Jakobus Andries Du Plessis

The Pitman model is widely used in South Africa for hydrological modelling and water resource management. For the model to assist with ongoing water management, it needs to use the most recent observed rainfall data, which has proved challenging over the past decade due to data scarcity. This research aimed at developing a CHIRPS (Climate Hazards Group Infrared Precipitation)–based Pitman model framework that instead uses satellite-derived rainfall estimates for the simulation of stream flows. The framework was developed and tested in Catchments G, B, V, and L, as case study catchments representative of the diverse hydroclimatic regions of South Africa. CHIRPS estimates (1981–2019), downloaded at a quaternary catchment scale for the study catchments, demonstrated a generally strong monthly correlation (R2 > 0.7) with the WR2012 rainfall data. The satellite rainfall data (CHIRPS) were adjusted to correspond to the WR2012 rainfall data, and the Pitman model was set up and calibrated for the period 1981–2009, using the satellite rainfall data. Validation was done for the period 2010–2019. Calibration and validation were performed for 351 quaternary catchments in evaluating the suitability of using satellite data in modelling hydrological catchment responses. The simulated CHIRPS-based flows illustrated good similarity (∆ ≤ 4%) to observed flows. Further, a goodness-of-fit assessment of CHIRPS-based flows using 8 hydrological indices at a ±15% threshold of acceptable error was performed. The results demonstrated 78%, 73%, and 80% suitability of simulated CHIRPS-based flows for Catchments B, V and G, respectively. Catchment L had ‘suspect’ results, with indices illustrating inadequate correspondence between observed and CHIRPS-based flows. Based on satisfactory performance of the developed CHIRPS-based Pitman model framework, complementary application of CHIRPS rainfall estimates with the declining available observed rainfall data for the simulation of observed flows in data-scarce South African catchments is recommended.

This study presents a hybrid approach combining machine learning (ML) and the Water Evaluation and Planning (WEAP) model to optimise water allocation in the Upper Awash–Akaki catchment, Ethiopia, under projected climate and population pressures. Population growth (2.5–3.5% annually) is expected to increase demand from 6.3 million in 2025 to nearly 39 million by 2075. Climate scenarios (SSP4.5 and SSP8.5) project temperature increases of 0.9 to 1.6 °C and precipitation changes ranging from +15.5% to −6.3%, intensifying hydrological stress. Hydrological impacts include a 46.9 mm decline in baseflow, reduced soil moisture (−124.9 to +16.4 mm), and evapotranspiration losses up to 1,010 mm, leading to unmet water demand exceeding 1.58 billion m3. Integrating ML improved WEAP's forecasting, with Random Forest outperforming Long Short-Term Memory (LSTM) (MAE: 0.41 vs. 0.46). SHapley Additive exPlanations (SHAP) analysis identified lagged population and unmet demand as key predictors. Using Non-dominated Sorting Genetic Algorithm II (NSGA-II), two Pareto-optimal strategies emerged: one prioritising equity (Gini index 0.22), the other economic efficiency (9% gain), highlighting allocation trade-offs. The framework, supported by SHAP interpretation and IoT-based monitoring, offers a replicable tool for adaptive, climate-resilient water governance in data-scarce and rapidly urbanising basins.

Rainfall intensity‒duration‒frequency (IDF) curves are essential for hydrological design and flood management, yet their development in Uganda is limited by scarce subdaily rainfall data. This research regionalized the IDF coefficients using physical and climatic predictors. IDF coefficients (a, b, c) were derived from observed hourly rainfall and from disaggregated daily rainfall using aligned time distribution (TD) factors. The aligned TD method produced consistent coefficients (b: 0.404 to 0.65; c: 1.01 to 1.079) with near-perfect R² values, while the empirical method based on observed hourly data exhibited greater variability, the coefficient a ranged from 32.37 at Soroti to 678.54 at Gulu. Multiple regression analysis (MRA) using elevation, mean, and standard deviation as predictors, performed strongly, particularly with disaggregated data (R² > 92% for all coefficients). Validation showed predicted coefficients closely matched actual values, especially coefficients b and c (percentage error within ±10%), though coefficient a exhibited larger deviations. The MRA model, based on observed data, also performed well for coefficients b and c (R² = 97.65% and 96.25%, respectively). This research demonstrates that regionalizing IDF coefficients using MRA is feasible and effective, providing practical tools for estimating design storm intensities in gauged and ungauged catchments, while recommending cautious application beyond the calibration domain.

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.

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.

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.

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.

This paper examines how machine learning (ML) techniques can enhance the Water Evaluation and Planning (WEAP) model for surface water distribution strategies in the context of climate variability. Recent advancements in ML, General Circulation Models (GCMs), satellite data, and climate projections offer new opportunities for improved water resource management. However, methodological challenges remain in integrating these techniques across scientific disciplines and translating theoretical research into practical applications. The paper evaluates the effectiveness of the WEAP model in scenario planning while identifying uncertainties arising from dynamic socio-economic changes and climate variability. It demonstrates how ML enhances WEAP’s capabilities by improving forecasting accuracy, recognising hydrological patterns, and reducing measurement uncertainties. Furthermore, scenario-based modelling, powered by ML, offers sustainable water management solutions tailored to water-stressed regions facing increasing environmental and demand pressures. By synthesising insights from diverse research, this paper offers actionable recommendations for researchers, policymakers, and practitioners seeking to develop resilient water distribution systems in vulnerable regions.

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.