FLOOD RISK FROM REGIONAL HAZARD TO ASSET-LEVEL INTELLIGENCE

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Regional flood-risk datasets play an important role in understanding where flooding may occur and identifying areas potentially exposed to inundation. They provide a valuable starting point for climate risk assessments, infrastructure planning and resilience strategies. However, when decisions need to be made at the level of individual buildings, properties or infrastructure assets, regional datasets may not provide sufficient spatial detail to capture important local differences in flood exposure.

RADIOM bridges this gap by combining established flood hazard information with high-resolution terrain data to develop a more detailed, asset-oriented perspective on potential flood risk. By moving beyond the regional scale, the analysis reveals how exposure can vary across relatively short distances, even within areas identified as similarly exposed by conventional hazard datasets. This provides a more refined understanding of the relationship between flood hazards, local topography and the built environment.

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FROM BROAD PATTERNS TO LOCAL INSIGHT

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At regional scale, flood hazard maps provide a useful overview of potential inundation, highlighting areas where water may reach the landscape under a given flood scenario. Yet the conditions within these areas are rarely uniform. Differences in ground elevation, local depressions and the position of individual assets can influence the potential depth and distribution of floodwater.

RADIOM's high-resolution approach helps bring these local variations into focus. By combining flood hazard information with detailed terrain data, the analysis provides a more granular representation of potential inundation and its relationship to the surrounding environment. This makes it possible to explore how exposure may differ between neighbouring locations, rather than treating an entire hazard zone as if it presents the same conditions everywhere.

The comparison between the original regional dataset and RADIOM's enhanced analysis illustrates an important distinction: identifying an area as potentially exposed is not the same as understanding the potential hazard experienced at a specific location. Greater spatial detail helps bridge that gap, providing a stronger basis for investigating flood exposure at the scale where practical decisions are made.

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THE ROLE OF LiDAR TECHNOLOGY

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A key component of RADIOM model is LiDAR, or Light Detection and Ranging, a remote-sensing technology that uses laser pulses to measure distances to the Earth's surface. By collecting large numbers of measurements, LiDAR can generate highly detailed representations of terrain, capturing subtle changes in elevation across the landscape.

These measurements are used to create a Digital Terrain Model (DTM), which represents the underlying ground surface. Unlike a conventional topographic overview, a high-resolution DTM can reveal small elevation differences that may influence how water accumulates, moves across or drains from an area. This makes LiDAR particularly valuable for flood-related analysis, where relatively small changes in terrain can be relevant to local inundation patterns.

For this case study, LiDAR-derived terrain information provides the high-resolution spatial foundation for examining potential flood exposure. It adds an important layer of physical detail to the original hazard information, allowing the analysis to reflect local terrain variations that may not be visible at regional scale.

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BRINGING FLOOD ANALYSIS TO 50 CM RESOLUTION

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RADIOM combines regional flood-depth information with high-resolution LiDAR terrain data to produce a terrain-informed flood-depth estimate at 50 cm spatial resolution. At this resolution, each output grid cell represents an area of 0.25 square metres. This provides a much finer spatial representation than the original regional dataset and allows potential flood-depth variations to be examined in relation to local ground elevation.

A finer spatial representation makes it easier to distinguish between locations that may have meaningfully different terrain conditions within the same broader flood zone. This is particularly relevant in built environments, where individual properties, access routes and infrastructure assets can occupy locations with different elevation profiles.

The resulting analysis offers a more detailed perspective on potential inundation and creates opportunities for more precise spatial screening. It can help identify assets that warrant closer investigation, compare potential exposure between locations and support more informed assessments of physical climate risk.

The 50 cm resolution describes the spatial detail of the resulting estimate, rather than the resolution of the original flood hazard model. The analysis uses detailed terrain information to refine the spatial representation of potential flood depth; it should not be interpreted as an independently validated hydraulic simulation at 50 cm resolution.

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BUILT FOR ASSET-LEVEL DECISIONS

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For organisations managing properties, infrastructure or investment portfolios, understanding risk at regional scale is only the first step. Decisions about resilience measures, asset management and investment prioritisation often require a more specific understanding of which assets may be exposed and how their exposure could differ.

RADIOM's approach helps connect broad-scale physical hazard information with the locations and characteristics of individual assets. Instead of relying solely on a regional indication of flood exposure, organisations can use more detailed geospatial information to screen assets, investigate local conditions and identify where further assessment may be most valuable.

This creates a stronger link between environmental data and practical decision-making. It supports more targeted risk assessments, more informed prioritisation and a clearer understanding of potential exposure across a portfolio. The analysis can also provide a useful foundation for further work, including the assessment of building footprints, infrastructure networks and other spatially defined assets.

Ultimately, the objective is to make complex geospatial information more relevant to the decisions organisations need to take. By combining flood hazard data, LiDAR technology and high-resolution spatial analysis, RADIOM helps translate broad regional information into more detailed, actionable intelligence.

Regional data indicates where flood hazards may occur. RADIOM helps organisations understand what may be at risk, where exposure is concentrated and where a closer look matters most.