The rainfall–runoff (RR) process in a catchment is non-uniform, complex, dynamic, and non-linear in nature. Although a number of advanced conceptual and data-driven techniques have been proposed in the past, the accurate estimation of daily runoff still remains a challenging task. A majority of conceptual models proposed so far suffer from the assumptions of linearity during their modeling. In this paper, novel hybrid approaches are proposed that are capable of exploiting the strength of both conceptual and data-driven techniques in RR modeling. A conceptual technique is first used to generate sub-basins’ runoff hydrographs in upstream reaches and then data-driven techniques are employed for routing them to the outlet of the catchment. The hybrid models’ performances are compared with standalone conceptual and data-driven models by employing the daily rainfall, runoff, and temperature data derived from the Kentucky River basin, USA. The results show that the proposed hybrid models, which do not assume the RR process to be a linear process to simulate the flow, outperform their individual counterparts. It is concluded that in order to achieve improved accuracy in RR modeling, the real-life process needs to be represented as accurately as possible in the modeling effort rather than making simplified assumptions.

流域内的降雨径流 (RR) 过程本质上是非均匀、复杂、动态和非线性的。尽管过去已经提出了许多先进的概念和数据驱动技术，但准确估计每日径流仍然是一项具有挑战性的任务。迄今为止提出的大多数概念模型在建模期间都受到线性假设的影响。在本文中，提出了新颖的混合方法，该方法能够在 RR 建模中利用概念和数据驱动技术的优势。首先使用概念技术生成上游河段的子流域径流水文过程线，然后采用数据驱动技术将它们路由到集水区的出口。通过使用来自美国肯塔基河流域的每日降雨量、径流和温度数据，将混合模型的性能与独立的概念和数据驱动模型进行比较。结果表明，所提出的混合模型（不假设 RR 过程是模拟流动的线性过程）优于它们各自的对应模型。得出的结论是，为了提高 RR 建模的准确性，需要在建模工作中尽可能准确地表示现实生活过程，而不是做出简化的假设。