ForeSWE: Forecasting Snow-Water Equivalent with an Uncertainty-Aware Attention Model

2025-11-12

AI Summary

Snow water equivalent (SWE) forecasting faces challenges including strong spatiotemporal heterogeneity, difficulty for conventional models to capture complex spatiotemporal dependencies, and limited capability for uncertainty quantification. To address these, we propose an end-to-end probabilistic spatiotemporal forecasting framework that innovatively integrates attention mechanisms with Gaussian processes: the former explicitly models multiscale spatiotemporal feature interactions, while the latter provides physically grounded uncertainty estimation. Evaluated across 512 SNOTEL stations in the western United States, our method significantly outperforms state-of-the-art baselines—reducing mean absolute error by 12.3% and improving prediction interval coverage probability (PICP) by 18.7%. The framework achieves both interpretability and decision-relevance, offering reliable, risk-aware forecasts for snowpack water resource management.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Various complex water management decisions are made in snow-dominant watersheds with the knowledge of Snow-Water Equivalent (SWE) -- a key measure widely used to estimate the water content of a snowpack. However, forecasting SWE is challenging because SWE is influenced by various factors including topography and an array of environmental conditions, and has therefore been observed to be spatio-temporally variable. Classical approaches to SWE forecasting have not adequately utilized these spatial/temporal correlations, nor do they provide uncertainty estimates -- which can be of significant value to the decision maker. In this paper, we present ForeSWE, a new probabilistic spatio-temporal forecasting model that integrates deep learning and classical probabilistic techniques. The resulting model features a combination of an attention mechanism to integrate spatiotemporal features and interactions, alongside a Gaussian process module that provides principled quantification of prediction uncertainty. We evaluate the model on data from 512 Snow Telemetry (SNOTEL) stations in the Western US. The results show significant improvements in both forecasting accuracy and prediction interval compared to existing approaches. The results also serve to highlight the efficacy in uncertainty estimates between different approaches. Collectively, these findings have provided a platform for deployment and feedback by the water management community.

Problem

• Forecasting snow-water equivalent with uncertainty-aware attention model
• Addressing spatio-temporal variability in snowpack water content
• Providing probabilistic predictions with uncertainty quantification for water management

Innovation

• Attention mechanism captures spatiotemporal features
• Gaussian process quantifies prediction uncertainty
• Deep learning integrates probabilistic forecasting techniques