Incorporation of soft data to describe uncertainty of data in model calibration

In this work we present an intuitive and simple framework for incorporating "soft data" about the accuracy, of different parts of calibration data into the calibration process. The framework makes use of a ranking method developed in operations research that requires only the specification of the importance order of the criteria used for ranking. The framework was demonstrated in the case of Fishtrap Creek catchment, where a short streamflow record with significant gaps was reconstructed using Support Vector Machines (SVM's). The reconstructed streamflow is inherently less accurate than the observed streamflow. Incorporating this educated Judgment about the relative accuracy of the calibration data in the proposed framework resulted in identification of faulty model calibration as well as errors in SVM's prediction of extreme streamflow events, which would have gone to go undetected otherwise.