Comparison of observed and DEM-driven field-to-river routing of flow from eroding fields in an arable lowland catchment

Favis-Mortlock, David; Boardman, John; Foster, Ian; Shepheard, Mark. 2022 Comparison of observed and DEM-driven field-to-river routing of flow from eroding fields in an arable lowland catchment. CATENA, 208, 105737.

Before downloading, please read NORA policies.
Catena text forPURE.pdf - Accepted Version

Download (3MB) | Preview


Field-to-river flow of runoff and sediment in a lowland arable catchment in the south of England is explored from both field and modelling perspectives. Routes observed to be taken by flow and sediment on five study areas include many interactions between flow and ‘landscape elements’ (LEs), including those (field boundaries, paths, roads) of anthropogenic origin. We were able to satisfactorily replicate observed flow routes using a simple steepest-descent-with-overtopping model with a 5 m DEM. This was unexpected, considering the narrowness of linear LEs such as paths and tracks. However LE attributes showed considerable sensitivity: changing just one attribute of a single FE-flow interaction notably altered the route taken by simulated flow, while changing LE attributes notably affected synthetic hydrographs for flow reaching the river, suggesting similar impacts upon transported sediment reaching the river. Thus while simple steepest-descent and overtopping permits satisfactory replication of observed flow routes, it is likely that more explicit representation of LE-flow interactions is necessary in order to adequately capture the dynamics of field-to-river runoff and sediment transport, as must be done by catchment-scale erosion models. This will enable such models to better represent runoff speed and volume, and the flux and size distribution of transported sediment, with the aim of overcoming some broad limitations of such models as noted in earlier model validation studies. Finally, we consider the representation of some LE-flow interactions in several catchment-scale models, and discuss the ways in which such representation might be improved.

Item Type: Publication - Article
Digital Object Identifier (DOI):
ISSN: 03418162
Date made live: 17 Jan 2022 14:03 +0 (UTC)

Actions (login required)

View Item View Item

Document Downloads

Downloads for past 30 days

Downloads per month over past year

More statistics for this item...