One of the world’s major issues is climate change, which has a significant impact on ecosystems, human beings, agricultural productivity, water resources, and environmental management. The General Circulation Models (GCMs), specially the recently released (coupled model intercomparison project six) CMIP6 are very indispensable to understand and support decision makers to identify adaptation strategies in response to future climate change in a basin. However, proper selection of skillful GCMs and future climate assessment is a prior task to climate impact studies. The objective of the study is an attempt to appraise the climate model’s performance and future climate scenarios of Shared Socioeconomic Pathways (SSPs) in the Omo River Basin. The performance evaluation of 20 GCMs of the CMIP6 was properly performed to reproduce the precipitation and the maximum temperature in the basin. Their performance has been carried out against the best selected mean monthly Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) precipitation and European Community Medium Water Range Weather Forecasts Version 5 (ECMWF-ERA5) maximum temperature. The GCMs of the CMIP6 were selected and ranked using the compromise programming method of multi-criteria decision making. The result shows that ensemble models and NorESM2-MM models have been designated to reproduce the precipitation and maximum temperature in the basin respectively. The Mann–Kendall trend test was executed to appraise the trend of selected CMIP6 models, and subsequently, downscaling and bias correction techniques were conducted. The projected seasonal precipitation of June, July, August, September (JJAS) and March, April, May (MAM) shows an increasing trend with 10.86, 17.66, 38.96 and 11.85, 22.1, and 40.7% under SSP2452031-2060, SSP5852031-2060, and SSP5852071-2100 scenarios respectively. Furthermore, increasing trends were detected in MAM by 12.8% and decreasing trends in JJAS were detected by 15.23% under SSP2452071-2100 scenario. The maximum temperature projection will be increased on average by 0.95, 1.78, 1.4, and 3.88 °C in JJAS and 1.53, 2.24, 1.56, and 3.89 °C in MAM under climate change scenarios of near-future SSP2452031-2060, SSP5852031-2060, far-future SSP2452071-2100, and SSP5852071-2100, respectively. Additionally, the basin has shown temporal-spatial climate fluctuation in terms of precipitation and maximum temperature.

The reduction of traffic crashes, as well as their socio-economic consequences, has captivated the attention of safety professionals and transportation agencies. The most important activity for an effective road safety practice is to identify hazardous roadway areas based on a spatial pattern analysis of crashes and an evaluation of crash spatial relations with neighboring areas and other relevant factors. For decades, safety researchers have adopted several techniques to analyze historical road traffic crash (RTC) information using the advanced GIS-based hot spot analysis. The objective of this study is to present a GIS technique for identifying crash hot spots based on spatial autocorrelation analysis using a four-year (2014–2017) crash data across Ethiopian regions, as well as zones and towns in the Oromia region. The study considered the corresponding severity values of RTCs for the analysis and ranking of crash hot spot areas. The spatial autocorrelation tool in ArcGIS 10.5 was used to analyze the spatial patterns of RTCs and then the Getis Ord Gi* statistics tool was used to identify high and low crash severity cluster zones. The results showed that the methods used in this analysis, which incorporated Moran’s I spatial autocorrelation of crash incidents, Getis Ord Gi* and crash severity index, proved to be a fruitful strategy for identifying and ranking crash hot spots. The identified crash hot spot areas are along the entrance to and exit from Addis Ababa, Ethiopia’s capital city, so the responsible bodies and traffic management agencies should give top priority attention and conduct a thorough study to reduce the socio-economic effect of RTCs.

For the selection of global climate models in the upper basin of the Blue Nile, an advanced envelope-based approach was used. Currently, the number of general circulations models (GCM) has increased extremely. The reliability of any general circulation model in a particular region is confronted, so the selection of the appropriate climate models that can predict the climate variable is essential. Representative concentration pathways RCP4.5 and RCP8.5 were taken into account. For RCP4.5 105 GCMs were used and for RCP8.5 78 GCMs were used to select the best performance models for the Upper Blue Nile Basin for a climate change impact study. Three steps were followed to derive the best performing models in the study area based on their range of projected mean temperature and precipitation changes, the range of projected extreme changes, and the ability to reproduce past climates between 1971 and 2000 and 2071–2100. Five corners of the spectrum were used, e.g., wet-warm, wet-cold, dry-warm, dry-cold, and the 50th percentile of the temperatures. For RCP4.5 and RCP8.5, a total of 25 GCMs were chosen based on the range of anticipated mean temperature and rainfall change. Based on the range of extreme changes, 10 GCMs were chosen. Finally, for each RCP4.5 and RCP8.5, five GCMs were chosen by combining all three stages.

This study has two goals: First, to fill a gap in the use of the predictive approach for evaluating road safety performance in Ethiopia, the most recent analytical methods of the HSM predictive approach in IHSDM software were used to evaluate the safety and operational effects of the existing roadway geometric design. Second, to assure safety and a sustainable transportation system, the relative safety effects of design changes made to hazardous road segments were quantified. Based on the Crash Prediction Module (CPM) evaluation of IHSDM software, the study identified fifteen hazardous road segments on the existing rural two-lane roads of Addis Ababa to Chacha and Addis Ababa to Dillela. The design changes made to the hazardous road segments have resulted in a remarkable reduction in crash rate, especially on the first top five hazardous segments, where incredible improvements have been observed. The total safety benefits acquired by applying engineering mitigations to the fifteen identified hazardous segments are described as follows: 17.18% reduction in total crash frequency (crashes/year), 58.94% reduction in crash rate (crashes/km/year), and 58.86% reduction in travel crash rate (crashes/million veh-km). In general, the study’s findings underlined the effectiveness of performance-based road safety evaluation and design in providing safe, efficient, and economically-feasible roadway infrastructure. The IHSDM reports and graphical outputs assist decision-making in the roadway design process by providing a quantitative evaluation of the safety impact of various design features and identifying roadway segments with safety concerns. Additionally, IHSDM is a tool capable of saving time for Road Safety Audit (RSA) teams. The paper also outlined the need for a computerized crash database recording system in Ethiopia to develop jurisdiction-specific Safety Performance Functions (SPFs).

Fatigue crack deterioration in flexible pavements results from structural loading, traffic demand, material aging, and climatic exposure; yet, Ethiopian pavement models remain largely empirical, with little mechanistic foundation. This study develops a hybrid mechanistic–empirical and artificial neural network framework to forecast fatigue crack progression along a five-kilometer segment of the Woldia–Jeneto road in northern Ethiopia, built in 2015 and assessed after ten years of service. ERAPave layered elastic analysis computed critical horizontal tensile strain at the asphalt base, using the ERA manual recommendation of the Australian fatigue criterion for tropical areas, deriving cumulative damage indices via Miner’s rule. These outputs, alongside material properties, soil indices, traffic, climate, and temporal variables, formed an 18-feature input vector, which was trained using Latin–Hypercube Sampling and leave-one-out cross-validation under data-scarce conditions. Critical tandem-axle loads of 200.2 kN produced tensile strains of 182.7–199.83 με and damage ratios of 0.39–0.76 within fatigue lifetimes of 10.46–20.12 million ESALs, exceeding the 7.93 million ESAL design threshold. The model achieved R² = 0.9997 and MAPE = 1.64%; these figures reflect five-station training conditions and synthetic augmentation rather than unconditional generalization accuracy. Ten-year forecasts place Station 5 at structural failure within three years, supporting evidence-based pavement maintenance planning.