Modelling properties and understanding processes across different spatial scales within the critical zone through environmental correlation.

Abstract:

An environmental correlation approach establishes predictive relationships between the measured properties of the critical zone with a comprehensive suite of environmental covariates. The environmental covariates ideally cover or represent proxies for the factors that control soil/regolith formation. These factors include parent material, time, climate, biological and landscape processes. The corresponding proxies include lithology maps, satellite imagery (e.g. Landsat TM, MODIS), geophysical imagery (e.g. magnetics, radiometrics and gravity), terrain attributes (e.g. slope, wetness index) and climate surfaces (e.g. annual rainfall).

Using this approach we model and spatially predict two important components of the critical zone including: depth of weathering and geochemistry. Predictive maps of these attributes are based on nested piecewise linear tree models. Models of critical zone thickness and geochemistry (including elements, element ratios and chemical indices) have been developed at the catchment scale and at the continental scale. Thickness and weathering intensity (determined through geochemical weathering indices) of the critical zone profoundly affects groundwater interactions, subsoil water movement, water storage and nutrient availability. In highly weathered Australian landscapes we commonly see geochemical convergence typified by the abundance of end-member weathering phases such as quartz, clays and oxyhydroxides. The modelling can be used to map elements of economic importance or those which are potentially hazardous to human health.

 

Modelling and integration of environmental covariates helps to facilitate our understanding of the processes occurring within the lithosphere, hydrosphere, atmosphere and biosphere that control the nature and distribution of the weathered materials. It also provides an approach to integrate and model the vast amount of spatial information we have from ground, airborne and satellite remote sensing.