Climate change is a highly contentious topic in the modern world. There is much evidence to indicate that climatic shifts and extreme weather anomalies are taking place globally, in some places more than others. This paper presents the findings on research to determine whether shifts in seasonal rainfall patterns are indeed already visible in historical rainfall data in the Western Cape of South Africa. The paper aims to provide some baseline information which can stimulate further research in this field. Different analytical methods were formulated to investigate the relationships between daily rainfall indices over a set timescale. Data was collected from the South African Weather Service (SAWS) in order to accumulate 20 rainfall stations, each with at least 100 years of historical daily rainfall data. Statistical analysis, linear trend line distributions, time lag comparisons, cumulative distributions, moving average plots and autocorrelation relationships were applied to the data. The results of the analysis indicated that (1) the rainfall season undergoes fluctuations of wetter and drier years (approximately 20-year cycles), (2) the South Coast region exhibits a shift towards a longer rainfall season, and in contrast the Mediterranean region is shifting to a shorter rainfall season when linear trend lines were analysed, and (3) the moving average plots showed only isolated seasonal shifts at the boundary months. Keywords: rainfall pattern, shifts in seasonal rainfall, climate change

Long-term rainfall data with good spatial and temporal distribution is essential for all climate-related analyses. The availability of observed rainfall data has become increasingly problematic over the years due to a limited and deteriorating rainfall station network, occasioned by limited reporting and/or quality control of rainfall and, in some cases, closure of these stations. Remotely sensed satellite-based rainfall data sets offer an alternative source of information. In this study, daily and monthly rainfall data derived from Climate Hazards Group InfraRed Precipitation (CHIRPS) is compared with observed rainfall data from 46 stations evenly distributed across South Africa. Various metrics, based on a pairwise comparison between the observed and CHIRPS data, were applied to evaluate CHIRPS performance in the estimation of daily and monthly rainfall. The results show that CHIRPS data correlate well with observed monthly rainfall data for all stations used, having an average coefficient of determination of 0.6 and bias of 0.95. This study concludes that monthly CHIRPS data corresponds well, with good precision and relatively little bias when compared to observed monthly rainfall data, and can therefore be considered for use in conjunction with observed rainfall data where no or limited data is available in South Africa for hydrological analysis.

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.

Extreme storms in South Africa and specifically in the Western Cape have been responsible for widespread destruction to property and infrastructure, even leading to displacement and death. The occurrences of these storms have been increasingly linked to human-induced climate change that is expected to cause more variable weather. Studies on climate circulation models for future climate conditions project that rainfall in the Western Cape and wider South African region is to become more intense and extreme. Sub-daily rainfall for 3 stations in the Western Cape and 4 stations in the rest of South Africa were analysed in order to determine if any trends towards more intense and extreme rainfall are observed and whether the trend is unique to the Western Cape or indicates a wider trend. This study explores this expectation by using historical short-duration rainfall (less than 24 h) for 7 stations in the Western Cape and South African region. Digitised autographic and automatic weather station 5-min rainfall data were combined to extend the effective record length. Both the magnitude and frequency of occurrence of rainfall events were analysed to assess if rainfall intensities are showing any evidence of increasing over time. For the magnitude of rainfall events, extreme value theory was applied to non-stationary sequences, using both a parametric and non-parametric approach for both event maxima and peaks over threshold modelling. The frequency analysis entailed measuring the frequency of exceedance of rainfall events over a certain threshold value. Both the magnitude and frequency analysis indicated that the combination of the two record types influenced the results of some of the stations, while the others showed no consistent evidence of changing rainfall intensities. This led to the conclusion that, from the available observed short-duration record, no evidence was found of trends or indications of changes in rainfall intensities. Keywords: climate change, short duration rainfall, extreme value theory, non-stationary

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.