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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 9
Hydrol. Earth Syst. Sci., 18, 3623–3634, 2014
https://doi.org/10.5194/hess-18-3623-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 18, 3623–3634, 2014
https://doi.org/10.5194/hess-18-3623-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 12 Sep 2014

Research article | 12 Sep 2014

Evaluating digital terrain indices for soil wetness mapping – a Swedish case study

A. M. Ågren1, W. Lidberg1, M. Strömgren2, J. Ogilvie3, and P. A. Arp3 A. M. Ågren et al.
  • 1Dept. of Forest Ecology and Management, Swedish University of Agricultural Sciences, 901 83 Umeå, Sweden
  • 2Dept. of Soil and Environment, Swedish university of Agricultural Sciences, P.O. Box 7014, 750 07 Uppsala, Sweden
  • 3Forest Watershed Research Centre, Faculty of Forestry & Environmental Management, 28 Dineen Drive, UNB, Fredericton, NB, E3B 583, Canada

Abstract. Trafficking wet soils within and near stream and lake buffers can cause soil disturbances, i.e. rutting and compaction. This – in turn – can lead to increased surface flow, thereby facilitating the leaking of unwanted substances into downstream environments. Wet soils in mires, near streams and lakes have particularly low bearing capacity and are therefore more susceptible to rutting. It is therefore important to model and map the extent of these areas and associated wetness variations. This can now be done with adequate reliability using a high-resolution digital elevation model (DEM). In this article, we report on several digital terrain indices to predict soil wetness by wet-area locations. We varied the resolution of these indices to test what scale produces the best possible wet-areas mapping conformance. We found that topographic wetness index (TWI) and the newly developed cartographic depth-to-water index (DTW) were the best soil wetness predictors. While the TWI derivations were sensitive to scale, the DTW derivations were not and were therefore numerically robust. Since the DTW derivations vary by the area threshold for setting stream flow initiation, we found that the optimal threshold values for permanently wet areas varied by landform within the Krycklan watershed, e.g. 1–2 ha for till-derived landforms versus 8–16 ha for a coarse-textured alluvial floodplain.

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