Evaluation of Merging Methods of Remotely Sensed and Gauged Precipitation for Rainfall Runoff Simulation in Da River Basin, Vietnam

Tuesday, 16 December 2014
Bui Thi Hieu and Hiroshi Ishidaira, University of Yamanashi, Yamanashi, Japan
Precipitation would be one of the major sources of uncertainty for hydrological analysis since precipitation is the main input of the hydrological models, in that sense, is one of the first factors controlling the accuracy of Rainfall Run-off (R-R) modeling. However, sparse meteorological data in un-gauged or poorly gauged basins has been a long concerning issue that bottlenecks the quantification of the hydrological budget. In recent decades, remote sensing techniques with their broad spatial coverage and repeat temporal coverage would grasp well the spatial distribution of precipitation over the river basin. However, remote sensing rainfall estimates are not direct measurements of rainfall that are subjects to variety of error sources and exhibit the limitation for rainfall budget accuracy. Therefore, it is appealing to incorporate the precision of point measurements of the local rainfall stations with the fine spatial distribution of satellite-based rainfall estimates to obtain a good quality of precipitation field in space and time to take the advantages of the both two datasets. The aim of my research is blending the remote sensing satellite precipitation, GsMAP-MVK (Global Satellite Mapping of Precipitation moving vector with Kalman filter) with a very high spatial distribution, with the local rainfall measurements for improvement of quantitative rainfall estimates and run-off predictions capability. Several satellite-gauge merging methods with various complexity degrees: from linear merging, power transformation merging to geo-statistical merging techniques were utilized to provide the precipitation input for conceptual hydrological model HBV for run-off simulation in Da river basin in Vietnam. The Geo-statistical merging methods give the best results for stream-flow simulation.

Key Words: Remote sensed satellite data, hydrological model, un-gauged basin, bias correction.