Cloud burst simulation at the end user - municipalities, county boards and private actors

With changing climates come altered weather patterns and extreme weather events, such as heavy rainfall, which lead to flooding in landscapes. Significant economic values are at stake in urban landscapes as well as in forest and agricultural landscapes.

We have developed a model and software that can simulate the path of water in both urban and rural areas, determining its speed and depth per square meter during and after heavy rainfall.

This marks a breakthrough in rainfall mapping, enabling rapid and reliable estimations of flood effects, as well as testing and optimizing climate adaptation measures to mitigate undesirable effects.

Such efforts could include vegetation areas to increase infiltration, fixed structures to temporarily store water in low-lying areas, small upstream detention basins, rain gardens, green roofs, and green walls.To accomplish this, a meticulous mapping and analysis process must precede implementation.

This is done using GIS, which stands for Geographic Information System.

GIS analysis is particularly suitable for such analyses because the key component, elevation data for analysing water movement, is typically stored as raster data.We have developed an algorithm and a tool that can simulate the effects (water depth and velocity) of heavy rainfall with high accuracy and very short response times.

Precipitation and water flow can be simulated both in time and space. The model we have developed is superior and faster than what is currently available on the market. Users can directly utilize data from existing GIS databases used by both public and commercial entities today.

This allows for much shorter preparation times than other models, such as hydraulic models, for example.Currently, the tool lacks a user-friendly interface. We aim to develop one in collaboration with users from municipalities, authorities, and companies. The interface will be simple, allowing "non-experts" to model flood effects.

Response times for users will be even shorter compared to today´s complex models, while reliability will be maintained.

The results will be presented attractively for various user categories, visually through maps and videos, and numerically through tables, for example. It will be possible to point to an image and retrieve flood data for a point or an area.

Currently, we can simulate preventive measures such as embankments, infiltration areas, and dams, and this will be further developed.

Furthermore, we want to incorporate the measures that users apply in reality for climate adaptation to floods.Flood risk management practices today involve multiple actors and stakeholders.

The goal of minimizing damage and controlling water inflow in urban areas requires planning with expertise from several disciplines.

The tool will continue to evolve through close dialogue with user groups from a number of pilot organisations.Leveraging GIS expertise within an organisation saves taxpayer money that would otherwise pay for expensive consultant hours.

With the possibility of realistic results within public entities´ organisations and the commercial sector without having to wait for an external party´s availability, decision-making data can be generated more efficiently. When the organisation conducts the analysis itself, it owns the entire issue and will obtain adequate results.

Climate adaptation and measures to reduce or eliminate flood risks are themselves an environmental gain and a sustainable solution as infrastructure enjoys a longer lifespan than if subjected to water stress.The social aspects align with environmental and economic sustainability as climate adaptation and risk minimization can be modeled, and the most vulnerable areas detected.

The data provided to decision-makers become more realistic, presenting several alternatives than if a static map were the basis for decisions. For individuals, much is at stake in terms of value, such as flooded basements and land. Insurance companies study natural risks, wh