Titlebar

Export bibliographic data
Literature by the same author
plus on the publication server
plus at Google Scholar

 

Investigating short-term dynamics and long-term trends of SO4 in the runoff of a forested catchment using artificial neural networks

Title data

Lischeid, Gunnar:
Investigating short-term dynamics and long-term trends of SO4 in the runoff of a forested catchment using artificial neural networks.
In: Journal of Hydrology. Vol. 243 (2001) Issue 1-2 . - pp. 31-42.
ISSN 0022-1694
DOI: https://doi.org/10.1016/S0022-1694(00)00399-1

Abstract in another language

The impact of long-lasting non-point emissions on groundwater and streamwater in remote watersheds has been studied at numerous sites. In spite of substantially decreasing emissions in the last decade, recovery has not yet been observed in all cases. This trend might be masked by the considerable short-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 models are fitted to the short-term dynamics at a time step of one day. About 75% of the variance of the SO4 data is explained by the instantaneous discharge, short-term history of discharge and the moving average of SO4 concentration in throughfall. In contrast, neither air temperature as an indicator for biological activity nor a snowmelt indicator based on the temperature sum 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 identified input variables. According to the model, decreasing emissions have a significant effect on runoff SO4 concentration only during the first severe storms at the end of the vegetation period. This suggests to focus on these events as indicators for recovery of the topsoil layers.Author Keywords: Artificial neural network; Discharge; Sulfate; Freshwater; Trend analysis

Further data

Item Type: Article in a journal
Refereed: Yes
Additional notes: BAYCEER42800
BAYCEER7496
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Chair Ecological Modelling
Research Institutions > Research Centres > Bayreuth Center of Ecology and Environmental Research- BayCEER
Faculties
Faculties > Faculty of Biology, Chemistry and Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences
Research Institutions
Research Institutions > Research Centres
Result of work at the UBT: Yes
DDC Subjects: 500 Science
Date Deposited: 30 Sep 2015 05:56
Last Modified: 13 Nov 2015 11:26
URI: https://eref.uni-bayreuth.de/id/eprint/19891