Hydrological ML requires costly upstream catchment aggregation. We present an efficient flow-accumulation-based method bypassing per-pixel delineation, achieving orders-of-magnitude speedups. Implemented in GRASS and Python, this open-source approach enables scalable, high-resolution modeling, demonstrated by a countrywide 90 m Random Forest nitrogen prediction.