Streamflow forecasts are critical to guide water resource management, mitigate drought and flood effects, and develop climate-smart infrastructure and industries. Many global regions, however, have limited streamflow observations to guide evidence-based management strategies. In this paper, we propose an attention-based domain adaptation streamflow forecaster for data-sparse regions. Our approach leverages the hydrological characteristics of a data-rich source domain to induce effective 24h lead-time streamflow prediction in a limited target domain. Specifically, we employ a deep-learning framework leveraging domain adaptation techniques to simultaneously train streamflow predictions and discern between both domains using an adversarial method. Experiments against baseline cross-domain forecasting models show improved performance for 24h lead-time streamflow forecasting.

翻译：径流预测对于指导水资源管理、减轻干旱和洪涝影响、以及发展气候适应性基础设施和产业至关重要。然而，全球许多地区的径流观测数据有限，难以支撑基于证据的管理策略。本文针对数据稀疏区域，提出了一种基于注意力的域自适应径流预测器。该方法利用数据丰富源流域的水文特征，在有限的目标流域中实现有效的24小时超前径流预测。具体而言，我们采用基于深度学习框架的域自适应技术，通过对抗性方法同时训练径流预测模型并区分两个域。与跨域基线预测模型的对比实验表明，所提方法在24小时超前径流预测方面性能更优。