Assessing Rainfall Disaggregation Techniques and Subdaily Rainfall Patterns Across Uganda’s Tropical Climatic Zones

Accurate subdaily rainfall data are critical for the design of hydrological infrastructure and effective flood risk assessment, particularly in data-scarce regions such as Uganda, where rainfall records are typically available only at daily resolutions. This research used observed hourly rainfall data to derive site-specific parameters for daily rainfall disaggregation. It then evaluated the performance of several disaggregation methods, including empirical approaches, statistical distributions, the Bartlett–Lewis rectangular pulse (BLRP) stochastic model, and the aligned time distribution (TD) method. The method performance was assessed via the root mean square error (RMSE), mean absolute error (MAE), percent bias (PBIAS), and Kolmogorov–Smirnov (KS) tests. To demonstrate the practical applicability of the derived parameters, the designed TD method was applied to disaggregate daily rainfall and construct intensity‒duration‒frequency (IDF) curves. The results revealed that rainfall events across all stations were temporally skewed, with peak intensities generally occurring within the first 1–3 hours of a storm. Among the methods tested, the aligned TD method consistently performed best, achieving the lowest RMSE values (1.02–1.35 mm) and KS statistics (0.14–0.24) across all stations. The derived IDF curves revealed sharper intensity peaks for shorter durations, closely reflecting the observed storm characteristics. The research concludes that the aligned TD method offers a robust and reliable approach for disaggregating daily rainfall into subdaily values suitable for IDF curve development. Finally, this research recommends the use of site-specific disaggregation parameters in hydrological infrastructure design and emphasizes the need for further research into the transferability of storm profiles across diverse regions.