Abstract
A hybrid rainfall-runoff model was developed in this study by integrating the variable infiltration capacity (VIC) model with artificial neural networks (ANNs). In the proposed model, the prediction interval of the ANN replaces separate, individual simulation (i.e., single simulation). The spatial heterogeneity of horizontal resolution, subgrid-scale features and their influence on the streamflow can be assessed according to the VIC model. In the routing module, instead of a simple linear superposition of the streamflow generated from each subbasin, ANNs facilitate nonlinear mappings of the streamflow produced from each subbasin into the total streamflow at the basin outlet. A total of three subbasins were delineated and calibrated independently via the VIC model; daily runoff errors were simulated for each subbasin, then corrected by an ANN bias-correction model. The initial streamflow and corrected runoff from the simulation for individual subbasins serve as inputs to the ANN routing model. The feasibility of this proposed method was confirmed according to the performance of its application to a case study on rainfall-runoff prediction in the Jinshajiang River Basin, the headwater area of the Yangtze River.
Highlights
The Jinshajiang River, the headwater area of China’s Yangtze River, is rife with hydropower resources that will be ready to put into use once the cascade power stations currently under construction in the area are complete
This paper presents a hybrid hydrology model comprised of a variable infiltration capacity (VIC) model and an artificial neural networks (ANNs) routing model
Two statistical criteria were selected to assess the predictive capability of the hybrid model: the relative error (RE) and the Nash and Sutcliffe efficiency efficiency (NSE)
Summary
The Jinshajiang River, the headwater area of China’s Yangtze River, is rife with hydropower resources that will be ready to put into use once the cascade power stations currently under construction in the area are complete. Given the numerous applications and updates to the VIC model throughout its nearly twenty years of existence, the current version of the model is well suited to parameterizing various factors of the water budget process, including horizontal soil moisture distribution, evapotranspiration, infiltration capacity and subsurface flow heterogeneities. Researchers have focused on integrating the ANN form with conceptual hydrological models as opposed to applying the ANN alone as a simple black-box model [29,30,31,32] These hybrid models can yield highly accurate forecasts of dynamic processes, as the combination appropriately captures unknown and nonlinear components of the mechanistic model via the neural network [29]. The flow inputs are routed by the ANN routing model to the outlet of the network forming a hydrograph of the simulation
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