http://ir.bdu.edu.et/handle/123456789/1990 2026-07-13T14:42:38Z http://ir.bdu.edu.et/handle/123456789/16866 Evaluating Satellite-Derived Precipitation Products and Climate Change Impact on Smallholder Livelihoods and Adaptation Strategies in West Gojjam Zone, Central Highlands of the Abbay Basin, Ethiopia Taye, Mulugojjam The central highlands of Ethiopia’s Abbay Basin, a region highly dependent on rain-fed agriculture, face significant challenges due to climate change and variability, threatening agricultural productivity. This dissertation address the critical need for reliable rainfall data, and understanding of rainfall variability and trends, projection future climate scenarios, and a clear assessment of the vulnerability and the dynamics of adaptation strategies implemented by smallholder farmers. The study seeks to answer: which satellite-based precipitation dataset is most reliable for the study area? How variable are rainfall patterns, and what are the trends? What are the likely future changes in rainfall and temperature? How vulnerable are local communities, and How adaptation strategies variable from time to time and place to place? The study area covers the west Gojjam Zone in the central highlands of Abbay Basin. Using four satellite precipitation data estimators (SPEs), Climate Hazards Group Infrared Precipitation Stations (CHIRPSv2), the Climate Prediction Center (CPC) morphing technique (CMORPH), the Integrated Multi-satellite Retrieval for GPM (IMERG-06) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSSIANN-CCS) and GCM/CMIP6 precipitation and temperature (max and min) data, against ground-based gauged data and blended/ENACTS data, respectively. Household survey and KI also included. The research applied categorical (Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), and Accuracy) and continuous (Root mean square error (RMSE), Relative bias, bias ratio) metrics for performance evaluation. Rainfall variability was analysed, parameters on onset and cessation dates, dry spells, Coefficient of Variation (CV %), and Standardized Rainfall Anomalies (SRA) were utilized to evaluate rainfall variability and seasonality. Trend analysis was carried out using Mann-Kendall test and Sen’s slope estimator. Climate projection relied on CMIP6 models under Shared Socioeconomic pathways (SSP2-4.5 and SSPS5-8.5) scenarios. Bias corrected data was extracted with Climate Model for hydrology (CMhyd) tool while vulnerability was assessed via Livelihood Vulnerability Index (LVI) and LVI-IPCC frame work. Descriptive statistics and Chi-square test was included to investigate the variation of adaptation strategies between the study agro ecological zones and decadal variation. IMERGE-06 emerged as the most reliable rainfall dataset, with CMORPH excelling in event detection (POD 0.9, CSI 0.74). The region showed moderate rainfall variability (CV: annual 10.7%, kiremt 11.7%, belg 10.6, and bega 22.6%), with delayed onsets (mean 142) Day of the Year (DOY) and early cessation date (279 DOY) shortening growing season. Trend analysis revealed significant annual (+9.14 mm/year) and belg (+6.94 mm/year) rainfall increases, while kiremt rainfall decline slightly. Future projection indicate annual rainfall increase up to 32% by mid-century and temperature rises up to 29oC under a high emission scenario. Vulnerability analysis found highland communities more exposed and less adaptive, with midland projected to be more vulnerable. Furthermore, the study area demonstrated significant variations in adaptation strategies within the Dega and W/Dega AEZs. During the 1990s, adaptation strategies focused on farmland expansion and intensification of irrigation. In the 2010s, farmers increasingly adopted row planting, greater use of fertilizer, crop diversification and improved seed varieties. By the 2020s, strategies shifted towards adjusting cropping calendars, further diversifying crops, and increasing the use of fertilizer and pesticides. In conclusion, context specific adaptation strategies, improved climate information, and institutional support are recommended to enhance resilience. Future research should incorporate additional climate variables and expand vulnerability assessment to better inform sustainable adaptation planning. 2025-11-01T00:00:00Z http://ir.bdu.edu.et/handle/123456789/16853 Assessment Of Groundwater Vulnerability To Pollution Using Modified Drastic Model In Bure Town, Ethiopia Atinkut, Kelemu Groundwater resources, which account for about 30 percent of global freshwater, are the most abundant, clean, and important resource for human and ecosystem adaptability. However, the quality of groundwater is gradually deteriorating due to rapid population increase, urbanization, climate change, and anthropogenic activities. So, groundwater quality protection has become a global concern. One of the mechanisms for protecting groundwater quality is vulnerability assessment to test its sensitivity to pollution. The study was carried out in Bure town which hosts a large number of population settlements and several industries with no good working waste stabilization ponds. The study aimed to assess the vulnerability of groundwater to pollution in Bure town using the modified DRASTIC model. In this study, the modified DRASTIC model was used which integrated eight parameters including depth to water, net recharge, aquifer media, soil media, topography, the impact of the vadose zone, hydraulic conductivity, and land use land cover. The spatial variation of vulnerability was identified by integrating the thematic layers using a raster calculator tool on the GIS environment. Primary data types such as remote sensing data and meteorological data were primary data as well as secondary data inputs such as geological maps, soil maps, hydrogeological log data, pumping test data, static water levels, and water quality data were used. The result of vulnerability revealed vulnerability index value of the study area ranges from 74 to 173. Accordingly, 67.05% of the area was under medium to high vulnerability zone. Areas with a high vulnerability index (35.82%) were found in the central and northwestern part of Bure town in association with industrial, residential, and intensive agricultural areas respectively while low vulnerability zones (32.99%) were found in most of the eastern peripheral part in association with plantation and other open areas. The validation of the model using Pearson's correlation coefficient "R2" was determined to be 0.55 suggesting a positive correlation between the nitrate value and vulnerability index. Sensitivity analysis revealed that the depth to water level and the land use factor were influencing factors. The spatial variation of water quality parameters showed variations from one site to another although most of them are within the acceptable limits. The value of pH ranges from 6.5 to 8.4, indicating the acidic to alkaline nature of the water which was supported by variations in EC and TDS which range from 331.9 μS/cm to 5429.6 μS/cm and 215.8 and 3555.3 mg/L respectively. Higher levels of nitrate (33.9 mg/L) were revealed in northwestern sites where intensive agriculture (as a non-point source) is practiced followed by residential and industrial areas. A strong correlation between agricultural 2025-01-01T00:00:00Z http://ir.bdu.edu.et/handle/123456789/16847 Groundwater Quality Potential Zones Delineation Using Geographic Information System and Analytical Hierarchical Process: the case of East Gojjam Zone, Amhara Regional State, Ethiopia Eskemeche, Temesgan Water is the prime natural resource, which is basic for healthful functioning of any ecosystem. However, estimating the groundwater quality potential zone has remained uncertain and less studied due to its complex nature. In the East Gojjam zone, the surroundings of Debre-Markos Town; in particular, groundwater development for various purposes is increasing. Therefore, assessment of the groundwater potential is very crucial for sustainable use of groundwater. Satellite images have been widely used for groundwater exploration because of its capability to identify indicators of groundwater potential zones. The main objective of this study it to delineate the groundwater potential of East Gojjam Zone, Amhara region, Ethiopia using geo information system combined with analytical hierarchal processes and water quality index methods. A purpose full sampling technique with a minimum of 50 sample size were collected from each category through observation and transact walkover. By consulting existing literatures, for this study ten most important groundwater controlling factors:- lithology, lineaments density, drainage density, geomorphology, rainfall, slope, land use land cover, elevation, and soil depth and soil texture that derived from satellite data and existing secondary data were selected. These thematic layers were prepared and converted to raster data format. Then, weightages were assigned for each thematic layers and ranked based on the knowledge that gained from literatures and expert opinions. Finally, the thematic layers overlaid to determine the groundwater potential based on the assigned rank and weight using Arc GIS spatial analysis tool. The groundwater potential map was validated through receiver operating characteristics (ROC) method of area under the curve (AUC) value were used based on wells yield data that are collected from different offices. The groundwater quality map was prepared based on the 52-groundwater point data of having quality test and yield results with geographic coordinates. The Ethiopian drinking water standard of 2013 using the WQI technique was used to test the groundwater quality. The results show that there are good agreements of 91.8% between the predicted groundwater potential map and the existing groundwater wells data. The groundwater quality potential map was reclassified into three groundwater quality potential zones of very high, high, and moderate. The result indicated that, about 92.09% (12,864.88 km2) of the study area was high, 6.24% (871.6 km2) of the study area was very high, and 1.67% (232.7728 km2) as moderate groundwater quality potential zone. The study suggested that, generated GWPZM would serve as useful guidelines for planners, engineers, and decision makers providing quick decision- making in the management of groundwater resources. 2025-01-01T00:00:00Z http://ir.bdu.edu.et/handle/123456789/16844 Integrated Geospatial-Based Suitable Site Identification for Forest Landscape Restoration in Libokemkem District of South Gonder, Ethiopia Zelalem, Teshager Forest degradation significantly impacts biodiversity, carbon storage, water regulation, and forest-dependent livelihoods, necessitating effective restoration efforts. Identifying optimal sites for forest landscape restoration is critical for sustainable forest management. This study employed a GIS-based Multi-Criteria Decision Analysis (MCDA) using the Analytical Hierarchy Process (AHP) to assess forest landscape restoration suitability in Ethiopia's Libokemkem District. Sampling techniques and data collection instruments involved acquiring high-resolution geospatial data from diverse sources, including ALOS PALSAR DEM (12.5m), PERSIANN-CCS rainfall (4km), Sentinel-2 LULC (10m), FAO soil maps, and OpenStreetMap for infrastructure. Ground truth data from Google Earth validated the LULC map, achieving an 85% overall accuracy and a Kappa coefficient of 74%. Expert judgment, formalized through AHP pairwise comparisons, determined factor weights. The Consistency Ratio (CR) of 0.056 (5.6%) confirmed high judgment reliability. LULC (26%), Slope (20%), Distance from Forest Patches (15%), and Proximity to River (13%) emerged as the most influential factors. Conversely, Distance from Roads and Settlements (both 3%) had minimal influence. The Weighted Overlay Analysis revealed that the majority of the district (61.29%) is Moderately Suitable (S2) for restoration, covering 845.93 km2. Marginally Suitable (S1) areas constitute 38.10% (525.84 km2), while Highly Suitable (S3) sites are scarce, accounting for only 0.62% (8.49 km2). Negligible areas were classified as Not Suitable (N). This geospatial framework provides a robust, data-driven foundation for strategically prioritizing and implementing targeted forest restoration initiatives across suitable zones. Its application is crucial for enhancing ecological resilience and fostering sustainable socio-economic benefits in the region. 2025-06-01T00:00:00Z