Shore, M. 1,2, Melland, A.R.1, Murphy, P.N.C.1, Jordan, P.3, Mellander P-E.1, Mechan, S.1, Shine, O.1, and Shortle, G.1.
1: Agricultural Catchments Programme, Teagasc, Johnstown Castle, Wexford, Ireland
2: School of Environmental and Biological Sciences, University College Dublin, Dublin 4, Ireland
3: School of Environmental Sciences, University of Ulster, Coleraine, N. Ireland
Identifying hydrological connectivity in the landscape is important for managing critical source areas of diffuse water pollution from agriculture but is technically difficult. The SCIMAP Network Index model offers the potential to predict surface connectivity using digital elevation models (DEMs). Using a 5 m DEM in Ireland the SCIMAP model was applied in six 3 – 30 km2 agricultural catchments. SCIMAP predictions of surface hydrological connectivity from field to catchment scale were tested in two of the catchments. At the stream subcatchment scale (ca. 1.3 km2), modelled surface connectivity generally matched observations in a poorly permeable catchment but predictions in a permeable catchment, and at smaller scales in both catchments, were poor. Poor surface connectivity predictions were attributed to inaccuracies in the modelled direction of flow, due to a prevalence of surface ditches in these catchments, and to overestimation of surface connectivity on naturally and artificially well drained soils.
The importance of considering field-scale surface ditch features, which were not represented on the 5m DEM, was evaluated by applying SCIMAP to a DEM that was manually corrected for observed ditch location and morphology. Hydrological correction improved the modelled direction of flow from field to subcatchment scales but distorted SCIMAP surface connectivity predictions due to discrepancies between the resolution of the DEM and ditch features. These limitations highlight the need to consider soil type and drainage features as well as topography when identifying critical sources areas at field scales. Future work will apply SCIMAP using a high resolution (0.25 m) LiDAR-derived DEM, which captures surface field-scale drainage features. Connectivity outputs will then be used to help identify critical source areas for phosphorus and sediment loss in combination with soil type and soil phosphorus data at field and subcatchment scales.