A multi-scale analysis of Namibian rainfall over the recent decade – comparing TMPA satellite estimates and ground observations

• Daily-scale ground-based rainfall records from a data scarce region were reported.
• TMPA satellite rainfall estimates were validated using the ground observations.
• Long-term trends of changes in rainfall amount, intensity and frequency were analyzed.
• The results showed increased rainfall frequency and variability for the driest location.
• This study provided a useful approach of using TMPA data for data scarce conditions.

Study regionNamibia.Study focusThe lack of ground observations has long been a major obstacle in studying rainfall patterns in many dryland regions, particularly in the data scarce African continent. In this study, a continuous 6-year (2008–2013) daily record of ground observations collected from Weltevrede Farm at the edge of the Namib Desert was used to evaluate TRMM Multi-satellite Precipitation Analysis (TMPA, 0.25° resolution) daily rainfall estimates of this area. A Mann-Kendall trend analysis was conducted using all the available annual TMPA satellite data (1998–2015) to examine long-term trends in rainfall amount, intensity, frequency and seasonal variations over four locations across a rainfall gradient.New hydrological insights for the regionThe agreement between ground and satellite rainfall data was generally good at annual/monthly scales but large variations were observed at the daily scale. Results showed a spatial variability of rainfall trends across the rainfall gradient. We observed significant changes in frequency along with insignificant changes in intensity and no changes in total amount for the driest location, but no changes in any of the rainfall parameters were observed for the three wetter locations. The results also showed increased rainfall variability for the driest location. This study provided a useful approach of using TMPA data associated with trend analysis to extend the data record for ecohydrological studies for similar data scarce conditions. The results of this study will also help constrain IPCC predictions in this region.