Development of IDF curves for Uganda Using Observed, Remotely Sensed, and Regional Climate Model rainfall data

Climate-resilient hydrological infrastructure design requires accurate estimation of design storm intensities, especially in data-scarce regions such as Uganda. This research investigates the reliability of bias-corrected remotely sensed rainfall (RSR) and regional climate model (RCM) data for constructing intensity‒duration‒frequency (IDF) curves under both current (1991–2020) and future (2036–2065) climate. The accuracy of RSR data in estimating intensities, the impacts of climate change, and the sensitivity of intensities to emission scenarios were assessed through comparisons of developed IDF curves. The performance of the RCMs was evaluated via metrics, including the root mean square error (RMSE) and the Kolmogorov–Smirnov (KS) test. Among the RCMs, REMO2009 performed best at Fort Portal and Mbarara, with the lowest RMSE values, whereas BCCR-WRF331 demonstrated better accuracy at Gulu, Jinja, and Soroti. Under the representative concentration pathway (RCP) 4.5 scenario for 2036–2065, the projected intensities consistently increase across all stations. For example, at Gulu, the 1-hour, 100-year intensity increased by 22.1%, from 94.58 mm/h to 115.45 mm/h. Rainfall intensity comparisons between the RCP4.5 and RCP8.5 scenarios reveal higher intensities under RCP8.5 at Mbarara, Fort Portal, and Soroti, e.g., Mbarara’s 100-year, 1-hour event increases by 15.15%. At Gulu and Jinja, intensities under RCP 8.5 are slightly lower, indicating spatial variability in emission scenario sensitivity. The research concludes that bias-corrected RSR are reliable alternative in IDF development in data-scarce regions, and that RCM outputs can inform future climate risk assessments. It recommends the integration of localized IDF projections into planning and policy, especially in infrastructure design.