Application of curve-fitting techniques to develop numerical calibration procedures for a river water quality model

River water quality models are often constrained by a lack of understanding of model structures and complicated estimation procedures for unknown parameters. This paper demonstrates a new calibration strategy by setting up a simple model structure for river water quality. The unknown parameters of RWQM were calibrated through the use of small river water quality data sets. In order to facilitate the calibration procedure, data reconstruction and parameter estimation were performed by the systematic application of cubic smoothing spline, polynomial curve-fitting and nonlinear least squares. The quality of calibrated parameters was estimated by developing a sensitivity ranking system. The variation of model outputs showed a slight difference at a sensitivity index of less than 10% and a significant difference at a sensitivity index of more than 50%. The one-way analysis of variance showed a large p-value of 0.8431, indicating that differences between model data and measured data means are not significant. The calibrated model responses and their statistical envelopes were in good agreement with the river water quality data. A MATLAB GUI platform was developed to perform numerical and graphical analysis, which can be used as a relatively simple but robust calibration tool to support model application and data analysis.