Summary
to develop a meteorological-hydrological forecasting system for the transboundary water management of the Blue Nile. The so-called "seamless prediction" approach combines overlapping forecast horizons ranging from days to several months. Key forecast variables – including precipitation, reservoir inflow, sediment input, and potential agricultural yields – enable a comprehensive assessment within the water-food-energy nexus, with a particular focus on the Grand Ethiopian Renaissance Dam (GERD). A special emphasis is placed on ensemble-based, probabilistic forecasting of hydro-meteorological extreme events: from heavy precipitation and floods (short-term, a few days) to droughts and heat periods in the subseasonal-to-seasonal range (S2S, weeks to months). For practical application, the approaches are translated into cloud-ready modules and microservices that allow flexible implementation adapted to local conditions. Development takes place in close collaboration with Ethiopian and Sudanese partners from academia, policy, and water management. Through sandwich doctoral programmes, joint workshops, and engagement in broader initiatives, an international expert dialogue is fostered between specialists from Ethiopia, Sudan, Germany, and other countries.
Funding code: 02WGR1643A-C
Project duration: 01.09.2022 - 30.04.2026
Publications & Products
Integrating dynamic planting dates into Noah-MP-Crop for sorghum simulation in semi-arid regions
By integrating dynamic, satellite-derived planting dates into the process-based crop-climate model Noah-MP-Crop, sorghum growth in the semi-arid eastern Nile Basin is simulated more realistically. Compared to fixed planting dates, dynamic sowing information substantially improves model performance with respect to leaf area index and crop yields — offering a transferable approach for crop-climate assessments in data-scarce regions.
Toward a Seamless Sub-Seasonal to Seasonal Prediction System for the Blue Nile Basin
By regionally enhancing ECMWF forecasting products (SEAS5 and sub-seasonal forecasts) through statistical and deep learning-based post-processing methods, a seamless S2S prediction system for the Blue Nile Basin is developed. Although the post-processed meteorological forecasts alone barely outperform climatology, they demonstrate clear added value for sector-specific decision-making when coupled with hydrological and crop models.
Improved seasonal precipitation forecasts for the Blue Nile Basin: a deep learning approach.
The study proposes Seasonal AFNOCast, a deep learning approach based on an Adaptive Fourier Neural Operator, for bias correction and downscaling of seasonal precipitation forecasts in the Blue Nile Basin, benchmarked against the established statistical method BCSD. Results show that both methods substantially improve forecast quality compared to the raw data; however, Seasonal AFNOCast demonstrates clear advantages over BCSD, particularly at longer lead times, in the representation of extreme events, and in computational speed (5–20× faster).
How to Improve Forecast Dissemination in the Blue Nile River Basin. Challenges, Opportunities and Ways Forward.
The paper examines how climate and weather forecasts are communicated across the Blue Nile River Basin and how governments, global and regional organisations, and development partners can support existing systems and practices, published in March 2026.
Suspended Sediment Fluxes and Decadal Trends in the Humid Tropics: Machine Learning Reconstruction and Coupled Modelling in Upper Blue Nile Tributaries
Using machine learning methods — in particular Quantile Regression Forests (QRF) — continuous daily sedigraphs (1990–2020) are reconstructed for two catchments in the Upper Blue Nile Basin and used for the first time to calibrate the coupled hydro-sedimentological model WASA-SED. Decadal analyses reveal contrasting trajectories: whilst intensifying rainfall in the Gilgel Abay catchment strongly increased streamflow but sediment loads declined in recent years, both streamflow and sediment loads rose markedly in the Gumara catchment — driven primarily by wetland loss and rapid urban expansion.
Seasonal forecast of streamflow and suspended sediment in the Blue Nile Basin, Ethiopia
Investigating the potential of a coupled hydro-meteorological seasonal forecasting system for streamflow and sediment transport in the Upper Blue Nile Basin (Ethiopia), upstream of the GERD dam, with a lead time of up to seven months. By combining the ECMWF-SEAS5 precipitation product with the hydro-sedimentological model WASA-SED and a downstream autoregressive bias-correction approach, results demonstrate that the system can reliably predict rainfall and streamflow on a seasonal scale.
Policy Brief. How to Improve Forecast Dissemination in the Blue Nile River Basin. Challenges, Opportunities and Ways Forward.
The Policy Brief summarises the findings of the Policy Paper, which examines how climate and weather forecasts are communicated across the Blue Nile River Basin and how governments, global and regional organisations, and development partners can support existing systems and practices. Published in March 2026.
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