Relations Between Topography and Water Table Depth in a Shallow Forest Soil

Abstract

The relations between water‐table depth in a shallow forest soil and topographic characteristics derived from a raster digital elevation model (DEM) were studied. Water‐table depths at times of observation, and peak levels between times of observation, were measured weekly during an autumn–winter rainy season at 59 wells in a catchment (ca 0.04 km2) drained by an ephemeral stream, located 80 km east of Vancouver, Canada. Digital elevation models with grid spacings of 4, 8 and 16 m were derived from a ground survey and values of a (upslope contributing area per unit contour length), tanβ (surface slope) and χ (a measure of surface curvature) were computed from the DEMs. To avoid problems with data censoring when the water‐table dropped below the bottom of some wells, the depths were discretized into classes and discriminant analysis was used to relate water‐table depth classes to the topographic indices. The relations were derived using data from 30 wells and were tested on data from the other 29 wells to provide an independent test of predictive capability. The index ln(a/tanβ) provided more reliable classifications than a multiple discriminant analysis using ln(a), tanβ and χ as separate predictor variables. Water‐table depths at some wells were consistently misclassified (e.g. 0–3% correct classifications), whereas at others they were classified correctly in up to 100% of cases. Consistent misclassification at individual wells could reflect either the effects of errors in the derived topographic indices (through errors in the DEM or the algorithms used to calculate the indices) or phenomena not related to surface topography, such as variability in soil properties or differences between the surface topography and the topography of the base of the soil profile. The predictive accuracy and statistical significance of derived relations decreased as the DEM grid size increased from 4 to 8 to 16 m. These results are valid only for predictions of water‐table depths at points. Further research should explore the effect of grid scale on relations between grid‐averaged water‐table depths and topographic indices and on methods to parameterize subgrid‐scale variability.

Evaluating topography‐based predictions of shallow lateral groundwater discharge zones for a boreal lake‐stream system

“…It is also possible that spatial variability in subsurface permeability and topography of a confining layer contributed to disagreements in predicted inflow contributions [ Hutchinson and Moore , ; Ameli et al ., ]. In addition, upslope contributing areas will be sensitive to DEM resolution and may have also contributed to errors in the predicted lateral inflow contributions [ Thompson and Moore , ; Sørensen and Seibert , ; Ågren et al ., ]; however, our comparison of different DEM resolutions and flow accumulation algorithms suggests that this a minor source of uncertainty for our site.…”

Empirical Stream Thermal Sensitivities May Underestimate Stream Temperature Response to Climate Warming

“…The prototype catchment used for the model simulations was East Creek, a forested 38-ha headwater catchment located in the Malcolm Knapp Research Forest near Vancouver, British Columbia. The East Creek catchment and other sites in Malcolm Knapp Research Forest have shallow (<1 m deep), permeable soils overlying compacted basal till or granitic bedrock, and streamflow generation is dominated by throughflow, which is defined as saturated subsurface flow within the soil (Hutchinson & Moore, 2000;Leach & Moore, 2014;Thompson & Moore, 1996). The elevation of Malcolm Knapp Research Forest spans from 100 to 1,000 m above sea level, which encompasses the rain-on-snow zone in this region (Beaudry & Golding, 1983).…”

A Test of Two Spatial Frameworks for Representing Spatial Patterns of Wetness in a Glacial Drift Watershed

“…The second type of spatial framework we used is a higherresolution topographic wetness index (TWI), where pixel-level values are calculated based on geospatial inputs. Topographic indices have commonly been correlated with soil moisture (e.g., Ali et al, 2010;Beaudette et al, 2013;James and Roulet, 2007;Liang et al, 2017;Lin et al, 2006;Tague et al, 2010) and water table (e.g., Detty and McGuire, 2010;Grabs et al, 2012;Monteith et al, 2006;Rinderer et al, 2014;Thompson and Moore 1996) measurements across many study systems. Topographic wetness indices have been related to hydropedologic unit, soil organic matter, and other soil properties (Gillin et al, 2015;Grabs et al, 2012;Laamrani et al, 2014;Lin et al, 2006;Moore et al, 1993;Murphy et al, 2011;Pei et al, 2010;Zinko et al, 2006).…”