Spatiotemporal Analysis of Precipitation in the Sparsely Gauged Zambezi River Basin Using Remote Sensing and Google Earth Engine

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 24; p. 2977
• Main Authors: Zeng, Hongwei, Wu, Bingfang, Zhang, Ning, Tian, Fuyou, Phiri, Elijah, Musakwa, Walter, Zhang, Miao, Zhu, Liang, Mashonjowa, Emmanuel
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 11.12.2019
• Subjects: Agricultural management; Agricultural production; Agriculture; Annual precipitation; Annual rainfall; Application programming interface; Climate change; Datasets; Developing countries; Drought; Dry season; Environmental risk; Flood management; Food; Food production; Gauges; LDCs; Pattern analysis; Precipitation; Rain; Rainfall; Remote sensing; Risk assessment; River basins; Rivers; Solar radiation; Spatial analysis; Spatial data; Temporal variations; Trends; Zimbabwe; Zambia; Mozambique; Malawi; Zambezi River; Africa
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11242977

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.