Multi-Criteria Process-Based Calibration Using Functional Data Analysis to Improve Hydrological Model Realism

It has been argued that rainfall-runoff model calibration based solely on streamflow is not sufficient to evaluate the realism of a hydrological model to represent the internal fluxes. Therefore, model calibration has evolved to evaluating model performance using a number of hydrological signatures that link the model to the underlying processes. However, this approach uses goodness-of-fit measures, unable to describe the entire dynamic of time series, to evaluate model consistency and to simulate hydrological signatures. The present paper develops a stepwise multicriteria calibration using hydrograph partitioning and calibration criteria defined on the basis of Functional Data Analysis (FDA), a statistical tool that conserves all important features of the hydrograph by approximating times series as a single function. The aim of this approach is to improve model realism by scrutinizing model components and by evaluating its ability to reproduce the entire flow dynamic. The proposed approach is compared to a calibration against daily streamflow only. The stepwise calibration improved the estimation of the flood curve, the annual peak volume as well as the performance of the model at sites other than the calibration station.