Use of Hydrological Models to Predict Risk for Rutting in Logging Operations
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 d...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/13/6/901 |
| _version_ | 1797487374402322432 |
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| author | Sima Mohtashami Tomas Thierfelder Lars Eliasson Göran Lindström Johan Sonesson |
| author_facet | Sima Mohtashami Tomas Thierfelder Lars Eliasson Göran Lindström Johan Sonesson |
| author_sort | Sima Mohtashami |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T23:47:43Z |
| format | Article |
| id | doaj.art-69932e87033247bcae12bdbca51babc6 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-09T23:47:43Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-69932e87033247bcae12bdbca51babc62023-11-23T16:41:03ZengMDPI AGForests1999-49072022-06-0113690110.3390/f13060901Use of Hydrological Models to Predict Risk for Rutting in Logging OperationsSima Mohtashami0Tomas Thierfelder1Lars Eliasson2Göran Lindström3Johan Sonesson4The Forestry Research Institute of Sweden, Skogforsk, 751 83 Uppsala, SwedenDepartment of Energy and Technology, Swedish University of Agricultural Sciences (SLU), 750 07 Uppsala, SwedenThe Forestry Research Institute of Sweden, Skogforsk, 751 83 Uppsala, SwedenThe Swedish Meteorological and Hydrological Institute (SMHI), 603 80 Norrköping, SwedenThe Forestry Research Institute of Sweden, Skogforsk, 751 83 Uppsala, SwedenUsing 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.https://www.mdpi.com/1999-4907/13/6/901rut formationforestry operationshydrological data |
| spellingShingle | Sima Mohtashami Tomas Thierfelder Lars Eliasson Göran Lindström Johan Sonesson Use of Hydrological Models to Predict Risk for Rutting in Logging Operations Forests rut formation forestry operations hydrological data |
| title | Use of Hydrological Models to Predict Risk for Rutting in Logging Operations |
| title_full | Use of Hydrological Models to Predict Risk for Rutting in Logging Operations |
| title_fullStr | Use of Hydrological Models to Predict Risk for Rutting in Logging Operations |
| title_full_unstemmed | Use of Hydrological Models to Predict Risk for Rutting in Logging Operations |
| title_short | Use of Hydrological Models to Predict Risk for Rutting in Logging Operations |
| title_sort | use of hydrological models to predict risk for rutting in logging operations |
| topic | rut formation forestry operations hydrological data |
| url | https://www.mdpi.com/1999-4907/13/6/901 |
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