Trend and Variability Analysis of Annual Maximum Rainfall Using Observed and Remotely Sensed Data in the Tropical Climate Zones of Uganda

Martin Okirya () and Du Plessis Ja
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Martin Okirya: Department of Civil Engineering, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa
Du Plessis Ja: Department of Civil Engineering, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa

Sustainability, 2024, vol. 16, issue 14, 1-44

Abstract: 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.

Keywords: CHIRPS; ERA5_AG; ERA5; MERRA2; NOAA_CPC; PERSIANN; GPCC; extreme rainfall; PBIAS; Kolmogorov–Smirnov test (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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