Investigating trends of hydrochemical time series of small catchments by artificial neural networks

Abstract

The short-term variation of discharge and solute concentration of the runoff of small catchments generally reflects the interplay of a variety of different processes. This makes the investigation of anthropogenic impacts on the catchment's runoff often rather difficult. On the other hand, short-term dynamics at the output boundary provide information about the system. This information can be used, in principle at least, to assess its long-term behaviour more precisely. In this paper examples of time series of sulphate and nitrate in the runoff of two small forested catchments are presented. To minimise the danger of over-parametrisation, the objective was to find a very simple empirical model to map a substantial portion of the observed variance (daily values). Here artificial neural networks were applied. They yield an efficiency of more than 0.7 for the solutes investigated, based on discharge depth and air temperature as input variables only. As a next step, the invariance of these relationships was investigated. In the case of sulphate, a significant trend is observed. However, it differs considerably for different subregions of the regression plane. Thus the neural network approach reveals a much more detailed insight into temporal shifts of the dynamics than an overall trend analysis.