Interpolation and approximation of water quality time series and process identification

Data records with equidistant time intervals are fundamental prerequisites for the development of water quality simulation models. Usually long-term water quality data time series contain missing data or data with different sampling intervals. In such cases "artificial" data have to be added to obtain records based on a regular time grid. Generally, this can be done by interpolation, approximation or filtering of data sets. In contrast to approximation by an analytical function, interpolation methods estimate missing data by means of measured concentration values. In this paper, methods of interpolation and approximation are applied to long-term water quality data sets with daily sampling intervals. Using such data for the water temperature and phosphate phosphorus in some shallow lakes, it was possible to identify the process of phosphate remobilisation from sediment.