> For the complete documentation index, see [llms.txt](https://docs.alphageo.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.alphageo.ai/climate-resilience-suite/data-products-faq.md).
# FAQ: Climate Resilience Suite
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{% tab title="Climate Risk and Resilience Index (CRRI)" %} **Overall**
**Are there plans to develop risk analytics for other climate hazards other than the current hazards?**\
CRRI currently covers nine acute and chronic hazards, including hail, landslide, and earthquake. We continue to evaluate additional hazards as suitable data and methods become available.
***
**Data sources**
**What are your primary data sources?**\
Our CRRI integrates data from 85 unique sources, encompassing satellite observations, climate model projections, historical weather records, and socio-economic datasets.
Notable sources include CMIP6 climate projections, Aqueduct 4.0, Copernicus, IPCC reports, CHAZ/CLIMADA tropical-cyclone wind, OpenStreetMap, WorldPop, and the World Bank.
**Why do you choose to use open-source data?**\
Utilizing open-source data ensures transparency, reproducibility, and broad accessibility. It allows AlphaGeo to build upon peer-reviewed and well-documented datasets, fostering trust and enabling users to understand and verify the underlying data and methodologies.
**What is the value you provide, if underlying data is open-source?**\
AlphaGeo adds value through rigorous data curation, advanced geospatial data processing and engineering, and the calculation of resilience-adjusted risk scores for actionable insights that go beyond raw data.
Learn more about how we transform raw adaptation data into standardized, actionable insight here: [Global Adaptation Layer](/global-adaptation-layer/methodology-2-3-global-adaptation-layer.md)
**What is the global coverage of your data?**\
AlphaGeo's datasets offer global coverage, enabling assessments of climate risk and resilience for any location worldwide.
**What is the data resolution?**\
Resolution depends on the source and feature. Each layer is used at an appropriate supported resolution. For details, see our [Data Dictionary](/for-developers/data-dictionary.md).
**What GCMs are used for future scenarios? How are they downscaled?**
We use NASA's NEX-GDDP-CMIP6 v2.0 ensemble, including models such as CanESM5 and MRI-ESM1. NASA supplies the peer-reviewed downscaled climate product; AlphaGeo does not perform additional in-house downscaling of that climate signal.
**The native resolution of some data sources are far coarser than the building/parcel level resolution of your analytics. How have you downscaled this data?**
Each layer is read at the governed resolution its prepared source supports. We do not treat indexing onto a finer grid as evidence of finer source precision. Address-level differentiation comes from combining layers at different resolutions: for example, flood-inundation depth, landslide probability, and adaptation inputs can distinguish locations more finely than the shared climate signal, while asset type and the Remediation Checklist add building-specific context.
**Is the resolution of your features uniform across all global locations? Specifically, how granular is your coverage for developing countries or rural zones? Does this differ depending on risk versus adaptation features?**
No. Resolution varies by feature because it follows the prepared source, while the same governed resolution is used consistently for that feature across locations. Coverage still depends on the underlying source inventory, particularly for local adaptation infrastructure.
**How frequent are data updates?**\
Quarterly.
**Do you have historical or current risk data?**
Yes, we have historic, current and future climate data from 1975-2100.
**Does your data incorporate historical weather events, i.e., incidents? Does it include the severity of the weather event and how is that measured?**
Historical observations can support validation and methodology provenance, but are not automatically scoring inputs. Hurricane Wind scoring uses CHAZ/CLIMADA modelled return-period wind speeds rather than observed storm tracks.
**How is AlphaGeo's data different from what FEMA provides?**
We have more granular data, and cover multiple SSP scenarios and time periods:
1. AlphaGeo's data is more granular. Our data is provided at the building/parcel-level, compared to FEMA's neighbourhood or block-level data.
2. AlphaGeo covers 3 emissions scenarios (SSP245, SSP370, SSP585) and 4 time periods (2025, 2035, 2050, 2100). FEMA focuses on current and historical flood risk based on observed data and past events. They do not project future flood risk under climate scenarios, and are less suitable for scenario analysis or forecasting.
**How do flood models account for precipitation?**
Our scores are based on rainfall intensity and flood inundation levels. For example, compared to Singapore, Bangkok receives approximately half the amount of precipitation and has fewer days with extreme precipitation, leading to lower levels of inundation due to rainfall.
***
**Methodology**
**What framework or methodology do you use to select and group risk features for each hazard?**
Features are grouped based on their correlation to key components of climate risk:
* **Exposure**: The degree to which a location is exposed to climate-related hazards.
* **Frequency**: How often a location might experience these hazards.
* **Intensity**: The severity of the impacts when these hazards occur.
**What is your hazard forecasting methodology?**
Drawing from risk engineering practices, CRRI applies feature- and hazard-specific damage curves to estimate impact. Feature impacts are then aggregated by hazard.
**How do you define, or where do you obtain, the damage functions for each hazard, and its change over time/scenarios?**
We define the range, threshold, and type of damage functions for each type of hazard through existing scientific literature and domain experience.
**Is the change in MDR modelled at the hazard (e.g., coastal flooding) or feature level (e.g., mean sea level rise)?**
Feature level. Each feature has a corresponding damage function.
**How are the resilience-adjusted risk scores calculated?**\
We combine local adaptation capacity and societal resilience, then apply a hazard-specific adjustment to Physical Risk Impact. Asset-level remediation is included when checklist data is available.
**How do you determine the degree of adaptation offsets? What thresholds are used, and is this based on any established method?**
The Resilience Score does not subtract a fixed number of points from Physical Risk. It attenuates the Physical Risk Impact by a proportion of itself.
For each hazard, local adaptation capacity and societal resilience contribute equally to a normalized combined-resilience value. A hazard-specific scaling factor then controls the maximum share of Physical Risk Impact that resilience can reduce. Flood, wildfire, drought, and hurricane wind are more mitigable than hail, landslide, or earthquake, so their maximum reductions differ. Available asset-level remediation data can provide an additional reduction after this combined-resilience adjustment.
The framework is described in [Resilience-adjusted Risk with Triple-layer Adaptation Offset](/methodology/access-to-our-methodology-docs.md). The specific per-hazard scaling factors are available on request.
**Are your methods peer-reviewed? How can we trust your methodology?**
AlphaGeo's analytics are built upon the evolving peer-reviewed data sources and literature. With the open access nature of our methodology and documentation, we invite the scrutiny and feedback from all experts in the field to continuously enhance our methods.
**What are the biases and limitations of your methodology?**
1. **Data Limitations**: Our methodology relies on the quality and quantity of available data. Insufficient or biased data can impact the results.
2. **Assumptions**: The analysis is based on certain assumptions that may not hold true in every scenario.
3. **Model Bias**: The algorithms used may have inherent biases, affecting the objectivity of predictions or outputs.
4. **Static Framework**: The methodology may not account for immediate changes in the environment or context.
Understanding these limitations helps in interpreting results more accurately and applying improvements to future iterations.
**Do you provide confidence intervals?**
For Hurricane Wind, the platform presents the median of the CHAZ/CLIMADA ensemble. P5 and P95 values are governed source statistics and are available on request; together they describe the central 90% ensemble interval, not a 95% confidence interval. Other uncertainty information depends on the source and metric.
***
**Indexing and scoring**
**Key links:** [Indexing & Score Interpretation](/climate-resilience-suite/climate-risk-and-resilience-index/methodology-3-3-indexing.md)
**What do your scores represent, and how are they calculated?**
Our hazard-specific risk scores (e.g., Hurricane Risk) are absolute risk scores, while the overall scores (e.g., Overall Physical Climate Risk) are relative scores.
* **Hazard risk scores**: **Absolute risk profiles for any o**ne type of risk for any given scenario and time period, on a scale of 0-100. Each score is assigned based on thresholds determined for each risk feature. The risk category thresholds are based on the Mean Damage Ratio (MDR) for the respective features.
* **Overall scores**: Global percentile scores that measure overall risk profile of a location relative to other locations, allowing users to compare across space.
Details are available at our scorecard here: [Indexing & Score Interpretation](/climate-resilience-suite/climate-risk-and-resilience-index/methodology-3-3-indexing.md)
**Do the overall risk scores change if a different benchmark is selected? Or is it always relative to a global distribution?**
No, the overall risk score are percentile scores on a global distribution. The benchmark helps the user to compare the performance of the selected location to a narrower geographic region (i.e. country or city).
**What are the implications in your CRRI scorecard based on?**\
The implications are based on the expected damages caused by the hazard for each risk category. This is based on industry standards and the thresholds in the damage functions
**How are features benchmarked?**
All features are benchmarked globally, i.e., the indicators are scored based on datasets that are globally available. For example, FEMA Flood Zone data is not used in scoring Singapore or the US. We continue to include these datasets, however, for users who wish to pull these engineered risk features to create their own models.
**How are "regional averages" calculated?**
We calculate the regional average by aggregating data from the sub-provincial level. In most countries, this means the data is aggregated at the city/county/municipality level. In the case of Singapore, for example, since it's a city, the sub-provincial level will be the five regions. When you access data for a property in Singapore, regional-level data aggregates data in those regions. If the access point is Asia Square, then the sub-provincial level is the average of the Central Region of Singapore.
***
**Scenarios**
**Which scenarios do you support, and why?**
We support three emissions scenarios: SSP2-4.5, SSP3-7.0, SSP5-8.5. These are the CMIP6/IPCC AR6 scenarios.
***
**Regulatory alignment**
**Key links:** [Use Case: Regulatory Disclosures](/climate-resilience-suite/use-case-regulatory-disclosures.md)
**Are your hazards aligned to the EU taxonomy?**\
Yes, AlphaGeo's assessments align with the EU Taxonomy Regulation by identifying material physical climate risks and evaluating adaptation measures that substantially reduce these risks. The methodology incorporates best practices and guidance from the IPCC and other scientific bodies.
**How does your data help with regulatory disclosures?**\
AlphaGeo's Climate Risk and Resilience Index supports compliance with various regulatory frameworks, including the Task Force on Climate-Related Financial Disclosures (TCFD), International Sustainability Standards Board (ISSB) IFRS S2, EU Taxonomy Regulation, and the EU Corporate Sustainability Reporting Directive (CSRD). The platform provides standardized risk scores, adaptation assessments, and financial impact metrics, facilitating comprehensive and transparent climate-related disclosures.
***
If you have further questions, please feel free to reach out to us at ****.
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{% tab title="Financial Impact Analytics" %}
**Methodology**
**Key links:** [Financial Impact Analytics](/climate-resilience-suite/financial-impact-analytics.md)
**How is physical risk translated into financial impact?**
Each financial impact metric is developed with its own unique methodology. In general, we define a change in the risk profile of a location, and apply an impact function to that delta in risk. These damage functions are derived from empirical, academic research. Only relevant risks (e.g., fire and flooding for insurance cost impact) are modelled in related to each metric.
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