Streamflow forecasts are critical to guide water resource management, mitigate drought and flood effects, and develop climate-smart infrastructure and governance. 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 24hr lead-time streamflow prediction in a data-constrained 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 24hr lead-time streamflow forecasting.

翻译：径流预测对于指导水资源管理、缓解干旱与洪水影响、以及发展气候适应性基础设施和治理至关重要。然而，全球许多地区的径流观测数据有限，难以制定基于证据的管理策略。本文提出了一种基于注意力的域自适应径流预测模型，用于数据稀疏区域。该方法利用数据丰富源域的水文特征，在数据受限的目标域中实现有效的前24小时径流预测。具体而言，我们采用深度学习框架结合域自适应技术，通过对抗方法同时训练径流预测模型并区分两个域。与基线跨域预测模型的实验对比表明，本方法在前24小时径流预测任务中表现更优。