Recently, the conceptual data-driven approach (CDDA) was proposed to correct residuals of ensemble hydrological models (HMs) using data-driven models (DDMs), followed by the stochastic CDDA (SCDDA) that used HM simulations as input to DDMs within a stochastic framework - both approaches improved ensemble HMs' simulations. Here, a new SCDDA is introduced where CDDA uncertainty is estimated (instead of DDM uncertainty in the original SCDDA). Using nine HM-DDM combinations for daily streamflow simulation in three Swiss catchments, the new SCDDA improved CDDA's mean continuous ranked probability score up to 15% and performed similarly without a snow-routine in a snowy catchment, suggesting that SCDDA may account for missing processes in HMs. The stochastic framework can convert unreliable ensemble models into more reliable (stochastic) models at the cost of simulation sharpness. The coverage probability plot is proposed as a diagnostic tool, predicting SCDDA's out-of-sample reliability using validation set data (CDDA simulations and observations).

最近，提出了概念数据驱动方法 (CDDA) 来使用数据驱动模型 (DDM) 校正集合水文模型 (HM) 的残差，然后是使用 HM 模拟作为 DDM 输入的随机 CDDA (SCDDA)。随机框架 - 两种方法都改进了集成 HM 的模拟。在这里，引入了一个新的 SCDDA，其中估计了 CDDA 不确定性（而不是原始 SCDDA 中的 DDM 不确定性）。在三个瑞士流域中使用九种 HM-DDM 组合进行每日流量模拟，新的 SCDDA 将 CDDA 的平均连续排名概率得分提高了 15%，并且在下雪的流域中没有下雪例行程序的情况下表现相似，这表明 SCDDA 可能是缺失过程的原因在HM。随机框架可以以模拟清晰度为代价将不可靠的集成模型转换为更可靠的（随机）模型。覆盖概率图被提议作为一种诊断工具，使用验证集数据（CDDA 模拟和观察）预测 SCDDA 的样本外可靠性。