Topographical attributes to predict soil hydraulic properties along a hillslope transect

Abstract

[1] Basic soil properties have long been used to predict unsaturated soil hydraulic properties with pedotransfer function (PTFs). Implementation of such PTFs is usually not feasible for catchment-scale studies because of the experimental effort that would be required. On the other hand, topographical attributes are often readily available. This study therefore examines how well PTFs perform that use both basic soil properties and topographical attributes for a hillslope in Basilicata, Italy. Basic soil properties and hydraulic data were determined on soil samples taken at 50-m intervals along a 5-km hillslope transect. Topographical attributes were determined from a digital elevation model. Spearman coefficients showed that elevation (z) was positively correlated with organic carbon (OC) and silt contents (0.62 and 0.59, respectively) and negatively with bulk density (r b ) and sand fraction (À0.34 and À0.37). Retention parameters were somewhat correlated with topographical attributes z, slope (b), aspect (cosf), and potential solar radiation. Water contents were correlated most strongly with elevation (coefficient between 0.38 and 0.48) and aspect during ''wet'' conditions. Artificial neural networks (ANNs) were developed for 21 different sets of predictors to estimate retention parameters, saturated hydraulic conductivity (K s ), and water contents at capillary heads h = 50 cm and 12 bar (10 3 cm). The prediction of retention parameters could be improved with 10% by including topography (RMSE = 0.0327 cm 3 cm À3 ) using textural fractions, r b , OC, z, and b as predictors. Furthermore, OC became a better predictor when the PTF also used z as predictor. The water content at h = 50 cm could be predicted 26% more accurately (RMSE = 0.0231 cm 3 cm À3 ) using texture, r b , OC, z, b, and potential solar radiation as input. Predictions of ANNs with and without topographical attributes were most accurate in the wet range (0 < h < 250 cm). Semivariograms of the hydraulic parameters and their residuals showed that the ANNs could explain part of the (spatial) variability. The results of this study confirm the utility of topographical attributes such as z, b, cosf, and potential solar radiation as predictors for PTFs when basic soil properties are available. A next step would be the use of topographical attributes when no or limited other predictors are available.

Assessment of soil particle redistribution on two contrasting cultivated hillslopes

“…The relationships obtained between topographical attributes (elevation and slope) and various size fractions may be valuable in the formulation of pedotransfer functions to predict spatially variable soil hydraulic properties at the field or catchment scale (Leij et al, 2004). The distribution of the silt fractions also has several significant environmental and economic implications.…”

Variation of soil hydraulic properties with alpine grassland degradation in the eastern Tibetan Plateau

“…FC and K s are mainly influenced by vegetation, soil (Pachepsky and Park, 2015), topography (Leij et al, 2004), climate (Jarvis et al, 2013) and human activities (Mubarak et al, 2009;Palese et al, 2014). In recent years, vegetation degradation has been widespread because of natural environmental changes and anthropogenic influences.…”

A Hydrophysical Database to Develop Pedotransfer Functions for Brazilian Soils: Challenges and Perspectives

“…Such tendency is observed worldwide. ISRIC-WISE (Harmonized Global Soil Profile Dataset, version 3.1) (Batjes 2009), UNSODA (UNsaturated SOil DAtabase) (Leij et al 1996;Nemes et al 2001) and HYPRES (HYdraulic PRoperties of European Soils) (Wösten et al 1999), were the first soil databases in which hydraulic properties associated with soil survey information were available. These datasets have (partially) satisfied the world's demand for hydrophysical information at the time.…”

“…The accuracy of these functions requires, among other aspects, databases that present the following characteristics: (a) precise measures of soil attributes (Wösten et al 2001;Mohanty and Shouse 2002;Perkins and Nimmo 2009;Vereecken et al 2010); (b) various soil properties and landscape features, such as topographic parameters and terrain vegetation Rawls and Pachepsky 2002;Leij et al 2004;Sharma et al 2006) that may serve as input variables in these functions; (c) a substantial number of data distributed in relatively homogenous quantities from diverse soils and environments (Perkins and Nimmo 2009;Vereecken et al 2010); and (d) standardization of measuring methods of soil attributes among data contained in the database (Schaap and Leij 1998;Pachepsky et al 1999;Perkins and Nimmo 2009;Vereecken et al 2010).…”