Ida B. Karlsson1, Anker Lajer Højberg1 and Bo Vangsø Iversen2
1 Geological Survey of Denmark and Greenland
2 Department of Agroecology, Aarhus University
Nitrate pollution from agriculture of surface water bodies are an increasing problem in countries all over the world. As nitrate leaches from the root zone it is exposed to degradation processes in the soil and saturated zone, however in areas with tile drainages the percolating nitrate may get transported to the drains; often bypassing reduction; ending as non-reduced nitrate components in water bodies. Therefore the correct partitioning of drain and groundwater flow is essential for modelling of nitrate transport in tile drained areas.
Unfortunately most catchment scale models have proven incapable of capturing local scale drain flow due to lack of information on the appropriate scale to guide the calibration and limitations in the model concept.
This work presents the development and testing of different drain concepts capable of being incorporated into a catchment scale model in the MIKESHE modelling framework; with the objective of improving drain flow modelling; and thereby nitrate transport modelling. The concepts are built on differentiation between different drainage types (tile, natural and urban drain) and hypothesises of tile drain typologies.
The concepts are tested in the agricultural dominated and extensively tile drained 101 km2 Norsminde catchment in Denmark (Hansen et al., 2014; He et al., 2015). The effect of the concepts on drain flow dynamics and magnitude is evaluated using drain flow measurements from eight small drain catchments (Kjærgaard et al., 2011-2015) within Norsminde.
Hansen, A. L., Gunderman, D., He, X., and Refsgaard, J. C.: Uncertainty assessment of spatially distributed nitrate reduction potential in groundwater using multiple geological realizations, Journal of Hydrology, 519, Part A, 225-237, http://dx.doi.org/10.1016/j.jhydrol.2014.07.013, 2014.
He, X., Højberg, A. L., Jørgensen, F., and Refsgaard, J. C.: Assessing hydrological model predictive uncertainty using stochastically generated geological models, Hydrological Processes, 29, 4293-4311, 10.1002/hyp.10488, 2015.
Kjærgaard, C. et al.: IDræn projektet. www.idraen.dk, 2011-2015.