**INTRODUCTION**

CityCAT is a software (developed by Newcastle University) tool for hydrodynamic modelling, analysis and visualisation of surface water flooding. It’s an urban flood modelling, analysis and visualisation tool. CityCAT uses accurate and computationally efficient solutions for free surface flow equations. It enables assessment of flood risk management measures with the following key features: uses standard, readily available data sets; efficient and fast set up; fast and accurate solutions of flow equations (surface and subsurface drainage network); accounts for “blue-green features” e.g.

infiltration, roof storage, swales. We will try to show the impact of the urbanization on a flood in Newcastle.

**I. IMPORT DATA**

The area of study is located in Newcastle, centered on Saint James Park. The buildings are delimited by red lines on the following picture, and the green areas by green lines.

The aim of the exercise is to determine effects of several parameters on the flow and the water level just in front of a shop (corresponding to the cell #41474 and her neighbouring cells). The zone studied is in the red circle.

The same rainfall data will be used for all computations in order to compare impacts of parameters on the water level.

**II. FIRST SIMULATION**

The first simulation consists in only considering buildings.

Here are the graphs. The first one represents the velocity of the flow on the X axis, the second represents the velocity on the Y axis, and the third one represent the water depth.

It can be seen that after 1000 sec of computation, the water level is growing, and more significantly after 1500 sec. The water depth keep growing until 2m. A such water depth is potentially dangerous for the shop.

In the next part of this report, impact of parameters such as green areas, roof permeability and roof storage on the water depth will be studied.

**III. IMPACT OF PARAMETERS**

After considering the green areas, here is the results of the simulation:

First, the growth of the water depth starts at 1200 sec of computation. It can be seen that the maximum depth is 1.5m instead of 2m.

It can be assumed that the Green Areas delayed the growth of depth by 200 sec and reduced the depth by 0.5m. So Green Areas have a significant impact during a rainfall event, but the water level is still dangerous for the shop. The Green Areas store the water and with a higher roughness, the water velocity is reduced.

In the following computation, all the pavements and roads have been converted into permeable surfaces.

Here the results are much more significant than the simulation with green areas. The water level starts growing after 1700 sec of computation and the maximum depth is around 1m. These results can be explain by the assumption which is very important. Considering all pavements and roads as permeable, surfaces doesn’t represent reality.

This is currently impossible to make pavements and roads permeable. And if it were possible it will be really expensive.

The third main parameter is the roof storage. Green areas are considered and the roof storage is set up to 0.1m. It means that the Green Areas store the water at a deep of 0.1m.

Here, water depth starts to grow after 1450 sec of computation and the maximum depth is around 1m.

The water storage affects the water level and the “lag time”, as long as the roof can absorb the water, the runoff is quite null. And once the roof is saturated the runoff grow rapidly.

This situation is much more realistic than the previous one. It considers green areas and their capacity to absorb water. Roads and pavements also have a roof storage up to 0.1m that can represent a storm sewer network which is not considered by implemented datas.

**IV. MAP VIEW**

On these two maps, the water depth is represented. The shop is in the red circle. Light blue represents a zone with a low depth, dark blue and purple represent higher water level (around 1.5 and more than 2m for purple).

On this picture, we can see that the zone in front of the shop is purple (in the red circle). This picture represents the case without the green areas and with no water storage surfaces. This picture was taken at the end of the simulation (after 1h).

The next picture represents the case with the green areas and the water storage set at 0.1m. There is a difference in the whole domain. The depth is generally lower in the second case. But a purple zone in front a the shop still remains. There is may be a lack of evacuation.

**CONCLUSION**

By using a simple application on CityCAT with the same rainfall data for all computations, we were able to understand the roles and effects of each parameters. The Green Areas for instance store the water and reduce the water surface velocity; and by setting the roof storage, we could also reduce water level in the studied area. Therefore, the CityCAT model is very useful for analysis the run-off across an environment and gives a realistic idea about a potential risk of important run-off.