Insights
When climate changes faster than infrastructure, what makes the difference is not the ability to react to an event, but the ability to understand its possible consequences in advance.
If Climate Risk Assessment defines the process through which hazards, exposure and vulnerability are analysed, the Climate Risk Model is the analytical engine that makes it possible to estimate the probability of climate events, the intensity of phenomena, asset fragility, economic and functional impacts, and intervention priorities.
Its goal is not to describe risk in theoretical terms, but to make it measurable and usable in decision-making.
In this article, we clarify:
A Climate Risk Model is an analytical model designed to estimate physical climate risk on assets, infrastructure, networks, operational processes and business systems. It combines climate, geospatial, physical, operational and economic data to transform complex information into indicators that can support decision-making.
In a Climate Risk Assessment, three fundamental dimensions are analysed: hazard, exposure and vulnerability.
The Climate Risk Model integrates these components and translates them into quantitative or semi-quantitative assessments of expected impacts.
| Component | Function in the model | Example |
|---|---|---|
| Hazard | Identifies the climate threat | Flood |
| Exposure | Assesses where the asset is located | Facility near a flood-prone area |
| Vulnerability | Measures the asset’s fragility | Insufficient protection |
| Impact | Estimates the potential effect | Downtime, physical damage, cost increase |
The goal is not to forecast the weather or determine with absolute certainty what will happen in the future. Rather, the model is used to estimate plausible risk scenarios and support concrete decisions on where to intervene, with what priority and with what level of investment.
The value of a model is not measured by its ability to eliminate uncertainty, but by its ability to make it readable and manageable.
In this way, climate risk stops being an isolated piece of information and becomes an element that can be integrated into business processes and adaptation strategies.
The two tools are different but complementary, because they answer different questions:
Climate Change Model → “How is the climate changing?”
Climate Risk Model → “What impact could this change have on my assets?”
Climate Change Models simulate the evolution of the climate system over time. They analyse variables such as:
These models help us understand how the climate could evolve over the coming decades based on different emissions scenarios.
Climate Risk Models add further layers of analysis to the variables of Climate Change Models, connected to the assets and activities that need to be protected:
Let us imagine an industrial facility located near a river in an area that has recorded increasingly frequent flood events over recent decades.
A Climate Change Model can show that, over the next thirty years, the intensity and frequency of extreme rainfall will increase in that territory, making flooding events more likely.
A Climate Risk Model uses this information to assess how that change could affect the facility. It can estimate the probability of flooding in production areas, potential damage to equipment, expected days of downtime and associated economic impacts, helping to define intervention priorities and adaptation investments.
| Aspect | Climate Change Model | Climate Risk Model |
|---|---|---|
| Objective | Simulate climate evolution | Estimate impacts on assets and operations |
| Main question | How is the climate changing? | Which assets are most at risk? |
| Output | Climate scenarios | Risk scores, expected damage, priorities |
| Main data | Climate variables | Climate, assets, vulnerability and costs |
| Use | Research and scenario planning | Operational decisions and investments |
Two apparently similar assets can present very different levels of risk depending on their geographical location, elevation, proximity to rivers, coastlines or landslide-prone areas, construction characteristics, maintenance level, operational redundancy, dependencies on external networks and their criticality for operational continuity.
A Climate Risk Model helps understand how risk can translate into concrete consequences for assets and which interventions can reduce impacts over time.
It makes it possible to:
It is through the ability to connect climate scenarios, vulnerability and possible consequences that risk becomes a concrete variable supporting decisions.
An accurate model can establish that:
| Asset | Relevant climate risk | Decision supported by the model |
|---|---|---|
| Electrical substation | Flood, extreme wind | Physical protection and continuity priorities |
| Railway line | Extreme heat, landslides, heavy rainfall | Predictive maintenance and adaptation |
| Logistics hub | Extreme rainfall, heat stress | Operational continuity and downtime reduction |
| Water network | Drought, landslides, floods | Investments in resilience and redundancy |
| Industrial facility | Wildfires, wind, extreme heat | Protection strategies and production continuity |
Probabilistic climate risk models are currently one of the most advanced approaches to physical risk assessment.
Unlike deterministic models, they do not return a single absolute value, but produce a distribution of possible scenarios, assigning each one a probability of occurrence.
This approach is particularly useful because climate risk inevitably contains margins of uncertainty.
This type of model can estimate:
Among the most commonly used outputs are:
When risk is translated into clear information, it becomes easier to assess the available options and guide decisions.
Those who manage assets and infrastructure can:
The quality of a model depends directly on the quality of the data that feeds it.
The more accurate, granular and up-to-date the data, the more reliable the estimates produced.
In Climate Risk Assessment, geospatial data already represents a fundamental component. Within a Climate Risk Model, it becomes one of the central elements for scenario simulation and impact quantification.
The main categories of input include:
Climate data
Geospatial data
Asset data
Operational and economic data
| Data category | Examples | Why it is useful |
|---|---|---|
| Climate | Rainfall, temperatures, wind | Defines the hazard |
| Geospatial | Coordinates, elevation | Measures exposure |
| Asset | Materials, age, design | Assesses vulnerability |
| Economic and operational | CAPEX, OPEX, downtime | Quantifies impact |
Risk model validation is what makes it possible to distinguish a reliable result from an output that is difficult to interpret or use in decision-making.
For this reason, every Climate Risk Model must undergo continuous checks that assess its robustness, consistency and ability to represent risk realistically.
This requires analysis conducted on multiple levels:
Validation is not only used to measure the accuracy of simulations. It is also used to understand how the model reacts to changes in initial conditions and how robust its estimates are across different contexts.
For this reason, a model should not be assessed solely on the basis of its technical complexity. Equally important are:
One of the most common mistakes is relying on a numerical output without understanding the assumptions, limitations and margins of uncertainty that influence the result.
The most advanced best practices do not simply return a numerical result. Each output is accompanied by methodological explanations, confidence levels and alternative scenarios that help interpret the risk correctly and use it in decisions.
A Climate Risk Model creates value only when it becomes part of the decision-making process. It is not a tool to be consulted occasionally, but an information base that supports operational, strategic and investment decisions.
Integrating models into business processes helps improve activities such as:
For this to happen, the model must be embedded in a continuous process that accompanies the entire asset management cycle.
The integration of the model into business processes develops through several key activities:
The model should not be considered a one-off analysis. To continue supporting effective decisions, it must evolve together with assets, operating conditions and climate scenarios, becoming a living tool capable of accompanying the organisation over time.
Every resilient decision begins with the ability to connect what could happen with what can be done today.
Airis is the platform developed by Eoliann to transform complex climate data into operational guidance.
By integrating climate, geospatial and asset-level data, it enables the analysis of physical risk on infrastructure and strategic assets, observing its evolution across different climate scenarios.
Through maps, indicators and high-resolution models, Airis makes it possible to:
In this way, climate risk becomes usable information to define priorities, guide investments and prepare assets and infrastructure for future conditions.
Discover how to turn climate risk into operational decisions: book a personalised Airis demo!
A Climate Risk Model uses different categories of data, including historical and future climate data, geospatial information, physical characteristics of assets, vulnerability indicators, operating costs, CAPEX, OPEX and information on the asset’s criticality for the business or for operational continuity.
Climate risk is characterised by an intrinsic level of uncertainty. Probabilistic models make it possible to assess multiple possible scenarios, estimate probabilities of occurrence and quantify different levels of impact, providing a more robust basis for adaptation and investment decisions.
A Climate Risk Model helps identify where to invest to reduce future vulnerabilities and where to intervene to contain operating costs, extraordinary maintenance, downtime and inefficiencies. In this way, it supports more effective planning for both infrastructure investments and long-term operational management.
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