Evaluation and bias correction of RCM data: Hydrology modeling applications
Jinwon Kim, National Institute of Atmospheric Sciences
The ongoing climate change and variations driven by anthropogenic emissions of greenhouse gases made it a critical task to assess the impacts of the climate change on various human sectors. There exists a substantial gap in the spatial scales of climate data and regional sectors of interests. Because of this, the climate research community has been working to develop the procedures and methodologies to apply climate model data to regional impact assessment. This presentation revisits model evaluation and bias correction of RCM data for the application to surface hydrology and stream flow model calculations. Application of bias correction schemes could successfully improve some climatological properties such as the mean and the spatial variability. It is also found that the errors in the sequence of events are virtually unchanged by the bias correction schemes. This can be critical in hydrology model simulations because the simulated stream flows, especially the peak flow magnitudes, usually depend on the sequence of events (or simultaneous effects of heavy precipitation and antecedent soil moisture conditions).