Topsoil porosity prediction across habitats at large scales using environmental variables.

Soil porosity and its reciprocal bulk density are important environmental state variables that enable modelers to represent hydraulic function and carbon storage. Biotic effects and their 'dynamic' influence on such state variables remain largely unknown for larger scales and may result in important, yet poorly quantified environmental feedbacks. Existing representation of hydraulic function is often invariant to environmental change and may be poor in some systems, particularly non-arable soils. Here we assess predictors of total porosity across two comprehensive national topsoil (0-15 cm) data sets, covering the full range of soil organic matter (SOM) and habitats (n = 1385 & n = 2570), using generalized additive mixed models and machine learning. Novel aspects of this work include the testing of metrics on aggregate size and livestock density alongside a range of different particle size distribution metrics. We demonstrate that porosity trends in Great Britain are dominated by biotic metrics, soil carbon and land use. Incorporating these variables into porosity prediction improves performance, paving the way for new dynamic calculation of porosity using surrogate measures with remote sensing, which may help improve prediction in data sparse regions of the world. Moreover, dynamic calculation of porosity could support representation of feedbacks in environmental and Earth System Models. Representing the hydrological feedbacks from changes in structural porosity also requires data and models at appropriate spatial scales to capture conditions leading to near-saturated soil conditions. Classification. Environmental Sciences.

Analytical modelling of soil porosity and bulk density across the soil organic matter and land-use continuum.

The thin layer of soil at the earth's surface supports life, storing water and nutrients for plant uptake. These processes occur in the soil pore space, often half the soil volume, but our understanding of how this volume responds to environmental change is poor. Convention, has been to predict soil porosity, or its reciprocal bulk density (BD), from soil texture using pedotransfer functions (PTFs). A texture based approach, invariant to environmental change, prevents feedback from land use or climate change to soil porosity. Moreover, PTFs are often limited to mineral soils with < 20% soil organic matter (SOM) content. Here, we develop an analytical model to predict soil porosity, or BD, as a function of SOM. We test it on two comprehensive, methodologically consistent, temperate national-scale topsoil data sets (0-15 cm) (Wales, n = 1385; Great Britain, n = 2570). The purpose of the approach is to generate an analytical function suitable for predicting soil porosity change with SOM content, while providing insight into the main grain-scale factors determining the porosity emergence. The newly developed function covering the entire SOM gradient allows for impacts of land use, management or climate change to feedback on soil porosity or bulk density through decadal dynamic changes in SOM.