Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices
<p>Soil moisture has important implications for drought and flooding forecasting, forest fire prediction and water supply management. However, mapping soil moisture has remained a scientific challenge due to forest canopy cover and small-scale variations in soil moisture conditions. When accur...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-10-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/26/4837/2022/hess-26-4837-2022.pdf |
| _version_ | 1797997401807519744 |
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| author | J. Larson W. Lidberg A. M. Ågren H. Laudon |
| author_facet | J. Larson W. Lidberg A. M. Ågren H. Laudon |
| author_sort | J. Larson |
| collection | DOAJ |
| description | <p>Soil moisture has important implications for drought and flooding forecasting, forest fire prediction and water supply management. However, mapping soil moisture has remained a scientific challenge due to forest canopy cover and small-scale variations in soil moisture conditions. When accurately scaled, terrain indices constitute a good candidate for
modelling the spatial variation of soil moisture conditions in many
landscapes. In this study, we evaluated seven different terrain indices at
varying digital elevation model (DEM) resolutions and user-defined
thresholds as well as two available soil moisture maps, using an extensive
field dataset (398 plots) of soil moisture conditions registered in five
classes from a survey covering a (68 km<span class="inline-formula"><sup>2</sup></span>) boreal landscape. We found that the variation in soil moisture conditions could be explained by terrain indices, and the best predictors within the studied landscape were the depth to water index (DTW) and a machine-learning-generated map. Furthermore, this study showed a large difference between terrain indices in the effects of changing DEM resolution and user-defined thresholds, which severely affected the performance of the predictions. For example, the commonly used topographic wetness index (TWI) performed best on a resolution of 16 m, while TWI calculated on DEM resolutions higher than 4 m gave inaccurate results. In contrast, depth to water (DTW) and elevation above stream (EAS) were more stable and performed best on 1–2 m DEM resolution. None of the terrain indices performed best on the highest DEM resolution of 0.5 m. In addition, this study highlights the challenges caused by heterogeneous soil types within the study area and shows the need of local knowledge when interpreting the modelled results. The results from this study clearly demonstrate that when using terrain indices to represent soil moisture conditions, modelled results need to be validated, as selecting an unsuitable DEM resolution or user-defined threshold can give ambiguous and even incorrect results.</p> |
| first_indexed | 2024-04-11T10:32:33Z |
| format | Article |
| id | doaj.art-64207e7b44e54744912879bf6b26b6b3 |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-04-11T10:32:33Z |
| publishDate | 2022-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-64207e7b44e54744912879bf6b26b6b32022-12-22T04:29:23ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382022-10-01264837485110.5194/hess-26-4837-2022Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indicesJ. LarsonW. LidbergA. M. ÅgrenH. Laudon<p>Soil moisture has important implications for drought and flooding forecasting, forest fire prediction and water supply management. However, mapping soil moisture has remained a scientific challenge due to forest canopy cover and small-scale variations in soil moisture conditions. When accurately scaled, terrain indices constitute a good candidate for modelling the spatial variation of soil moisture conditions in many landscapes. In this study, we evaluated seven different terrain indices at varying digital elevation model (DEM) resolutions and user-defined thresholds as well as two available soil moisture maps, using an extensive field dataset (398 plots) of soil moisture conditions registered in five classes from a survey covering a (68 km<span class="inline-formula"><sup>2</sup></span>) boreal landscape. We found that the variation in soil moisture conditions could be explained by terrain indices, and the best predictors within the studied landscape were the depth to water index (DTW) and a machine-learning-generated map. Furthermore, this study showed a large difference between terrain indices in the effects of changing DEM resolution and user-defined thresholds, which severely affected the performance of the predictions. For example, the commonly used topographic wetness index (TWI) performed best on a resolution of 16 m, while TWI calculated on DEM resolutions higher than 4 m gave inaccurate results. In contrast, depth to water (DTW) and elevation above stream (EAS) were more stable and performed best on 1–2 m DEM resolution. None of the terrain indices performed best on the highest DEM resolution of 0.5 m. In addition, this study highlights the challenges caused by heterogeneous soil types within the study area and shows the need of local knowledge when interpreting the modelled results. The results from this study clearly demonstrate that when using terrain indices to represent soil moisture conditions, modelled results need to be validated, as selecting an unsuitable DEM resolution or user-defined threshold can give ambiguous and even incorrect results.</p>https://hess.copernicus.org/articles/26/4837/2022/hess-26-4837-2022.pdf |
| spellingShingle | J. Larson W. Lidberg A. M. Ågren H. Laudon Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices Hydrology and Earth System Sciences |
| title | Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices |
| title_full | Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices |
| title_fullStr | Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices |
| title_full_unstemmed | Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices |
| title_short | Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices |
| title_sort | predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices |
| url | https://hess.copernicus.org/articles/26/4837/2022/hess-26-4837-2022.pdf |
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