Translator: May Yee Chan Reviewer: Leonard Ryunosuke
I believe in the past several months you all have similar feelings that
the flood has occupied headlines for a while.
Germany, Zhengzhou, New York
and the current flood victim - Shanxi,
they all have experienced historic flood,
and suffered from enormous economic losses.
We can imagine:
when a flood hits us suddenly,
and we are just trapped in the flood.
What can we do?
The only thing might be waiting.
But there is an objective issue:
once there is a flood,
the traffic will shut down, and transport will be disrupted.
Then rescue workers would also be hampered from arriving on time.
Rescue is always the biggest issue.
I would like to briefly introduce this to you all.
For example, the process from
raining to a flood on a street.
When the rain starts,
there is no water over the road
because of the drainage system.
But when the drainage is overloaded,
the water will gather on the road surface.
And if the rain continues,
the water level will rise.
Probably, cars cannot move by then.
Buildings along will be flooded and
people will then be trapped.
This is the start of our rescue work.
At the beginning,
people are already trapped because of the gathered water.
Rescue workers then head to the location.
At this point, first you need time.
Next, as I said, traffic was disrupted.
These are the few reasons that greatly affect our rescue effectiveness.
For those who are trapped in the floods,
rescuers arriving a minute early or late.
could be life or death for them.
So, is there any solution to this issue?
Can we use
Is it possible to determine in advance,
at a time when it was still raining and before the floods have even formed,
to identify the areas that are likely to be affected?
Wouldn’t that allow us to deploy in advance and
increase our rescue efficiency?
So what I want to talk about today is
some of the past project I’ve done.
and to share about what we can do,
before the floods come.
We were actually in 2016 when
my team and a few of our partners in Henan Province,
with funding from the World Bank,
we went to this Dar es Salaam in Tanzania.
It is the largest city in Tanzania, and is also its economic capital.
We went there for a project on the city’s
flood mitigation project.
At the time it was a city that, because of its climatic conditions,
economy, infrastructure, and for various reasons,
flooded frequently.
Although they would issue the flood warning,
But then, apart from the warning,
they don’t have more of a systematic and developed relief system.
Dar es Salaam is a city where over 50% of the population
live in slums.
As you can imagine, that’s one out of two people in a slum
Slum areas are very difficult to manage.
These are the photos we took in the slums.
We took some for our research.
You can see that the conditions are not good.
For these people who live in the slums,
For the floods, the people are actually very vulnerable.
One big flood could very well,
in one time make them lose all their homes and livelihoods.
And the local Red Cross
would have special funds and staff to
help these people
to rebuild their homes after the disaster.
Our job is to
help the Red Cross,
help them find ways to
identify in advance,
before a disaster strikes, identify potentially affected areas, and
help them to effectively
deploy their supplies and personnel,
as well as to improve the rescue efforts.
First of all, to do this,
we need an accurate topographical map.
This should be easy to understand.
You need to know the place
to simulate how the water flows,
and where it will affects when it rains.
As for cites,
its terrain is fairly complex.
We can image, for instance,
a road next to a river, it is usually elevated.
We need to know the height of the elevation,
to be able to simulate
how much time it takes to rain
before the water floods the road
Another example
we have drains next this road.
As I said, be it visible or invisible.
We need to know its drainage capacity
to be able to calculate precisely
when a flood is likely to occur.
Next example, our city have elevated roads.
We need to know which is on top
or at the bottom.
These structures on top.
How has it been supported?
Is it by these pillars, or these solid walls?
These details will change the flow of the water and
the area of the affected streets.
During the project, we had a problem.
The local authorities were not able to provide us with an accurate map.
We had to make one ourselves.
As you can imagine, there are ways to make a map.
We could put on our helmets and
go out in the field and
scan the whole city.
But obviously,
It was impractical for a one-year project
to work for us.
So we ended up with one solution
that requires three things
to produce this precise topographic map.
The first is through radar satellite,
the global radar satellite with elevation data.
We were able to get a rough idea of how the terrain is like.
Then, we use street maps
to add details to the topographic map including
houses, streets, etc.
Finally, we utilize the community experience.
We communicate with the local community workers
We talk to the experts who know about the areas,
and invite them to help us
refine the accuracy of the map.
Now we can have a look on the topographic map.
We used the NASA’s
digital elevation map called SRTM.
It’s a global digital product.
It has an accuracy of about 30 metres by 30 metres.
It’s like a grid, a small grid.
Its accuracy is fairly high at global level.
But think about it, for someone like us
who demands an accurate street-level flood simulation,
it’s probably not enough.
I’ve talked to the staff.
Our venue,
is no more than 900 square metres.
As you can imagine then, the whole venue, all of us,
were just a dot in this database,
just like a pixel,
without any details.
That is why we need to add to this map
with more details.
But how?
We thought of using “Open Street Map”
with its Chinese translation.
It is similar to the map we normally use
such as Gao De and Baidu
It contains information about buildings,
roads,
rivers, and even drainage system.
But what makes it so special
is in its name: it’s called “Open”.
It is based on people going about uploading the information themselves.
So that everyone can use it for free.
We then took this...
our SRTM elevation map
and Open Street Map
which contains these data together,
and we did a geo-fusion.
Then we obtained a high resolution topographic map.
You can see on the map,
Not only do we have the topographic information,
but also the details: we have houses,
rivers, streets,
and even the drainage system.
But are these enough?
They are not.
Each place, in fact, has some of its own characteristics.
It has some traits of its own.
This is a picture
we took in the slum area.
A picture of its drainage channel.
You can see that it’s blocked with rubbish.
This is very common in slums.
As you can imagine, the water in this drainage canal.
It’s almost impossible for it to flow.
As for the flood model,
it increases the Manning’s co-efficiency and
reduces the capacity.
That is all.
Another situation, I found
that is very common in this slum. It’s the household.
Some families will a build waterproof wall
around their house, like this.
It’s about 10 to 15 centimetres high.
It looks simple
but works very well.
It works for small to medium floods.
It’s really effective. It stops floods.
It keeps the flood out of their home.
Just all these details, it’s imaginable
that these are important for us in this simulation.
But we don’t have the means to research door to door.
So what we came up with was to use the community experience.
We worked with community workers, with experts who know the local context.
We had a meeting together,
we invited them to input these parameters
directly into our simulation software
to incorporate these local features
and so to help us refine the topography.
We can see that this is the topographic map before and after the refinement.
If we make a change in this map,
you can see that this is the difference.
The red coverage are areas where the height of the land
is lower before the refinement.
The blue ones are higher after the refinement
Once we’ve made these simulations,
we take this accurate topographic map
into our flood model,
and this is what we got.
Again, this is before and after refinement.
You can see that the blue coverage are
affected areas, the flooded areas.
In these red circles that I’ve drawn here.
You can see that
the difference is huge.
So we shared the refined map
with the local experts,
and had them carefully review it.
The feedback was positive.
We felt that this information…
The information we provided
is helpful to the local Red Cross
by giving them two important layers of information.
The first will be the blue areas.
which are also areas with potential disaster.
Those are where they need to focus on.
And those next to the blue areas
that aren’t flooded are safe areas.
It’s those safe areas that
they can go set up
their relief supplies and personnel in advance,
or perhaps, in the process of relief,
moving these people from the affected areas to safer places.
it’s not surprising to see that our projects
is to focus only for a few hours.
But we believe,
in terms of flood mitigation,
what we need to focus on more is the next few decades.
Why?
Because of climate change.
If the climate change is described as a shark,
the water will be its teeth.
In other words,
As to each and every one of us,
the climate change is the most immediate threat.
We can feel it directly
through water-related weather extremes.
We have numerous data which has proven
that the changing climate
has already been making our extreme weather,
now than it used to be,
more frequent and extreme.
I will use an example from United States
to share with you.
This chart on the left
is from the US Federal Emergency Management Agency.
It uses historical data to create a
100-year flood map of the Chicago area.
The right side is from First Street.
It’s an organization that focuses on climate risk, flood risk.
Its flood experts have updated the latest data based on this
100-year flood map of the Chicago area.
As you can see, the difference is huge.
The most significant cause
is the climate change.
My team is here to help the policy makers and the worldwide public
to better understand the impact that climate change on flooding
will likely have in the coming decades.
We’ve built Aqueduct,
it’s a global open source, an online platform for water risk.
Everyone can access and download the data
or have a look at it.
We used the global climate models,
with different climate scenarios
and socio-economic changing scenarios.
We have simulated the next 80 or even 100 years
of climate change that may bring
differences in the flooding areas in worldwide.
The map you see here is
Guangdong, Hong Kong and Macao.
You can see: now
2030,
2050, and 2080.
According to our data analysis,
one of the results is that,
take China for an example,
our average annual population affected by floods
is now about 6.5 million.
For China,
by the year 2080,
it could go beyond 10 million.
That’s about a 60% increase.
And that’s basically and almost entirely due to climate change.
We certainly don’t want climate change
to continue this way,
But as you may have heard last year
in the news,
China made a promise to the world:
We will reach the emission peak by 2030 and achieve
carbon neutrality by 2060.
This is what our country has done
to mitigate the global climate change.
All these things we did
is also to reduce
the impact of the extreme weather to the world and all mankind.
Last, I’d like to say that
I hope that this sharing today
would let you know that
before the flood,
whether it’s hours or decades,
There’s still a lot of things we can do.
I hope that our flood preparation will get stronger and better,
so that the next time it hits, we will no longer be vulnerable.
Thank you.