HydroGEM: A Self Supervised Zero Shot Hybrid TCN Transformer Foundation Model for Continental Scale Streamflow Quality Control

• URL: http://arxiv.org/abs/2512.14106v1
• Date: Tue, 16 Dec 2025 05:39:26 GMT
• Title: HydroGEM: A Self Supervised Zero Shot Hybrid TCN Transformer Foundation Model for Continental Scale Streamflow Quality Control
• Authors: Ijaz Ul Haq, Byung Suk Lee, Julia N. Perdrial, David Baude,
• Abstract summary: We introduce HydroGEM, a foundation model for continental-scale streamflow quality control.<n>HydroGEM uses two-stage training: self-supervised pretraining on 6.03 million sequences from 3,724 stations.<n>On held-out synthetic tests, HydroGEM F1 = 0.792 for detection and 6 reconstruction-error reduction, a 36.3% improvement over existing methods.
• Score: 2.153503585184628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time streamflow monitoring networks generate millions of observations annually, yet maintaining data quality across thousands of remote sensors remains labor-intensive. We introduce HydroGEM (Hydrological Generalizable Encoder for Monitoring), a foundation model for continental-scale streamflow quality control. HydroGEM uses two-stage training: self-supervised pretraining on 6.03 million sequences from 3,724 USGS stations learns hydrological representations, followed by fine-tuning with synthetic anomalies for detection and reconstruction. A hybrid TCN-Transformer architecture (14.2M parameters) captures local temporal patterns and long-range dependencies, while hierarchical normalization handles six orders of magnitude in discharge. On held-out synthetic tests comprising 799 stations with 18 expert-validated anomaly types, HydroGEM achieves F1 = 0.792 for detection and 68.7% reconstruction-error reduction, a 36.3% improvement over existing methods. Zero-shot transfer to 100 Environment and Climate Change Canada stations yields F1 = 0.586, exceeding all baselines and demonstrating cross-national generalization. The model maintains consistent detection across correction magnitudes and aligns with operational seasonal patterns. HydroGEM is designed for human-in-the-loop workflows - outputs are quality control suggestions requiring expert review, not autonomous corrections.

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