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Comparative simulation of the nitrogen dynamics using the INCA model and a neural network analysis: implications for improved nitrogen modelling

Title data

Lischeid, Gunnar ; Langusch, Jens:
Comparative simulation of the nitrogen dynamics using the INCA model and a neural network analysis: implications for improved nitrogen modelling.
In: Hydrology and Earth System Sciences. Vol. 8 (2004) . - pp. 742-750.
ISSN 1607-7938
DOI: https://doi.org/10.5194/hess-8-742-2004

Abstract in another language

Abstract: Continuing deposition of nitrogen in forested catchments affects stream and groundwater quality. However, the dependence of nitrogen dynamics on climatic and hydrological boundary conditions is still poorly understood. These dynamics have been investigated by applying the process-oriented Integrated Nitrogen in CAtchments (INCA) model and an artificial neural network to the data set from the forested Steinkreuz catchment in South Germany. The data comprise daily values of precipitation, air temperature and discharge of the catchment runoff. The INCA model simulated the mean nitrate concentration in the stream as well as seasonal fluctuations but it underestimated the short-term variance of the observed stream water nitrate concentration, especially the pronounced concentration peaks in late summer. In contrast, the artificial neural network matched the short-term dynamics using non-linear regressions with stream discharge and air temperature data. The results provide strong evidence that the short-term dynamics of stream nitrate concentration during storm-flow were generated in the riparian zone, which is less than 1% of the catchment area, and is not considered explicitly in the INCA model. The concentration peaks have little effect on the catchment’s nitrogen budget and the shallow groundwater data suggest that the short-term hydrological dynamics also govern groundwater recharge in the upland parts of the catchment. This substantial underestimate by the INCA model parameterisation is balanced by a corresponding underestimate of denitrification in clayey layers of the deeper aquifer. A better understanding of these processes is necessary to improve long-term risk assessments.Keywords: catchment, runoff, nitrogen, INCA, artificial neural network, flushing

Further data

Item Type: Article in a journal
Refereed: Yes
Additional notes: BAYCEER20741
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Chair Ecological Modelling
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Chair Soil Ecology
Research Institutions > Research Centres > Bayreuth Center of Ecology and Environmental Research- BayCEER
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences
Faculties
Faculties > Faculty of Biology, Chemistry and Earth Sciences
Research Institutions
Research Institutions > Research Centres
Result of work at the UBT: Yes
DDC Subjects: 500 Science
Date Deposited: 30 Sep 2015 05:55
Last Modified: 30 Sep 2015 05:55
URI: https://eref.uni-bayreuth.de/id/eprint/19850