ORCID Profile
0000-0003-0256-1496
Current Organisations
CSIRO
,
CSIRO Queensland Bioscience Precinct
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: CSIRO
Date: 2018
Publisher: Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc.
Date: 12-2013
Publisher: Informa UK Limited
Date: 04-2009
Publisher: CSIRO
Date: 2017
Publisher: Elsevier BV
Date: 12-2017
Publisher: CSIRO
Date: 2015
Publisher: Wiley
Date: 14-09-2018
DOI: 10.1002/LDR.3130
Publisher: Copernicus GmbH
Date: 12-2017
DOI: 10.5194/HESS-21-6049-2017
Abstract: Abstract. Soil moisture plays a critical role in land surface processes and as such there has been a recent increase in the number and resolution of satellite soil moisture observations and the development of land surface process models with ever increasing resolution. Despite these developments, validation and calibration of these products has been limited because of a lack of observations on corresponding scales. A recently developed mobile soil moisture monitoring platform, known as the rover, offers opportunities to overcome this scale issue. This paper describes methods, results and testing of soil moisture estimates produced using rover surveys on a range of scales that are commensurate with model and satellite retrievals. Our investigation involved static cosmic-ray neutron sensors and rover surveys across both broad (36 × 36 km at 9 km resolution) and intensive (10 × 10 km at 1 km resolution) scales in a cropping district in the Mallee region of Victoria, Australia. We describe approaches for converting rover survey neutron counts to soil moisture and discuss the factors controlling soil moisture variability. We use independent gravimetric and modelled soil moisture estimates collected across both space and time to validate rover soil moisture products. Measurements revealed that temporal patterns in soil moisture were preserved through time and regression modelling approaches were utilised to produce time series of property-scale soil moisture which may also have applications in calibration and validation studies or local farm management. Intensive-scale rover surveys produced reliable soil moisture estimates at 1 km resolution while broad-scale surveys produced soil moisture estimates at 9 km resolution. We conclude that the multiscale soil moisture products produced in this study are well suited to future analysis of satellite soil moisture retrievals and finer-scale soil moisture models.
Publisher: Elsevier BV
Date: 11-2020
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 29-11-2015
Publisher: Elsevier BV
Date: 2014
DOI: 10.1016/J.SCITOTENV.2013.07.049
Abstract: The use of river basin modelling to guide mitigation of non-point source pollution of wetlands, estuaries and coastal waters has become widespread. To assess and simulate the impacts of alternate land use or climate scenarios on river washload requires modelling techniques that represent sediment sources and transport at the time scales of system response. Building on the mean-annual SedNet model, we propose a new D-SedNet model which constructs daily budgets of fine sediment sources, transport and deposition for each link in a river network. Erosion rates (hillslope, gully and streambank erosion) and fine sediment sinks (floodplains and reservoirs) are disaggregated from mean annual rates based on daily rainfall and runoff. The model is evaluated in the Burdekin basin in tropical Australia, where policy targets have been set for reducing sediment and nutrient loads to the Great Barrier Reef (GBR) lagoon from grazing and cropping land. D-SedNet predicted annual loads with similar performance to that of a sediment rating curve calibrated to monitored suspended sediment concentrations. Relative to a 22-year reference load time series at the basin outlet derived from a dynamic general additive model based on monitoring data, D-SedNet had a median absolute error of 68% compared with 112% for the rating curve. RMS error was slightly higher for D-SedNet than for the rating curve due to large relative errors on small loads in several drought years. This accuracy is similar to existing agricultural system models used in arable or humid environments. Predicted river loads were sensitive to ground vegetation cover. We conclude that the river network sediment budget model provides some capacity for predicting load time-series independent of monitoring data in ungauged basins, and for evaluating the impact of land management on river sediment load time-series, which is challenging across large regions in data-poor environments.
Publisher: CSIRO Publishing
Date: 2017
DOI: 10.1071/SR16140
Abstract: The development and implementation of a national data schema for soil data in Australia over the last two decades, coupled with advances in information technology, has led to the realisation of more comprehensive state and national soil databases. This has facilitated increased access to soil data for many purposes, including the creation of many digital soil-mapping products, such as the Soil and Landscape Grid of Australia. Consequently, users of soil data have a growing need for clarity concerning the quality of the data many new users have little understanding of the varying quality of the data. To date, statements about the quality of primary soil data have typically been qualitative and/or judgemental rather than explicit. The consequences of poor-quality primary data and of the lack of a coding system for data quality are growing with increased usage and with demand for soil data at the regional to national scale. Pillar 4 of the Global Soil Partnership and the National Soil Research, Development and Extension Strategy both identify the need to improve the quality of soil data. Various international standards do exist with respect to the quality of soil data but these tend to focus on general principles and quality-assurance frameworks rather than the detail of describing data quality. The aim of this paper is to stimulate a discussion in the Australian soil science community on how to quantify and describe the quality of primary soil data. We provide ex les of the data quality issues and propose a framework for structured data-quality checking procedures and quality coding of soil morphological and analytical data in Australia.
Publisher: CSIRO Publishing
Date: 2015
DOI: 10.1071/SR14366
Abstract: Information on the geographic variation in soil has traditionally been presented in polygon (choropleth) maps at coarse scales. Now scientists, planners, managers and politicians want quantitative information on the variation and functioning of soil at finer resolutions they want it to plan better land use for agriculture, water supply and the mitigation of climate change land degradation and desertification. The GlobalSoilMap project aims to produce a grid of soil attributes at a fine spatial resolution (approximately 100 m), and at six depths, for the purpose. This paper describes the three-dimensional spatial modelling used to produce the Australian soil grid, which consists of Australia-wide soil attribute maps. The modelling combines historical soil data plus estimates derived from visible and infrared soil spectra. Together they provide a good coverage of data across Australia. The soil attributes so far include sand, silt and clay contents, bulk density, available water capacity, organic carbon, pH, effective cation exchange capacity, total phosphorus and total nitrogen. The data on these attributes were harmonised to six depth layers, namely 0–0.05 m, 0.05–0.15 m, 0.15–0.30 m, 0.30–0.60 m, 0.60–1.00 m and 1.00–2.00 m, and the resulting values were incorporated simultaneously in the models. The modelling itself combined the bootstrap, a decision tree with piecewise regression on environmental variables and geostatistical modelling of residuals. At each layer, values of the soil attributes were predicted at the nodes of a 3 arcsecond (approximately 90 m) grid and mapped together with their uncertainties. The assessment statistics for each attribute mapped show that the models explained between 30% and 70% of their total variation. The outcomes are illustrated with maps of sand, silt and clay contents and their uncertainties. The Australian three-dimensional soil maps fill a significant gap in the availability of quantitative soil information in Australia.
Publisher: Elsevier BV
Date: 06-2014
Publisher: CSIRO
Date: 2014
Publisher: Copernicus GmbH
Date: 30-07-2012
DOI: 10.5194/ISPRSARCHIVES-XXXIX-B8-507-2012
Abstract: Abstract. This study demonstrates the potential applicability of high temporal frequency information on the biophysical condition of the vegetation from a time series of the global Moderate Resolution Imaging Spectroradiometer (MODIS) Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) from 2000 to 2006 (collection 4 8-day composites in 1 km spatial resolution) to improve modelling of soil loss in a tropical, semi-arid catchment in Queensland. Combining the biophysical information from the MODIS FPAR with structural vegetation information from the Geoscience Laser Altimeter System on the Ice, Cloud, and land Elevation Satellite (ICESat) for six vegetation structural categories identified from a Landsat Thematic Mapper 5 (TM) and Enhanced Thematic Mapper 7 (ETM+) woody foliage projective cover product representing floristically and structurally homogeneous areas, dynamic vegetative cover factor (vCf) estimates were calculated. The dynamic vCf were determined in accordance with standard calculation methods used in erosion models worldwide. Time series of dynamic vCf were integrated into a regionally improved version of the Universal Soil Loss Equation (USLE) to predict daily soil losses for the study area. Resulting time series of daily soil loss predictions averaged over the study area coincided well with measures of total suspended solids (TSS) (mg/l) at a gauge at the outlet of the catchment for three wet seasons (R2 of 0.96 for a TSS-event). By integrating the dynamic vCf into modified USLE, the strength of the dependence of daily soil loss predictions to the only other dynamic factor in the equation – daily rainfall erosivity – was reduced.
Publisher: Elsevier BV
Date: 03-2019
Publisher: Elsevier BV
Date: 03-2016
Publisher: Informa UK Limited
Date: 03-09-2019
Publisher: CSIRO
Date: 2018
Publisher: Elsevier BV
Date: 11-2014
Publisher: CRC Press
Date: 27-01-2014
DOI: 10.1201/B16500-48
Publisher: CRC Press
Date: 27-01-2014
DOI: 10.1201/B16500-26
Publisher: CSIRO Publishing
Date: 2015
DOI: 10.1071/SR15191
Abstract: The Soil and Landscape Grid of Australia (SLGA) is the first continental version of the GlobalSoilMap concept and the first nationally consistent, fine spatial resolution set of continuous soil attributes with Australia-wide coverage. The SLGA relies on digital soil mapping methods and integrates historical soil data, new measurement with spectroscopic sensors, novel spatial modelling and a web-service delivery architecture. The SLGA provides soil, regolith and landscape estimates at the centre point of 3 arcsecond grid cells (~90 × 90 m) across Australia. At each point, there are estimates of 11 soil attributes and confidence intervals for each estimate to a depth of 2 m or less, depth of regolith and a set of terrain descriptors. The information system also includes a library of mid-infrared spectra, an inference engine that allows estimation of additional soil parameters and an information model that enables users to access the system via web services. The explicit mapping of depth, bulk density and coarse fragments allows estimation of material stores and fluxes on a volumetric basis. The SLGA therefore has immediate applications in carbon, nitrogen and water process modelling. The map of regolith depth will find immediate application to studies of vadose zone processes, including solute transport, groundwater and nutrient fluxes beyond the root zone. Landscape attributes at 1 and 3 arcseconds are useful for a wide spectrum of ecological, hydrological and broader environmental applications. The SLGA can be accessed at no cost from www.csiro.au/soil-and-landscape-grid. It is managed and delivered as part of the Australian Soil Resource Information System (ASRIS).
Publisher: Institute of Mathematical Statistics
Date: 09-2016
DOI: 10.1214/16-AOAS950
Publisher: Elsevier BV
Date: 09-2020
Publisher: Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc.
Date: 12-2013
Publisher: Elsevier BV
Date: 12-2020
Publisher: Elsevier BV
Date: 03-2021
Publisher: Elsevier BV
Date: 09-2020
Publisher: CSIRO Publishing
Date: 2015
DOI: 10.1071/SR15100
Abstract: Better understanding the spatial distribution of soil organic carbon (SOC) stocks is important for the management and enhancement of soils for production and environmental outcomes. We have applied digital soil mapping (DSM) techniques to combine soil-site datasets from legacy and recent sources, environmental covariates and expert pedological knowledge to predict and map SOC stocks in the top 0.3 m, and their uncertainty, across South Australia’s agricultural zone. In achieving this, we aimed to maximise the use of locally sourced datasets not previously considered in national soil C assessments. Practical considerations for operationalising DSM are also discussed in the context of working with problematic legacy datasets, handling large numbers of potentially correlated covariates, and meeting end-user needs for readily interpretable results and accurate maps. Spatial modelling was undertaken using open-source R statistical software over a study area of ~160 000 km2. Legacy-site SOC stock estimates were derived with inputs from an expert-derived bulk-density pedotransfer function to overcome critical gaps in the data. Site estimates of SOC were evaluated over a consistent depth range and then used in spatial predictions through an environmental-correlation regression-kriging DSM approach. This used the contemporary Least Absolute Shrinkage and Selection Operator penalised-regression method, which catered for a large number (63 numeric, four categorical, four legacy-soil mapping themes) of potentially correlated covariates. For efficient use of the available data, this was performed within a k-fold cross-validation (k = 10) modelling framework. Through this, we generated multiple predictions and variance information at every node of our prediction grid, which was used to evaluate and map the expected value (mean) of SOC stocks and their uncertainty. For the South Australian agricultural zone, expected value SOC stocks in the top 0.3 m summed to 0.589 Gt with a 90% prediction interval of 0.266–1.086 Gt.
Location: Australia
No related grants have been discovered for Ross Searle.