A Hierarchical Deep Learning Framework for Runoff Prediction Using Raster-Based Spatial Representations

De-Hui Ouyang

doi:10.1029/2026wr043815

A Hierarchical Deep Learning Framework for Runoff Prediction Using Raster-Based Spatial Representations

2026-08-30

0

OA

AI

DOI：10.1029/2026wr043815

Original

Save

View PDF

Abstract

En 中文

Large-sample deep learning runoff models rely primarily on aggregated catchment attributes, which may obscure informative spatial patterns. Using 531 watersheds in the contiguous United States, we develop a hierarchical deep learning framework that represents static watershed properties from watershed-averaged raster aggregation to convolutional neural network encodings at fixed-grid and adaptive multi-patch scales, thereby providing a controlled test of whether increasingly explicit spatial encoding improves temporal generalization and spatial transferability to ungauged watersheds. Watershed-averaged rasters perform comparably to curated attribute tables and show a slight advantage under the prediction-in-ungauged-basins benchmark. Incorporating intermediate spatial structure via fixed-grid encoding provides the clearest temporal-split benefit relative to watershed-averaged aggregation, with 26.2% of watersheds showing uncertainty-supported Nash–Sutcliffe efficiency improvement and 6.0% showing uncertainty-supported degradation, although this advantage weakens under spatial holdout. Under spatial holdout, diagnostic analyses indicate that fixed-grid encoding tends to outperform watershed-averaged raster aggregation in higher-elevation, steeper watersheds. The adaptive multi-patch level further reveals that finer spatial representation does not necessarily provide consistent gains, highlighting a trade-off between spatial detail and computational demand. Overall, this framework provides practical guidance for selecting raster-based spatial representations in large-sample deep learning runoff prediction.

Keywords:

runoff prediction

spatial encoding

deep learning

large-sample hydrology

watershed characteristics

A Hierarchical Deep Learning Framework for Runoff Prediction Using Raster-Based Spatial Representations

Abstract

AI Summary

Journal

Organization

Cited Papers