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Addis Ababa, Ethiopia

Birhanu K.,Haramaya University | Alamirew T.,Water and Land Resource Center | Olumana Dinka M.,Tshwane University of Technology | Ayalew S.,Addis Ababa Institute of Technology | Aklog D.,Tottori University
Water Resources Management | Year: 2014

One of typical problems in water resources system modeling is derivation of optimal operating policy for reservoir to ensure water is used more efficiently. This paper introduces optimization analysis to determine monthly reservoir operating policies for five scenarios of predetermined cropping patterns for Koga irrigation scheme, Ethiopia. The objective function of the model was set to minimize the sum of squared deviation (SSD) from the desired targeted supply. Reservoir operation under different water availability and thresholds of irrigation demands has been analyzed by running a chance constraint nonlinear programming model based on uncertain inflow data. The model was optimized using Microsoft Excel Solver. The lowest SSD and vulnerability, and the highest volumetric reliability were gained at irrigation deficit thresholds of 20 % under scenario I, 30 % under scenario II, III and V, and at 40 % under scenario IV when compensation release is permitted for downstream environment. These thresholds of deficits could be reduced by 10 % for all scenarios if compensation release is not permitted. In conclusion the reservoir water is not sufficient enough to meet 100 % irrigation demand for design command areas of 7,000 ha. The developed model could be used for real time reservoir operation decision making for similar reservoir irrigation systems. In this specific case study system, attempt should be made to evaluate the technical performance of the scheme and introduce a regulated deficit irrigation application. © 2014, Springer Science+Business Media Dordrecht. Source


Gebere S.B.,TU Bergakademie Freiberg | Alamirew T.,Water and Land Resource Center | Merkel B.J.,TU Bergakademie Freiberg | Melesse A.M.,Florida International University
Remote Sensing | Year: 2015

Accurate estimation of rainfall in mountainous areas is necessary for various water resource-related applications. Though rain gauges accurately measure rainfall, they are rarely found in mountainous regions and satellite rainfall data can be used as an alternative source over these regions. This study evaluated the performance of three high-resolution satellite rainfall products, the Tropical Rainfall Measuring Mission (TRMM 3B42), the Global Satellite Mapping of Precipitation (GSMaP_MVK+), and the Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Networks (PERSIANN) at daily, monthly, and seasonal time scales against rain gauge records over data-scarce parts of Eastern Ethiopia. TRMM 3B42 rain products show relatively better performance at the three time scales, while PERSIANN did much better than GSMaP. At the daily time scale, TRMM correctly detected 88% of the rainfall from the rain gauge. The correlation at the monthly time scale also revealed that the TRMM has captured the observed rainfall better than the other two. For Belg (short rain) and Kiremt (long rain) seasons, the TRMM did better than the others by far. However, during Bega (dry) season, PERSIANN showed a relatively good estimate. At all-time scales, noticing the bias, TRMM tends to overestimate, while PERSIANN and GSMaP tend to underestimate the rainfall. The overall result suggests that monthly and seasonal TRMM rainfall performed better than daily rainfall. It has also been found that both GSMaP and PERSIANN performed better in relatively flat areas than mountainous areas. Before the practical use of TRMM, the RMSE value needs to be improved by considering the topography of the study area or adjusting the bias. © 2015 by the authors. Source


Yesuf H.M.,Wollo University | Melesse A.M.,Florida International University | Zeleke G.,Water and Land Resource Center | Alamirew T.,Water and Land Resource Center
Environmental Earth Sciences | Year: 2016

Appropriate implementation of Soil and Water Assessment Tool (SWAT) hydrologic model requires prediction uncertainty analysis, calibration and validation of the model against historical output records. Sequential Uncertainty Fitting-2 (SUFI-2) and Generalized Likelihood Uncertainty Estimation (GLUE) algorithms with ArcSWAT2009 and ArcGIS10.0 were used in this research to conduct uncertainty analysis, calibration and validation of the SWAT model using monthly observed streamflow data in Maybar experimental watershed, Ethiopia. The results revealed that the model was generally satisfactory as proved by the uncertainty, calibration and validation goodness of fit indicators: The goodness of fit and the degree to which the calibrated SWAT model accounted for the uncertainties assessed by: P-factor (72, 70 %) and (65, 69 %) for (calibration, validation) stages of SUFI-2 and GLUE algorithms, respectively, and R-factor (0.97, 0.90) and (0.89, 0.95) for (calibration, validation) stages of SUFI-2 and GLUE algorithms, respectively, are reached acceptable values, then the parameter uncertainties used were the desired parameter ranges. Further model evaluation statistics: Coefficient of Determination (R2 ≥ 0.76), Nash–Sutcliffe efficiency (NSE ≥ 0.63), Percent Bias (PBIAS ≤ ± 7.10 %) and Root Mean Square Error-observations Standard deviation Ratio (RSR ≤ 0.46) for both calibration and validation periods were quantified and the extent of similarity between predicted and recorded streamflow data suggests that SWAT model can adequately simulate monthly streamflow at Maybar gauged watershed. © 2016, Springer-Verlag Berlin Heidelberg. Source


Ambachew S.,Sekota Dryland Agricultural Research Center | Alamirew T.,Water and Land Resource Center | Melese A.,Florida International University
Agricultural Water Management | Year: 2014

Knowing the performance of short season crops under deficit irrigation has a paramount importance for arid and semi-arid regions with limited access to on-farm water harvesting or other irrigation infrastructure. In this research, the performance of mungbean, a newly introduced crop into the cropping systems in Ethiopia, to stage-wise and uniform deficit irrigation was tested at Sekota Dryland Agricultural Research Center, Northern Ethiopia. Eight treatments - four stage-wise deficit and four uniform deficit irrigation applications were evaluated during the 2010/2011 dry season. Plant phenological variables, grain yield and irrigation water use efficiency were used for performance evaluation. The results showed that a uniform water stress shortened the dates to 50% flowering and maturity, but with proportionate reduction in yield. The yield obtained varied between 1366kg/ha under 331mm optimal seasonal irrigation to 492kg/ha when one-fourth of ETc (102mm) was uniformly applied though out the growing season. The flowering/reproductive stage was noted as the most sensitive growth stage with a 24.9% yield reduction compared to the control treatment. In all other stages, yield is linearly associated (R2=0.93) with the amount of irrigation water applied, over the range tested. IWUE values ranged from 0.248 to 0.304kg/m3. It can therefore be concluded that provided stress at the midseason stage is avoided and depending on the volume of water available, different deficit irrigation arrangements using on-farm pond water is possible. © 2014 Elsevier B.V. Source


Shimelis B.G.,TU Bergakademie Freiberg | Merkel B.,TU Bergakademie Freiberg | Agumassie T.A.,Water and Land Resource Center
FOG - Freiberg Online Geoscience | Year: 2015

The knowledge of land use and land cover is important for properly managing, planning and monitoring natural resources. The aim of this study was to generate land use maps for the study area and to understand the land use and land cover changes using remotely sensed satellite imageries from 1985 to 2011. The images were geometrically corrected to a common map projection followed by image processing operations. In ERDAS, supervised classification based on the maximum likelihood algorithm was applied to the Landsat images acquired in 1985, 1995, 2006 and 2011. To check the accuracy of the classification, ground truth data was also collected. Post classification change detection was applied in order to assess changes in land use and land cover over time using IDRISI software. Rapid population growth has created land use misbalances. The results showed that dramatic changes in land use and land cover have occurred with shrinkage of water bodies, cultivated land, forests and grassland and at the same time expansion of Catha edulis (chat)/shrubs as well as settlement areas. The result revealed that an absolute shrinkage and loss of water bodies has occurred due to an extensive and massive clearance of forests and grasslands. Between 1985 and 2011, forests became smaller and more fragmented and declined from 202.6 ha to 101.6 ha. Only patches of mature forests are left. They are under threat from expansion of land for chat production and settlements. In the mentioned period, the total area of water bodies decreased from 3.5 % to 1.0 % while the area of grassland and cultivated land decreased from 29.2 % to 19.6 % and 42.4 % to 32.6 %, respectively. For a sustainable development of the watershed resources, proper land use planning is essential. © 2015, Technical University Freiberg. All rights reserved. Source

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