A Clustering Preprocessing Framework for the Subannual Calibration of a Hydrological Model Considering Climate‐Land Surface Variations. Issue 12 (14th December 2018)

• Record Type: Journal Article
• Authors: Lan, T.; Lin, K. R.; Liu, Z. Y.; He, Y. H.; Xu, C. Y.; Zhang, H. B.; Chen, X. H.
• Is Part Of: Water resources research. Volume 54:Issue 12(2018)
• Page Start: 10, 034
• Page End: 10, 052
• Publication Date: 2018-12-14
• Subjects: clustering preprocessing -- time‐variant parameters -- subannual calibration -- hydrological prediction -- anomalous parameter values; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2018WR023160
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: 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. Key Points: A new framework for subannual calibration is developed to cope with discrepancy in the forecast accuracy under different flow phases The CPP framework improves transposability of model parameters between calibration period and validation period The candidate mechanism of the anomalous model parameter values is investigated using violin plots