Dataset Information

Description and spatial inference of soil drainage using matrix soil colours in the Lower Hunter Valley, New South Wales, Australia.

ABSTRACT: Soil colour is often used as a general purpose indicator of internal soil drainage. In this study we developed a necessarily simple model of soil drainage which combines the tacit knowledge of the soil surveyor with observed matrix soil colour descriptions. From built up knowledge of the soils in our Lower Hunter Valley, New South Wales study area, the sequence of well-draining ? imperfectly draining ? poorly draining soils generally follows the colour sequence of red ? brown ? yellow ? grey ? black soil matrix colours. For each soil profile, soil drainage is estimated somewhere on a continuous index of between 5 (very well drained) and 1 (very poorly drained) based on the proximity or similarity to reference soil colours of the soil drainage colour sequence. The estimation of drainage index at each profile incorporates the whole-profile descriptions of soil colour where necessary, and is weighted such that observation of soil colour at depth and/or dominantly observed horizons are given more preference than observations near the soil surface. The soil drainage index, by definition disregards surficial soil horizons and consolidated and semi-consolidated parent materials. With the view to understanding the spatial distribution of soil drainage we digitally mapped the index across our study area. Spatial inference of the drainage index was made using Cubist regression tree model combined with residual kriging. Environmental covariates for deterministic inference were principally terrain variables derived from a digital elevation model. Pearson's correlation coefficients indicated the variables most strongly correlated with soil drainage were topographic wetness index (-0.34), mid-slope position (-0.29), multi-resolution valley bottom flatness index (-0.29) and vertical distance to channel network (VDCN) (0.26). From the regression tree modelling, two linear models of soil drainage were derived. The partitioning of models was based upon threshold criteria of VDCN. Validation of the regression kriging model using a withheld dataset resulted in a root mean square error of 0.90 soil drainage index units. Concordance between observations and predictions was 0.49. Given the scale of mapping, and inherent subjectivity of soil colour description, these results are acceptable. Furthermore, the spatial distribution of soil drainage predicted in our study area is attuned with our mental model developed over successive field surveys. Our approach, while exclusively calibrated for the conditions observed in our study area, can be generalised once the unique soil colour and soil drainage relationship is expertly defined for an area or region in question. With such rules established, the quantitative components of the method would remain unchanged.

SUBMITTER: Malone BP

PROVIDER: S-EPMC5907776 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Description and spatial inference of soil drainage using matrix soil colours in the Lower Hunter Valley, New South Wales, Australia.

Malone Brendan P BP McBratney Alex B AB Minasny Budiman B

PeerJ 20180416

Soil colour is often used as a general purpose indicator of internal soil drainage. In this study we developed a necessarily simple model of soil drainage which combines the tacit knowledge of the soil surveyor with observed matrix soil colour descriptions. From built up knowledge of the soils in our Lower Hunter Valley, New South Wales study area, the sequence of well-draining → imperfectly draining → poorly draining soils generally follows the colour sequence of red → brown → yellow → grey → b ...[more]

PMID: 29682425

Similar Datasets

Project description:IntroductionDespite being a preventable cause of death, drowning is a global public health threat. Australia records an average of 288 unintentional drowning deaths per year; an estimated annual economic burden of $1.24 billion AUD ($2017). On average, a further 712 hospitalisations occur due to non-fatal drowning annually. The Australian state of New South Wales (NSW) is the most populous and accounts for 34% of the average fatal drowning burden. This study aims to explore the demographics and outcome of patients who are admitted to hospitals for drowning in NSW and also investigates prediction of patients' outcome based on accessible data.Methods and analysisThis protocol describes a retrospective, cross-sectional data linkage study across secondary data sources for any person (adult or paediatric) who was transferred by NSW Ambulance services and/or admitted to a NSW hospital for fatal or non-fatal drowning between 1/1/2010 and 31/12/2019. The NSW Admitted Patient Data Collection will provide data on admitted patients' characteristics and provided care in NSW hospitals. In order to map patients' pathways of care, data will be linked with NSW Ambulance Data Collection and the NSW Emergency Department Data Collection. Finally patient's mortality will be assessed via linkage with NSW Mortality data, which is made up of the NSW Register of Births, Deaths and Marriages and a Cause of Death Unit Record File. Regression analyses will be used to identify predicting values of independent variables with study outcomes.Ethics and disseminationThis study has been approved by the NSW Population & Health Services Research Ethics Committee. Results will be disseminated through peer-reviewed publications, mass media releases and at academic conferences. The study will provide outcome data for drowning patients across NSW and study results will provide data to deliver evidence-informed recommendations for improving patient care, including updating relevant guidelines.

Project description:1. Human activities have been a significant driver of environmental changes with tremendous consequences for carbon dynamics. Peatlands are critical ecosystems because they store ~30% of the global soil organic carbon pool and are particularly vulnerable to anthropogenic changes. The Zoige peatland on the eastern Tibet Plateau, as the largest alpine peatland in the world, accounts for 1‰ of global peat soil organic carbon storage. However, this peatland has experienced dramatic climate change including increased temperature and reduced precipitation in the past decades, which likely is responsible for a decline of the water table and facilitated earthworm invasion, two major factors reducing soil organic carbon (SOC) storage of peatlands. 2. Because earthworms are often more active in low- than in high- moisture peatlands, we hypothesized that the simultaneous occurrence of water table decline and earthworm invasion would synergistically accelerate the release of SOC from peatland soil. We conducted a field experiment with a paired split-plot design, i.e. presence vs. absence of the invasive earthworms (Pheretima aspergillum) nested in drained vs. undrained plots, respectively, for three years within the homogenous Zoige peatland. 3. Water table decline significantly decreased soil water content and bulk density, resulting in a marked reduction of SOC storage. Moreover, consistent with our hypothesis, earthworm presence dramatically reduced SOC in the drained but not in the undrained peatland through the formation of deep burrows and decreasing bulk density of the lower soil layer over three years. The variation in SOC likely was due to changes in aboveground plant biomass, root growth, and earthworm behavior induced by the experimental treatments. 4. Synthesis and applications. We suggest that incentive measures should be taken to prevent further water table decline and earthworm invasion for maintaining the soil C pool in Zoige peatland. Artificial filling of drainage canals should be implemented to increase the water table level, facilitating the recovery of drained peatlands. Moreover, the dispersal of earthworms and their cocoons attached to the roots of crop plants and tree saplings from low-lying areas to the Zoige region should be controlled and restricted.

Dataset Information

Description and spatial inference of soil drainage using matrix soil colours in the Lower Hunter Valley, New South Wales, Australia.

Publications

Description and spatial inference of soil drainage using matrix soil colours in the Lower Hunter Valley, New South Wales, Australia.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets