Relating Cone Penetration and Rutting Resistance to Variations in Forest Soil Properties and Daily Moisture Fluctuations

Abstract

Soil resistance to penetration and rutting depends on variations in soil texture, density and weather-affected changes in moisture content. It is therefore difficult to know when and where off-road traffic could lead to rutting-induced soil disturbances. To establish some of the empirical means needed to enable the "when" and "where" determinations, an effort was made to model the soil resistance to penetration over time for three contrasting forest locations in Fredericton, New Brunswick: a loam and a clay loam on ablation/ basal till, and a sandy loam on alluvium. Measurements were taken manually with a soil moisture probe and a cone penetrometer from spring to fall at weekly intervals. Soil moisture was measured at 7.5 cm soil depth, and modelled at 15, 30, 45 and 60 cm depth using the Forest Hydrology Model (ForHyM). Cone penetration in the form of the cone index (CI) was determined at the same depths. These determinations were not only correlated with measured soil moisture but were also affected by soil density (or pore space), texture, and coarse fragment and organic matter content (R 2 = 0.54; all locations and soil depths). The resulting regression-derived CI model was used to emulate how CI would generally change at each of the three locations based on daily weather records for rain, snow, and air temperature. This was done through location-initialized and calibrated hydrological and geospatial modelling. For practical interpretation purposes, the resulting CI projections were transformed into rut-depth estimates regarding multi-pass off-road all-terrain vehicle traffic.

Modeling boreal forest evapotranspiration and water balance at stand and catchment scales: a spatial approach

“…The proposed modular framework can provide support to a variety of questions benefiting from spatial and temporal hydrological predictions. These include, but are not limited to the following: (1) predicting soil moisture necessary for forecasting forest soil trafficability (Vega-Nieva et al, 2009;Jones and Arp, 2017), precision forestry and confronting climate-induced risks (Muukkonen et al, 2015); (2) identifying how saturated areas, considered as biogeochemical and biodiversity hotspots particularly sensitive to negative environmental impacts of human activities, evolve over time (Laudon et al, 2016;Ågren et al, 2015); (3) addressing impacts of forest structure, management and climate change on ET partitioning, streamflow dynamics and soil moisture (Zhang et al, 2017;Karlsen et al, 2016); (4) supporting water-quality modeling in headwater catchments (Guan et al, 2018); and (5) providing a starting point for developing a spatially distributed forest productivity and sustainability framework that combines open data streams, statistical approaches and mechanistic models. Moreover, we propose the Canopy submodel, in particular the leaf-to-canopy upscaling of canopy conductance, to be tested more widely in other ecosystems.…”

Soil Trafficability Forecasting

“…In this regard, the above regression results are at least consistent with general expectations. For example, moist to wet soils have low physical strength due to low particle cohesion (Kumar et al, 2012), and are therefore prone to traffic-induced compaction, displacement and rutting (Sutherland, 2003;Børgesen et al, 2006;Nikooy et al, 2016;Jones & Arp, 2017). To illustrate,…”

“…This measure varies from weak to strong as soil moisture content decreases, and this is particularly so in fine-textured soils. In contrast, sandy soils remain friable from wet to dry (Earl, 1997;Vaz, 2003;Dexter et al, 2007;Tekeste et al, 2008;Vaz et al, 2011;Kumar et al, 2012;Jones & Arp, 2017).…”

Analyzing and Projecting Soil Moisture and Cone Penetrability Variations in Forest Soils

“…3) an assessment of the season and weather dependent minimum upslope open-channel flow initiation area (FIA) as this could vary from ≤0.25 to ≥8 ha ( Figure 4); 4) a forest hydrology model that can be used to estimate weather-and season-affected soil moisture content (MC PS0 ) for ridge-top soils, to initiate, e.g., the Equation (15) and Equation (18) calculations (Jones & Arp, 2017), based on block-by-block elevation, slope, aspect, texture, D b , OM, CF, vegetation type and % canopy closure.…”