Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices

Abstract

Abstract. 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 km2) 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.

Tree growth potential and its relationship with soil moisture conditions across a heterogeneous boreal forest landscape

“…Similar findings for soil moisture conditions and soil types have been observed in studies of the relationship between normalised mean annual increment in relation to soil properties across Sweden 48 . Previous studies have also proven the utility of soil moisture maps in comparison to other terrain indices 49 , as well as the ability to predicting thick organic soil layers based on soil moisture 50 . Lower site quality is expected in these areas because the saturated soil conditions decrease tree growth 48 , 51 .…”

Soil moisture conditions control nutrient accumulation, carbon storage and tree growth in boreal forest landscapes

“…Code and data availability. The code and data used in this study for aggregating the DEM and generating the different terrain indices are available at https://doi.org/10.17632/dg64p8wmj9.1 (Larson et al, 2022) in addition to an interactive/electronic version of Fig. The magnitude and variation in the total soil C stocks observed in this work were comparable to those found at the national level in Sweden, suggesting that C accumulation in boreal landscapes is more sensitive to local variation resulting primarily from differences in soil moisture conditions than to regional differences in climate, nitrogen deposition, and parent material.…”

Water Retention Characteristics of Mineral Forest Soils in Finland: Impacts for Modeling Soil Moisture

“…Accounting for these factors requires process-based models. The model results (Figures 7 and 8) demonstrate how mean soil moisture content and its temporal variability are dependent on the soil properties, such as the WRC, but also on the depth of the ground water [85,86] as well as transient water storage changes due to the weather conditions [6,36]. Landscape patterns of soil moisture are also strongly related to topography, which has given a foundation for using topographic wetness indices (TWI, DTW etc.)…”