Auburn University, Department of Biosystem Engineering, 350 Mell St, Auburn, AL, 36849, USA.
University of Maryland, Agricultural Experiment Station, Symons Hall, 7998 Regents Drive, College Park, MD, 20742, USA.
Sci Data. 2022 May 16;9(1):211. doi: 10.1038/s41597-022-01304-7.
Climate change impacts on precipitation characteristics will alter the hydrologic characteristics, such as peak flows, time to peak, and erosion potential of watersheds. However, many of the currently available climate change datasets are provided at temporal and spatial resolutions that are inadequate to quantify projected changes in hydrologic characteristics of a watershed. Therefore, it is critical to temporally disaggregate coarse-resolution precipitation data to finer resolutions for studies sensitive to precipitation characteristics. In this study, we generated novel 15-minute precipitation datasets from hourly precipitation datasets obtained from five NA-CORDEX downscaled climate models under RCP 8.5 scenario for the historical (1970-1999) and projected (2030-2059) years over the Southeast United States using a modified version of the stochastic method. The results showed conservation of mass of the precipitation inputs. Furthermore, the probability of zero precipitation, variance of precipitation, and maximum precipitation in the disaggregated data matched well with the observed precipitation characteristics. The generated 15-minute precipitation data can be used in all scientific studies that require precipitation data at that resolution.
气候变化对降水特征的影响将改变流域的水文特征，如洪峰流量、洪峰到达时间和侵蚀潜力。然而，目前许多可用的气候变化数据集的时间和空间分辨率不足，无法量化流域水文特征的预计变化。因此，将粗分辨率降水数据时间离散化到更精细的分辨率对于对降水特征敏感的研究至关重要。在这项研究中，我们使用改进的随机方法，从五个根据 RCP8.5 情景下的 NA-CORDEX 降尺度气候模型获得的每小时降水数据集中生成了 15 分钟降水数据集，用于研究美国东南部历史时期（1970-1999 年）和预测时期（2030-2059 年）的降水特征。结果表明降水输入的质量守恒。此外，离散化数据中的零降水概率、降水方差和最大降水与观测到的降水特征吻合较好。生成的 15 分钟降水数据可用于所有需要该分辨率降水数据的科学研究。
