The modelling of soil-process functional units based on three-dimensional soil horizon cartography, with an example of denitrification in a riparian zone

Abstract

This article aims to propose an approach for estimating the three-dimensional (3D) variability of denitrification. The concept of functional horizons is applied to the process of biological denitrification and 3D soil horizon cartography is used to estimate its spatial variation. On one hand, detailed fieldwork (186 pedological auger holes) was undertaken to map 3D horizon distribution within a 3-ha riparian area using Geographical Information Systems (GIS). On the other hand, three classes of denitrifying capacities were defined according to the distribution of the denitrifying enzyme activity of 51 samples. The relationship between the process of denitrification and the cartography is assessed through soil characteristics, which both differentiate soil horizons and control the process of denitrification: organic carbon and textural fractions. This allows a class of denitrifying capacity to be attributed to each soil horizon. This information was inserted into the 3D soil horizon cartography and the denitrifying functional horizons could be delimited. With this approach, field criteria are used and variations of the 3D distribution of

Distribution of bacteria and nitrogen-cycling microbial communities along constructed Technosol depth-profiles

“…Accordingly, analysis of the Technosol physico-chemical properties revealed that the T2 upper horizon differ the most with, for example, a much lower gravel content, C/N ratio and total CaCO 3 concentration (Table 1). A decrease in potential denitrification with soil depth was reported in previous studies [34][35][36][37] and has been attributed to changes in organic carbon and soil texture, which in turn influences the oxygen partial pressure (pO 2 ) [38]. Indeed it is well known that the activity of the denitrifiers is controlled by the pO 2 , the availability of organic carbon and nitrogen oxides [39,40].…”

Identification of facies models in alluvial soil formation: The case of a Swiss alpine floodplain

“…This limit was chosen because it represents the bottom of the studied system and was considered as almost similar throughout the study area. This sampling technique is commonly used to provide an indication of the soils represented in the field and to describe the soil types, if soil profiles have been previously determined (Cosandey et al, 2003;Earl et al, 2003;Bragato, 2004). This is the case for this site where previous studies have already been published (Bureau et al, 1995;Fierz et al, 1995;Mendonça Santos et al, 2000).…”

Searching for biogeochemical hot spots in three dimensions: Soil C and N cycling in hydropedologic settings in a northern hardwood forest

“…It is important to note that microbial C and N cycling and soil inorganic N pools are more temporally dynamic than soil C concentrations. However, the strong link we found between water availability and soil %C suggests that during the growing season, microbial processes throughout soil profiles and across watersheds are mappable based on soil C, as has been shown in other forests [ Cosandey et al ., ; Webster et al ., ]. Although the relationships between soil microbial processes and soil C concentrations are likely to change seasonally based on forest phenology or in response to climate patterns, the potential microbial biomass and activity parameters that we measured show much less temporal variation than soil moisture and temperature [ Groffman and Tiedje , ], which supports the idea that the relationships reported here are not ephemeral.…”

“…Although biogeochemical variability occurs at multiple spatial scales, there has long been considerable interest in understanding variability at the watershed scale for both research and management purposes [McGuire and Likens, 2011]. Studies of linkages between watershed hydrology and biogeochemistry have been productive [Burt and Pinay, 2005;Lohse et al, 2009], integrating surface and subsurface processes and yielding conceptual advances, such as simulation models of watershed carbon (C) and nitrogen (N) fluxes [Band et al, 2001], ways to estimate and map soil C pools and microbial processes [Cosandey et al, 2003;Webster et al, 2011], and recognition of biogeochemical hot spots in the landscape [Burt and Pinay, 2005;McClain et al, 2003].…”