arXiv cs.AI· Pengfei Qu, Lei Ye, Wenyu Ouyang, Chi Zhang, Yikai Chai, Shuolong Xu, Yongri Piao, Miao Zhang, Huchuan Lu·· 11 小时前AI 评分24
利用地球基础模型与 AlphaEarth 嵌入提升水文模型模拟性能
Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings
AI 导读
研究评估了 64 维 AlphaEarth Foundations（AEF）嵌入作为静态流域表示，在 CAMELS 数据集上用于径流预测。AEF 嵌入在样本外场景下比 CAMELS 属性更稳健，尽管两者样本内表现几乎相同。在 100 个目标流域中，AEF 静态输入总体提升了无观测流域预测（PUB）性能，且在小规模候选池下基于相似度的供体流域选择优于随机选择。
正文 · 原文
Abstract:Accurate prediction in ungauged basins (PUB) requires effective transfer across hydrologically diverse regions. While deep learning has advanced large-sample hydrological modeling, its cross-regional generalization depends in part on the static basin descriptors used to guide model behavior. Earth Foundation Models (EFMs) provide a promising alternative by learning environmental representations from multi-modal satellite data. In this study, we evaluate the 64-dimensional AlphaEarth Foundations (AEF) embeddings as a new form of static basin representation and assess their value for streamflow prediction under both in-sample (IS) and out-of-sample (OOS) settings using the CAMELS dataset. Results show that AEF embeddings offer higher OOS robustness than CAMELS attributes, despite nearly identical IS performance. Mutual-information analysis reveals statistical associations between AEF embeddings and conventional catchment attributes related to terrain, vegetation, and rooting characteristics. We further investigate similarity-based donor-basin selection for PUB using CAMELS attributes and AEF embeddings. Across 100 target basins, AEF static inputs generally improved PUB performance. With AEF inputs, similarity-based donor selection outperformed random selection for smaller donor pools, but its advantage diminished as pool size increased. Adding more donor basins did not consistently improve performance. Leave-one-cluster-out experiments showed AEF input benefits depended on basin partitioning, suggesting that transferability remains sensitive to the catchment conditions represented during training. Overall, this study demonstrates that Earth Foundation Model embeddings provide a transferable representation for PUB prediction and underscores the importance of basin representation quality and donor pool design, motivating future research toward unified hydrological foundation models.
| Comments: | 26 pages, 10 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2601.01558 [cs.LG] |
| (or arXiv:2601.01558v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2601.01558 arXiv-issued DOI via DataCite |
Submission history
From: Wenyu Ouyang [view email]
[v1]
Sun, 4 Jan 2026 15:14:16 UTC (7,997 KB)
[v2]
Wed, 1 Jul 2026 07:18:11 UTC (8,369 KB)
[v3]
Wed, 4 Nov 2026 11:43:31 UTC (6,636 KB)
来源：arXiv cs.AI · arxiv.org