Understanding rainfall variability and trends is crucial for effective water resource management and disaster preparedness, particularly in tropical regions like Uganda. This study analyzes the trends and variability of the Annual Maximum Series (AMS) and seasonal rainfall data across four rainfall stations in Uganda, comparing observed data with various Remotely Sensed Rainfall (RSR) products. The key methods used in this study include the Mann–Kendall test and Sen’s slope estimator for trend analysis, AMS rainfall variability analysis using statistical performance metrics such as the Nash–Sutcliffe Coefficient of Efficiency (NSE) and Percent Bias (PBIAS), and data distribution comparisons based on goodness-of-fit evaluation using the Kolmogorov–Smirnov (KS) test. The results indicate that most trends in the seasonal rainfall and AMS data are statistically insignificant. However, the September to November (SON) observed rainfall at the Gulu station shows a statistically significant increasing trend of 7.68 mm/year (p-value = 0.03). Based on the PBIAS metric, GPCC and NOAA_CPC products outperform other RSR data products. At the Jinja station, NOAA_CPC has a PBIAS value of −12.93% and GPCC, −14.64%; at Soroti, GPCC has −9.66% and NOAA_CPC, −14.79%; at Mbarara, GPCC has −5.93% and NOAA_CPC, −11.63%; and at Gulu, GPCC has −3.05% and NOAA_CPC, −19.23%. The KS test results show significant differences in the distribution of RSR data and observed rainfall data, though GPCC shows significant agreement at the Gulu (p-value = 0.60) and Mbarara (p-value = 0.14) stations. Additionally, NOAA_CPC outperforms other RSR data products at the Mbarara station, with a KS p-value of 0.24. This study highlights the limitations of current RSR datasets in replicating observed AMS rainfall data. Based on KS test results, GPCC is identified as a better product for hydrological applications at the Gulu, Jinja, and Soroti station areas compared to other RSR products. For the Mbarara station, NOAA_CPC outperforms other RSR products.

This research addresses the challenge of bias in Remotely Sensed Rainfall (RSR) datasets used for hydrological planning in Uganda’s data-scarce, ungauged catchments. Four bias correction methods, Quantile Mapping (QM), Linear Transformation (LT), Delta Multiplicative (DM), and Polynomial Regression (PR), were evaluated using daily rainfall data from four gauged stations (Gulu, Soroti, Jinja, Mbarara). QM consistently outperformed other methods based on statistical metrics, e.g., for National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA_CPC) RSR data at Gulu, Root-Mean-Square Error (RMSE) was reduced from 29.20 mm to 19.00 mm, Mean Absolute Error (MAE) reduced from 22.44 mm to 12.84 mm, and Percent Bias (PBIAS) reduced from −19.23% to 1.05%, and improved performance goodness-of-fit tests (KS = 0.03, p = 1.00), while PR, though statistically strong, failed due to overfitting. A bias correction framework was developed for ungauged catchments, using predetermined bias factors derived from observed station data. Validation at Arua (tropical savannah) and Fort Portal (tropical monsoon) demonstrated significant improvements in RSR data when the bias correction framework was applied. At Arua, bias correction of Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data reduced RMSE from 49.14 mm to 21.41 mm, MAE from 45.74 mm to 17.38 mm, and PBIAS from −59.83% to −8.18%, while at Fort Portal, bias correction of the CHIRPS dataset reduced RMSE from 28.35 mm to 15.02 mm, MAE from 25.28 mm to 11.35 mm, and PBIAS from −46.2% to 4.74%. Our research concludes that QM is the most effective method, and that the framework is a tool for improving RSR data in ungauged catchments. Recommendations for future work includes machine learning integration and broader regional validation.

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.

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 study 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 h 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.

This research addresses the challenge of bias in Remotely Sensed Rainfall (RSR) datasets used for hydrological planning in Uganda’s data-scarce, ungauged catchments. Four bias correction methods; Quantile Mapping (QM), Linear Transformation (LT), Delta Multiplicative (DM), and Polynomial Regression (PR), were evaluated using daily rainfall data from four gauged stations (Gulu, Soroti, Jinja, Mbarara). QM consistently outperformed other methods across statistical metrics (e.g., for National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA_CPC) RSR data at Gulu, Root Mean Square Error (RMSE) was reduced from 29.20 mm to 19.00 mm, Mean Absolute Error (MAE) reduced from 22.44 mm to 12.84 mm, and Percent Bias (PBIAS) reduced from -19.23% to 1.05%, and goodness-of-fit tests (KS = 0.03, p = 1.00), while PR, though statistically strong, failed due to overfitting. A bias correction framework was developed for ungauged catchments, using predetermined bias factors derived from observed station data. Validation at Arua (tropical savannah) and Fort Portal (tropical monsoon) demonstrated significant improvements in RSR data when the bias correction framework was applied. At Arua, bias correction of Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) data reduced RMSE from 49.14 mm to 21.41 mm, MAE reduced from 45.74 mm to 17.38 mm, and PBIAS reduced from -59.83% to -8.18%, while at Fort Portal, bias correction of CHIRPS dataset reduced RMSE from 28.35 mm to 15.02 mm, MAE from 25.28 mm to 11.35 mm, and PBIAS from -46.2% to 4.74%. The research concludes that QM is the most effective method and that the framework is a tool for improving RSR data in ungauged catchments. Recommendations for future work includes machine learning integration and broader regional validation.

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.