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

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

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

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

Joint presentation by the Karlsruhe Institute of Technology & University of Augsburg, University of Potsdam and adelphi

Auftaktveranstaltung der BMBF-Fördermaßnahme GRoW