A novel ensemble-based conceptual-data-driven approach (CDDA) is developed where a data-driven model (DDM) is used to “correct” the residuals from an ensemble of hydrological model (HM) simulations. The CDDA respects hydrological processes via the HM and it benefits from the DDM's ability to simulate the complex relationship between residuals and input variables. The CDDA can accomodate any HM and DDM, allowing for different configurations to be tested. The CDDA is tested for ensemble streamflow simulation in three Swiss catchments where the HM, HBV (Hydrologiska Byråns Vattenbalansavdelning), is coupled with eight different DDMs: Multiple Linear Regression, k Nearest Neighbours Regression, Second-Order Volterra Series Model, Artificial Neural Networks, and two variants of eXtreme Gradient Boosting (XGB) and Random Forests (RF). The proposed CDDA was able to improve the mean continuous ranked probability score by 16–29% over the standalone HM. Since XGB and RF demonstrated the best performance, they are recommended for simulating the HM residuals.

开发了一种新的基于集合的概念数据驱动方法 (CDDA)，其中使用数据驱动模型 (DDM) 来“校正”来自水文模型 (HM) 模拟集合的残差。CDDA 通过 HM 尊重水文过程，它受益于 DDM 模拟残差和输入变量之间复杂关系的能力。CDDA 可以容纳任何 HM 和 DDM，允许测试不同的配置。CDDA 在三个瑞士流域中进行了集合流模拟测试，其中 HM、HBV (Hydrologiska Byråns Vattenbalansavdelning) 与八种不同的 DDM 耦合：多重线性回归、k 最近邻回归、二阶 Volterra 系列模型、人工神经网络、以及极限梯度提升 (XGB) 和随机森林 (RF) 的两种变体。与独立 HM 相比，提议的 CDDA 能够将平均连续排名概率得分提高 16-29%。由于 XGB 和 RF 表现出最佳性能，因此建议将它们用于模拟 HM 残差。