Improving the flood prediction capability of the Xin’anjiang model by formulating a new physics-based routing framework and a key routing parameter estimation method

Abstract

The well-known conceptual Xin'anjiang model (XAJ) has limited capability for application in the basins with limited data due to two major runoff routing problems: relying on the observed flow data to estimate the parameters of the Muskingum method and lack of physics-based representation of spatial heterogeneity in key routing parameters such as the river network recession constant (Cs). To tackle these issues, this study first developed a new hybrid rainfall-runoff model named the Xin’anjiang Digital CHannel (XAJ-DCH) model by coupling the XAJ model with the Muskingum-Cunge-Todini (MCT) and diffusion wave methods to improve the river network routing. Then a physics-based Cs estimation method was developed to conquer the data limitation by deriving the quantitative relationships of the Cs parameter with basin characteristics such as sub-catchment area, average river slope, average rainfall intensity, and average river width based on the mass conservation. The flood prediction capabilities of the XAJ and XAJ-DCH models were further tested and compared in the Tunxi Catchment with two nested sub-catchments. The results show that the XAJ-DCH model can predict stream flows well at both catchment outlet and internal channel grid cells without recalibration. Moreover, the derived Cs values based on the proposed method enable both XAJ and XAJ-DCH models to gain good results with the averaged Nash-Sutcliffe efficiency ranging between 0.91 and 0.95.