Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 165 00, Praha-Suchdol, Czech Republic.
Schulich School of Engineering, University of Calgary, Calgary, Canada.
Sci Data. 2024 Mar 15;11(1):298. doi: 10.1038/s41597-024-03109-2.
Time series of annual maxima daily precipitation are crucial for understanding extreme precipitation behavior and its shifts toward nonstationarity with global warming. Extreme precipitation insight assists hydraulic infrastructure design, water resource management, natural hazard prevention, and climate change adaptation. However, not even a third of the records are of sufficient length, and the number of active stations keeps decreasing. Herein, we present HYADES: archive of yearly maxima of daily precipitation records, a global dataset derived from the Global Historical Climatology Network database of daily records (GHCN-Daily). The HYADES dataset contains records from 39 206 stations (heterogeneously distributed worldwide) with record lengths varying from 16 to 200 years between 1805 and 2023. HYADES was extracted through a methodology designed to accurately capture the true maxima even in the presence of missing values within the records. The method's thresholds were determined and evaluated through Monte Carlo simulations. Our approach demonstrates a 96.73% success rate in detecting the true maxima while preserving time series statistical properties of interest (L-moments and temporal monotonic trend).
年极大日降水量时间序列对于理解极端降水行为及其在全球变暖背景下向非平稳性转变至关重要。极端降水洞察有助于水力基础设施设计、水资源管理、自然灾害预防和气候变化适应。然而,即使是记录的三分之一也不够长,并且活跃站的数量不断减少。在此,我们介绍 HYADES:每日最大降水量记录的档案,这是一个源自全球历史气候网络日记录数据库(GHCN-Daily)的全球数据集。HYADES 数据集包含来自 39206 个站点(全球分布不均)的记录,记录长度在 1805 年至 2023 年之间从 16 年到 200 年不等。HYADES 是通过一种旨在即使在记录中存在缺失值的情况下也能准确捕捉真实最大值的方法提取的。该方法的阈值是通过蒙特卡罗模拟确定和评估的。我们的方法在检测真实最大值时成功率为 96.73%,同时保留了感兴趣的时间序列统计特性(L 矩和时间单调趋势)。