Open global hydrological products are often the only continuous source available for data-scarce locations, but their local use is stronger when the modelled signal is connected to documented hydrometric evidence. This study evaluates an explainable machine-learning workflow for anticipating the GloFAS v4-derived discharge series returned by the Open-Meteo Flood API near Babahoyo, Ecuador, and complements the modern 2017–2026 analysis with a historical hydrometric-context protocol based on offic