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Arua, Uganda

Muni University is a public multi-campus university in Uganda. It is one of the public universities and degree-awarding institutions in the country. The list includes the following: Makerere University - Founded in 1924 Uganda Management Institute - Founded in 1968 Mbarara University - Founded in 1989 Metropolitan University Business School - Founded in 1997 Kyambogo University - Founded in 2003 Gulu University - Founded in 2002 Busitema University - Founded in 2007 Kigumba Petroleum Institute - Founded in 2009 Muni University - Founded in 2012↑ ↑ Wikipedia.

Five hydrological models were applied based on data from the Blue Nile Basin. Optimal parameters of each model were obtained by automatic calibration. Model performance was tested under both moderate and extreme flow conditions. Extreme events for the model performance evaluation were extracted based on seven criteria. Apart from graphical techniques, there were nine statistical "goodness-of-fit" metrics used to judge the model performance. It was found that whereas the influence of model selection may be minimal in the simulation of normal flow events, it can lead to large under- and/or overestimations of extreme events. Besides, the selection of the best model for extreme events may be influenced by the choice of the statistical "goodness-of-fit" measures as well as the criteria for extraction of high and low flows. It was noted that the use of overall water-balance-based objective function not only is suitable for moderate flow conditions but also influences the models to perform better for high flows than low flows. Thus, the choice of a particular model is recommended to be made on a case by case basis with respect to the objectives of the modeling as well as the results from evaluation of the intermodel differences. © 2016 Charles Onyutha. Source

Variability analyses for the rainfall over the Nile Basin have been confined mostly to sub-basins and the annual mean of the hydroclimatic variable based on observed short-Term data from a few meteorological stations. In this paper, long-Term country-wide rainfall over the period 1901-2011 was used to assess variability in the seasonal and annual rainfall volumes in all the River Nile countries in Africa. Temporal variability was determined through temporal aggregation of series rescaled nonparametrically in terms of the difference between the exceedance and non-exceedance counts of data points such that the long-Term average (taken as the reference) was zero. The co-occurrence of the variability of rainfall with those of the large-scale ocean-atmosphere interactions was analyzed. Between 2000 and 2012, while the rainfall in the equatorial region was increasing, that for the countries in the northern part of the River Nile was below the reference. Generally, the variability in the rainfall of the countries in the equatorial (northern) part of the River Nile was found to be significantly linked to occurrences in the Indian and Atlantic (Pacific and Atlantic) Oceans. Significant linkages to Niño 4 regarding the variability of both the seasonal and annual rainfall of some countries were also evident. © IWA Publishing 2016. Source

Edemacu K.,Muni University | Bulega T.,Makerere University
Proceedings of 2014 8th International Conference on Telecommunication Systems Services and Applications, TSSA 2014 | Year: 2015

Machine-to-Machine (M2M) communication is becoming a commonly used terminology due to the idea of Internet of Things (IoT). M2M communication has many areas of application, such as in; medical, transport, environmental monitoring, smart grids among others. As the field of its application expands, the number of M2M devices is expected to grow exponentially in the next few years. Long Term Evolution (LTE) has been identified as one of the suitable wireless communication technologies for M2M communication. Incorporating M2M communication on top of regular Human-to-Human (H2H) communication in LTE is a challenging task due to the expected increase in the number of M2M devices coupled with the unique characteristics of M2M traffic. Therefore, the current scheduling and resource allocation techniques among others being used in LTE need to be refined to efficiently accommodate M2M communication. A scheduling scheme called fixed Access Grant Time Interval (AGTI) time-controlled scheduling scheme was proposed for scheduling M2M traffic in LTE. Resource sharing and utilization under this scheme is inefficient due to fixed AGTI assignment which results into fixed nature of resource allocation. In this work, we propose a scheduling scheme called Dynamic AGTI Time-controlled Scheduling Scheme in which the AGTI is dynamically assigned basing on M2M and H2H traffic intensities. We model the proposed scheme using M/G/m/m queuing technique focusing on resource utilization and Quality of Service (QoS) of both M2M and H2H traffic. The analytical results show that, the Dynamic AGTI Time-controlled Scheduling Scheme achieves; better percentage resource utilization as compared to Fixed AGTI Time-controlled Scheduling Scheme while providing optimal blocking probability for both M2M and H2H traffic. However, the monitoring of resource usage and reassignments of AGTI in Dynamic AGTI Time-controlled Scheduling Scheme increases scheduler complexity. © 2014 IEEE. Source

Onyutha C.,Catholic University of Leuven | Onyutha C.,Muni University | Tabari H.,Catholic University of Leuven | Rutkowska A.,Agricultural University of Krakow | And 3 more authors.
Journal of Hydro-Environment Research | Year: 2016

In this study, outputs of three statistical downscaling (SD) methods including the change factor (Delta), simplified (simQP) and advanced (wetQP) quantile-perturbation-based approaches were compared based on daily rainfall series at 9 meteorological stations in the Lake Victoria basin (LVB) in Eastern Africa. The comparison was made considering phase 5 and phase 3 of the Coupled Model Inter-comparison Project, i.e. CMIP5 and CMIP3 respectively. For the CMIP5 (CMIP3) at each station, there were a total of 7 (14) GCMs, 18 (20) daily historical (control) simulations over the period 1961-2000, and 35 (49) daily future projection series of the periods 2050s and 2090s. The ensemble mean of the GCMs' Bias in reproducing rainfall extremes for return periods in the range of 1 to 40 years for the CMIP5 (CMIP3) varied from -19.05% to 3.11% (-65.85% to -4.86%). For the high greenhouse gas scenario rcp8.5 (A2) of the CMIP5 (CMIP3), the ensemble mean of the projected changes over the LVB in the 10-year rainfall intensity quantile obtained from the Delta, simQP, wetQP SD goes up to 5.8, 10 and 22.4% (11.7, 15.9 and 43.6%) in the 2050s and 8, 11.4, and 25.4% (14.2, 23.3 and 40.6%) in the 2090s. Rainfall totals of the main wet (dry) season are generally projected to increase (decrease) in both the 2050s and 2090s. Because the outputs from the three SD methods captured well the pattern of monthly rainfall totals, the difference between the projected changes of seasonal or annual rainfall totals from the Delta, simQP and wetQP was shown to be insignificant. However, the differences in the results from the Delta, simQP and wetQP methods with respect to the projections of rainfall quantiles indicate that the choice of the SD method can be made on a case by case basis in line with the objectives of the climate change impact study, e.g. the Delta does not capture well the changes in rainfall extremes, whereas the wetQP is suitable for both rainfall extremes and rainfall totals at both seasonal and annual time scales. The findings of this study also show the need to consider evaluations of the inter-GCM differences in the LVB as a data scarce region in assessing the discernible impact of climate change on rainfall extremes and/totals for decision making related to water resources management and engineering. © 2016 International Association for Hydro-environment Engineering and Research, Asia Pacific Division. Source

Onyutha C.,Muni University
Stochastic Environmental Research and Risk Assessment | Year: 2016

In hydro-meteorological trend analysis, an alteration in the given variable is detected by considering the long-term series as a whole. Whereas the long-term trend may be absent, the significance of hidden (short-durational) sub-trends in the series may be important for environmental management practices. In this paper, a graphical approach of identifying trend or sub-trends using nonparametric cumulative rank difference (CRD) was proposed. To confirm the significance of the visualized trend, the CRD was translated from the graphical to a statistical metric. To assess its capability, the performance of the CRD method was compared with that of the well-known Mann–Kendall (MK) test. The graphical and statistical CRD techniques were applied to detect trends and sub-trends in the annual rainfall of 10 River Nile riparian countries (RNRCs). The co-occurrence of the trend evolutions in the rainfall with those of the large-scale ocean–atmosphere interactions was analyzed. The power of the CRD method was shown to closely agree with that of the MK test under the various circumstances of sample sizes, variations, linear trend slopes, and serial correlations. At the level of significance α = 5 %, the long-term trends were found present in 30 % of the RNRCs. However at α = 5 %, the main downward (upward) sub-trends were found significant in 30 (60 %) of the RNRCs. Generally at α = 1 %, linkages of the trend evolutions in the rainfall of the RNRCs were found to those of the influences from the Atlantic and Indian Oceans. At α = 5 %, influences from the Pacific Ocean on the rainfall trends of some countries were also evident. © 2015, Springer-Verlag Berlin Heidelberg. Source

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