Assessing the Reliability of Satellite and Reanalysis Estimates of Rainfall in Equatorial Africa

Resolution-Sensitive Added Value Analysis of CORDEX-CORE RegCM4-7 Past Seasonal Precipitation Simulations over Africa Using Satellite-Based Observational Products

This study adopts a two-way approach to CORDEX-CORE RegCM4-7 seasonal precipitation simulations’ Added Value (AV) analysis over Africa, which aims to quantify potential improvements introduced by the downscaling approach at high and low resolution, using satellite-based observational products. The results show that RegCM4-7 does add value to its driving Global Climate Models (GCMs) with a positive Added Value Coverage (AVC) ranging between 20 and 60% at high resolution, depending on the season and the boundary conditions. At low resolution, the results indicate an increase in the positive AVC by up to 20% compared to the high-resolution results, with an up to 8% decrease for instances where an increase is not observed. Typical climate zones such as West Africa, Central Africa, and Southern East Africa, where improvements by Regional Climate Models (RCMs) are expected due to strong dependence on mesoscale and fine-scale features, show positive AVC greater than 20%, regardless of the season and the driving GCM. These findings provide more evidence for confirming the hypothesis that the RCMs AV is influenced by their internal physics rather than being the product of a mere disaggregation of large-scale features provided by GCMs. Although the results show some dependencies to the driving GCMs relating to their equilibrium climate sensitivity nature, the findings at low resolutions similar to the native GCM resolutions make the influence of internal physics more important. The findings also feature the CORDEX-CORE RegCM4-7 precipitation simulations’ potential in bridging the quality and resolution gap between coarse GCMs and high-resolution remote sensing datasets. Even if further post-processing activities, such as bias correction, may still be needed to remove persistent biases at high resolution, using upscaled RCMs as an alternative to GCMs for large-scale precipitation studies over Africa can be insightful if the AV and other performance statistics are satisfactory for the intended application.

Rainfall Variability and Trends over the African Continent Using TAMSAT Data (1983–2020): Towards Climate Change Resilience and Adaptation

This study reveals rainfall variability and trends in the African continent using TAMSAT data from 1983 to 2020. In the study, a Mann–Kendall (MK) test and Sen’s slope estimator were used to analyze rainfall trends and their magnitude, respectively, under monthly, seasonal, and annual timeframes as an indication of climate change using different natural and geographical contexts (i.e., sub-regions, climate zones, major river basins, and countries). The study finds that the highest annual rainfall trends were recorded in Rwanda (11.97 mm/year), the Gulf of Guinea (river basin 8.71 mm/year), the tropical rainforest climate zone (8.21 mm/year), and the Central African region (6.84 mm/year), while Mozambique (−0.437 mm/year), the subtropical northern desert (0.80 mm/year), the west coast river basin of South Africa (−0.360 mm/year), and the Northern Africa region (1.07 mm/year) show the lowest annual rainfall trends. There is a statistically significant increase in the rainfall in the countries of Africa’s northern and central regions, while there is no statistically significant change in the countries of the southern and eastern regions. In terms of climate zones, in the tropical northern desert climates, tropical northern peninsulas, and tropical grasslands, there is a significant increase in rainfall over the entire timeframe of the month, season, and year. This implies that increased rainfall will have a positive effect on the food security of the countries in those climatic zones. Since a large percentage of Africa’s agriculture is based only on rainfall (i.e., rain-fed agriculture), increasing trends in rainfall can assist climate resilience and adaptation, while declining rainfall trends can badly affect it. This information can be crucial for decision-makers concerned with effective crop planning and water resource management. The rainfall variability and trend analysis of this study provide important information to decision-makers that need to effectively mitigate drought and flood risk.

Artificial Intelligence-Based Techniques for Rainfall Estimation Integrating Multisource Precipitation Datasets

This study presents a comprehensive investigation of multiple Artificial Intelligence (AI) techniques—decision tree, random forest, gradient boosting, and neural network—to generate improved precipitation estimates over the Upper Blue Nile Basin. All the AI methods merged multiple satellite and atmospheric reanalysis precipitation datasets to generate error-corrected precipitation estimates. The accuracy of the model predictions was evaluated using 13 years (2000–2012) of ground-based precipitation data derived from local rain gauge networks in the Upper Blue Nile Basin region. The results indicate that merging multiple sources of precipitation substantially reduced the systematic and random error statistics in the Upper Blue Nile Basin. The proposed methods have great potential in predicting precipitation over the complex terrain region.