Calibration of hydrological models on hydrologically unusual events

The length of the observation period used for model calibration has a great influence on the identification of the model parameters. In this contribution it is shown that a relatively small number of so called unusual time periods are sufficient to specify the model parameters with the same certainty as using the whole observation period. The unusual events are identified from discharge or precipitation observations series using the statistical concept of data depth. The idea is to distinguish between model states which are covered by previously observed states (interpolation case), and those for which no similar events occurred (extrapolation case). Depth functions are used to identify unusual events from four days lagged discharge or API (antecedent precipitation index) series. Data with low depth are near the boundary of the multivariate set and are thus considered as unusual. The depth is calculated using the observations, their natural logarithms, their rank and their first differences. Model calibration using the selected critical periods is only slightly worse than using all data. The transferability of the parameters for different time periods is equally good as using all the data and significantly better than random selection. Two different models (HBV and HYMOD) are used to demonstrate the methodology for the Neckar catchment in South-West Germany. The methodology developed in this study can be potentially useful for developing monitoring strategies.

► Hydrological models can be calibrated using a small number of critical events equally well as using all data. ► Critical events can be identified as specific sequences from the observed discharge or precipitation series. ► Critical events can be identified with the help of their geometrical properties of the set of subsequent precipitation or discharge data. ► An algorithm (ICE) was presented to perform the identification of the critical events.