资源环境科技发展态势分析平台

One model structural deficiency is that some dynamic characteristics (such as seasonal dynamics) in catchment conditions are not explicitly represented by hydrological models. This study integrates data mining techniques to develop a clustering preprocessing framework for the subannual calibration of hydrological models to simulate seasonal dynamic behaviors. The proposed framework aims to solve the problems caused by missing processes and deficiencies of hydrological models, providing guidance for future model development. A set of climatic-land surface indices is provided and preprocessed using the maximal information coefficient and the principal component analysis. Two clustering operations are performed based on the preprocessed climatic index and land-surface index systems. Hydrological data are clustered into subannual periods for calibration. The parameters are independently optimized for each subperiod using a modified parallel calibration scheme and are then combined to generate a continuous simulation. The framework is applied in calibrating the TOPMODEL. The results show that the performance of the model with a clustering preprocessing framework in the middle- and low-flow conditions is significantly improved without reducing the simulation accuracy for high flows. The transposability of the model parameters from the calibration to validation period has been improved significantly as well. The anomalous parameter values may be attributed in part to the convergence problem when using an optimization algorithm. Though well applied in the TOPMODEL, the framework has the potential to be used in other hydrological models.