Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, China.
Institute of Qinling Mountains, Northwest University, Xi'an, 710127, China.
Sci Data. 2024 Feb 15;11(1):207. doi: 10.1038/s41597-024-03044-2.
The spatial distribution and data quality of curve number (CN) values determine the performance of hydrological estimations. However, existing CN datasets are constrained by universal-applicability hypothesis, medium resolution, and imbalance between specificity CN tables to generalized land use/land cover (LULC) maps, which hinder their applicability and predictive accuracy. A new annual CN dataset named CUSCN30, featuring an enhanced resolution of 30 meters and accounting for temporal variations in climate and LULC in the continental United States (CONUS) between 2008 and 2021, was developed in this study. CUSCN30 demonstrated good performance in surface runoff estimation using CN method when compared to observed surface runoff for the selected watersheds. Compared with existing CN datasets, CUSCN30 exhibits the highest accuracy in runoff estimation for both normal and extreme rainfall events. In addition, CUSCN30, with its high spatial resolution, better captures the spatial heterogeneity of watersheds. This developed CN dataset can be used as input for hydrological models or machine learning algorithms to simulate rainfall-runoff across multiple spatiotemporal scales.
曲线数 (CN) 值的空间分布和数据质量决定了水文估算的性能。然而，现有的 CN 数据集受到通用性假设、中等分辨率以及特异性 CN 表与广义土地利用/土地覆盖 (LULC) 图之间的不平衡的限制，这阻碍了它们的适用性和预测精度。本研究开发了一个新的年度 CN 数据集，名为 CUSCN30，其分辨率提高到 30 米，并考虑了 2008 年至 2021 年美国大陆（CONUS）气候和 LULC 的时间变化。与选定流域的观测地表径流相比，CUSCN30 在使用 CN 方法进行地表径流估算方面表现出良好的性能。与现有的 CN 数据集相比，CUSCN30 在正常和极端降雨事件的径流估算中表现出最高的精度。此外，CUSCN30 具有较高的空间分辨率，更好地捕捉了流域的空间异质性。这个开发的 CN 数据集可用于水文模型或机器学习算法，以模拟多个时空尺度的降雨径流。
