Abstract
Abstract The Xin'anjiang model and the Sacramento model are two widely used short-term watershed rainfall-runoff forecasting models, each with their own unique model structure, strengths, weaknesses and applicability. This paper introduces a weight factor to integrate the two models into a combined model, and uses the cyclic coordinate method to calibrate the weight factor and the parameters of the two models to explore the possibility of the complementarity between the two models. With application to the Yuxiakou watershed in Qingjiang River, it is verified that the cyclic coordinate method, although simple, can converge rapidly to a satisfactory calibration accuracy, mostly after two iterations. Also, the results in case studies show that the forecast accuracy of the new combined rainfall-runoff model can improve the forecast precision by 4.3% in a testing period, better in runoff process fitting than the Xin'anjiang model that performs better than the Sacramento model.
Highlights
Stream flow, a major part in the water cycle, plays an essential role in managing and planning water resources systems (Hansen & Hallam 1991), and the objective existence of the lead time between the rainfall and its caused runoff in river networks opens an opportunity for us to reduce the uncertainty of stream flows by forecasting the runoff based on rainfall observed (Anderson et al 2002)
The model has been improved in its structure and simulation procedure by previous works, including the following: Jayawardena & Zhou (2000) modified the spatial distribution curve of soil moisture storage capacity from the traditional single parabolic to a general double parabolic in order to differentiate the wet from dry condition; Meng et al (2018) included both the infiltration and saturation excess runoff mechanisms, coupled to a two-source potential evapotranspiration model (TSPE), to simulate the hydrological process; Yao et al (2014) coupled the Xin’anjiang model with the geomorphologic instantaneous unit hydrograph to achieve better flood predictions in ungauged catchments; and Liao et al (2016) employed the antecedent precipitation to correct the real-time forecast
The combined model improves the Nash–Sutcliffe efficiency (NSE) by 4.3% compared with the Xin’anjiang model, the better one when individually applied
Summary
A major part in the water cycle, plays an essential role in managing and planning water resources systems (Hansen & Hallam 1991), and the objective existence of the lead time between the rainfall and its caused runoff in river networks opens an opportunity for us to reduce the uncertainty of stream flows by forecasting the runoff based on rainfall observed (Anderson et al 2002). Many efforts have long been made in previous works to address this engineering problem by modeling the relationship between rainfall and runoff (Todini 1988; Aytek et al 2008; Asadi et al 2019). Among these rainfall and runoff models, the physically based models are best used when precise data are available, physical properties of the hydrological processes are accurately understood, and applied on fine scales due to computational time, while conceptual models have gained popularity in the modeling community because they are easy to use and calibrate (Sitterson et al 2018). The sensitivity of forecasting accuracy to the parameters was investigated by, for example, Zhang
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