Comparison of BIAS correction techniques for GPCC rainfall data in semi-arid climate

Long-term historical precipitation data are important in developing metrics for studying the impacts of past hydrologic events (e.g., droughts) on water resources management. Many geographical regions around the world often witness lack of long term historical observation and to overcome this challenge, Global Precipitation Climatology Center (GPCC) datasets are found to be useful. However, the GPCC data are available at coarser scale (0.5A degrees resolution), therefore bias correction techniques are often applied to generate local scale information before it can be applied for decision making activities. The objective of this study is to evaluate and compare five different bias correction techniques (BCT's) to correct the GPCC data with respect to rain gauges in Iraq, which is located in a semi-arid climatic zone. The BCT's included in this study are: Mean Bias-remove (B) technique, Multiplicative Shift (M), Standardized-Reconstruction (S), Linear Regression (R), and Quantile Mapping (Q). It was observed that the Performance Index (PI) of BCT's differs in space (i.e., precipitation pattern) and temporal scale (i.e., seasonal and monthly). In general, the PI for the Q and B were better compared to other three (M, S and R) bias correction techniques. Comparatively, Q performs better than B during wet season. However, both these techniques performed equally well during average rainy season. This study suggests that instead of using a single bias correction technique at different climatic regimes, multiple BCT's needs to be evaluated for identifying appropriate methodology that suits local climatology.