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Research article2022Peer reviewedOpen access

Use of Hydrological Models to Predict Risk for Rutting in Logging Operations

Mohtashami, Sima; Thierfelder, Tomas; Eliasson, Lars; Lindstrom, Goran; Sonesson, Johan


Using hydrological models with a high temporal resolution to predict risk for rutting may be a possible method to improve planning of forwarder trails or to schedule logging operations in sites with low bearing capacity to periods when soil moisture content is at a minimum. We have studied whether descriptions of rut variations, collected in 27 logging sites, can be improved by using hydrological data, modeled by Swedish HYdrological Prediction for Environment (S-HYPE). Other explanatory variables, such as field-surveyed data and spatial data, were also used to describe rut variations within and across logging sites. The results indicated that inclusion of S-HYPE data led to only marginal improvement in explaining the observed variations of the ruts in terms of both "rut depths" within the logging sites and "proportion of forwarder trails with ruts" across the logging sites. However, application of S-HYPE data for adapting depth-to-water (DTW) maps to temporal changes of soil moisture content may be a way to develop more dynamic soil moisture maps for forestry applications.


rut formation; forestry operations; hydrological data

Published in

2022, Volume: 13, number: 6, article number: 901

      UKÄ Subject classification

      Forest Science

      Publication identifier


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