水文学中基于过程的模型和数据驱动模型的外推能力增强方法
Approaches for enhancing extrapolability in process-based and data-driven models in hydrology
AI总结：
该研究回顾对比了水文学中基于过程与数据驱动两类模型的外推能力评估及增强方法，分析其前景局限，提出相关技术策略与发展方向以提升模型全球适用性。
摘要：
基于过程的水文学模型与数据驱动的水文学模型的应用在现代水文学研究中至关重要，尤其在预测径流、蒸散（ET）、土壤湿度等关键水循环变量方面。这些模型为水资源管理、洪水预报和生态保护提供了科学依据。基于过程的模型模拟流域水文过程的物理机制，数据驱动模型则依托大型数据集与先进的机器学习算法。本文回顾并比较了评估与增强这两类模型外推能力的方法，探讨了其前景与局限性。关键策略包括采用留一交叉验证和基于相似性的方法，来评估模型在无资料地区的性能。深度学习、迁移学习与领域自适应技术在提升模型在数据稀缺及极端条件下的预测效果方面也颇具潜力。跨学科合作与算法的持续进步对于提升水文学模型的全球适用性与可靠性同样重要。
英文摘要：
The application of process-based and data-driven hydrological models is crucial in modern hydrological research, especially for predicting key water cycle variables such as runoff, evapotranspiration (ET), and soil moisture. These models provide a scientific basis for water resource management, flood forecasting, and ecological protection. Process-based models simulate the physical mechanisms of watershed hydrological processes, while data-driven models leverage large datasets and advanced machine learning algorithms. This paper reviewed and compared methods for assessing and enhancing the extrapolability of both model types, discussing their prospects and limitations. Key strategies include the use of leave-one-out cross-validation and similarity-based methods to evaluate model performance in ungauged regions. Deep learning, transfer learning, and domain adaptation techniques are also promising in their potential to improve model predictions in data-sparse and extreme conditions. Interdisciplinary collaboration and continuous algorithmic advancements are also important to strengthen the global applicability and reliability of hydrological models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
