Run DynamicFlood in Norway with ready-made local rain events
- Uudet julkaisut
DynamicFlood now automatically selects local design rains in Norway. Now it is even easier to get started with hydrodynamic simulations.
This spring, we launched DynamicFlood, powered by TUFLOW, the industry leader in hydraulic modelling accuracy. With DynamicFlood you can run 2D flood simulations directly in Scalgo Live with just a few clicks using preset inputs.
In Norway, we are now excited to announce a significant improvement to the preset inputs of DynamicFlood. We present to you nationwide pre-configured rain events and climate scenarios based on local rain data, making DynamicFlood even more precise and easy to use.
Get the most suitable design rains for your project area
Traditionally, modellers create design rains using data from the nearest rain gauge, a manual and time-consuming process that requires careful data quality checks and adjustments. Once prepared, these design rain events are fed into the hydrodynamic model.
We've automated this process by developing a new dataset that describes local rain events in all of Norway, and we have integrated this rain data into DynamicFlood. Now, when you create your Workspace, you can choose from a selection of return periods and climate scenarios, all based on data from the nearest rain gauge. This ensures that when you run simulations, you automatically receive the most suitable design rainfall for your location.
Figure 1. Norway is now divided into polygons that define local rain regions. This serves as automatic input into DynamicFlood simulations.
An automated process based on best practice
The new rain dataset divides Norway into polygons corresponding to the locations of rain gauges. To develop this data, we used rainfall records from a high-resolution rain gauge network provided by the Norwegian Meteorological Institute (MET), along with Intensity-Duration-Frequency (IDF) curves for each station. These IDF curves summarise a statistical analysis of the frequency of high-intensity rain events, and are important input for creating design rains.
We followed the MET's recommendation to use IDF curves only from stations that are currently operational and have over 20 years of rainfall observations. However, for northern Norway, where stations are less common, we adjusted the requirement to 10 years. After this filtering process, 23 weather stations remained, and we divided the country into polygons, each representing the area closest to a given station.
Using IDF curves from the 23 approved MET stations, we created design rain events with several return periods (2, 5, 10, 20, 25, 50, 100 and 200 years) based on the Chicago Design Storm (CDS) method. This method is a widely used approach for generating synthetic rain events and is ideal for testing stormwater management measures. For each station, two sets of rain events are available: one representing current climate conditions (2024) and another projecting the future climate (2100). The first set is valuable for analysing existing conditions, while the second is recommended for future planning.
You can view the entire data set in the Library - look for the dataset “Rain”.
Figure 2. In the Library, the data is called “Rain”. Use Point query and click on one of the polygons to get information about available local design rain events.
Use the new rains in your DynamicFlood simulations
To use the new design rain events, you simply need to initiate a DynamicFlood simulation and choose which return periods and climate scenarios you want to run in your Workspace.
DynamicFlood automatically suggests rain events based on the local rain polygons. If your project area spans over multiple polygons, rain events are proposed based on the centerpoint of the project area. You can easily choose data from another station using the “+” button.
Figure 3. To start a DynamicFlood simulation you just need to choose which return periods and climate scenarios you want to apply.
Further development to create automated rain events at scale
The current methodology relies solely on rain gauge data, which means that it does not account for the spatial variability between rain gauge stations. To address this, MET is collaborating with other countries around the Baltic to develop a more refined and comprehensive grid of rain statistics for the entire region.
Meanwhile, at Scalgo, we are working on creating a rain dataset for all of Europe. As these new data sets become available, we will integrate them to DynamicFlood for the benefit of our users.
