Hierarchical Conditional Multi-Task Learning for Streamflow Modeling

TL;DR

This paper introduces HCMTL, a hierarchical multi-task learning model that incorporates causal hydrological processes to improve streamflow prediction accuracy and interpretability.

Contribution

The paper presents HCMTL, a novel hierarchical multi-task learning framework that models causal hydrological processes and enhances long-term streamflow prediction.

Findings

HCMTL outperforms five baseline models across numerous drainage basins.

Incorporating domain-specific causal knowledge improves prediction accuracy.

HCMTL enhances interpretability of hydrological modeling.

Abstract

Streamflow, vital for water resource management, is governed by complex hydrological systems involving intermediate processes driven by meteorological forces. While deep learning models have achieved state-of-the-art results of streamflow prediction, their end-to-end single-task learning approach often fails to capture the causal relationships within these systems. To address this, we propose Hierarchical Conditional Multi-Task Learning (HCMTL), a hierarchical approach that jointly models soil water and snowpack processes based on their causal connections to streamflow. HCMTL utilizes task embeddings to connect network modules, enhancing flexibility and expressiveness while capturing unobserved processes beyond soil water and snowpack. It also incorporates the Conditional Mini-Batch strategy to improve long time series modeling. We compare HCMTL with five baselines on a global dataset. HCMTL's superior performance across hundreds of drainage basins over extended periods shows that integrating domain-specific causal knowledge into deep learning enhances both prediction accuracy and interpretability. This is essential for advancing our understanding of complex hydrological systems and supporting efficient water resource management to mitigate natural disasters like droughts and floods.