The introduction of iterative ensemble smoothers (IES) for parameter calibration opens avenues for expanding parameter space in surface water hydrologic modeling. Here, we have introduced independent parameters into a model calibration experiment to estimate errors in rainfall forcing data. This approach has the potential to estimate rainfall errors using other hydrological observations and to improve model calibration. Using high-resolution rain gauge data, we estimated “real” rainfall errors across the Turkey River watershed at storm and daily scales. Tests on synthetic and real-world scenarios successfully estimated errors correlated with observed values – even at daily scales. However, a bias remained from model parameter compensation, and identifying errors was challenging for low precipitation and snowfall. Despite synthetic results showing good error correlation, the biases in parameter identification masked potential improvements in hydrological calibration. This study highlights the potential of IES to provide additional information on rainfall errors, even only using streamflow observations.

中文翻译：

---

使用迭代集合平滑器进行降水不确定性估计和降雨径流模型校准

用于参数校准的迭代集成平滑器（IES）的引入为扩展地表水水文建模中的参数空间开辟了途径。在这里，我们在模型校准实验中引入了独立参数，以估计降雨强迫数据的误差。这种方法有可能利用其他水文观测来估计降雨误差并改进模型校准。使用高分辨率雨量计数据，我们估计了土耳其河流域风暴和每日尺度的“真实”降雨误差。对合成场景和现实场景的测试成功地估计了与观测值相关的误差——即使是在日常规模上。然而，模型参数补偿仍然存在偏差，并且对于低降水量和降雪量来说，识别误差具有挑战性。尽管综合结果显示出良好的误差相关性，但参数识别中的偏差掩盖了水文校准的潜在改进。这项研究强调了 IES 提供有关降雨误差的额外信息的潜力，即使仅使用径流观测。