德布雷马克洛斯大学

Accurate simulation and projection of precipitation extremes are critical for climate adaptation planning. This study aims to evaluate the performance of the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) in simulating extreme precipitation and to project future changes in the Jemma subbasin. Daily precipitation data from seven meteorological stations in subtropical and temperate agroecological zones (AEZs) were used to evaluate CMIP6 GCM performance based on ten precipitation extreme indices. The top-performing GCMs were selected based on their skill assessed through the Comprehensive Rating Index (CRI). The GCMs were then used to project changes under three greenhouse gas emission scenarios (SSP24.5: medium emissions, SSP3-7.0: high emissions, and SSP5-8.5: very high emissions) for near (2030-2049), mid (2060-2079), and far future (2080-2099) periods relative to 1995-2014. The selected 8 GCMs demonstrated moderate skill, with CRI values ranging from 0.29 to 0.78 in the subtropical AEZ and 0.44 to 0.80 in the temperate AEZ. Indices such as Rx5day (multi-day maximum rainfall) and R10mm (heavy rainfall frequency) were captured more reliably than dry-spell metrics like consecutive dry days (CDD). Projections reveal notable variability in extreme precipitation across scenarios, periods, and AEZs. In the subtropical AEZ, R10mm and R20mm are projected to rise by 1.57 and 0.28 days/year, respectively, under SSP3-7.0 (near-term). Rx5day is expected to increase by 1.5 mm (SSP2-4.5, far-term) and 1.54 mm (SSP3-7.0, mid-term), while R95p (very wet day precipitation) rises by 7.51 mm (SSP3-7.0, near-term). In the temperate AEZ, mixed trends are observed: Rx1day increases by 0.88 mm (SSP5-8.5, far-term), Rx5day rises by 1.5 mm (SSP3-7.0, mid-term), but drops by 1.29 mm (SSP2-4.5, far-term). R95p generally trends upward, although a 5.36 mm decline occurs under SSP3-7.0 (near-term). Annual total wet-day rainfall (PRCPTOT) and SDII (simple daily intensity index) mostly increase, though occasional SDII declines suggest heightened uncertainty. The projected changes in precipitation extremes suggest increasing challenges for agricultural systems and water resource management, underscoring the need for climate-resilient planning in the Jemma sub-basin.

This study evaluates landslide susceptibility in the Sego catchment, southern Ethiopia, by integrating a detailed landslide inventory with bivariate statistical modelling in a GIS environment. This study addresses a key gap in Ethiopian Rift catchments by providing the first quantitatively validated landslide susceptibility assessment for the Sego catchment, and by explicitly comparing Information Value (IV) and Weight of Evidence (WoE) using a high-resolution inventory and multi-source conditioning factors in a data-scarce setting. Landslide locations (125 events) were identified through field surveys and visual interpretation of high-resolution Google Earth imagery; 94 landslides (75

ObjectiveThis study evaluated the phytochemical composition and antibacterial activity of extracts of Rumex abyssinicus roots and Verbascum sinaiticum leaves, two medicinal plants traditionally used in Ethiopia.MethodsExtracts were prepared by cold maceration, and preliminary toxicity was assessed in Galleria mellonella larvae. Antibacterial activity was determined by the disc diffusion method against Staphylococcus aureus (ATCC 25923), Salmonella typhi (ATCC 19430), Escherichia coli (ATCC 25922), and Shigella sonnei (ATCC 25931). Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values were established by broth microdilution. Qualitative phytochemical screening was conducted, and principal bioactive constituents were characterized by thin-layer chromatography (TLC) and nuclear magnetic resonance (NMR) spectroscopy.ResultsThe extracts demonstrated dose-dependent toxicity in G. mellonella, with up to 40% mortality at 100 mg/mL. Ethanol extracts of R. abyssinicus exhibited the strongest antibacterial effects, notably against S. aureus (21.3 +/- 1.21 mm at 200 mg/mL), whereas chloroform extracts of V. sinaiticum showed modest inhibition at higher concentrations. The MIC ranged from 16-32 mg/mL for the ethanol extracts and 32-64 mg/mL for the chloroform extracts of both plant materials. Phytochemical analysis identified anthraquinones, saponins, and tannins in R. abyssinicus, and flavonoids and phenols in V. sinaiticum. NMR profiling revealed nine major compounds, including chrysophanol, emodin, physcion, helminthosporin, and chrysophanol-bianthrone from R. abyssinicus, and luteolin, chrysoeriol-7-O-glucoside, aucubin, and ajugol from V. sinaiticum.ConclusionThese results provide a chemical basis for the observed antibacterial activity and support further investigation of these extracts and isolated metabolites as potential leads for antibacterial drug development.

The Northern Ethiopian Highlands are a critical agroecological zone where climate change poses serious challenges to rainfed farming, yet the region’s climatic dynamics remain poorly quantified. This study addresses that gap by examining spatiotemporal trends in temperature, precipitation, and agroclimatic indices, Rainfall Onset Date (ROD), Cessation Date (RCD), and Growing Season Length (GSL) from 1981 to 2020. To overcome sparse station networks in complex terrain, we utilized the ENACTS dataset, providing a robust 4-km gridded product. Using an analytical framework integrating the Modified Mann–Kendall test, Theil–Sen slope estimator, Innovative Trend Analysis (ITA), and Inverse Distance Weighting (IDW), we detected significant (p < 0.05) warming trends across all stations. Mann-Kendall values for maximum (8.64–14.5) and minimum (1.45–2.07) temperatures confirmed monotonic warming over the 40 years. Precipitation trends were heterogeneous, with 75 This graphical abstract presents the spatiotemporal dynamics of climate and agroclimatic shifts in the Northern Ethiopian Highlands from 1981 to 2020. Using ENACTS data, the visualization presents statistical trends in rainfall and temperature analyzed through a multi-method framework involving the Modified Mann-Kendall test, ITA, and the Theil-Sen estimator. Variability indices, including the Precipitation Concentration Index (PCI), Coefficient of Variation (CV), and Rainfall Anomaly Index (RAI) are integrated to map the distribution of Rainfall Onset Date (ROD), Cessation Date (RCD), and Growing Season Length (GSL). The visualization indicates a pronounced east-west climate dipole, characterized by a spatial asymmetry in agroclimatic stability. The western region generally exhibits longer growing seasons and earlier rainfall onset, whereas the eastern sector shows drying trends and a higher frequency of contracted growing seasons. These patterns are presented as statistical associations consistent with regional atmospheric forcing and complex topography, not as deterministic predictions. The evidence suggests that addressing regional food security requires location-specific adaptation strategies that account for these divergent spatiotemporal trajectories Significant warming trends were detected in both Tmin and Tmax across all stations from 1981 to 2020. Rainfall declined markedly in the east; Meher season remained stable, and Belg became highly erratic. A novel east–west climate dipole identified using Mann-Kendall, ITA, and Theil-Sen methods. Rains start later and end earlier in the northeast, shortening the growing season length (GSL). Agroclimatic shifts threaten food security, demanding spatially targeted adaptation strategies.

Gram-Schmidt (GS) sharpening is a component-substitution method that uses GS orthogonalization to inject spatial detail from the panchromatic (PAN) image into the multispectral (MS) bands. A simulated PAN is formed from the MS bands, a forward GS transform decorrelates the data, the high-resolution PAN replaces the first component, and the inverse GS transform produces the sharpened MS image. In this work, an adaptive feedback-driven pansharpening (AFDP) framework is proposed to enhance the GS method. The GS result is low-pass filtered to create an updated low-resolution MS image, which is fed back into the GS process and iterated until an established quality criterion, the QNR index in our case, is met. The mathematical analysis of the AFDP framework shows that PAN details are transferred gradually across iterations, not in a single step like with the classical GS method, allowing controlled and data-driven detail injection. Experiments conducted on three satellite datasets, including WorldView-2, QuickBird and Ikonos demonstrate that the AFDP-GS method consistently enhances the fusion results, numerically and visually, compared to the classical GS method, along with a set of baseline methods including intensity-hue-saturation, principal component analysis, high-pass filtering and high frequency modulation methods.