一个用于在大型且极度稀疏的时空图上插补水面高程的数据集与模型

中文摘要 AI 辅助

跨河网连续监测水面高程对于洪水预报、水资源管理以及理解全球水循环至关重要。然而，全球大部分地区原位测站的稀缺制约了可靠建模框架的发展。卫星测高有潜力缓解这一问题，但其目前的使用受到稀疏时间覆盖的限制。为此，我们引入了AmazonSWE，一个用于训练和评估大规模时空图插补方法的数据集，该数据集整合了来自多种来源的处理后的卫星测高测量数据，包括近期宽幅的SWOT传感器。该数据集覆盖亚马逊河流域超过19,000个河段和10年（2016-2026）的数据，并保留原位测站用于评估。除了贡献一个具有潜在社会影响的新颖真实世界用例之外，AmazonSWE还引入了显著的技术挑战：每天观测到的河段少于1%，该数据集比现有插补基准稀疏得多，并且其有向无环河流拓扑在结构上既不同于现有数据集中的图，也大于这些图。我们表明，先前的时空图插补方法不适应这种拓扑、规模和稀疏性，并提出一个简单的双向选择性状态空间模型，通过采样连通子图并将空间和时间展平为具有拓扑感知位置编码的单一标记序列，该模型优于先前方法。与已发表的基于SWOT的水面高程加密的最先进方法（该方法将统计与物理建模相结合）相比，我们的模型将相对于原位测站的均方根误差降低了18-39%，同时为每个河段生成预测，而不仅仅是那些具有足够邻近卫星覆盖的河段。

英文摘要

Continuous monitoring of water surface elevation across river networks is critical for flood forecasting, water resource management, and understanding the global water cycle. Yet, the scarcity of in situ gauges across much of the globe constrains the development of reliable modeling frameworks. Satellite altimetry has the potential to alleviate this problem but its use is currently hindered by sparse temporal coverage. To this end, we introduce AmazonSWE, a dataset for training and evaluating large-scale spatiotemporal graph imputation methods that integrates processed satellite altimetry measurements from a range of sources, including the recent wide-swath SWOT sensor. The dataset covers over 19K river sections and 10 years (2016-2026) in the Amazon river basin, with in situ gauges held out for evaluation. Besides contributing a novel real-world use case with the potential for societal impact, AmazonSWE introduces significant technical challenges: with fewer than 1% of sections observed per day, the dataset is far sparser than existing imputation benchmarks, and its directed acyclic river topology is both structurally different from and larger than graphs in existing datasets. We show that prior spatiotemporal graph imputation methods are not adapted to this topology, scale and sparsity, and propose a simple bidirectional selective state space model that outperforms them by sampling connected subgraphs and flattening space and time into a single token sequence with topology-aware positional encodings. Compared to the state-of-the-art published method for SWOT-based WSE densification, which integrates statistics with physical modeling, our model reduces RMSE against in situ gauges by 18-39%, while producing predictions for every river section rather than only those with sufficient nearby satellite coverage.

发表机构

• European Space Agency, Science Hub（欧洲空间局科学中心）
• University of Florence（佛罗伦萨大学）
• Imperial College London（伦敦帝国理工学院）
• Université Bretagne Sud, IRISA（南布列塔尼大学，IRISA）
• University of Tromsø（特罗姆瑟大学）
• Serco Italia SpA（意大利Serco股份公司）

机构由 AI 辅助整理，请以论文原文为准。