Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world's freshwater resources have inadequate monitoring of critical environmental variables needed for management. Yet, the need to have widespread predictions of hydrological variables such as river flow and water quality has become increasingly urgent due to climate and land use change over the past decades, and their associated impacts on water resources. Modern machine learning methods increasingly outperform their process-based and empirical model counterparts for hydrologic time series prediction with their ability to extract information from large, diverse data sets. We review relevant state-of-the art applications of machine learning for streamflow, water quality, and other water resources prediction and discuss opportunities to improve the use of machine learning with emerging methods for incorporating watershed characteristics into deep learning models, transfer learning, and incorporating process knowledge into machine learning models. The analysis here suggests most prior efforts have been focused on deep learning learning frameworks built on many sites for predictions at daily time scales in the United States, but that comparisons between different classes of machine learning methods are few and inadequate. We identify several open questions for time series predictions in unmonitored sites that include incorporating dynamic inputs and site characteristics, mechanistic understanding and spatial context, and explainable AI techniques in modern machine learning frameworks.

翻译：无监测站点动态环境变量的预测仍是水资源科学中长期存在的挑战。全球大部分淡水资源对管理所需的关键环境变量缺乏充分监测。然而，由于过去数十年气候与土地利用变化及其对水资源的相关影响，对河流流量、水质等水文变量进行广泛预测的需求日益迫切。现代机器学习方法凭借其从大规模多样化数据集中提取信息的能力，在水文时间序列预测中日益超越基于过程的模型与经验模型。本文综述了机器学习在径流、水质及其他水资源预测中的前沿应用，探讨了通过新兴技术提升机器学习效能的机遇，包括将流域特征融入深度学习模型、迁移学习以及将过程知识整合至机器学习模型。分析表明，现有研究主要集中于基于多站点日尺度预测的深度学习框架（以美国为主），但不同类别机器学习方法间的比较研究仍显不足且不够充分。我们提出了无监测站点时间序列预测中若干待解问题，包括动态输入与站点特征的融合、机理理解与空间背景的结合，以及现代机器学习框架中可解释人工智能技术的应用。