Predicting river flow in places without streamflow records is challenging because basins respond differently to climate, terrain, vegetation, and soils. Traditional basin attributes describe some of these differences, but they cannot fully represent the complexity of natural environments. This study examines whether AlphaEarth Foundation embeddings, which are learned from large collections of satellite images rather than designed by experts, offer a more informative way to describe basin characteristics. These embeddings summarize patterns in vegetation, land surface properties, and long-term environmental dynamics. We find that models using them achieve higher accuracy when predicting flows in basins not used for training, suggesting that they capture key physical differences more effectively than traditional attributes. We further investigate how selecting appropriate donor basins influences prediction in ungauged regions. Similarity based on the embeddings helps identify basins with comparable environmental and hydrological behavior, improving performance, whereas adding many dissimilar basins can reduce accuracy. The results show that satellite-informed environmental representations can strengthen hydrological forecasting and support the development of models that adapt more easily to different landscapes.

翻译：在缺乏径流记录的地区预测河流流量具有挑战性，因为流域对气候、地形、植被和土壤的响应各不相同。传统的流域属性描述了部分差异，但无法完全表征自然环境的复杂性。本研究探讨AlphaEarth基础模型嵌入——这些嵌入是从大规模卫星图像集合中学习得到而非专家设计——是否能提供更具信息量的流域特征描述方式。这些嵌入总结了植被格局、地表特性及长期环境动态。我们发现，使用此类嵌入的模型在预测未参与训练的流域径流时获得了更高的精度，表明其比传统属性更有效地捕捉了关键物理差异。我们进一步研究了如何选择适宜的供体流域以影响无测站区域的预测效果。基于嵌入的相似性有助于识别具有可比环境与水文行为的流域，从而提升预测性能；而引入大量不相似流域则会降低精度。研究结果表明，基于卫星信息的环境表征能够增强水文预报能力，并支持开发更易适应不同景观的模型。