Ethiopian Journal of Water Science and Technology

Modelling Future Climate Changes Impacts on Precipitation Pattern Using a Multi-Model Ensemble of CMIP6 Scenarios for the Abaya-Chamo Sub-Basin, Ethiopia.

Desalegn Laelago, Admasu Gebeyehu Awoke — Fri, 27 Feb 2026 09:30:27 +0300

Climate change disrupts the natural water cycle and agriculture, hindering the progress toward achieving sustainable development goals. Employing bias-corrected climate model simulations is crucial for future climate change patterns prediction and informing policy decisions. This research employs a multi-model ensemble from the Coupled Model Intercomparison Project Phase 6 to assess how climate change affects precipitation patterns in the Abaya-Chamo Sub-basin located in southern Ethiopia. Future predicted precipitation datasets were evaluated under Shared Socioeconomic Pathway scenarios. The Climate Data Operators (CDOs) tool was used to interpolate global climate model results. A power transformation method was utilized to address systematic biases in the outputs of the multi-model ensemble. Spatial patterns of precipitation maps in ArcMap were generated using the inverse distance weighting method. The findings revealed that the bias-corrected mean monthly and annual precipitations were lower than the observed precipitations. The SSP2-4.5 scenario forecasted a decrease in mean annual precipitation of 6.6% to 25.85% over the near periods (2021-2064) and a decrease of 2.25% to 20.24% in the long term future (2065-2100). The spring (MAM) season experienced the largest percentage reduction of all seasons. The spatial distribution of mean annual precipitation varied widely across watersheds, ranging from 450 to 1,140 millimeters. The multi-model ensemble projection for precipitation indicates a more significant decrease in the Gidabo watersheds during the summer (JJA) and spring (MAM) seasons, highlighting spatial variability. Projected future precipitation declines are expected to reduce the amount of water available to ecosystems. Therefore, developing comprehensive, effective water resource management strategies is extremely important to adapt to these changes.

Keywords: Abaya-Chamo, Bias Correction, CMIP6, Climate Change, Multi-Model Ensemble, Precipitation.

Synchronizing Time Series Satellite Data and Real-Time Reservoir Level Measurement to Assess Sedimentation in Koka Reservoir of Ethiopia

Mon, 04 May 2026 22:05:06 +0300

Regular reservoir surveys are necessary to assess the reduction in storage capacity caused by sediment deposition. Optimal water allocation requires up-to-date information on reservoir storage and sedimentation status, yet such information is often limited. This research aimed at assessing the sedimentation of Koka reservoir by integrating time series satellite image data and real-time reservoir water level measurements. The reservoir surface area was extracted from satellite images using QGIS), aided by Automated Water Extraction Index (AWEI) to identify water pixels. Water level records from the reservoir gauging station were combined with surface area to estimate reservoir volume using a prismoidal formula. The resulting estimates were used to update the elevation-capacity curve. The findings show that the current storage capacity at full reservoir level was 780.01 Mm³. This indicates that 869.99 Mm³ of silt has accumulated in the reservoir since initial operation. This corresponds to a loss of approximately 52.73% of the original storage capacity. The mean annual sediment deposition rate over the 62 years of operation is 14.03 Mm³, equivalent to an annual sedimentation rate of 0.85%. This rate is comparable to the global index of annual reservoir sedimentation, which ranges between 0.5% and 1%. The research findings provide up-to-date information on the sedimentation status of Koka. The results support informed water allocation planning for effective reservoir water management. Scheduling sediment flushing during the peak flood periods of the upper Awash River provides an adaptive approach to managing sedimentation in the Koka Reservoir.

Advances in Remote Sensing and Machine Learning for Land Use/Land Cover Change Detection and Climate Model Bias Correction: Methods and Applications

Teshale Tirulo, Dagnachew Daniel, Ayano Hirbo — Tue, 05 May 2026 09:32:10 +0300

Ecosystems globally are increasingly threatened by the integrated impacts of human activities and climate change, especially in the Ethiopian Rift Valley, where data are scarce. Therefore, there is an urgent need for implementing advances in sustainable resource management. This study presented a systematic meta-analysis that synthesized more than 100 peer-reviewed studies (2004-2025) to critically evaluate how remote sensing (RS) and machine learning (ML) could be integrated for detecting LULC change and climate model bias. Using a PRISMA protocol, the methodological progression from traditional statistical approaches was evaluated through advanced deep-learning and hybrid methods. The results provided strong evidence that integrating RS and ML had significant transformative potential; however, there still remained a "gap" in their integration. Both fields advanced greatly, yet each existed as a largely independent entity. Recent studies addressed this disintegration, however operational frameworks were scarce. In order to close this gap, a novel integrated conceptual framework was proposed. This would dynamically integrate multi-sensor data fusion, ML-based LULC mapping, hybrid ML-statistical bias correction, and SWAT+ hydrological models into a single end-to-end processing pipeline designed specifically for basins that contained little or no data. Transfer learning and explainable artificial intelligence (XAI) could be used to solve the problem of applying data-hungry models in situations where very little or no data were available. The integration of XAI methods such as SHAP and LIME gave a promise for improving model interpretability in environmental applications. The review suggests that robust-policy-relevant environmental prediction depended on designing combined systems that were not only accurate but also interpretable and adaptable to local conditions.

Keywords: Climate Model Bias Correction; Data Integration; Explainable AI; Land Cover Change Detection; Machine Learning; Remote Sensing