Overcoming Data Limitations in Sub-Daily Rainfall Simulation

The need for long-term synthetic sub-daily rainfall time series is crucial in various hydrological applications, particularly in flood frequency analysis. Traditional sub-daily rainfall simulation models rely on high time-resolution data, typically spanning only 20–30 years, which is insufficient for generating the long synthetic time series required for high return period design value estimation. In contrast, longer datasets of daily rainfall records and annual maximum values are more widely available, often covering 50–80 years. These datasets underpin the derivation of Intensity-Duration-Frequency (IDF) curves, a cornerstone of current hydrological practice.

This study introduces an innovative framework for simulating sub-daily rainfall time series using only daily rainfall records and IDF curves, thus eliminating the need for sub-daily observational data. The approach integrates a daily rainfall simulation model, Complete Stochastic Modelling Solution, calibrated with observed daily data, with a multifractal disaggregation scheme informed by IDF curves. The resulting framework offers a robust and parsimonious solution for generating sub-daily rainfall data.

By leveraging readily available datasets, this method expands the applicability of sub-daily rainfall simulations to a broader range of hydrological and climate modeling contexts, providing a valuable tool for advancing flood frequency analysis and related applications.