evidence across Contrasting Elevation in the Blue Nile Basin. Gebrehiwot Niguse Tesfay 1, Menberu Bitew 2, Mekonnen Gebremichael 2 1 Ethiopian Institute of Water Resources, Addis Ababa University, Ethiopia. 2 Department of Civil and Environmental Engineering, University of Connecticut, USA. Introduction Accurate observation of precipitation is essential to hydrological applications and water resources planning and management aspects. In complex terrain region of the Blue Nile, the effect of topography on rainfall variability was not clearly estabilished. Accurate measurement of rainfall using dense rain gauges was very essential for the assessment spatial rainfall variability. We deployed 70 recording rain gauges across the Blue Nile over elevation ranging from 600 m.a.s.l to 3300 m.a.s.l with a focus on capturing spatial rainfall variability within fine resolution ground rainfall grid. The spatial rainfall variability is assessed based on the relatively dense rain gauge networks. The main goal of this study is to evaluate the effect of topography on rainfall variability in two contrasting elevated gridded areas using newly deployed dense rain gauge networks in the Blue Nile basin. Understanding and quantifying rainfall variability at the contrasting elevation locations will provide important information in quantifying the errors associated to the representation of arial rainfall from ground data. Methodology Results Conclusions Acknowledgment Contact Information INSERT LOGO HERE Figure 2. DEM of Study site, gauge location and the vertical difference between the two sites Table 2. Precipitation correlations in the highland area Figure 3. Relationship of distance and Rainfall between stations in lowland site Figure 4. Relationship between distance and Rainfall between stations in highland site It has been observed that, elevation and horizontal distance difference have significant effect on total amount and the frequency of rainfall. In this experimental investigation, maximum rainfall event in the study time is obtained in the lowland but the accumulated is less than the other. This project is made possible by the generous support of the American people through the United State Agency for International Development (USAID) and Higher Education for Development (HED) office. Gebrehiwot Niguse Tesfay, INSERT LOGO HERE 1.Field Work and Analysis of Data  To achieve the stated objective, the following three procedures has been used: 2.Data Used  For the purpose of this study, the following precipitation data has been collected: Precipitation (Total) Distance and Elevation Data UnitMillimeter (mm)Meter, m Data RangeJuly 01 – Sep 30, Time series15 minutes1m Grid Resolution0.25 o X 0.25 o 3.Study Area  The correlation shows quick response to change in horizontal distance between stations in the lowland as compared to other site. Rainfall variability  Statistical indicator analysis Station Code Elevation, a m.s.l Elevation difference between consecutive station, km Distance between station, km Correlation between station N N N N N N N N  In site with smaller elevation( averagely 600m a.s.l) 12.6km, 8km and 2.73km are correlated respectively by , 0.38 and The three hourly average rainfalls is mm with maximum event value of mm. This is an indicator that, the average rainfall value is not a representative of all point rainfall data.  Similarly, in the higher elevation (averagely 2500m a.s.l) area; for rain gauges departed by 20.5km, 9km, 6.5km and 3.4 are related respectively by 0.14, 0.37 and 0.48 and 0.6. The three hourly average rainfalls is mm with maximum value of 63.25mm. Figure 1. DEM of Blue Nile Basin Table 1. Precipitation correlation in the low land area Station Code Elevation, a m.s.l in m Elevation difference between station, m Distance between station, km Correlation between consecutive station G G G G G G G G9707 Deploying rain gauges within Blue Nile Basin  Studying the rainfall variability  Standard statistical analysis was used. is done based on correlation (R 2 ) between consecutive rainfall stations with in lower elevation and higher elevation grid sites. Collect primary rainfall data over 2012 summer monsoon July 1, 12 to Sep 30, 12 from the deployed rain gauges Low land High land