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.

我们探索可减少水文模型（HYMOD）中模拟流量偏差的后处理方法。在此，使用一组校准参数作为基准比较了三种偏差校正方法（案例1和5）。提出的偏差校正方法基于流量持续时间曲线（案例2），基于从模拟流量获得的残差的自回归模型（案例3）和额定曲线（案例4）。在所有情况下，明显的季节性代表了冬天比夏天更大的可变性。通常在冬季观察到残留物范围扩大，这表明HYMOD可能无法适当地重现高流量。这项研究证实，在校正模拟流量中的系统误差方面，偏差校正流量比基线模型更有效。而且，比较不同流态下的均方根误差表明，案例3在校正整个流态下的系统偏差方面最有效。最后，在校准和验证期间评估并比较所有情况下的每月水平衡。情况3中的水平衡也更接近于观测值。研究并讨论了不同的后处理方法对偏差校正性能的影响。