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Abstract:The objective of this study is to evaluate the relationships of El Niño Southern Oscillation (ENSO) indices and the Blue Nile River Basin hydrology using a new approach that tracks cumulative ENSO indices. The results of this study can be applied for water resources management decision making to mitigate drought or flood impacts with a lead time of at least few months. ENSO tracking and forecasting is relatively easier than predicting hydrology. ENSO teleconnections to the Blue Nile River Basin hydrology were evaluated using spatial average basin rainfall and Blue Nile flows at Bahir Dar, Ethiopia. The ENSO indices were sea surface temperature (SST) anomalies in region Niño 3Ð4 and the Southern Oscillation Index (SOI). The analysis indicates that the Upper Blue Nile Basin rainfall and flows are teleconnected to the ENSO indices. Based on event correspondence and correlation analysis, high rainfall and high flows are likely to occur during La Niña years and dry years are likely to occur during El Niño years at a confidence level of 90%. Extreme dry and wet years are very likely to correspond with ENSO events as given above. The great Ethiopian famine of 1888-1892 corresponds to one of the strongest El Niño years, 1888. The recent drought years in Ethiopia correspond to strong El Niño years and wet years correspond to La Niña years. In this paper, a new approach is proposed on how to classify the strength of ENSO events by tracking consecutive monthly events through a year. A cumulative SST index value of ½5 and cumulative SOI value of Ä 7 indicate strong El Niño. A cumulative SST index value of Ä 5 and cumulative SOI index of ½7 indicate strong La Niña.

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In this study, monthly and annual Upper Blue Nile Basin rainfall data were analyzed to learn the rainfall statistics and its temporal and spatial distribution. Frequency analysis and spatial characterization of rainfall in the Upper Blue Nile Basin are presented. Frequency analysis was performed on monthly basin rainfall. Monthly basin average rainfall data were computed from a network of 32 gauges with varying lengths of records. Monthly rainfall probability distribution varies from month to month fitting Gamma‐2, Normal, Weibull and Log‐Normal distributions. The January, July, October and November basin rainfall fit the Gamma‐2 probability distribution. The February, June and December ones fit Weibull distribution. The March, April, May and August rainfall fit Normal distribution. The September rainfall fits Log‐Normal distribution. Upper Blue Nile Basin is relatively wet with a mean annual rainfall of 1423 mm (1960–2002) with a standard deviation of 125 mm. The annual rainfall has a Normal probability distribution. The 100‐year‐drought basin annual rainfall is 1132 mm and the 100‐year‐wet basin annual rainfall is 1745 mm. The dry season is from November through April. The wet season runs from June through September with 74% of the annual rainfall. October and May are transition months. Monthly and annual rainfalls for return periods 2‐, 5‐, 10‐, 25‐, 50‐ and 100‐year dry and wet patterns are presented. Spatial distribution of annual rainfall over the basin is mapped and shows high variation with the southern tip receiving as high as 2049 mm and the northeastern tip as low as 794 mm annual average rainfall. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract:The low and high flow characteristic of the Blue Nile River (BNR) basin is presented. The study discusses low and high flow, flow duration curve (FDC) and trend analysis of the BNR and its major tributaries. Different probability density functions were fitted to better describe the low and high flows of the BNR and major tributaries in the basin. Wavelet analysis was used in understanding the variance and frequency-time localization and detection of dominant oscillations in rainfall and flow. FDCs were developed, and low flow (below 50% exceedance) and high flow (over 75% exceedance) of the curves were analysed and compared. The Gravity Recovery and Climate Experiment (GRACE) satellite-based maps of monthly changes in gravity

Evapotranspiration (ET) accounts for a substantial amount of the water flux in the arid and semi-arid regions of the World. Accurate estimation of ET has been a challenge for hydrologists, mainly because of the spatiotemporal variability of the environmental and physical parameters governing the latent heat flux. In addition, most available ET models depend on intensive meteorological information for ET estimation. Such data are not available at the desired spatial and temporal scales in less developed and remote parts of the world. This limitation has necessitated the development of simple models that are less data intensive and provide ET estimates with acceptable level of accuracy. Remote sensing approach can also be applied to large areas where meteorological data are not available and field scale data collection is costly, time consuming and difficult. In areas like the Rift Valley regions of Ethiopia, the applicability of the Simple Method (Abtew Method) of lake evaporation estimation and surface energy balance approach using remote sensing was studied. The Simple Method and a remote sensing-based lake evaporation estimates were compared to the Penman, Energy balance, Pan, Radiation and Complementary Relationship Lake Evaporation (CRLE) methods applied in the region. Results indicate a good correspondence of the models outputs to that of the above methods. Comparison of the 1986 and 2000 monthly lake ET from the Landsat images to the Simple and Penman Methods show that the remote sensing and surface energy balance approach is promising for large scale applications to understand the spatial variation of the latent heat flux.

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