A variety of different processes is known that determine water and solute f
luxes in headwater catchments. Water resources management of these systems,
however, relies in most cases on empirical experience with respect to its
overall response. A promising method to bridge the gap between comprehensiv
e scientific investigations and the need to manage the systems on the basis
of limited data sets seems to be the application of artificial neural netw
orks (ANN). Here, time series of NO, concentrations in the runoff of two fo
rested headwater catchments in south Germany are investigated. Furthermore,
the application of nonlinear methods presented here reveals a rather intri
cate behaviour also on the temporal scale, and considerable differences bet
ween the two catchments. This demonstrates the validity of ANN as universal
descriptive tools.