Abstract This study develops a novel approach to data-driven hydrological modeling. The approach adopts the feature representation technique in computer vision to effectively exploit spatial information contained in time-variant input data fields and seamlessly fuse multisource information via machine learning. The new approach overcomes a major limitation of existing approaches in which the spatial heterogeneity of input variables cannot be sufficiently accounted for. The approach is applied to predict the streamflow in a watershed on the northern margin of the Qinghai-Tibetan Plateau, and its performance is compared with various data-driven and process-based models. The major findings are as follows. First, the new approach represents a general framework for the fusion of multisource spatiotemporal data for hydrological modeling and demonstrates great potential to incorporate fast-growing environmental big data. Second, the new approach demonstrates satisfactory short-term forecasting, long-term simulation, and transfer learning performances and is promising for addressing predictions in ungauged basins. Third, the predictors, including precipitation, temperature, leaf area index, and historical streamflow, play markedly distinct roles in modeling streamflow with the novel approach. Finally, topographic information is not a necessary model input in the proposed approach because spatial patterns can be well embodied by other inputs (e.g., temperature) that have high similarities with topography. This study represents the first attempt to bring computer vision into data-driven hydrological modeling and may inspire future studies in this promising direction.

中文翻译：

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基于计算机视觉的融合时空数据的水文建模方法

摘要 本研究为数据驱动的水文建模开发了一种新方法。该方法采用计算机视觉中的特征表示技术，有效利用时变输入数据字段中包含的空间信息，并通过机器学习无缝融合多源信息。新方法克服了现有方法的一个主要限制，即无法充分考虑输入变量的空间异质性。将该方法应用于青藏高原北缘流域的水流预测，并将其性能与各种数据驱动和基于过程的模型进行比较。主要发现如下。第一的，新方法代表了用于水文建模的多源时空数据融合的一般框架，并展示了整合快速增长的环境大数据的巨大潜力。其次，新方法展示了令人满意的短期预测、长期模拟和迁移学习性能，并有望解决未测量盆地的预测问题。第三，预测因子，包括降水、温度、叶面积指数和历史流量，在使用新方法模拟流量方面发挥着明显不同的作用。最后，在所提出的方法中，地形信息不是必要的模型输入，因为空间模式可以由与地形具有高度相似性的其他输入（例如，温度）很好地体现。