Showing results for: [ Environmental Impact Assessment ]
This is an application of the Spark framework configured for interactive use through a graphical user interface on a personal computer.
Design, validate and employ custom fire propagation models, usi... moreng Spark’s computational fire-front propagation solver. Incorporate any number of modules specifically designed for wildfire spread, including readers and writers for geospatial data and a wide range of visualisations and tools to analyse the resulting data.less
Natural Hazards and Infrastruture - Spark - Published 29 Oct 2019
This is an application of the Spark framework configured for command-line use on a server.
Design, validate and employ custom fire propagation models, using Spark’s computational fire-front propagati... moreon solver. Incorporate any number of modules specifically designed for wildfire spread, including readers and writers for geospatial data and a wide range of visualisations and tools to analyse the resulting data.less
Natural Hazards and Infrastruture - Spark Batch Demo - Published 29 Oct 2019
18S rDNA, 16S rDNA, and diatom 18S v4 rDNA amplicons from sediment DNA collected from Vavouto Bay New Caledonia.
NiPERA - Analysis of sediment nickel concentrations and benthic community composition - Published 17 Oct 2019
This data collection include the APSIM simulation outputs and the relevant data assessment on the trade-offs between crop yield, soil sequestration and N2O emissions under different nitrogen managemen... moret scenarios at 613 sites across Australian cropping regions. At each site, the model was run for 100 years under different N applications rates, while crop yield, soil carbon stock, N2O emissions from the soil were output. Based on the these dataset, the best N management practices were identified. less
Potential soil carbon sequestrati - - Published 20 Jun 2019
This data collection includes the APSIM-simulated soil organic carbon (SOC) dynamics in the top 30 cm soil layers at 1890 sites across Australian cropping areas. To generate the data, APSIM was firstl... morey constrained by observed data reported in NCAS report No. 36 (See the data here: https://doi.org/10.4225/08/54F0786D6D923). Then, the constrained APSIM model will run from 2009 to 2070 at the daily time step using current comment management practices and under different nitrogen, residue management and climate change scenarios. The output variables include a series of attributes of soil carbon, nitrogen, water dynamcis and crop biomass, yield. less
Grains Industry Life Cycle Inventory - - Published 20 Jun 2019
This data collection includes APSIM simulation of crop yield, nitrogen and water management, soil nitrogen and carbon dynamics in Huang-Huai-Hai Plain, China. A winter-wheat and summer maize double cr... moreopping system was simulated under a series of management scenarios including nitrogen and water management. less
Scientific benchmarks for sustainable agricultural intensification - - Published 20 Jun 2019
Raw photographs and georectified orthopotographs of three wetland sites on the Kakadu Flood Plain, East Alligator section. Flights captured areas that are under intensive weed management regimes (aer... moreial spraying and ground spraying) to control Para grass (Urochloa mutica).less
Nthn NESP 5.5 Kakadu bushtucker monitoring - UAS photographs - Published 12 Dec 2018
RNA Seq reads (Illumina HiSeq 2500) from the liver and gill of spotted dragonet (Repomucenus calcaratus) exposed to either control conditions of 2 mg/kg unweathered crude oil. Fish tissues were colle... morected 24 and 90 hours into exposure as well as 20 and 90 hours into recovery.less
Chevron_GAB_Phase 2 - determining monitoring targets - Published 18 Jul 2018
Bio-physical, ecological and social information have been used to parameterise two computer models able to simulate (ALCES and Ecopath with Ecosim [EwE]) land, coastal and marine processes. A careful ... moreexamination of a large volume of publications from the academic, private and public sector has also allowed us to identify a number of climate and social economic development scenarios the Kimberly region may experience in the decades to comeless
WAMSI-Kim 2.2.8 MSE Modelling Knowledge - MSE modelling and ecosystem modelling - Published 09 Mar 2018
A spatially disaggregated global livestock dataset containing information on biomass use, production, feed efficiency, excretion, and greenhouse gas emissions for 28 world regions, 8 livestock product... moreion systems, 4 animal species (cattle, small ruminants, pigs, and poultry), and 3 livestock products (milk, meat, and eggs) for the year 2000.
The dataset highlights: (i) feed efficiency as a key driver of productivity, resource use, and greenhouse gas emission intensities, with vast differences between production systems and animal products; (ii) the importance of grasslands as a global resource, supplying almost 50% of biomass for animals while continuing to be at the epicentre of land conversion processes; and (iii) the importance of mixed crop–livestock systems, producing the greater part of animal production (over 60%) in both the developed and the developing world. These data provide critical information for developing targeted, sustainable solutions for the livestock sector and its widely ranging contribution to the global food system.
Legacy data - - Published 08 Mar 2018
In Western Australia (WA), the Environmental Impact Assessment process requires dredging proponents to make scientifically sound predictions of the likely extent, severity, and persistence of environm... moreental impacts of the proposed activity under a spatially defined zoning pattern. This is achieved by using coupled hydrodynamic, wave and sediment transport models in conjunction with water quality (ecological) thresholds for sensitive receptors such as corals, filter feeders, or seagrasses/macroalgae. These predictions guide the scale and scope of associated monitoring programs, providing assistance to proponents as to where to establish environmental monitoring and reference sites. Increasingly, modelling is also being used by dredging programs to forecast a few days in advance, so as to understand the potential consequence of various dredging scenarios and optimize the dredging programs to minimize environmental damage.
The overall objective of Project 2/3.4 was to improve the predictive capabilities of sediment dispersion modelling that incorporate dynamic plume and passive plume processes through assessing model sensitivity to key forcing and parameter values, such as met-ocean condition, particle settling velocity distribution, critical shear stress, sediment erosion and deposition, provide frequency and duration of biological stressor fields including suspended sediment concentration, sediment accretion and erosion, and available light; and provide guidance on developing best practice algorithms and parametrizations for dredge plume modelling.
Based on the outcome Project 2/3.1, an appropriate modelling suite that includes hydrodynamics, waves, and sediment transport was chosen (Delft3D) to model the far-field passive plume. The model was set up and validated using the bathymetry and baseline data collected as part of the Chevron Australia Wheatstone Project, located near Onslow, Western Australia.
The model outputs were assessed against monitoring data from Chevron Australia's Wheatstone Dredging program, including, remote sensing and in-situ data collected in Project 2/3.2. A 20 month hindcast of passive plume dispersal from the dredging project to the furthest extent of the passive plume were compared with the field data and MODIS images (where available). Spatial and temporal variability of plume dispersal under different forcing scenarios and sediment release rates were investigated and reported.
This record pertains to the simulation data files. less
WAMSI Dredg T2-3 Gen Sediment - Sediment Plume Modelling - Published 10 Jan 2018
The remote Kimberley coast of north-western Australia is one of the few marine environments domains on earth largely unaffected by human use. However, the region is undergoing increasing economic impo... morertance as a destination for tourism and significant coastal developments associated with oil and gas exploration. The objective of the project was to reconstruct a timeline of inferred water quality changes from the sediment record for a selected set of sites in the Kimberley, Western Australia.
The project made use of palaeoecological approaches to reconstruct a chronology of change over the last approximately 100 years using a series of biogeochemical proxies for phytoplankton composition and biomass, temperature and terrestrial influences. Where possible these were matched to historical land/water use, meteorological or hydrological observational records. The project examined sediment cores from three coastal locations, Koolama Bay (King George River), Cygnet Bay and Roebuck Bay. Each sampling location provided a contrast with which to evaluate changes over either a spatial or temporal gradient of human or natural influence.
Sediment cores (up to 1.5 m) were obtained from each of these locations in the expectation that they would provide a time series for about the last 100 years. A set of parameters was measured along the core length (every 1-2 cm) for some or all cores depending on the particular focus for the location: 210Pb and 137Cs; 15N isotope; 13C isotope; Carbon/Nitrogen ratio; Sedimentation rate and grain size; Total Organic Carbon (TOC) and Total Nitrogen (TN); Biosilicate; Biomarkers; TEX86; long chain n-alkanes (C27+C29+C31); Elemental carbon (or black carbon).
Rainfall data was obtained from the Australian Bureau of Meteorology website (www.bom.gov.au). Stream flow data was obtained from the Western Australian Department of Water website (www.water.wa.gov.au). Historical bushfire data was obtained from the Western Australian Department of Parks and Wildlife.
The metadata record only relates to data generated as part of the sediment analysis.
WAMSI - KIM 2.2.9 Sediment Record - Sediment coring for water quality assessment - Published 08 Jan 2018
Reconstructed sea-surface heights for 1950 to 2001 as described in Church et al. (2004), except that it has been extended to the end of 2001. Briefly, this data set is: - near-global (65°S to 65°N) fr... moreom January 1950 to December 2001 on a 1° × 1° × 1 month grid - seasonal signal removed - inverse barometer correction made - GIA (Mitrovica) correction made to tide gauge data
Updated to 2012 following Church and White (2011).less
3.3 ACCSP-SL change, storm surge - ACCSP - Published 19 Oct 2017
This collection provides additional analyses, figures and tables for an integrated risk assessment of natural, cultural and economic assets in the Kakadu Region of northern Australia, from the combine... mored threats of invasive species (feral animals & aquatic weeds) and climate change induced sea level rise saltwater inundation. It addresses cumulative multiple risks to multiple values over different time frames (Present-day, 2070 & 2100).less
Legacy data - Modelling - Published 25 Sep 2017
Context: While the impact of seismic surveys on marine mammals has been well studied, there has been little work on zooplankton. The study by McCauley et al. (2017) published on the 23 June 2017 is th... moree first large-scale field experiment on the impact of seismic activity on zooplankton. Their study overturns the conventional idea of limited and localised impact on zooplankton. They found that air gun exposure significantly decreased zooplankton abundance, and increased the mortality rate from a natural level of 19% per day to 45% per day (on the day of exposure). These impacts were observed out to the maximum assessed range of 1.2 km. McCauley et al. (2017) state that there is an urgent need to conduct further study to mitigate, model and understand potential impacts on plankton. Methods: Here we simulate the large-scale impact of a seismic survey on zooplankton, assuming the mortality rate associated with air gun exposure reported by McCauley et al. (2017). Our approach models a hypothetical survey on the edge of the Northwest Shelf during summer. The survey area was 80 km by 36 km in water 300-800 m deep and the simulation was conducted over a 35-day period,. To simulate the movement of zooplankton by currents, we used a hydrodynamic model that seeded 0.5 million particles into CSIRO’s Ocean Forecast Australia Model. Zooplankton particles could be hit multiple times by the air gun if they were carried by currents into the future survey path. Each particle represents a zooplankton population exhibiting logistic population growth. The greatest limitation in this approach was accurate knowledge of the natural growth and mortality rates of zooplankton. We thus tested the sensitivity of the model to different recovery (growth-mortality) rates, and also the sensitivity of our results to ocean circulation by undertaking simulations with and without water motion. We report the relative zooplankton biomass in our simulations – the ratio of zooplankton biomass following a seismic survey relative to biomass in the absence of a survey, from 0 (all dead) to 1 (no impact). We report results on four regions relevant to management and of varying size. Results: Simulations that included ocean circulation showed that the impact of the seismic survey on zooplankton biomass was greatest in the Survey Region (0.78, i.e., 22% of the zooplankton biomass was removed) and declines moving to the Survey Region + 15 km (0.86), and the Survey Region + 150 km regions (0.98, see Table for values); there was no discernible effect on the entire Northwest Shelf Bioregion. The time to recovery (to 95% of the original level) for the Survey Region and Survey Region + 15 km recovery was 39 days (38-42 days) after the start of the survey and 3 days (2-6 days) after the end of the survey. Simulations with no ocean circulation showed a much greater impact of the seismic survey on relative zooplankton biomass: 0.65 for the Survey Region; 0.78 for the Survey Region + 15 km; 0.97 (0.97-0.97) for the Survey Region + 150 km; and no discernible effect on the entire Northwest Shelf Bioregion. The time to recovery for the Survey Region from the start of the survey was 64 days (49-100 days) and from the end of the survey was 26 days. Discussion: Applying the mortality rate from McCauley et al. (2017), we found considerable impact within the seismic survey area and within 15 km of it. However, these impacts are not discernible at the largest scale of the Northwest Shelf Bioregion and are barely discernible within 150 km of the survey area. Zooplankton populations recovered quickly after seismic exposure due to their fast growth rates, and the dispersal and mixing of zooplankton from both inside and outside of the impacted region. Finally, we make suggestions about how future studies could be designed and optimized using tools developed in the current study – to test the findings of McCauley et al. (2017).less
Coastal Development & Mgt - IP Costs - Potential impacts on zooplankton of seismic surveys - Published 20 Jul 2017
This collection provides PIRI as a software tool to assess environmental impact of pesticides, especially via their impact on water quality, and provide safer choices of pesticides products.
The soft... moreware has been written in Tcl/Tk but is compiled to make it a platform free application and therefore does not require any particular program to run it.
The downloadable version of the software package is a zipped package that includes an executable file, together with a data folder with input data files. A tutorial file is also included.
A fact sheet on the application of PIRI is available under "Supporting Materials" and two research papers describing the science underpinning the PIRI risk indicator and a report on its cost: benefit analysis are available under "Related Materials".
Please note there are two versions of the software: English and Spanish. The data is located in two separate folders.
Please register your details using the form (in English/Spanish) available under "Related Materials" before downloading the data.
Legacy data - Minimising environmental impact of pesticides - Published 06 Feb 2017
Amicus is a multi-platform computer application developed by CSIRO that enables the easy calculation of expected fire behaviour from burning conditions that you enter. It synthesises our current knowl... moreedge for predicting the behaviour and spread of bushfires in a range of vegetation types as well as providing simple calculations of expected fire danger.
This is a beta release for public evaluation.less
Natural Hazards and Infrastruture - Amicus beta release - Published 20 Dec 2016
This file contains the monthly Global Mean Sea Level (GMSL) time series as described in Church and White (2011).
3.3 ACCSP-SL change, storm surge - ACCSP - Published 13 Dec 2016
This file contains the monthly Global Mean Sea Level (GMSL) time series as shown on figure 2 of Church and White (2006). The sea level was reconstructed as described in Church et al (2004).
3.3 ACCSP-SL change, storm surge - details - Published 13 Dec 2016
16S rRNA gene amplification and sequencing were performed to assess the effects of uranium on sediment microbial communities. A total of 12 treatments along a uranium concentration gradient of 0 - 4,0... more00 mg/kg were interrogated, with four replicates per treatment. Data here include: raw MiSeq sequences (paired-end 250 base reads) as well as a mapping file for demultiplexing and a fasta file containing sequences of OTUs not deposited at DDBJ/ENA/GenBank (due to poor sequence similarity with existing entries). less
Estuarine Health Assessment - Next generation sequencing - Published 13 May 2016
A global map of 5 land use types at 30s (approx. 1km) resolution for 2005. The data set was generated through the statistical downscaling of the Land-use Harmonisation data set (Hurt et al 2011) at ht... moretp://luh.umd.edu/. Five land use types (primary, secondary, pasture, crop, urban) are provided as separate raster layers, with the value of each cell representing the proportion of the grid cell occupied by that land use type. An additional layer representing cells defined as permanent ice (value of 1) is also provided.less
1173.3 Strat: WPC Global Prot Area Ass - Land-use downscaling - Published 04 Apr 2016
The data set comprises trace element concentration data obtained through a series of leach tests applied to a set of eight Australian coals samples. Raw data (triplicate measurements) for the leach te... morests are reported. The leach tests were carried using the following extractants: dilute hydrochloric acid, pH 5 buffer with citrate added, pH 7 buffer and pH 7 buffer with citrate added.less
NICNAS Collaboration-Environ fate - Leach tests - Published 17 Oct 2015
eDNA metabarcoding was performed to assess the effects of copper on the establishment of the benthic eukaryote community within a series of environmentally relevant copper contaminated fresh... morewater mesocosms. The data is obtained from a recolonization experiment contained five treatments : control (C) and four sediment copper concentrations (very low (VL), low (L), high (H) and very high (VH)), each with four replicates. The data represents samples obtained on seven occasions subsequent to the experiment being established: 15, 31, 135, 161, 365, 407 and 497 days.
Details on the experimental design are available in:
Gardham, S., G. C. Hose, S. L. Simpson, C. Jarolimek, and A. A. Chariton. 2014. Long-term copper partitioning of metal-spiked sediments used in outdoor mesocosms. Environmental Science and Pollution Research 21:7130-7139.
1177.2 Estuarine Health Assessment - Experiment - Published 29 Sep 2014
125 samples of benthic eukaryotic communities from SE Queensland. Samples were collected from the Noosa, Maroochydore, Pine, Logan and Currumbin estuaries in February 2010. Five sites per estuary, wit... moreh 5 samples per site.
18S rRNA using the following primers:
All18SF-TGGTGCATGGCCGTTCTTAGT and All18SR-CATCTAAGGGCATCACAGACC
1177.2 Estuarine Health Assessment - Experiment - Published 22 Jan 2014
This dataset depicts a national map of available ASS mapping and ASS qualification inferred from surrogate datasets. ASS mapping is classified with a nationally consistent legend that includes risk as... moresessment criteria and correlations between Australian and International Soil Classification Systems.
Existing digital datasets of ASS mapping have been sourced from each coastal state and territory and combined into a single national dataset. Original state classifications have been translated to a common national classification system by the respective creators of the original data and other experts. This component of the Atlas is referred to as the “Coastal” ASS mapping. The remainder of Australia beyond the extent of state ASS mapping has been “backfilled” with a provisional ASS classification inferred from national and state soils, hydrography and landscape coverages. This component is referred to as the “Inland” ASS mapping.
For the state Coastal ASS mapping, the mapping scale of source data ranges from 1:10K aerial photography in SA to 1:250K vegetation mapping in WA and NT, with most East coast mapping being at the 1:100K scale. For the backfilled inferred Inland ASS mapping the base scale is 1:2.5 million (except Tas.) overlaid with 1:250k hydography. As at 06/08, the Tasmanian inland mapping has been re-modelled using superior soil classification map derived from 1:100k landscape unit mapping.
NOTE: This is composite data layer sourced from best available data with polygons depicted at varying scales and classified with varying levels of confidence. Great care must be taken when interpreting this map and particular attention paid to the “map scale” and confidence rating of a given polygon. It is stressed that polygons rated with Confidence = 4 are provisional classifications inferred from surrogate data with no on ground verification. Also some fields contain a “-“, denoting that a qualification was not able to be made, usually because a necessary component of source mapping coverage did not extend to the given polygon.less
Legacy data - Australian Soil Resource Information System - Published 28 Feb 2013