A Pure Data‐Driven Fully Distributed Model for Spatially Consistent Streamflow Prediction

Abstract

Streamflow prediction is essential for water resources management and flood mitigation but remains challenging in large basins due to spatial heterogeneity. Machine learning (ML) models have been widely applied to streamflow prediction, typically relying on explicit routing modules, whereas the potential of pure ML models remains underexplored. This study presents a purely ML‐based model (Convolutional Entity‐Aware Long Short‐Term Memory) for fully distributed streamflow prediction. Spatial cross‐validation across 50 gauges in the Amazon Basin yields a median Nash‐Sutcliffe Efficiency of 0.73 and an R2 of 0.84, comparable to or exceeding benchmark models with routing modules. The model reduces annual peak flow bias (APFB = 0.12) and accurately reproduced spatial autocorrelation patterns (Pearson's r > 0.6, Moran's I > 0.8, and Geary's C < 0.2), indicating spatially consistent grid‐scale discharge predictions. These findings demonstrate the potential of a pure data‐driven ML model for fully distributed streamflow prediction in ungauged basins while ensuring spatial consistency.