Multi-Scale Graph Learning for Anti-Sparse Downscaling

TL;DR

This paper introduces a novel multi-scale graph learning framework that leverages cross-scale hydrological connections and asynchronous training to improve fine-scale water temperature predictions from coarse data, enhancing water resource management.

Contribution

The paper presents a multi-scale graph learning method with cross-scale interpolation and asynchronous training, addressing data scarcity and improving fine-scale water temperature prediction accuracy.

Findings

Achieved state-of-the-art performance in stream temperature downscaling.

Demonstrated the effectiveness of cross-scale interpolation in hydrological modeling.

Validated the approach in the Delaware River Basin with extensive experiments.

Abstract

Water temperature can vary substantially even across short distances within the same sub-watershed. Accurate prediction of stream water temperature at fine spatial resolutions (i.e., fine scales, $\leq$ 1 km) enables precise interventions to maintain water quality and protect aquatic habitats. Although spatiotemporal models have made substantial progress in spatially coarse time series modeling, challenges persist in predicting at fine spatial scales due to the lack of data at that scale.To address the problem of insufficient fine-scale data, we propose a Multi-Scale Graph Learning (MSGL) method. This method employs a multi-task learning framework where coarse-scale graph learning, bolstered by larger datasets, simultaneously enhances fine-scale graph learning. Although existing multi-scale or multi-resolution methods integrate data from different spatial scales, they often overlook the spatial correspondences across graph structures at various scales. To address this, our MSGL introduces an additional learning task, cross-scale interpolation learning, which leverages the hydrological connectedness of stream locations across coarse- and fine-scale graphs to establish cross-scale connections, thereby enhancing overall model performance. Furthermore, we have broken free from the mindset that multi-scale learning is limited to synchronous training by proposing an Asynchronous Multi-Scale Graph Learning method (ASYNC-MSGL). Extensive experiments demonstrate the state-of-the-art performance of our method for anti-sparse downscaling of daily stream temperatures in the Delaware River Basin, USA, highlighting its potential utility for water resources monitoring and management.