On calibration data selection: The case of stormwater quality regression models

Abstract

A stormwater quality model should be calibrated and verified against available data before it can be confidently used. This paper mainly examines two questions: how do the size and selection of calibration data sets affect model performances and how should the calibration data sets be selected. Regression models are used to simulate stormwater quality (TSS and COD) with variables characterizing rainfall and flow characteristics. Based on large databases of three catchments in France, several models are calibrated and verified with different data subsets. It is confirmed that the selection of calibration data sets leads to significant uncertainty in model performance. The information content in the calibration data sets is also important in addition to their size. Generally model performances can be improved by using a large size of calibration data sets and by selecting calibration data that are representative of all data. Three methods endeavoring to improve model performance by selecting calibration data either according to model outputs or model inputs are developed based on the principle of choosing calibration data that are representative of the whole data set. The effectiveness of the three selection methods is demonstrated by their application on databases of the three catchments. Model performances can be generally improved by selection methods. The selection methods based on model inputs that consider multi-dimension information perform better than the method with one-dimension information consideration.