G. Lischeid, Investigating short-term dynamics and long-term trends of SO4 in the runoff of a forested catchment using artificial neural networks, J HYDROL, 243(1-2), 2001, pp. 31-42
The impact of long-lasting non-point emissions on groundwater and streamwat
er in remote watersheds has been studied at numerous sites. In spite of sub
stantially decreasing emissions in the last decade, recovery has not yet be
en observed in all cases. This trend might be masked by the considerable sh
ort-term variability of the chemical hydrographs. In this study, artificial
neural networks are applied to investigate the SO4 dynamics in the runoff
of a small forested catchment susceptible to SO4 deposition. Empirical mode
ls are fitted to the short-term dynamics at a time step of one day. About 7
5% of the variance of the SOS data is explained by the instantaneous discha
rge, short-term history of discharge and the moving average of SOS concentr
ation in throughfall. In contrast, neither air temperature as an indicator
for biological activity nor a snowmelt indicator based on the temperature s
um increase the performance of the model. The model is used to investigate
long-term trends in sub-regions of the phase space spanned by the identifie
d input variables. According to the model, decreasing emissions have a sign
ificant effect on runoff SO4 concentration only during the first severe sto
rms at the end of the vegetation period. This suggests to focus on these ev
ents as indicators for recovery of the topsoil layers. (C) 2001 Elsevier Sc
ience B.V. All rights reserved.