Design of urban drainage systems or flood risk assessment in small catchments often requires knowledge of very short-duration rainfall events (less than 1 h). Unfortunately, data for these events are often unavailable or too scarce for a reliable statistical inference. However, regularities in the temporal pattern exhibited by storm records, known as scaling properties of rainfall, could help in characterizing extreme storms at partially gauged sites better than the application of traditional statistical techniques. In this work, a scaling approach for estimating the distribution of sub-hourly extreme rainfall in Sicily (Italy) is presented based on data from high-resolution rain gauges with a short functioning period and from low-resolution rain gauges with longer samples. First, simple scaling assumption was tested for annual maxima rainfall (AMR) data from 10 min to 24 h duration, revealing that the simple scaling regime holds from 20 to 60 min for most of the stations. Then, scaling homogeneous regions were classified based on the values of the scaling exponent. In each region, this parameter was regionalized through power-law relationships with the median of 1 h AMR data. After that, regional Depth Duration Frequency (DDF) curves were developed by combining the scale-invariant framework with the generalized extreme value (GEV) probability distribution and used to estimate T-year sub-hourly extreme rainfalls at sites where only rainfall data for longer durations (≥ 1 h) were available. The regional GEV simple scaling model was validated against sub-hourly historical observations at ten rain gauges, generally yielding, in relation to the scaling exponent value, to similar or better sub-hourly estimates than empirical approach.

在小型流域设计城市排水系统或进行洪水风险评估通常需要了解非常短时的降雨事件（少于1小时）。不幸的是，这些事件的数据通常不可用或太少，无法进行可靠的统计推断。但是，风暴记录显示的时间模式规律性（称为降雨的缩放特性）比传统统计技术的应用更好地帮助表征了部分测距地点的极端风暴。在这项工作中，提出了一种缩放方法，该方法可根据来自运行周期短的高分辨率雨量计和来自采样时间较长的低分辨率雨量计的数据，估算西西里岛（意大利）的亚小时极端降雨的分布。第一，对10分钟至24小时的年度最大降雨量（AMR）数据进行了简单缩放比例假设的测试，表明大多数站点的简单缩放比例范围为20至60分钟。然后，基于缩放指数的值对缩放均匀区域进行分类。在每个区域，该参数通过幂律关系与1 h AMR数据的中值进行区域化。此后，通过将尺度不变框架与广义极值（GEV）概率分布相结合，开发了区域深度持续时间频率（DDF）曲线，并用于估计仅降雨数据较长的站点的T年亚小时每小时极端降雨。持续时间（≥1小时）可用。根据十个雨量计的亚小时历史观测值，验证了区域GEV简单缩放模型，通常得出