Arba Minch University

Understanding resilience dynamics remains a central challenge in social-ecological systems research, particularly in marginalised and rapidly transforming local contexts. Although resilience theory offers a compelling framework for how systems absorb disturbances, adapt, and transform, managing system resilience remains difficult without explicitly foregrounding human agency. This paper introduces an enriched model for analysing resilience dynamics at local scales by integrating the adaptive cycle framework with human responses conceptualised as coping, adaptation, and transformation. Applied to one of Ethiopia’s most marginalised local contexts—the agropastoral Lower Omo Valley, which bears the burden of state-led mega-developments such as hydroelectric dams and sugar plantations—the model examines how local actors navigate the externally imposed changes and either sustain or lose resilience. Findings reveal that while national narratives frame these developments as transformative, local responses are predominantly characterised by coping. The study documents declining agricultural, livestock, and decision-making potential, weakened social and economic connectedness, and limited adaptive capacity. It argues that resilience at local scales is undermined by large-scale interventions that exclude community agency, pushing systems toward maladaptive responses and poverty traps. By foregrounding local agency and integrating resilience responses into adaptive cycle analysis, the study provides a nuanced understanding of resilience dynamics in marginalised social-ecological systems highly vulnerable to regime shifts. Our findings have broader implications for resilience-building in marginalised regions, particularly where external interventions reshape local systems without community participation.

The occurrence, strength, and geographical spread of flooding events are greatly affected by climate change. Rising temperatures, changing precipitation patterns, and increased extreme weather events are likely to increase the severity and frequency of flood risk. The study is to assess flood risk mapping for the Afia River Basin using projected climate change scenarios SSP2–4.5 and SSP5–8.5 for the mid-2050s (2041–2070) and late 2080s (2071–2100). Using HEC-HMS and HEC-RAS, flood scenarios and mapped flooded areas for different return intervals (50 and 100 years), were modelled. The results show notable differences in flood risk levels between the scenarios. The total expected flooded area over the 50-year period in the 2050s is projected to be 117.92 ha in SSP2–4.5 and 129.13 ha in SSP5–8.5. The flooded area increases to 155.01 ha and 165.15 ha for the 100-year return period. By the 2080s, these areas further expand, with 50-year return periods showing inundation areas of 151.84 ha for SSP2–4.5 and 158.27 ha for SSP5–8.5, and 100-year return periods reaching 178.72 ha and 186.50 ha detailed assessment of flood depths reveals that in the 2050s, low hazard zones (D < 0.6 m) range from 86.35 to 96.60 ha, while very high hazard zones (D > 3.5 m) span from 4.95 to 4.98 ha. By 2080s, low-hazard areas increase to 114.42 and 119.98 ha, with very high-hazard areas extending from 6.10 to 6.39 ha. The combination of HEC-HMS and HEC-RAS modeling offers a strong platform for simulating and studying potential flood scenarios.

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

Extreme climate and weather conditions pose significant risks to public health, economic stability, and the quality of both built and natural environments. This study evaluates projected changes in climate extreme events over the Omo-Gibe River Basin (OGRB), Ethiopia, using 12 CMIP6 Global Climate Models (GCMs). Additionally, rainfall-runoff simulations were assessed using the HEC-HMS hydrological model. Due to the region's susceptibility to extreme hydro-meteorological events like floods and droughts, gaining insight into future climate variability is essential for managing water resources effectively and reducing disaster risks. This analysis examines extreme precipitation and temperature indices under two future climate scenarios, SSP2-4.5 and SSP5-8.5, across the short-term (2023-2053) and mid-term (2054-2084) periods. Daily precipitation and temperature observations from 1984 to 2014, provided by the Ethiopian Meteorology Institute, were utilized for model validation. Bias correction was applied using the distribution mapping method to enhance the accuracy of CMIP6 simulations. Model performance was assessed using statistical evaluation metrics, including the coefficient of determination (R2), mean bias error (MBE), root mean square error (RMSE), and categorical indices such as the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). Results indicate a significant increase in extreme precipitation indices such as R95pTOT (up to +8.73 mm/year) and PRCPTOT (+7.49 mm/year) in specific clusters, while consecutive dry days (CDD) show decreasing trends. Temperature extremes are projected to rise, with TXn and TNn increasing by approximately 0.03 circle C per year. Bias correction substantially improved model performance, reducing mean precipitation bias by approximately 60% and lowering RMSE for extreme indices such as R95pTOT and Rx1day by about 40% relative to the raw simulations. The projections also indicate a decline in consecutive dry days (CDD), with reductions of about 9 days under both SSP2-4.5 and SSP5-8.5 compared to the historical period. Streamflow simulations using the HEC-HMS model reveal a projected increase of 36.3%-92.8% in annual discharge by 2073, with shifting seasonal peaks. These findings highlight the growing risk of extreme climate events in the basin, necessitating adaptive water resource management strategies and improved climate resilience planning.

Abstract Groundwater is Ethiopia’s principal source of domestic, industrial, and agricultural water, yet its quality is increasingly stressed in flood-prone, rapidly urbanising basins. This study assessed seasonal and flood-period variations in surface water and groundwater quality and well resilience in the Akaki well field using an integrated hydroclimatic–hydrochemical framework. Long-term rainfall records (1994–2024) were analysed with seasonal and event-based (pre-, peak-, and post-flood) hydrochemical data from surface water and production wells to compare dry-, wet-, and flood-period conditions. Rainfall during the main wet season (June–August) averaged 230–280 mm with high interannual variability (CV > 35%), coinciding with recurrent flooding in low-lying areas. Surface water showed marked seasonal contrasts: dry-season low dilution raised EC (850–1900 µS/cm), TDS (550–1200 mg/L), major cations, and chromium (1.8–4.1 mg/L), whereas wet-season flooding lowered EC (600–1050 µS/cm) and TDS (400–700 mg/L) but increased turbidity (16–34 NTU), nitrate, and Escherichia coli (~ 975 CFU/100 mL). Wells with damaged seals exhibited turbidity up to 45 NTU, nitrate up to 180 mg/L (NO₃⁻), and Cr, Pb, and Cd levels beyond WHO drinking-water guidelines. In contrast, seal-intact wells maintained consistent Ca–HCO₃⁻ facies, fluoride levels of 0.6–3.9 mg/L, and only mild seasonal variation. Multivariate analysis attributed 60.2% of groundwater variability to anthropogenic and microbiological influences and 20.5% to geogenic processes. An AHP-based resilience index (0.282–0.577), derived from three dimensions and five indicators, indicates low to moderate adaptive capacity, highlights the need for better wellhead protection and maintenance under recurrent floods.