Bias-correction schemes for calibrated flow in a conceptual hydrological model

Abstract

We explore post-processing methods that can reduce biases in simulated flow in a hydrological model (HYMOD). Here, three bias-correction methods are compared using a set of calibrated parameters as a baseline (Cases 1 and 5). The proposed bias-correction methods are based on a flow duration curve (Case 2), an autoregressive model based on residuals obtained from simulated flows (Case 3), and a rating curve (Case 4). A clear seasonality representing a more substantial variability in winter than summer was evident in all cases. The extended range of residuals was usually observed in winter, indicating that the HYMOD may not reproduce high flows appropriately. This study confirmed that bias-corrected flows are more effective than the baseline model in terms of correcting a systematic error in the simulated flow. Moreover, a comparison of root mean square error over different flow regimes demonstrates that Case 3 is the most effective at correcting systematic biases over the entire flow regime. Finally, monthly water balances for all cases are evaluated and compared during both calibration and validation periods. The water balance in Case 3 is also closer to the observed values. The effects of different post-processing approaches on the performance of bias-correction are examined and discussed.