Impacts of bias nonstationarity of climate model outputs on hydrological simulations
2020
Yu Hui | Yuni Xu | Jie Chen | Chong-Yu Xu | Hua Chen
Bias correction methods are based on the assumption of bias stationarity of climate model outputs. However, this assumption may not be valid, because of the natural climate variability. This study investigates the impacts of bias nonstationarity of climate models simulated precipitation and temperature on hydrological climate change impact studies. The bias nonstationarity is determined as the range of difference in bias over multiple historical periods with no anthropogenic climate change for four different time windows. The role of bias nonstationarity in future climate change is assessed using the signal-to-noise ratio as a criterion. The results show that biases of climate models simulated monthly and annual precipitation and temperature vary with time, especially for short time windows. The bias nonstationarity of precipitation plays a great role in future precipitation change, while the role of temperature bias is not important. The bias nonstationarity of climate model outputs is amplified when driving a hydrological model for hydrological simulations. The increase in the length of time window can mitigate the impacts of bias nonstationarity for streamflow projections. Thus, a long time period is suggested to be used to calibrate a bias correction method for hydrological climate change impact studies to reduce the influence of natural climate variability. HIGHLIGHTS The biases of GCM precipitation and temperature vary with time, due to natural climate variability.; The bias nonstationarity of precipitation plays a great role in future precipitation change, while the role of temperature bias is not important.; The bias nonstationarity of precipitation and temperature has great considerable impacts on future streamflow changes.;
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