Incorporating multiple observations for distributed hydrologic model calibration: An approach using a multi-objective evolutionary algorithm and clustering

The use of distributed data for model calibration is becoming more popular in the advent of the availability of spatially distributed observations. Hydrological model calibration has traditionally been carried out using single objective optimisation and only recently has been extended to a multi-objective optimisation domain. By formulating the calibration problem with several objectives, each objective relating to a set of observations, the parameter sets can be constrained more effectively. However, many previous multi-objective calibration studies do not consider individual observations or catchment responses separately, but instead utilises some form of aggregation of objectives. This paper proposes a multi-objective calibration approach that can efficiently handle many objectives using both clustering and preference ordered ranking. The algorithm is applied to calibrate the MIKE SHE distributed hydrologic model and tested on the Karup catchment in Denmark. The results indicate that the preferred solutions selected using the proposed algorithm are good compromise solutions and the parameter values are well defined. Clustering with Kohonen mapping was able to reduce the number of objective functions from 18 to 5. Calibration using the standard deviation of groundwater level residuals enabled us to identify a group of wells that may not be simulated properly, thus highlighting potential problems with the model parameterisation.