The rainfall intensity–duration–frequency (IDF) curves play an important role in water resources engineering and management. The applications of IDF curves range from assessing rainfall events, classifying climatic regimes, to deriving design storms and assisting in designing urban drainage systems, etc. The deriving procedure of IDF curves, however, requires long-term historical rainfall observations, whereas lack of fine-timescale rainfall records (e.g. sub-daily) often results in less reliable IDF curves. This paper presents the utilization of remote sensing sub-daily rainfall, i.e. Global Satellite Mapping of Precipitation (GSMaP), integrated with the Bartlett-Lewis rectangular pulses (BLRP) model, to disaggregate the daily in situ rainfall, which is then further used to derive more reliable IDF curves. Application of the proposed method in Singapore indicates that the disaggregated hourly rainfall, preserving both the hourly and daily statistic characteristics, produces IDF curves with significantly improved accuracy; on average over 70% of RMSE is reduced as compared to the IDF curves derived from daily rainfall observations.

中文翻译：

---

使用与遥感数据同化的缩减原位降雨推导强度-持续时间-频率（IDF）曲线

降雨强度-持续时间-频率（IDF）曲线在水资源工程和管理中起着重要作用。IDF曲线的应用范围包括评估降雨事件，对气候状况进行分类，得出设计暴风雨以及协助设计城市排水系统等。但是，IDF曲线的推导过程需要长期的历史降雨观测，而缺乏精细的观测。时标降雨记录（例如，次日记录）通常会导致IDF曲线的可靠性降低。本文介绍了遥感次日降水量的利用，即全球降水卫星图（GSMaP）与Bartlett-Lewis矩形脉冲（BLRP）模型相集成，以分解每日的原地降水量，然后将其进一步用于得出更可靠的IDF曲线。建议的方法在新加坡的应用表明，每小时的分类降雨保持了每小时和每天的统计特征，从而产生了IDF曲线，其准确性大大提高。与每日降雨观测得出的IDF曲线相比，平均RMSE平均减少了70％以上。