Temporal and Spatial Variations of Potential and Actual Evapotranspiration and the Driving Mechanism over Equatorial Africa Using Satellite and Reanalysis-Based Observation

Abstract

This study investigated the actual evapotranspiration (AET) and potential evapotranspiration (PET) seasonality, trends, abrupt changes, and driving mechanisms with global sea surface temperature (SST) and atmospheric circulation patterns over Equatorial Africa (EQA) during 1980–2020. The spatiotemporal characteristics of mean ET were computed based on a 40-year average at annual and seasonal scales. The Mann-Kendall statistical test, the Sen slope test, and the Bayesian test were used to analyze trends and detect abrupt changes. The results showed that the mean annual PET (AET) for 1980–2020 was 110 (70) mm. Seasonal mean PET (AET) values were 112 (72) in summer, 110 (85) in autumn, 109 (84) in winter, and 110 (58) in spring. The MK test showed an increasing (decreasing) rate, and the Sen slope identified upward (downward) at a rate of 0.35 (0.05) mm yr−10. The PET and AET abrupt change points were observed to happen in 1995 and 2000. Both dry and wet regions showed observed weak (strong) correlation coefficient values of 0.3 (0.8) between PET/AET and climate factors, but significant spatiotemporal differences existed. Generally, air temperature, soil moisture, and relative humidity best explain ET dynamics rather than precipitation and wind speed. The regional atmospheric circulation patterns are directly linked to ET but vary significantly in space and time. From a policy perspective, these findings may have implications for future water resource management.

The Relationship between Changes in Hydro-Climate Factors and Maize Crop Production in the Equatorial African Region from 1980 to 2021

“…However, both types of evapotranspiration increased at rates of 0.03 mm/10a −1 and 0.02 mm10a −1 , respectively. In the context of a warming climate, increasing evapotranspiration may lead to an increase in surface water losses and increased water availability, as supported by previous studies [37,52,62]. The spatial patterns of the minimum and maximum temperatures showed heterogeneity in their trends, but the overall values showed an identical trend rate of 0.01 • C 10a −1 .…”

“…These non-parametric methods assume datasets to have independent and random distribution. An advantage of using non-parametric estimators is that they can handle outliers and these methods are widely used in many areas [35,52]. All statistically significant trends were calculated using Student's t-test at a 95% confidence level.…”