AGCM3.2S RUNOFF DATA BIAS CORRECTION OVER UPPER CHAO PHRAYA RIVER BASIN BASED ON LAND COVER GROUPING

Abstract

 This paper shows a bias correction method for runoff estimation by a global climate model that considers land cover characteristics. In GCM simulation processes, runoff data is generated by a land surface model that incorporates the direct influence of land cover. This makes the bias correction possible to adapt to the different types of land cover setting in GCMs. In this study, a bias correction method considering land cover characteristics with a combination of a linear scaling factor and an empirical quantile mapping was implemented to MRI-AGCM3.2S runoff data over the Chao Phraya River basin in Thailand. The daily runoff spatial pattern and daily river discharge were used to evaluate the performance of the bias correction. The result shows that the proposed bias correction has better performance than a direct application of empirical quantile mapping bias correction in a sub-catchment and grid to grid-scale grouping for the rainy season. These results underpin the incorporation of land cover information into a bias correction method to improve the performance of runoff and river discharge predictions.