The ClimateIndex™ Report Limitations

This report has been prepared with the assumption that the site is in residential use and that no significant (re)development is planned.

Flooding

Data suppliers:

Floodmaps

We have sourced and used the Australia FloodMap™ modelled flood surface datasets from Ambiental Risk Analytics. Flood depths and extent are modelled for various return periods and climate change scenarios. The supplier retains copyright over the material. 

Areas benefiting from flood defences

We sourced data on areas benefiting from flood defences from Ambiental Risk Analytics. The data shows the extent and degree of protection offered by river and tidal flood defences where they exist. The supplier retains copyright over the material.

Floodscore

We sourced the Australia FloodScore™ property-level flood scores from Ambiental Risk Analytics. Designed to support insurers and brokers, we utilise this to make assessments of insurance risk to each property. The supplier retains copyright over the material.

Limitations: 

FloodScore

Ambiental’s FloodScore™ risk rating gives an indicative assessment of the potential insurance risk classification from flooding. The assessment is based on Ambiental’s river, tidal and surface water flood data and other factors which some insurers may use in their assessment are not included. 

The Ambiental FloodScore™ insurance rating is classified into six different bandings: 

• Very Low indicates a level of flood risk that should not have any impact on the provision of flood cover for the property.
• Low indicates a level of risk that is likely to mean standard cover and premiums are available for flood cover.
• Moderate indicates a level of risk that may make it more likely that standard insurance premiums will be higher, or additional terms may apply to the provision of flood cover.
• Moderate-High indicates a level of risk that may make it more likely that standard insurance premiums will be higher, or additional terms may apply to the provision of flood cover.
• High indicates a level of risk that may make it more likely that standard insurance premiums will be higher, or additional terms may apply to the provision of flood cover.
• Very High indicates a level of risk that may make it more likely that standard insurance premiums will be higher, or additional terms may apply to the provision of flood cover.

Flood risk – today

The flood risk assessment section is based on datasets covering river, surface water and tidal flooding types. Other types of flooding are not considered as part of this assessment. No inspection of the property or the surrounding area has been undertaken by Groundsure or the data providers. The modelling of flood hazards is extremely complex and in creating a statewide dataset certain assumptions have been made and all such datasets will have limitations. These datasets should be used to give an indication of relative flood risk rather than a definitive answer. Local actions and minor variations, such as blocked drains or streams etc. can greatly alter the effect of flooding. A low or negligible modelled flood risk does not guarantee that flooding will not occur. Nor will a high risk mean that flooding definitely will occur. 

Our overall flood risk assessment takes account of the cumulative risk of river, surface water and coastal flood hazard mapping provided by Ambiental Risk Analytics. The flood hazard maps were used to identify areas likely to flood by simulating 1 in 20 year (5% annual chance), 1 in 100 year (1% annual chance) and 1 in 500 year (0.2% annual chance) flood events. The flood risks for these rainfall events are reported where the depth would be greater than 0.2m. 

Flood risk – climate

Ambiental’s Australia Floodmap (Climate) data provides flood risk information from river, tidal and surface water flooding for the high emission RCP 8.5 climate scenario. The models are based on the CORDEX Region 9: Australia climate projections. CORDEX (Coordinated Regional Downscaling EXperiment) is a World Climate Research Programme (WCRP) framework. It is generally considered that climate change will increase the severity and frequency of flood events in the future for most locations.  Flood hazard maps using the forecasted climatic conditions were then used to identify areas likely to flood by simulating 1 in 20 year (5% annual chance), 1 in 100 year (1% annual chance) and 1 in 500 year (0.2% annual chance) flood events. The flood risks for these rainfall events are reported where the depth would be greater than 0.2m. 

Bushfires

Data suppliers:

NSW Rural Fire Service 

• Bushfire Prone Land

The NSW Bushfire Prone Land data has been sourced from the NSW Rural Fire Service. This dataset identifies areas within New South Wales that are prone to bushfires based on established criteria, including vegetation vulnerability and effective slope of the land. The dataset is released with three categories of vulnerability and defines the extent of the bushfire prone areas. The data is made available for use under the Creative Commons Licence. Copyright is retained by the supplier. 

In reference to this data the NSW Rural Fire Service state: 

“Bush Fire Prone Land mapping is intended to designate areas of the State that are considered to be higher bush fire risk for development control purposes. Not being designated bush fire prone is not a guarantee that losses from bush fires will not occur. Changes to the landscape may occur from time to time and therefore the certified bush fire prone land maps may not be a true indication of bush fire risk.”

Department of Planning and Environment

• Fire Extent and Severity Mapping

The NSW Fire Extent and Severity Mapping (FESM) data has been sourced from the NSW Department of Planning and Environment and comprises a semi-automatic classification and assessment of areas affected by bushfire for a given fire season. The dataset delineates the boundaries of the burn scar and evaluates the severity of the fire’s impact on vegetation and ecosystems. The data is made available for use under the Creative Commons Licence. Copyright is retained by the supplier. 

• Fire History

The National Parks and Wildlife Service (NPWS) NSW Fire History data has been sourced from the NSW Department of Planning and Environment and comprises a record of historic wildfires and prescribed burns in NSW for over 100 years. The data is made available for use under the Creative Commons Licence. Copyright is retained by the supplier. 

CSIRO – Commonwealth Scientific and Industrial Research Organisation

• Decadal Forest Fire Danger Index (FFDI) (2006-2096)

CSIRO scientists modelled and produced FFDI data for every decade up until 2096. We use the 2026 (today) and 2056 (30 year) modelled data in our assessments, and apply the A1FI SRES scenario. This FFDI data is used as our indicator of fire weather conditions, both with a view today and in 30 years, to inform our climate projection assessment. We use the same modelled parameters for our today and 30 year assessment. The data is made available for use under the Creative Commons Licence. Copyright is retained by the supplier (Leonard, Justin; Opie, Kimberley; Wang, Chi-Hsiang (2016): Decadal Forest Fire Danger Index (2006-2096). v3. CSIRO. Data Collection. https://doi.org/10.25919/5f2b593653ad6).

NASA

• Lightning

NASA and the Global Hydrometeorology Resource Centre (GHRC) release gridded climatologies of lightning flash rates, which contain mean annual flash rate per year data (Cecil, Daniel J.2015. LIS/OTD 0.5 Degree High-Resolution Full Climatology (HRFC) [HRFC_COM_FR]. Dataset available online from the NASA Global Hydrometeorology Resource Center DAAC, Huntsville, Alabama, U.S.A. DOI: http://dx.doi.org/10.5067/LIS/LIS-OTD/DATA302). These datasets are made available by agreement from the supplier. Copyright is retained by the supplier.

Geoscience Australia

• Elevation

A Digital Elevation Model provided by Geoscience Australia and produced from Shuttle Radar Topography Mission (SRTM) satellite data is supplied under the Creative Commons Licence. Copyright is retained by the supplier (Gallant, J., Wilson, N., Dowling, T., Read, A., Inskeep, C. 2011. SRTM-derived 1 Second Digital Elevation Models Version 1.0. Record 1. Geoscience Australia, Canberra. https://pid.geoscience.gov.au/dataset/ga/72759).

• Urban Landscapes

Areas designated as urban (a concentration of buildings surrounding a network of roads and supported by other associated infrastructure) are supplied by Geoscience Australia under the Creative Commons Licence. Copyright is retained by the supplier. 

Limitations: 

The bushfire risk assessment section is primarily based on datasets identifying potential areas of land that are susceptible to fire events occurring with an assessment based on the proximity of those areas from the property and supplemented with ancillary datasets.  

No inspection of the property or the surrounding area has been undertaken by Groundsure or the data providers. 

The modelling of bushfire hazards is exceptionally complex and in creating a statewide dataset certain assumptions have been made and all such datasets will have limitations. The key aspects of bushfire dynamics and spread that could not be considered. These include (but are not limited to) local fire management activities (e.g. maintenance of land, prescribed burns, suppression by emergency services etc, implementation of the mitigation measures within the buildings, urban density, topography, localised climate variables etc).

The datasets used should be used to give an indication of relative bushfire risk rather than a definitive answer. A low-assessed bushfire risk does not guarantee that an event will not occur that could impact the property. Nor will a higher risk mean that a bushfire that impacts the property definitely will occur. 

It is important to note that the bushfire risk assessment report based on proximity to bushfire prone land has its own innate limitations. One significant limitation is that it primarily focuses on the proximity of the property to bushfire prone areas, without taking into account the specific characteristics of the property itself. The report does not consider factors such as construction materials, fire-resistant landscaping, or the presence of adequate fire suppression systems, which can greatly influence the property’s vulnerability to bushfire.

This report’s reliance on statewide datasets also means that it cannot always reliably account for fine-resolution variations in topography, urban density, and localised climate variables. These factors can play a crucial role in fire behaviour and the potential spread of a bushfire. Therefore, while the assessment indicates relative bushfire risk, it should not be considered as a definitive answer or a guarantee of safety.

It is essential for property owners and occupants to exercise caution and implement appropriate mitigation measures, even if the assessed bushfire risk is low. Similarly, a high-risk assessment does not guarantee that a bushfire event will definitely occur, but it emphasises the need for heightened preparedness and proactive measures to minimise the potential impact on the property and its occupants.

Our bushfire climate risk assessment is principally based on changes in potential weather deemed to influence fire probability and intensity, specifically modelled Forest Fire Danger Index (FFDI) data. This data indicates if there is a predicted increase or decrease in the potential for bushfires as a result of climate change. With modelled changes to climate variables (e.g. temperature, rainfall, wind speeds, humidity, etc.), there is a possibility that bushfires will occur more frequently, and their intensity may increase. This data has been produced using the modelled change in maximum potential Forest Fire Danger Index (FFDI) in a given year produced by CSIRO scientists, specifically based on the A1FI scenario of the Special Report on Emissions Scenarios (SRES). The A1FI scenario represents a future emissions pathway with high greenhouse gas concentrations. It provides an indicator of how the likelihood of weather conditions that could lead to bushfire events could change in the future. However, it’s important to note that predicting future bushfire behaviour involves numerous variables and uncontrollable events, including land management practices, land clearance, new construction, and infrastructure development. While the data indicates the likely changes due to climate change, it does not guarantee the manifestation or absence of these changes.

Coastal erosion

Data suppliers:

Geoscience Australia

• Smartlines

The Australia Smartline data, a consistent geomorphological classification of Australia’s coastal zone, has been sourced from Geoscience Australia under the Creative Commons Licence. Copyright is retained by the supplier. 

Digital Earth Australia

• DEA Australia coastlines

The DEA Coastlines and data have been sourced from Digital Earth Australia under the Creative Commons Licence. Copyright is retained by the supplier. 

Limitations: 

The coastal erosion assessment section is based on historic erosion rates obtained from the DEA (Digital Earth Australia) erosion rate dataset and local geomorphology information derived from the Australia Smartlines dataset. It is important to note that no physical inspection of the property or its surrounding area has been conducted by Groundsure or the data providers.

Assessing coastal erosion is a highly complex task, and the creation of a comprehensive statewide dataset involves making certain assumptions and acknowledging inherent limitations. Several crucial aspects of coastal erosion dynamics could not be fully considered, including local factors and actions that may influence erosion processes. These factors encompass the maintenance of coastal land, implementation of erosion control measures, human interventions such as seawalls or breakwaters, as well as the influence of natural forces like storms and tides.

The datasets used for the assessment should be utilised to provide an indication of relative coastal erosion risk rather than offering a definitive answer. A low-assessed erosion risk does not guarantee that erosion will not occur in a way that may affect the property. Similarly, a highly assessed risk does not necessarily mean that erosion leading to property impact will definitely occur.

Furthermore, it is important to recognize that coastal erosion is influenced by a multitude of variables, including but not limited to sea-level rise, sediment availability, climate change, and storm events. These factors can introduce significant uncertainty into erosion predictions and may result in unforeseen changes in erosion patterns over time.

Property owners, developers, and relevant stakeholders must consider these limitations when utilising the coastal erosion assessment. Additional local studies and site-specific evaluations may be necessary to complement the dataset information and provide a more accurate understanding of the specific erosion risks and potential impacts on a property.

The specific datasets used have their inherent limitations, which we outline in more detail below. 

Smartlines

As with any dataset produced on this scale, Smartlines is not without some inherent limitations. These include:

• Spatial resolution: 
  • The dataset’s spatial resolution may vary depending on the available data sources used to create the Smartlines. In some cases, the resolution may not be detailed enough to capture small-scale coastal features accurately.
• Date and coastline position: 
  • Similar to the above point, the coastline position in the Smartlines dataset is not always accurate. This is partly due to the collection method but also the date of data production (2009). 
  • In some locations where erosion and progradation are high, the coastline has moved considerably. 
• Data availability: 
  • The availability of comprehensive and up-to-date data for all coastal areas in Australia may pose a limitation. The dataset’s coverage may vary, and there could be gaps or areas where data collection has not been conducted or integrated into the Smartlines.
• Interpretation and classification: 
  • The classification of coastal landforms in the Smartlines dataset relies on certain assumptions and interpretations. While efforts are made to apply consistent classification principles, there may still be subjective judgments and generalisations involved, leading to potential inconsistencies or misinterpretations in some areas. 

DEA

Coastlines are based on the annual mean sea level, which may not show the influence of intense storm events on the coastline. Also, the coastal environment can change rapidly (e.g. in the vicinity of rivers), the contours can show quite messy and convoluted results over time and representative results can not be calculated.  

In a peer-reviewed article on behalf of Geoscience Australia, it was suggested that if using an extrapolation of DEA Coastline predicting future shoreline trajectories, it should be done so with caution. The article advised that a rapidly moving tidal bar may be perceived by the points as inland erosion when coastline movement can be lateral as well as perpendicular to the coast.  

Other limitations that should be considered include:

• Temporal resolution: 
  • The availability and updating frequency of the Coastlines data may vary, and there could be a lag between the actual changes in coastal features and the incorporation of those changes into the dataset. This limitation can affect the accuracy and currency of the information, especially in dynamic coastal environments.
• Scale and detail: 
  • It may not capture fine-scale coastal features or variations in geomorphology, particularly in areas with complex or small-scale coastal landforms. This can impact the precision and specificity of the dataset for certain applications.
• Data uncertainty: 
  • Like any geospatial dataset, the Coastlines data may contain inherent uncertainties and errors. These could arise from data collection methods, sensor limitations, processing techniques, or other factors. Understanding the potential uncertainty of the data is crucial for appropriate interpretation and decision-making.
• Data gaps: 
  • Despite efforts to provide comprehensive coverage, there may be gaps in the Coastlines data due to limitations in data availability or data acquisition challenges in certain regions. These gaps may limit the dataset’s usefulness for specific areas or research questions.
• Coastline position:
  • The mean high average sea level used may not represent the coastline that is formed under extreme cases such as intense storms etc.