Evaluation of GSMaP Daily Rainfall Satellite Data for Flood Monitoring: Case Study—Kyushu Japan

Abstract

In this paper, the Global Satellite Mapping of Precipitation Moving Vector with Kalman filter (GSMaP_MVK) was evaluated and corrected at daily time scales with a spatial resolution of 0.1˚ latitude/longitude. The reference data came from thirty- four rain gauges on Kyushu Island, Japan. This study focused on the GSMaP_MVK’s ability to detect heavy rainfall patterns that may lead to flooding. Statistical analysis was used to evaluate the GSMaP_MVK data both quantitatively and qualitatively. The statistical analysis included the relative bias (B), the mean error (E), the Nash- Sutcliffe coefficient (CNS), the Root Mean Square Error (RMSE) and the correlation coefficient (r). In addition, Generalized Additive Models (GAMs) were used to conduct GSMaP_MVK data correction. The results of these analyses indicate that GSMaP_MVK data have lower values than observed data and may be significantly underestimated during heavy rainfall...