http://ir.bdu.edu.et/handle/123456789/11547 Mon, 13 Jul 2026 15:00:43 GMT 2026-07-13T15:00:43Z http://ir.bdu.edu.et/handle/123456789/16920 Impact of Climate Change and Variability on Pastand Future Rice (Oryza Satival) Productivity in Fogera Plain, Lake Tana Sub Basin Ethiopia Wubet Ashagrie Future climate change is expected to impact on rice (Oryza sativa L.) productivity. The study tried to investigate the impact of climate change and variability on past and future rice (Oryza sativa L.) productivity in Fogera plain. In this study, long-term observed climate data (1993–2022) and projected (2025–2086) rainfall and temperature used to analyze change and variability, projected change and impact on rice yield. Variability was analyzed through coefficient of variation, precipitation concentration index and Standard anomaly index. Whereas trend was analyzed through Sen’s slop, percent change and Modified Mann kandel test. CMhyd software were used for both extracted Netcdf file and bias correction the future precipitation and temperature for three shared socioeconomic pathways SSP1-2.6 (sustainable / with climate policies), SSP2-4.5 (medium) and SSP5-8.5(worst / in the absence of climate policies) of future socioeconomic conditions. The start, length of kiremt season and maximum temperature coefficient of variation was less than 10% in the plain. In Fogera plain over all mean precipitation concentration index were 23.1 which show very high concentrations of annual rainfall distribution. The annual and kiremt season rainfall insignificantly increase in Wereta and Addis Zemen but significant decreasing in Wanzaye. Late start of season (SOS) and decreasing in length of growing season was observed in the plain. Annual rainfall totals decreasing in Addis Zemen station in all scenarios except SSP58.5(2056-2086) but annual rainfall at Wereta station and Wanzaye stations is expected to increase by SSP1-2.6 and SSP5-8.5 both in 2025-2055 and 2056-2086. Projected change of annual and seasonal minimum and maximum temperature showed rise in under SSP1-2.6, SSP2-4.5 and SSP5-8.5 by 2025-2055 and 2056-2086 in the plain. Rice yields showed positive correlation with length and end of growing season. Rice yields showed negative correlation with onset of season, maximum and minimum temperature in all stations. The rice yield variation in Wanzaye, Wereta and Addis Zemen station were explained jointly by 77%, 62% and 64% of the rainfall and temperature variables while the remaining 23%, 38 % and 36% of the variations could be by other factors. The simulated yield of rice is varied with location and type of scenarios used in the future. Yield of rice would be expected to increase under the SSP1-2.6 scenario in all growing locations but decrease under the SSP5-8.5 scenario in all growing locations of Fogera plain. Fri, 01 Nov 2024 00:00:00 GMT http://ir.bdu.edu.et/handle/123456789/16920 2024-11-01T00:00:00Z http://ir.bdu.edu.et/handle/123456789/16919 Modeling Groundwater Vulnerability Using A Modified Drastic Model in the Case of Bahir Dar City, Northwest, Ethiopia Wasie Asmamaw Ensuring access to improved sources of water and safe drinking water for all has been the Nation’s concern which is one of the 17th UN Sustainable Development Goals set to be achieved by 2030. Groundwater is one of the most valuable natural resources in the world. However, groundwater resources, especially in shallow aquifers, are susceptible to contamination. Groundwater pollution is one part of environmental pollution that harms human health, livestock, and agricultural productivity, and other adverse impacts on the world's economic growth. With this respect, the quality of groundwater in the study area has deteriorated due to industrial effluents, domestic wastes, and extensive use of fertilizer for agricultural purposes. Once the groundwater gets polluted, treatment is very difficult and expensive because prevention is much better than protection. With this in mind, this study aims to assess groundwater vulnerability using the Modified DRASTIC model in the Case of Bahir Dar City. The modified DRASTIC model uses eight attributes including, aquifer media, net recharge, Depth to water table, soil media, topography, impact of vadose zone, land use land cover, and hydraulic conductivity of the aquifer were collected from both primary and secondary data sources. These thematic layers were integrated using the Raster calculator tool in a GIS environment to identify and map the spatial distribution of hotspot areas. Single parameter and map removal sensitivity analysis methods are used to identity significant parameters. The result of the study indicates that 5899.73 ha (27.63%) are put under the low vulnerable zone, 7344.92 ha (34.40%) are put under the moderate vulnerable zone, 5589.87 ha (26.18%) are put under the highly vulnerable zone and 2514.28 ha (11.78%) are put under the very high vulnerable zone. The vulnerability of the city is heavily influenced by net recharge (R) and land use land cover (Lu). GIS and Remote Sensing application of the DRASTIC model and integrating Lu in the model was found to be a suitable method for analyzing the groundwater vulnerability in Bahir Dar City. Thus, the study will be helpful in the proper management and development of available groundwater resources in the study area. Wed, 01 May 2024 00:00:00 GMT http://ir.bdu.edu.et/handle/123456789/16919 2024-05-01T00:00:00Z http://ir.bdu.edu.et/handle/123456789/16918 Farmers Perception, Vulnerability and Adaptation Strategies to Climate Change in North Mecha District, North Gojam Zone, Amhara Region, Ethiopia Tewachew Alem Climate change and variability is causing the greatest environmental, social and economic threats to all countries across the world; however, the problem is severe to many developing countries including Ethiopia emanated from their weak adaptive strategies. This study intends to assess farmers perception, vulnerability, adaptation strategies used and the determinants that hinder to cope up to climate change and variability in North Mecha district, Amhara Region, Ethiopia. The study employed both quantitative and qualitative approaches. Primary data for the study were collected through questionnaire from 138 respondents, focus group discussion, key informant interviews and field observation selected through stratified random sampling technique based on agro-ecology. Secondary data were collected from NASA power. LVI and LVI-IPCC methods were used to assess farmers vulnerability to climate change and variability using SPSS version 26. Descriptive statistics are used to characterize perception and dummy variables and to compare them using the chisquare test. The statistical data was entered and analyzed using SPSS version 26. The overall vulnerability result in the case of LVI is 0.394 and 0.382 for Woyna Dega and Dega agroecological zones respectively. Similar result was found using the LVI-IPCC approach which was 0.051 for Woyna Dega and 0.019 for Dega agro-ecology. Both LVI and LVI-IPCC results revealed that Woyna Dega was greater vulnerable to climate change and variability than Dega agro-ecology. The LVI-IPCC also implied that households of Woyna Dega were more exposed, relatively more sensitive and relatively less adaptive capacity to climate change and variability than Dega agro-ecology. The results revealed significant increasing trend in annual average temperature and decrease annual rainfall trend as well as a higher rainfall variability. Similarly, 75.4% and 73.9% of the interviewed households were aware of an increase in temperature and a decrease in rainfall respectively. The multiple response model results revealed that the main adaptation strategies of farmers include soil and water conservation, use improved variety, planting tree, water harvesting and irrigation. Binary logistic model revealed determinant factors such as gender, age, family size, educational status, farming experience, access to extension service, access to climate information, lack of infrastructure, farmland size and financial problem which has a positive and negative impact on different adaptation strategies used by farmers in the district. The findings can be used by the government of Ethiopia reduce vulnerability and enhance adaptive capacity of both agro-ecologies. In addition, income and livelihood diversification may be one of the options to reduce vulnerability in both agro-ecologies of the study area. Sun, 01 Jun 2025 00:00:00 GMT http://ir.bdu.edu.et/handle/123456789/16918 2025-06-01T00:00:00Z http://ir.bdu.edu.et/handle/123456789/16917 Evaluation of Satellite/Reanalysis Rainfall Products for Simulating Rainfall Extremes in Jemma Sub-Basin Selemon Tsegaye Reliable rainfall extreme simulation is critical for understanding extreme precipitation patterns and guiding climate adaptation strategies in regions like Jemma sub-basin, Ethiopia. This study evaluates the performance of five satellite/reanalysis rainfall datasets such as the Climate Hazards Group Infrared Precipitation with Stations, version 2.0 (CHIRPS v2.0), Tropical Applications of Meteorology using Satellite and ground-based observations, version 3.1 (TAMSAT v3.1), Multi-Source Weighted-Ensemble Precipitation, version 2.8 (MSWEP v2.8), Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA v2) and ERA5 against observed data (2000-2023). The evaluation focused on simulating daily rainfall, detecting rainfall events, and capturing extreme rainfall indices across sub-tropical and temperate agroecological zones (AEZs) of Jemma sub-basin. For this study point to pixel evaluation approach was used and the Statistical metrics, including continuous statistical metrics such as root mean square error (RMSE), percent bias (PBIAS), Kling–Gupta efficiency (KGE’), and correlation coefficient (R), and categorical statistical metrics such as probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and frequency bias Index (FBI), were used to assess their performance by give the rank after calculating comprehensive rating index (CRI) value. The results revealed that based on CRI value MSWEP v2.8 has first ranked better performance than others products over sub-tropical and temperate AEZs at the daily time scales and also it is a better detecting capability of rainfall events. CHIRPS v2.0 demonstrated superior performance across different indices by holding first rank based on CRI value, proving particularly effective in simulating extreme rainfall indices. MSWEP v2.8 ranked second, showing notable strengths in the temperate AEZ. In contrast, ERA5 and MERRA v2 exhibited substantial biases and weaker correlations, indicating limited applicability. These findings highlight the importance of selecting region-specific rainfall datasets to improve disaster management, water resource planning, and sustainable agriculture in climatevulnerable regions like the Jemma sub-basin. Wed, 01 Jan 2025 00:00:00 GMT http://ir.bdu.edu.et/handle/123456789/16917 2025-01-01T00:00:00Z