To identify and quantify the impact of climate change on socio-economic sectors and ecosystems, many global studies have been carried out and policy changes for mitigation and adaptation were recommended. However, the old-fashioned top-down approach, global study, has little local and regional specificity and has failed to address the regional and local consequences of climate change. Thus, this research was conducted in filling such knowledge gaps in Lake Tana sub-basin. The research was aimed at addressing the national and local issues of climate change and was done by analyzing time series temperature and rainfall trends in the highlands of Ethiopia and LTSB in particular. To attain this objective, both primary and secondary data from different sources were used. The collected data were analyzed following quantitative and qualitative analysis methods and Manna Kendall Trend test method was used. The main findings revealed that the mean, maximum and minimum temperature had a general increasing trend; whereas, rainfall amount showed a general decreasing trend in Lake Tana Sub-basin. Analysis of the 40 years annual total rainfall data from 109 representative ground based meteorological stations in Ethiopia indicated a coefficient of variation ranging from 20 to 89 %. Of the 109 stations considered, 17 had above 42 % coefficient of variation highlighting the extreme variability of rainfall over the country To conclude, development planners should design strategies and plans by taking into account a declining summer rainfall and increasing temperature impacts on rural livelihoods.

Background: Land degradation is one of the major threats to food security and natural resources conservation Zikre watershed. The objective of the study was to investigate the effects of land use types, management practices and slope classes on soil physico-chemical properties.Results: Results of the experiment indicated highest mean values of total nitrogen (0.23%), organic matter (5.01%) and cation exchange capacity (35.44 cmol₍₊₎kg⁻¹) were recorded under the natural forest and the lowest values of the same (0.12%, 2.57% and 26.08 cmol₍₊₎kg⁻¹, respectively) were registered in crop lands. Available phosphorus content was the highest (6.18 mg kg¹) in crop lands and the lowest (1.33 mg kg⁻¹) in grazing lands. Comparing management practices the highest mean values of available phosphorus (18.41 mg kg⁻¹), organic matter (5.88%) and total nitrogen (0.29%) were recorded from the cultivated land treated with both manure and soil bund compared to sole soil bund, sole manure and the control plots. Considering the slope classes, the higher mean values of total nitrogen (0.19%), organic matter (4.49%) and cation exchange capacity (33.09 cmol₍₊₎kg¹) were recorded in the lower slope classes followed by middle (0.17%, 3.39% and 30.58 cmol₍₊₎kg⁻¹, respectively) and upper slope classes (0.14%, 2.65% and 27.36 cmol₍₊₎kg⁻¹, respectively).Conclusions: To conclude, conversion of forest lands to cultivated and grazing lands had detrimental effects on the soil physico-chemical properties; whereas construction of soil bunds on farm fields and application of manure improve the same under subsistence farming systems.

Climate predictions using recent and high-resolution climate models are becoming important for effective decision-making and for designing appropriate climate change adaptation and mitigation strategies. Due to highly variable climate and data scarcity of the upper Blue Nile Basin, previous studies did not detect specific unified trends. This study discusses, the past and future climate projections under CMIP6-SSPs scenarios for the basin. For the models’ validation and selection, reanalysis data were used after comparing with area-averaged ground observational data. Quantile mapping systematic bias correction and Mann–Kendall trend test were applied to evaluate the trends of selected CMIP6 models during the 21st century. Results revealed that, ERA5 for temperature and GPCC for precipitation have best agreement with the basin observational data, MRI-ESM2-0 for temperature and BCC-CSM-2MR for precipitation were selected based on their highest performance. The MRI-ESM2-0 mean annual maximum temperature for the near (long)-term period shows an increase of 1.1 (1.5) °C, 1.3 (2.2) °C, 1.2 (2.8) °C, and 1.5 (3.8) °C under the four SSPs. On the other hand, the BCC-CSM-2MR precipitation projections show slightly (statistically insignificant) increasing trend for the near (long)-term periods by 5.9 (6.1)%, 12.8 (13.7)%, 9.5 (9.1)%, and 17.1(17.7)% under four SSPs scenarios.

Lake Tana is Ethiopia’s largest lake and is infested with invasive water hyacinth (E. crassipes), which endangers the lake’s biodiversity and habitat. Using appropriate remote sensing detection methods and determining the seasonal distribution of the weed is important for decision-making, water resource management, and environmental protection. As the demand for the reliable estimation of E. crassipes mapping from satellite data grows, comparing the performance of different machine learning algorithms could help in identifying the most effective method for E. crassipes detection in the lake. Therefore, this study aimed to examine the ability of random forest (RF), support vector machine (SVM), and classification and regression tree (CART) machine learning algorithms to detect E. crassipes and estimating seasonal spatial coverage of the weed on the Google Earth Engine (GEE) platform using Landsat 8 and Sentinel 2 images. Cloud-masked monthly median composite Landsat 8 and Sentinel 2 data from October 2021 and 2022, January 2022 and 2023, March 2022, and June 2022 were used to represent autumn, winter, spring, and summer, respectively. Four spectral indices were derived and used in combination with spectral bands to improve the E. crassipes detection accuracy. All methods achieved greater than 95% and 90% overall accuracy when using Sentinel 2 and Landsat 8 images, respectively. Using both data sets, all methods achieved a greater than 93% F1 score for E. crassipes detection. Though the difference in performance between the methods was small, the RF was the most accurate, while the SVM and CART methods had the same accuracy. The maximum E. crassipes coverage area was observed in autumn (22.4 km²), while the minimum (2.2 km²) was observed in summer. Based on Sentinel 2 data, the E. crassipes area coverage decreased significantly by 62.5% from winter to spring and increased significantly by 81.7% from summer to autumn. The findings suggested that the RF classifier was the most accurate E. crassipes detection algorithm, and autumn was an appropriate season for E. crassipes detection in Lake Tana.

Agriculture is the most susceptible sector to climate change related hazards. Unusual temperature and rainfall occurrence in terms of amount and distribution usually lead to poor harvest and/or complete crop failure and shortage of pasture and animal feed in Ethiopia. Such extreme conditions ultimately result in drought with a resultant depletion of assets, societal vulnerability, mass migration and loss of life. This research work has been conducted to fill such knowledge gaps of the target population in Lake Tana Sub-Basin. The objectives of the research were to assess the perception of the rural people about climate change and adaptation models. To attain this research objective, both primary and secondary data from different sources were collected. The collected data statistical analyses were done by STATA version 11 computer program.