Ghaneei Parnian, Moradkhani Hamid
Department of Civil, Construction and Environmental Engineering, University of Alabama, AL, Tuscaloosa, USA.
Center for Complex Hydrosystems Research, University of Alabama, AL, Tuscaloosa, USA.
Sci Data. 2025 Jan 7;12(1):25. doi: 10.1038/s41597-025-04389-y.
High quality baseflow data is important for advancing water resources modeling and management, as it captures the critical role of groundwater and delayed sources in contributing to streamflow. Baseflow is the main recharge source of runoff during the dry period, particularly in understanding the interaction between surface water and groundwater systems. This study focuses on estimating baseflow using deep learning algorithms that enhance the estimation capabilities in both gauged and ungauged basins. Recognizing the shortage in accessible high quality daily baseflow data, our objective is to generate a daily baseflow dataset across the contiguous United States (CONUS) for 1661 basins from 1981 to 2022. This dataset provides valuable information for earth and environmental scientists, and water resource managers, enhancing our understanding of the water cycle. It also provides an important foundation for enhancing the study of baseflow contributions to extreme events such as droughts and floods. The dataset can be used as a new benchmark for future studies aimed at improving hydrological predictions and managing water resources more effectively.
高质量的基流数据对于推进水资源建模和管理至关重要，因为它捕捉到了地下水和延迟水源对河流流量贡献的关键作用。基流是干旱时期径流的主要补给源，特别是在理解地表水与地下水系统之间的相互作用方面。本研究专注于使用深度学习算法估算基流，这些算法可增强在有测量数据和无测量数据流域的估算能力。认识到可获取的高质量每日基流数据的短缺，我们的目标是生成1981年至2022年期间美国本土（CONUS）1661个流域的每日基流数据集。该数据集为地球和环境科学家以及水资源管理者提供了有价值的信息，增强了我们对水循环的理解。它还为加强对基流对干旱和洪水等极端事件贡献的研究提供了重要基础。该数据集可作为未来旨在改进水文预测和更有效管理水资源研究的新基准。
