Author(s)

Martiwi Diah Setiawati^1*, Fusanori Miura²

¹Integrated Research System for Sustainability Science (IR3S), The University of Tokyo, Bunkyo, Japan.
²Graduate School of Science and Engineering, Yamaguchi University, Ube, Japan.

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. By applying GAM to bias correction, GSMaP_MVK’s ability to detect heavy rainfall was improved. In addition, GAM for bias correction could effectively be applied for significant underestimates of GSMaP_ MVK (i.e., bias of more than 55%). GAM is a new approach to predict rainfall amount for flood and landslide monitoring of satellite base precipitation, especially in areas where rain gauge data are limited.

Evaluation, GSMaP_MVK, Flood Monitoring, Kyushu

Setiawati, M. and Miura, F. (2016) Evaluation of GSMaP Daily Rainfall Satellite Data for Flood Monitoring: Case Study—Kyushu Japan. Journal of Geoscience and Environment Protection, 4, 101-117. doi: 10.4236/gep.2016.412008.