The Sensitivity of Meteorological Dynamics to the Variability in Catchment Characteristics

Evaluating meteorological dynamics is a challenging task due to the variability in hydroclimatic settings. This study is designed to assess the sensitivity of precipitation and temperature dynamics to catchment variability. The effects of catchment size, land use/cover change, and elevation differences on precipitation and temperature variability were considered to achieve the study objective. The variability in meteorological parameters to the catchment characteristics was determined using the coefficient of variation on the climate data tool (CDT). A land use/cover change and terrain analysis was performed on Google Earth Engine (GEE) and ArcGIS. In addition, a correlation analysis was performed to identify the relative influence of each catchment characteristic on the meteorological dynamics. The results of this study showed that the precipitation dynamics were found to be dominantly influenced by the land use/cover change with a correlation of 0.65, followed by the elevation difference with a correlation of -0.47. The maximum and minimum temperature variations, on the other hand, were found to be most affected by the elevation difference, with Pearson correlation coefficients of -0.53 and -0.57, respectively. However, no significant relationship between catchment size and precipitation variability was observed. In general, it is of great importance to understand the relative and combined effects of catchment characteristics on local meteorological dynamics for sustainable water resource management

Downscaling Africa’s Drought Forecasts through Integration of Indigenous and Scientific Drought Forecasts Using Fuzzy Cognitive Maps

In the wake of increased drought occurrences being witnessed in Sub-Saharan Africa, more localized and contextualized drought mitigation strategies are on the agendas of many researchers and policy makers in the region. The integration of indigenous knowledge on droughts with seasonal climate forecasts is one such strategy. The main challenge facing this integration, however, is the formal representation of highly-structured and holistic indigenous knowledge. In this paper, we demonstrate how the use of fuzzy cognitive mapping can address this challenge. Indigenous knowledge on droughts from five communities was modeled and represented using fuzzy cognitive maps. Maps from one of these case communities were then used in the implementation of the integration framework, called itiki

Building A High-Resolution Vegetation Outlook Model to Monitor Agricultural Drought for the Upper Blue Nile Basin, Ethiopia

To reduce the impacts of drought, developing an integrated drought monitoring tool and early warning system is crucial and more effective than the crisis management approach that is commonly used in developing countries like Ethiopia. The overarching goal of this study was to develop a higher-spatial-resolution vegetation outlook (VegOut-UBN) model that integrates multiple satellite, climatic, and biophysical input variables for the Upper Blue Nile (UBN) basin. VegOut-UBN uses current and historical observations in predicting the vegetation condition at multiple leading time steps of 1, 3, 6, and 9 dekades. VegOut-UBN was developed to predict the vegetation condition during the main crop-growing season locally called “Kiremt” (June to September) using historical input data from 2001 to 2016. The rule-based regression tree approach was used to develop the relationship between the predictand and predictor variables. The results for the recent historic drought (2009 and 2015) and non-drought (2007) years are presented to evaluate the model accuracy during extreme weather conditions. The result, in general, shows that the predictive accuracy of the model decreases as the prediction interval increases for the cross-validation years. The coefficient of determination (R2) of the predictive and observed vegetation condition shows a higher value (R2 \u3e 0.8) for one-month prediction and a relatively lower value (R2 = 0.70) for three-month prediction. The result also reveals strong spatial integrity and similarity of the observed and predicted maps. VegOut-UBN was evaluated and compared with the Standardized Precipitation Index (SPI) (derived from independent rainfall datasets from meteorological stations) at different aggregate periods and with a food security status map. The result was encouraging and indicative of the potential application of VegOut-UBN for drought monitoring and prediction. The VegOut-UBN model could be informative in decision-making processes and could contribute to the development of operational drought monitoring and predictive models for the UBN basin

Using Satellite Images for Drought Monitoring: A Knowledge Discovery Approach

The main objective of this research was to develop a new concept and approach to extract knowledge from satellite imageries for near real-time drought monitoring. The near real-time data downloaded from the Atlantic Bird satellite were used to produce the drought spatial distribution. Our results showed that approximately 40% of the observed areas exhibited negative deviation. In this study, the possibility of using the near real-time spatio-temporal Meteosat Second Generation (MSG) data for drought monitoring in food insecure areas of Ethiopia was tested, and promising results were obtained. The output of this research is expected to assist decision makers in taking timely and appropriate action in order to save millions of lives in drought-affected areas

Analysis of genetic diversity in some durum wheat (Triticum durum Desf) genotypes grown in Ethiopia

An experiment was conducted to examine the magnitude of genetic diversity and characters contributing to genetic diversity among 23 durum wheat genotypes grown at Adet, northwest Ethiopia, in 2010 main cropping season. Genetic divergence was carried out according to Mahalanobis D2 statistics. Genetic distance analysis revealed that the Euclidean genetic distance values ranged from 2.69 (between D-1 and D-11) to 9.68 (between D-13 and D-21) and 82.2% of the pair comparisons had values between 3.76 and 7.50. Cluster analysis grouped genotypes into six genetically distinct clusters. The highest inter-cluster distance was 8.30 (between clusters III and VI) followed by 7.99 (between clusters V and VI), indicating the wide genetic diversity among these clusters. The highest intra-cluster distance was observed in cluster I (4.91) and the lowest in cluster III (2.36). The average inter-cluster distances were higher than the average intra-cluster distances, which showed the presence of wide genetic diversity among the genotypes of different clusters than those of the same cluster. The first four principal components whose Eigen values are greater than one, accounted for 80.46% of the total variation of the original variables. The information obtained from the study is useful in planning further crossing programme for durum wheat crop improvement.Key words: Cluster analysis, genetic distance, principal component analysis

Bovine trypanosomosis and its vectors: prevalence and control operations in Kellem Wollega, Western Ethiopia

A cross-sectional study was conducted to estimate the prevalence of bovine trypanosomosis and to assess farmers’ perception of the  disease and its control operations. From October to April 2012, a total of 586 cattle were sampled for the prevalence study. Buffy coat procedure and haematocrit value determination were performed. To capture the fly that was involved in the transmission dynamics, one hundred monopyramidal traps were deployed for 72 hours. A semi-structured questionnaire was conducted to study farmers’  perceptions of the diseases and their control operations. Trypanosomal infections were diagnosed in only 8.7 % (51) of animals. The overall prevalence of trypanosome infection in cattle was significantly varied between study districts (33.1% Dale Sadi and 66.9% Dale Wabera). Most  infections were due to Trypanosoma congolense (81.8%) followed by T. vivax (15.6%) and mixed infections (2.6%). The association of hematological value changes and trypanosome infections was profound. The overall Packed Cell Volume (PCV) values of sampled cattle were 25.8%. A significant (P< 0.05) variation in PCV values was recorded in infected (20.8%) and non-infected (26.5%) cattle. In the study period, a total of 2055 flies were captured and of which 92% belong to the genus Glossina followed by Stomoxys and Tabanids. Four types of tsetse species (G. pallidpes, G. m. morsitans, G. tachinoides, and G. f. fuscipes) were identified. The questionnaire survey revealed that trypanosomosis is the most important problem for agricultural activity and animal production in the study areas. Farmers are well aware of the problem, means of transmission, and the different control methods. Integrating tsetse control program with other trypanosomosis control options is recommended

Precipitation Extremes in Dynamically Downscaled Climate Scenarios over the Greater Horn of Africa

This study first assesses the performance of regional climate models in the Coordinated Regional Climate Downscaling Experiment (CORDEX) in reproducing observed extreme precipitation indices over the Greater Horn of Africa (GHA) region during 1989–2005. The study then assesses projected changes in these extremes during 2069–2098 compared to 1976–2005. The Regional Climate Model (RCM) simulations are made using two RCMs, with large-scale forcing from four CMIP5 Global limate Models(GCMs) under two Representative Concentration Pathways (RCP4.5 and RCP8.5). We found that RCM simulations have reasonably captured observed patterns of moderate precipitation extreme indices (MPEI). Pattern correlation coefficients between simulated and observed MPEI exceed 0.5 for all except the Simple Daily Intensity Index (SDII). However, significant overestimations or underestimations exist over localized areas in the region. Projected changes in Total annual Precipitation (PRCPTOT) and the annual number of heavy (\u3e10 mm) and very heavy (\u3e20 mm) precipitation days by 2069–2098 show a general north-south pattern, with decreases over the southern half and increases over the northern half of the GHA. These changes are often greatest over parts of Somalia, Eritrea, the Ethiopian highlands and southern Tanzania. Maximum one- and five-day precipitation totals over a year and SDII (ratio of PRCPTOT to rainy days) are projected to increase over a majority of the GHA, including areas where PRCPTOT is projected to decrease, suggesting fewer, but heavier rainy days in the future. Changes in the annual sum of daily precipitation above the 95th and 99th percentiles are statistically significant over a few locations, with the largest projected decrease/increase over Eritrea and northwestern Sudan/Somalia. Projected changes in Consecutive Dry Days (CDD) suggest longer periods of dryness over the majority of the GHA, except the central portions covering northern Uganda, southern South Sudan, southeastern Ethiopia and Somalia. Substantial increases in CDD are located over southern Tanzania and the Ethiopian highlands. The magnitude and the spatial extent of statistically-significant changes in all MPEI increase from RCP4.5 to RCP8.5, and the separation between positive and negative changes becomes clearer under RCP8.5

Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

Accurate measurement of rainfall is vital to analyze the spatial and temporal patterns of precipitation at various scales. However, the conventional rain gauge observations in many parts of the world such as Ethiopia are sparse and unevenly distributed. An alternative to traditional rain gauge observations could be satellite-based rainfall estimates. Satellite rainfall estimates could be used as a sole product (e.g., in areas with no (or poor) ground observations) or through integrating with rain gauge measurements. In this study, the potential of a newly available Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) rainfall product has been evaluated in comparison to rain gauge data over the Upper Blue Nile basin in Ethiopia for the period of 2000 to 2015. In addition, the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT 3) and the African Rainfall Climatology (ARC 2) products have been used as a benchmark and compared with CHIRPS. From the overall analysis at dekadal (10 days) and monthly temporal scale, CHIRPS exhibited better performance in comparison to TAMSAT 3 and ARC 2 products. An evaluation based on categorical/volumetric and continuous statistics indicated that CHIRPS has the greatest skills in detecting rainfall events (POD D0.99, 1.00) and measure of volumetric rainfall (VHID1.00, 1.00), the highest correlation coefficients (r D0.81, 0.88), better bias values (0.96, 0.96), and the lowest RMSE (28.45mmdekad 1, 59.03mmmonth 1) than TAMSAT 3 and ARC 2 products at dekadal and monthly analysis, respectively. CHIRPS overestimates the frequency of rainfall occurrence (up to 31% at dekadal scale), although the volume of rainfall recorded during those events was very small. Indeed, TAMSAT 3 has shown a comparable performance with that of the CHIRPS product, mainly with regard to bias. The ARC 2 product was found to have the weakest performance underestimating rain gauge observed rainfall by about 24 %. In addition, the skill of CHIRPS is less affected by variation in elevation in comparison to TAMSAT 3 and ARC 2 products. CHIRPS resulted in average biases of 1.11, 0.99, and 1.00 at lower (\u3c 1000ma.s.l.), medium (1000 to 2000ma.s.l.), and higher elevation (\u3e 2000ma.s.l.), respectively. Overall, the finding of this validation study shows the potentials of the CHIRPS product to be used for various operational applications such as rainfall pattern and