CLIMATE CHANGE IMPACT ASSESSMENT METHOD IN OBSERVATION DATA SCARCE BASIN

Abstract

 In previous studies, reanalysis data has been used instead of observation data for bias correction of GCM outputs in observation data scarece basin. However, reanalysis data does not match the real weather characteristics (e.g. precipitation, temperature) in a local scale since it is targeted at a global scale. Therefore, this study aims to develop a bias correction method for re-analysis data based on observation data. As a result, it is revealed that time-series pattern of precipitation in a reanalysis data can be corrected using three relations of monthly precipitation in reanalysis data and 1) number of precipitation events in each month, 2) average and 3) standard deviation of 6 hourly precipitation in observation data. Furthermore, it is suggested that water resources will decrease in this study site in the future by runoff analysis using GCM outputs with bias correction based on reanalysis data.