Adapting Noah-MP-Crop for sorghum modeling in a semi-arid environment: a case study of Gedaref region in East Sahel

Semi-arid agricultural regions are highly vulnerable to rainfall variability and heat stress, yet crop modeling in these areas is often constrained by limited field observations. Land surface models provide an important advantage over standalone crop models by simulating crop growth within a coupled water-energy-carbon framework, enabling interactions between vegetation development and land-atmosphere exchanges. This study adapted the Noah-MP-Crop model to simulate rainfed sorghum in Sudan’s Gedaref region by introducing sorghum-specific phenological and physiological parameters. To represent spatial and interannual variability in sowing timing, we used normalized difference vegetation index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate sowing dates. The model was calibrated using a stepwise approach constrained by satellite-derived leaf area index (LAI), observation-based surface energy fluxes, and multi-year field yield observations. The adapted model reproduced the seasonal evolution of sorghum canopy development and surface energy fluxes across different hydro-climatic conditions. Compared with a conventional fixed sowing-date approach, dynamic sowing improved LAI simulation during validation, increasing Nash-Sutcliffe efficiency (NSE) from 0.27 to 0.57 while reducing mean absolute error (MAE) from 0.21 to 0.16. ... mehrDynamic sowing also improved yield simulation, reducing MAE by 27% during calibration and 55% during validation relative to the fixed sowing date approach. The results indicate that integrating satellite-derived sowing dates with crop-specific calibration improves sorghum growth simulation in the rainfed Gedaref region.