We've had a quite a big landslide that's come down and blocked a river. So the river can't
get through so it's formed a lake upstream of the dam. And it's causing all sorts of problems
for the locals, because it's closed the road they can't get access along the road to the properties
anymore and the water has gone up into some buildings and wool sheds and the farmers have had
to move with the stock out of the floodplain. And when a when the landslide comes down and blocks a
river one of two things can happen, the landside dam can either stay there for a very long time
and just cause a lake upstream, that can stay in the landscape for hundreds of thousands of years,
or if the landslide dam is stable or if the river can move the material, then there's quite a high
likelihood that the dam will fail. Which would send a huge flood it could be catastrophic. So
as soon as we heard that there was a landside dam, we got over there to check it out to make
some make some measurements and assess the situation to see whether that the landslide
dam could fail rapidly and send a big flood downstream. During a GeoNet landslide response,
we can use unmanned aerial vehicles or drones to rapidly collect lots and lots of high-resolution
images over the entire area that's affected by the landslide and the dam. So on the screen
here we can see all the hundreds of photos that we took out in the field with the UAV,
and these then get stitched together into one mega photo that covers the entire area. So this is what
we call a digital surface model, which basically quantifies that information for us which we can
then use to compare it with what the landscape used to look like before the landslide happened.
Blues and greens are areas where the surface has got lower, so that's where the material has been
eroded and fallen down the slope, and areas of yellows oranges and reds where that material has
been deposited. We can use that data to calculate volumes of the landslide dam as well as the volume
of the lake that's building up behind the landslide dam. The problem with landslide
dams is the the lake that forms behind them, and the speed at which that lake fills and if it fills
quickly and the dam over tops which many do, then it can cause a major hazard. And the problem with
dam breaches is that all of that debris comes as a big pulse that travels down the river,
and it can go very quickly and it can cover large areas and some of them fail with little
or no warning. Typically dams that are formed at big boulders that are the size of buses and cars,
they may survive for longer on the landscape because the water when it comes over the top
of them can't erode through. Now compared those to other dams where we have very fine particles,
and the water can easily erode through. And so what we have to do is get an understanding of
that material, because the failure mode of the dam is influenced by that material. Initially
the landslide dam blocked the entire river, so it blocked all the water from going through.
And so within about a week the river had eroded a little channel around the toe and it was starting
to cut down through that material because it was quite weak. We noticed that there was some little
cracks or slumps forming on the downstream face of the dam, and we could also see water coming
through the dam so through flow or piping which means there's a possibility that the water could
erode through the middle of the dam as well. So we had lots of warning bells going off in our heads
that there's possibility that the dam could go pretty soon. We then have to take a precautionary
approach and say well okay if the dam were to fail by overtopping which most dams do, then what's the
likely discharge and the discharge is the volume of water and debris that could be released per sec
and down the river. And to do that we use past dam failures, where we've got data, and so this work
we're doing now is going to add another dataset to that body of knowledge that we've collated
from all over the world. So about two weeks after the landslide dam had been formed, we had a rainy
day in the Wairarapa that was enough to bring the lake level up to the same level as the top of the
dam. And as soon as it reached the top of the dam it was eroded. Within about an hour and a half
most of the water from the lake had been emptied out. We estimate that the maximum discharged, so
that's the flow of water when the dam collapsed, was about 900 qmix which is a massive flood the
the 100-year flood for the Kaikoura River has expected to be about 15 to qmix and this was
900 so it was off the scale. so when we initially go to a dam that's formed, and we're assessing how
it's likely to fail, we don't have the ability to get into the dam material because it's a you know
emergency environment. So what we do is we do a simple appraisal of the source where the debris
is come from, and the shape of the dam and the materials exposed on the surface of the dam, to
try to understand how you know what the materials are like how big the particles are. After its
failed, then one of the things we do is we then go back into the the dam and we receive the material,
and you can see that what we do is we set up a crucible with some sibs and we systematically
sift the core material of the dam, and this helps us to refine the models. Now that we've got all
this data we can rerun the models of the flood outburst and train the model so that it fits
with the actual flood extent downstream. It's very rare to capture the kind of data that we've got,
we have this unique data set where we have the lake level, and we have the outflow when the
dam breeched. We've got the geometry of the dam from the actual change modeling and the and this
drone surveys and laser surveys, we also have the particle size distribution and we also have
the area that the flood inundated because we've mapped the the trash lines. So we put all of that
into our model and we back analyze how how the flood occurred by using all of the information to
control the model parameters that we use. What we want to do is we want to put all the information
with our other datasets from around New Zealand and from overseas so that we can produce these
different models and forecast tools that will help the emergency managers in the future.