Abstract
Glacial Lake Outburst Floods (GLOFs) pose a serious threat in high mountain regions. They are hazardous to communities, infrastructure, and ecosystems further downstream. The classical methods of GLOF detection and prediction have so far mainly relied on hydrological modeling, threshold-based lake monitoring, and manual satellite image analysis. These approaches suffer from several drawbacks: slow updates, reliance on manual labor, and losses in accuracy when clouds interfere and/or lack on-site data. To tackle these challenges, we present IceWatch: a novel deep learning framework for GLOF prediction that incorporates both spatial and temporal perspectives. The vision component, RiskFlow, of IceWatch deals with Sentinel-2 multispectral satellite imagery using a CNN-based classifier and predicts GLOF events based on the spatial patterns of snow, ice, and meltwater. Its tabular counterpart confirms this prediction by considering physical dynamics. TerraFlow models glacier velocity from NASA ITS_LIVE time series while TempFlow forecasts near-surface temperature from MODIS LST records; both are trained on long-term observational archives and integrated via harmonized preprocessing and synchronization to enable multimodal, physics-informed GLOF prediction. Both together provide cross-validation, which will improve the reliability and interpretability of GLOF detection. This system ensures strong predictive performance, rapid data processing for real-time use, and robustness to noise and missing information. IceWatch paves the way for automatic, scalable GLOF warning systems. It also holds potential for integration with diverse sensor inputs and global glacier monitoring activities.
Abstract (translated)
冰川湖突发洪水（GLOFs）在高山地区构成了严重的威胁，对下游社区、基础设施和生态系统造成了危害。传统的GLOF检测与预测方法主要依赖于水文学建模、基于阈值的湖泊监测以及手动分析卫星图像。这些方法存在若干缺点：更新慢、依赖人工劳动，并且当云层遮挡或缺乏现场数据时准确性会下降。 为了解决这些问题，我们提出了IceWatch：一种新的深度学习框架，用于GLOF预测，并结合了空间和时间两个维度的视角。IceWatch中的视觉部分名为RiskFlow，使用基于CNN（卷积神经网络）的分类器处理Sentinel-2多光谱卫星图像，依据雪、冰以及融水的空间模式来预测GLOF事件。其表格对应的组件通过考虑物理动态确认这一预测。 TerraFlow利用NASA ITS_LIVE时间序列数据模型冰川速度，而TempFlow则从MODIS地表温度记录中预测接近地面的温度；二者都基于长期观测档案训练，并通过统一预处理和同步整合来实现多模态、基于物理学信息的GLOF预测。这两个组件共同提供了交叉验证，从而提高了GLOF检测的可靠性和可解释性。 该系统确保了强大的预测性能，快速的数据处理能力以支持实时应用，并具有抗噪能力和缺失信息的鲁棒性。IceWatch为自动化的、可扩展的GLOF预警系统的实现铺平道路，同时还具备与其他传感器输入和全球冰川监测活动集成的可能性。
URL
https://arxiv.org/abs/2601.12330
