Adaptation to Urban Peak Precipitation is not hard.
As an introduction to the Tygron platform and as a small challenge, I have tried to make my own Use Case on analysing measures for flooding in neighbourhoods. I am a Master student International Land and Water Management at the Wageningen University & Research and currently fulfilling my graduate internship at Tyron. In this blog I am describing the steps I took to build my own Neighbourhood Surface Indicator.
After completing the basic Tygron tutorials and some small practice projects (https://support.tygron.com/wiki/Practice), I decided that I wanted to make my own indicator in my Use Case. Hereby, I could test my understanding of the software and get a better impression of all the possibilities of the Tygron platform. An Indicator is used to measure elements of a project or digital twin and show insights into the progression of possible measures or adjustments.
My first step in creating the indicator was setting up some of the boundary conditions. By asking myself:
- How can I make a use case related to climate change, that will be widely applicable and fits in my personal interests and study background?
- Is it possible that the issue highlighted by my indicator can be mitigated using easily accessible climate adaptation measures?
After doing some research, I encountered the following issue and initiative. Climate change, as known, is changing weather patterns in the Netherlands, extreme precipitation events are getting more severe and occurring more often. Using data of the KNMI I could project how future extreme precipitation scenarios (t=10y) are showing significant severity increases for the coming decades. Using the Tygron platform, these rain events can be simulated on a neighbourhood or streets scale and the current precipitation resilience can be assessed. This way, weaker areas can be mapped and impacts can be visualised.
Subsequently, I started looking for possible mitigation options. And after some research and some tips in the office, I encountered greener residential gardens as a possible flood reduction measure. By increasing the greenness of the garden, the water storage capacity is increased and as a natural consequence surface water runoff will be reduced. In many Dutch cities so-called break the stone initiatives are being set up. An example is Amersfoort, where residents were able to swap a tile for a garden plant to promote green gardens.
After consulting some different websites and scientific literature, I decided how I wanted to shape my indicator. By indicating how the surfaces of a neighbourhood are set-up, it will become more convenient indicating areas where gardens are too hard/paved. Therefore my indicator should be able to project the status of direct (soil) infiltration of (rain)water in a neighbourhood. I’ve divided the indicator into two categories; bad infiltrating surfaces and good infiltrating surfaces. The bad infiltrating surfaces include roofs, paved gardens and paved public spaces as parameters. And the good infiltrating spaces include green gardens and public green as parameters. Subsequently, I started looking at a location to make my Digital Twin, hereby I lay my eye on the Leusderkwartier neighbourhood in Amersfoort. A neighbourhood with a lot of paved areas, so green garden measures would make a significant positive contribution. And a neighbourhood that was part of a previous case study of the municipality whereby the selected categories were partially mapped. This enabled me to know in which direction the projected results of my parameters should be.
The next step was to translate this plan into a usable indicator that would actually work. This was the most difficult but also the most rewarding step. Before setting up my own indicator I’ve looked into the framework of previous existing indicators that had overlap in their design with my use case. Thereafter I started working on my own indicator, to do so I began by mapping the queries I would need to use my parameters. Queries are tasks in the Tygron platform to request certain data from the 3D model. The query tool of the platform helped me to arrange the correct queries. Using the queries I was able to request every parameter independently. Hereby I manually had to separate ‘green’ and ‘paved’ gardens and adjusted the values for each type of garden myself, since there is no standard distinction between these garden types yet. These values are for example the infiltration speed and the manning number, values directly influencing the draining capabilities of a surface. After some trial and error, I managed to get the indicator working and project the values I was expecting. My last step was to make green improvements available for the stakeholders of this use case. These stakeholders were predominantly the municipality (of Amersfoort) and residents.