A Satellite-Based Analysis of Precipitation Temporal Trends and Spatial Patterns in Ghana

Climate change is causing unprecedented changes in the frequency and intensity of hydroclimatic extremes around the world, with disastrous consequences in vulnerable regions and communities. West Africa faces substantial challenges due to its limited adaptive capacity and the high sensitivity of its socio-economic systems. Recent records show how Ghana has been exposed to flash flood events in major cities of the Ashanti and Greater Accra regions during 2019, 2020, and 2021, and this emphasizes the importance of comprehensive investigations into extreme precipitation trends and patterns in this region.

However, regions like West Africa have limited coverage of in situ observations and further ground weather radar networks are generally absent. Space-based platforms offer a unique opportunity to monitor rainfall in such regions. This study adopts the final run product of the Integrated Multisatellite Retrievals for GPM (IMERG), to investigate spatio-temporal changes of daily extreme precipitation in Ghana from 2001 to 2020. Specifically, indices from the Expert Team on Climate Change Detection and Indices (ETCCDI) are used, including maximum daily (RX1day) and maximum consecutive 5-day precipitation (RX5day) at an annual scale. Additionally, changes in the threshold-based indices are analyzed, such as the number of days exceeding the 95^th and 99^th climatological percentiles, as well as their fractional change to annual precipitation (R95pTOT and R99pTOT).

To ascertain the significance of temporal trends, the non-parametric Mann-Kendall test is applied to the IMERG time series at each grid point, evaluated at three significance levels (0.01, 0.05, and 0.10). The non-parametric Theil-Sen slope estimator is utilized to investigate changes in extreme precipitation magnitudes. Additionally, trends in extreme precipitation frequency, such as the number of days exceeding the 95^th and 99^th percentiles, are identified through the least-square method in a traditional linear regression. Results obtained in this work reveal the trends at 0.05 significance level in RX1day, RX5day, R95pTOT, and R99pTOT in the region. Outcomes of this study will help to understand trends and patterns in extreme precipitation in Ghana, which is crucial to empower informed decision-making for future flood forecasting and climate change mitigation efforts.