Papers by Keyword: Xinanjiang Model

Abstract: This paper discusses the conceptual stepped calibration approach (SCA) which has been developed for the Xinanjiang (XAJ) model. Multi-layer and multi-objective functions which can make optimization work simpler and more effective are introduced in this procedure. In all eight parameters were considered, they were divided into four layers according to the structure of XAJ model, and then calibrated layer by layer. The SCA procedure tends to improve the performance of the traditional method of calibration (thus, using a single objective function, such as root mean square error RMSE). The compared results demonstrate that the SCA yield better model performance than RMSE.

Abstract: Soil moisture plays an important role in agricultural drought predicting, therefore there is an increasing demand for detailed predictions of soil moisture, especially at basin scales. However, so far soil moisture predictions are usually obtained as a by-product of climate and weather prediction models coupled with a land surface parameterization scheme, and there has been little dedicated work to meet this urgent need at basin scales. In order to improve the basin hydrological models’ performance in the soil moisture forecasting, an integrated soil moisture predicting model based on Artificial Neural Network (ANN) and Xinanjiang model is proposed and presented in this paper. The performance of the new integrated soil moisture predicting model was tested in the Linyi watershed with a drainage area of 10040 km2, located in the semi-arid area of the eastern China. The results suggest that the soil moisture simulated by the integrated ANN-Xinanjiang model is more agree with the observed ones than that simulated by Xinanjiang, and that the simulated soil moisture by both the models has the similar trend and temporal change pattern with the observed one.