Abstract
Precipitation plays an important role in the food production of Southern Africa. Understanding the spatial and temporal variations of precipitation is helpful for improving agricultural management and flood and drought risk assessment. However, a comprehensive precipitation pattern analysis is challenging in sparsely gauged and underdeveloped regions. To solve this problem, Version 7 Tropical Rainfall Measuring Mission (TRMM) precipitation products and Google Earth Engine (GEE) were adopted in this study for the analysis of spatiotemporal patterns of precipitation in the Zambezi River Basin. The Kendall’s correlation and sen’s Slop reducers in GEE were used to examine precipitation trends and magnitude, respectively, at annual, seasonal and monthly scales from 1998 to 2017. The results reveal that 10% of the Zambezi River basin showed a significant decreasing trend of annual precipitation, while only 1% showed a significant increasing trend. The rainy-season precipitation appeared to have a dominant impact on the annual precipitation pattern. The rainy-season precipitation was found to have larger spatial, temporal and magnitude variation than the dry-season precipitation. In terms of monthly precipitation, June to September during the dry season were dominated by a significant decreasing trend. However, areas presenting a significant decreasing trend were rare (<12% of study area) and scattered during the rainy-season months (November to April of the subsequent year). Spatially, the highest and lowest rainfall regions were shifted by year, with extreme precipitation events (highest and lowest rainfall) occurring preferentially over the northwest side rather than the northeast area of the Zambezi River Basin. A “dry gets dryer, wet gets wetter” (DGDWGW) pattern was also observed over the study area, and a suggestion on agriculture management according to precipitation patterns is provided in this study for the region. This is the first study to use long-term remote sensing data and GEE for precipitation analysis at various temporal scales in the Zambezi River Basin. The methodology proposed in this study is helpful for the spatiotemporal analysis of precipitation in developing countries with scarce gauge stations, limited analytic skills and insufficient computation resources. The approaches of this study can also be operationally applied to the analysis of other climate variables, such as temperature and solar radiation.
Highlights
Rain-fed agriculture is the livelihood foundation for the majority of the rural poor in sub-Saharan Africa [1]
Considering the multiple global precipitation products included and stored in Google Earth Engine (GEE), such as the Global Satellite Mapping of Precipitation, Global Precipitation Measurement (GPM) V5, PERSIANN-CDR, Tropical Rainfall Measuring Mission (TRMM) and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), this study provides a good opportunity to conduct precipitation analysis using GEE
This study offers new insights into basin-scale precipitation trends across different temporal scales, and provides a suggestion on agriculture management according to the precipitation patterns of this region
Summary
Rain-fed agriculture is the livelihood foundation for the majority of the rural poor in sub-Saharan Africa [1]. In sub-Saharan Africa, nearly 90% of staple food production is provided by rain-fed farming systems [1]. Both the totals and the variations of annual, seasonal and sub-seasonal precipitation have major effect on crop productivity [2,3]. Nicholson et al [10] examined rainfall variability in 13 sectors that cover most of the African continent using the available rainfall stations. This method cannot be applied to undeveloped and developing regions where ground gauge data are sparse [11]. The modeled precipitation is biased against observation, and there have been large uncertainties in modeled results due to the parametrization, the selection of forcing data, and the model used
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