PubTalk 11/2019 —Sea-Level Rise, Extreme Water Levels, Coastal Erosion

Video Transcript
Download Video
Right-click and save to download

Detailed Description

Title: Sea-Level Rise, Extreme Water Levels, and Coastal Erosion ... How bad could it possibly be?

  • Sea-level rise represents an unprecedented civil engineering challenge.
  • Small amounts of sea-level rise can disproportionately increase the frequency of coastal flooding.
  • One-third to two-thirds of the beaches in Southern California may disappear by 2100 under sea-level rise of 1-2 meters.
  • A combination of satellite observations and modeling will help to understand and predict coastal change.
     

Details

Date Taken:

Length: 01:23:09

Location Taken: Menlo Park, CA, US

Transcript

0:01
… and welcome to the United States Geological Survey.
0:04
I’m pleased to see you all here for our November 21, 2019, public lecture.
0:10
I’m Diane Garcia, and I’m with our Science Information Services.
0:15
Before we start tonight’s presentation, I want to let you know we will
0:18
not be having a December talk – holidays and everything like that,
0:23
as you can all imagine. But the good news is, we will be back
0:27
in 2020, so please save the date – January 23rd, 2020.
0:34
And we’re going to have Justin Hagerty talk about our [chuckles] –
0:41
our Astrogeology Science Center. I had to stop and think –
0:44
our Astrogeology Science Center out of Flagstaff, Arizona.
0:48
So, like I said, do save the date and come back for that.
0:53
But tonight’s presentation is Sea Level Rise – Extreme Water
0:59
Levels and Coastal Erosion – How Bad Could it Possibly Be?
1:04
And it’s going to be presented by Dr. Sean Vitousek.
1:08
Sean was born and raised in Hawaii. He attended high school at
1:12
Hawaii Preparatory Academy. And then next, he attended Princeton
1:17
University and majored in civil and environmental engineering.
1:22
Outside of the classroom, Sean played on the Princeton volleyball team and
1:26
was the president of the Princeton Surf Club.
1:28
Sean received his master’s in geology and geophysics from the
1:32
University of Hawaii and was advised by Chip Fletcher.
1:37
With a strong interest to pursue numerical modeling, he attended
1:40
Stanford University and obtained his Ph.D. in civil and environmental
1:45
engineering, where he was advised by Oliver Fringer and supported by the
1:50
Department of Energy Computational Science Graduate Fellowship.
1:56
Following his time at Stanford, he received a Mendenhall postdoctoral
1:59
fellowship here with the United States Geological Survey in Santa Cruz.
2:05
Sean worked as a research assistant professor and later as an assistant
2:10
professor in the Department of Civil and Materials Engineering at the
2:14
University of Illinois in Chicago. Sean returned to the U.S. Geological
2:21
Survey, thank god [laughs], at the end of the government shutdown
2:26
this year in 2019, working as a research oceanographer to develop
2:31
numerical models to predict coastal climate change impacts.
2:35
In his spare time, him and his wife Sylvia enjoy playing with their 2-month
2:40
old daughter, Merigold, who likes to go swimming …
2:43
- Two years. - … and surfing with Daddy.
2:45
- Two years. - Oh, I’m sorry. I said two-month.
2:48
That was what was on here. - Oh.
2:50
- Two years. [laughter] - Two years. Yep.
2:52
- Oh. Oh. The fun is just beginning. [laughter] Sorry about that.
2:59
Thank you.
3:01
Let’s go ahead and give a warm round of applause and welcome Sean.
3:05
[Applause]
3:10
- Thanks very much. - Thank you.
3:13
- Thanks. Can everybody hear me okay? - [inaudible responses]
3:16
- So a little louder? Okay. [laughs]
3:20
Well, thanks very much for coming. This talk is about sea level rise, extreme
3:24
water levels, and coastal erosion. How bad can it possibly be?
3:28
And the title is a little bit of a joke because, as you can imagine, if you get
3:33
pretty high end sea level rise scenarios, it can be – things can be pretty bad.
3:38
So this is the outline for the talk. I’ll leave a little brief intro on sea level
3:42
rise, and then I’ll talk – the first section of the talk about coastal flooding –
3:46
extreme waves and water levels. And the second section about coastal
3:51
erosion using a data-assimilated model of coastal change in California
3:56
that we’ve developed. And to do something fun, I decided
4:01
to associate a movie with each one of these sections. [laughter]
4:05
So, Waterworld, Point Break, and True Lies. [laughter]
4:11
And each of these movies should be consistent with the theme of these talks,
4:16
and there should be a real specific Easter egg associated with each of these
4:22
movies in these sections of the talk. And so, while I’m going through and
4:27
presenting about this, you could maybe think about it in your mind, how this
4:31
movie is relating to this particular section of the talk.
4:36
So, well, Waterworld is relatively obvious
4:39
when you’re talking about sea level rise.
4:42
This is the very introductory scene to Waterworld.
4:47
And the concept behind the movie Waterworld is that, with global
4:51
warming, the ice on the planet melts, and so that melting ice raises sea level
4:58
to the point where there’s no more land on the planet.
5:03
Which geologically speaking, is not entirely correct.
5:07
I’ll talk a little bit about why that’s the case. [laughter]
5:12
But I think Waterworld was really one of my main motivations
5:17
to study sea level rise.
5:19
I actually grew up on the Big Island of Hawaii.
5:23
And, in Kawaihae Harbor here, pretty close to where I lived, they built this
5:30
post-apocalyptic atoll when I was a kid. And we’d have Mexican food down at
5:36
the harbor when we’d go to the beach. And so, when I was a kid going to the
5:39
beach, we would drive by this thing being built and the movie being filmed.
5:46
And so I think that was probably one of the main motivations for why
5:49
I studied sea level rise as a kid.
5:52
Because Waterworld was filmed right on the Big Island at the same time.
5:58
Okay. So Waterworld. The concept behind that movie is not entirely correct.
6:04
Because if you – if you melted all the ice on the planet, you’d probably only
6:10
raise sea level by about 100 meters. So there’d be plenty of land left on
6:16
planet Earth, even if you melted all of that ice.
6:19
However, as you can imagine, the half a meter to 2-1/2 meters
6:27
of sea level rise that we expect in about the next 80 years could be pretty
6:34
significant. And so we want all of that ice to remain as ice for as long as possible.
6:41
So currently, sea level is rising at about 3 to 4 millimeters per year.
6:46
And this is very well-measured from a network of tide gauges all around
6:51
the world and from satellite altimetry – so satellites that are –
6:56
that are measuring sea level over time.
6:59
So that’s what sea level is currently rising at.
7:03
But the question of, how much sea level rise are we going to get
7:06
by 2100 is unknown and is one of the most important
7:11
scientific questions of the 21st century, in my opinion.
7:16
So, if you go to Google Scholar, which is a great website, and you
7:21
type in “sea level rise projections,” since 2019 – so just this year,
7:27
you’ll get almost 13,000 papers. [laughter]
7:30
So a lot of papers that have been written on this subject.
7:34
You know, this is a very important subject, and a lot of implications –
7:40
a lot of things that are riding on how much sea level rise are we
7:42
going to get in the next 80 years.
7:46
So most of these sea level projections range from about
7:50
a half-meter to 2-1/2 meters. And pretty much all the variability
7:55
is controlled by how much of the large land-based ice masses –
8:01
Greenland and Antarctica – are going to melt.
8:05
Carbon emissions also, as you can imagine, play a big role.
8:09
You see these two different scenarios, which are sort of a low emissions
8:12
and a higher emissions scenario.
8:15
But the major uncertainty between the medium-range sea level scenarios
8:19
and the high end is just the stability of those large ice masses.
8:25
As you can see.
8:28
So my own global mean sea level projection of how much I think
8:35
we’re going to get by that time, based on everything that I’ve read,
8:40
is probably about 1-1/2 to 2 meters of sea level rise.
8:44
And the reason that I wanted to make this projection is because
8:47
this lecture is being recorded. And so, in 2100, when my grandkids
8:55
watch this lecture [laughter] on their holographic iPads [laughter]
8:59
or their augmented reality goggles, they could see how far off their
9:06
grandpa’s prediction was. [laughter]
9:08
But anyway, that’s my prediction for what we’re going to get.
9:14
And those predictions are not unreasonable given the
9:17
large sea level swings that the Earth has seen over the past long time.
9:22
So this is in millions of years before present.
9:26
And you can see that sea level has swung more than 100 meters in the past.
9:35
And this is all due to orbital motions – the orbit of the sun and the Earth.
9:41
And, when we transition from two glacial periods, ocean water is
9:49
occupied more so as ice than normal. So, during the transition to glacial
9:56
periods, you have about 1 meter – 1 millimeter per year of sea level fall.
10:01
And you have sea level low stands that are about
10:02
100 meters lower than present.
10:05
And then, when you transition out of glacial periods to inter-glacial periods,
10:10
you have about 5 millimeters per year of sea level rise.
10:14
And so, interestingly – I just put here this timeline for – at the – at the
10:21
glacial period where the city of Atlantis is built – this ancient civilization.
10:28
And, after that, you can – you can guess what happened to Atlantis.
10:32
Well, sea level rised rapidly from its low stand of about 100 meters
10:38
to go away. And so this is kind of a half-joke of, you know, consequences
10:44
for civilizations with rapidly rising sea levels.
10:50
So, right now, Earth should be in a cooling phase.
10:55
We’re coming out of this inter-glacial. So we should be getting about
10:58
1 millimeter per year of sea level fall. But that’s not what we’re seeing.
11:04
So, looking at the sea level impacts, by 2050 to 2100, sea level rise
11:10
could displace about 200 million people.
11:13
As I’ll talk later in this section of this talk.
11:17
It could make extreme water levels become the mean –
11:21
or happen – extreme water levels happen all the time.
11:24
It can erode sandy beaches by dozens to hundreds of meters.
11:28
And also, even if you sort of build a wall to keep all the sea level out,
11:34
you can still have what’s called groundwater flooding or groundwater
11:37
inundation, where that higher water table can actually go through the
11:42
groundwater and cause flooding through the ground,
11:45
even if you have a barrier on the coastline.
11:51
Okay. So that concludes the introduction about sea level rise.
11:55
And you can see how Waterworld is a – is a very strong connection there.
11:59
The next section is coastal flooding and extreme waves.
12:02
And the movie related to that is Point Break.
12:05
So you can think about, in the back of your mind, how the movie Point Break,
12:08
if you’ve seen it, might relate to what I’m talking about.
12:13
Okay. So say we wanted to assess flooding and erosion on a –
12:17
on a given beach. What are the processes that we need to consider?
12:21
So here’s a few.
12:24
A very important process is waves.
12:28
What are the waves doing?
12:31
So waves, when they break, they create a mean sea level elevation
12:38
that’s right near the shore, which is called wave setup.
12:41
Additionally, on a beach, you have this runup motion, called wave swash,
12:46
back and forth, or wave overtopping, just from those individual waves
12:50
that are propagating over the beach.
12:53
Another important process in flooding is the tide.
12:57
High tide versus low tide can be a pretty big difference in water level.
13:03
Another term here, which I’m showing just these – these are residual terms.
13:08
For instance, storm surge, which is caused by decreases in atmospheric
13:12
pressure, which cause water level to rise, and also winds.
13:18
And climatic cycles like El Niño, which, in California, can elevate
13:24
mean sea level around California by 10 or 15 centimeters
13:26
when you have a El Niño year. And other sea level anomalies,
13:31
like eddies and things like that, can contribute to variation
13:34
of sea level, which you can see in tide gauges.
13:38
So, sea level rise can be very important in the range of all these processes
13:46
because all of these processes essentially contribute to sort of
13:50
the variability in the water level. But sea level sort of represents
13:54
the mean. So if you take the mean of that varying water level, and you
14:00
just raise that mean, and then everything’s varying about that
14:03
new mean, the extreme thresholds where all the infrastructure is built
14:09
can be overtopped more and more easily with lesser and lesser storms, say.
14:14
And we’ll talk a little bit about that later in the – in the talk.
14:18
Okay. So here I’m just showing a nice day in Capitola,
14:24
where there’s no waves. There’s really nothing going on.
14:28
And you can see it’d probably be a nice time to go swimming
14:30
or go to the beach. So this is without waves.
14:33
And then this is the setup with waves, which you can see looks like a very
14:38
different place with these waves breaking very, very far out to shore.
14:45
Here you see another example of Capitola flooding where you
14:50
have these big wave events that are sort of coming up
14:53
and washing into these little structures here.
14:56
So pretty serious situation there if you –
15:02
if you happen to live in one of those places.
15:05
Okay, so how do we characterize extreme events
15:09
like extreme waves and extreme water levels?
15:13
Well, to characterize those, generally you’ll use some
15:16
sort of statistical model. And the most popular statistical model
15:19
to describe the distribution, or the occurrence, of those extreme events is
15:25
this model called the GEV – the Generalized Extreme Value distribution.
15:30
And there’s some really nice mathematical theory that basically
15:36
shows that, if you take a random variable – any kind of random variable,
15:42
the extremes of that random variable should follow a
15:46
Generalized Extreme Value distribution.
15:49
So here I’m showing an example of – this is a buoy in Hawaii, and you
15:55
look at a wave height record. And you can generally see big waves
15:59
in the wintertime, smaller waves in the summertime.
16:02
And these red circles are the top three largest events in a given year.
16:09
And if you fit those top three events per year for 30 years here to a distribution,
16:18
the GEV distribution generally holds pretty well.
16:22
And, for engineering design, you can – you can write this in a different way,
16:26
where you look at a return period, and you look at wave height,
16:30
where this is the one year, or annual, wave height.
16:34
This is the 10-year wave height. This is the 100-year wave height.
16:37
And another way to think of the 100-year wave height is just –
16:41
it’s a wave that has a 1% chance of occurring in a given year.
16:46
So, in 100 years, you might expect one of them.
16:49
So, in this example – so the 50-year storm, which corresponds to this return
16:56
period of 50 years, or a 2% annual chance of occurrence,
17:02
is a little bit more than 2 meters – or, sorry, 10 meters.
17:08
So this is what the Generalized Extreme Value distribution looks like.
17:12
And you don’t have to worry too much about the equation.
17:16
But essentially, this is a complicated version of a – of a Gaussian bell curve.
17:21
So it has a parameter representing the mean of that distribution, mu.
17:26
The width of that distribution, sigma.
17:29
And the skewness of that distribution, k. Okay?
17:32
And those parameters have pretty interesting consequences
17:37
on the behavior of extremes.
17:39
And here I sort of identify the 50-year storm as sort of a critical threshold
17:45
because, if you look in the coastal engineering manual, there’s just this
17:49
direct quote here that basically says that most coastal engineering
17:54
infrastructure is designed for return periods of 50 years or less.
17:59
The Dutch take a more conservative approach
18:01
to their coastal engineering structures.
18:03
They design structures for a 1 in every 10,000 year event. [laughter]
18:07
But here, we’re a little more economical.
18:10
Unfortunately, as I’ll talk a little bit about later in this talk,
18:14
when most of this coastal infrastructure was designed, they didn’t make
18:19
an allowance – what’s called an allowance for additional sea level.
18:24
That it was designed with the assumption that sea level
18:26
would remain as it is right now. They didn’t make any, necessarily,
18:30
additional factor, or allowance, for future sea level rise,
18:34
unfortunately, in some cases.
18:37
Okay. So this is – we did a little study where we took global models for the
18:47
tide, global models for storm surge, and global models for wave setup,
18:51
combined them to generate what’s called a total water level,
18:55
which represents sort of a extreme flood hazard level, around the world.
19:00
And then we looked at – fit a distribution to the top three extreme
19:08
water level events around the globe. And now we’re looking at the
19:12
different parameters involved at particular locations.
19:16
The mean, the standard deviation, and the skewness.
19:25
So whenever I look at this diagram, even though I’ve looked at it a lot,
19:30
I kind of always find really new, interesting things to understand
19:36
and relate from this diagram. So this parameter, which represents
19:41
the mean of that distribution, looks very much like the amplitude
19:47
of the largest tidal component, which is driven – which is the
19:52
M2 component, which is the lunar semi-diurnal component.
19:57
So it’s the component of the tide that’s associated with the orbit of the moon,
20:01
which is almost the largest tidal component everywhere you go.
20:05
So if you looked at the amplitude of the M2 tidal component, that would look
20:08
almost identical to this with a little bit of differences in the high latitudes,
20:15
which a little bit of this red area is associated with waves.
20:18
But this mean parameter looks very much like you’d
20:21
expect as a high tide level. So the parameter sigma represents
20:28
sort of the variance of the extremes. And when you look at this, you can
20:32
see very interesting things happening. So the northern hemisphere,
20:37
Pacific and north Atlantic, show very strong interannual
20:41
variability in water level. And this is really driven by the
20:46
interannual variability of extratropical storms, which generate waves.
20:52
In comparison, the tropics are much more consistent, much lower,
20:57
in terms of wave activity and water level activity.
21:00
And compared to the northern high latitudes, the southern ocean is very
21:05
much consistent in terms of its sort of extratropical wave activity.
21:12
And finally, in the shape parameter, the behavior of this form of the shape
21:19
parameter, you see these hot spots in red, which are very much
21:24
associated with tropical cyclones. So these are the hot spots for tropical
21:34
cyclones around the world, which show a very distinct behavior in terms of the
21:41
statistical occurrence of extremes. So kind of a nice way to describe this –
21:48
this parameter is really driven by the tide.
21:51
This parameter is really driven by extratropical activity.
21:54
And this parameter is really driven by tropical storm activity.
22:00
Okay. So now that we’ve investigated the statistics a little bit,
22:05
we’re going to try to see what happens with sea level rise.
22:08
So if you took this statistical model – this distribution in blue, or this
22:13
distribution over here, and you didn’t change the underlying behavior of the
22:18
wave dynamics or the climate, all you did was take the mean water level,
22:24
and you increased it, then essentially you’re taking this distribution –
22:28
and in this case, you’re shifting that distribution just right there you see
22:34
to the right. Or you’re taking this distribution, and you’re shifting it up.
22:40
And that’s problematic because, if you remember what I was talking
22:44
about in engineering design, you figure out what your 50-year water level is,
22:48
or your 100-year water level is, you put all your infrastructure there, and
22:53
then your infrastructure remains fixed. But, as you increase the mean of this
22:57
distribution, it allows so that lesser and lesser storms can exceed that threshold.
23:05
And so if you looked at, as you slowly shift sea level – slowly increases,
23:13
and you look at the factor of increase from the baseline state to the future
23:18
state, well, based on the Generalized Extreme Value distribution –
23:22
so I’m just taking this distribution, shifting it associated with sea level rise,
23:27
and looking at sort of the factor of increase in events that
23:30
exceed that threshold.
23:33
Well, those – the increase in frequency of those events grows exponentially.
23:41
So it sort of – you can see, it starts out slow, and then it slowly ramps up
23:45
and ramps up and ramps up and gets pretty out of hand.
23:50
So that is behavior based on the Generalized Extreme Value statistical
23:54
model, but we’re going to try to take a different approach where, rather than
23:58
rely on a statistical model, we’ll just use the empirical data and try to
24:02
come up with a empirical estimate of this rate of growth of flooding
24:09
frequency associated with sea level rise. And that was the approach that
24:13
I worked on with former graduate student Mohsen Taherkhani, and
24:17
we’re trying to get this work published right now in Scientific Reports.
24:22
So the data source that we used for this was tide gauges.
24:27
So we also wanted to take a data-driven approach,
24:29
use tide gauges rather than models.
24:32
So NOAA has a really nice network of tide gauges that they serve
24:37
all around the U.S. So you can go to this nice website,
24:41
NOAA Tides & Currents, and you can get historical data or predictions
24:45
of what the water level is going to be almost anywhere around the U.S.
24:48
And it has a really nice application programming interface, so if you use
24:54
a URL with some of these keywords, like the begin date or the end date, the
24:59
station ID, the product, you can just go to this URL, and it’ll send you data.
25:03
So we wrote a little code to get a bunch of data from
25:08
all the stations around the world and try to analyze that.
25:13
And now, tide gauges are a little bit different situation because most of the
25:18
tide gauges are really sheltered from impacts due to waves.
25:22
And if you look at most of those areas, a lot of big population centers,
25:26
like Boston in particular, you have less exposure to waves, but there’s
25:30
more of an importance of tide and storm surge and things like that.
25:34
So this analysis that I’ll show is mostly focusing on water levels driven by
25:41
tides, storm surge, but the impact of waves is a little bit more sheltered,
25:47
which is characteristic of a lot of population centers where the
25:50
wave exposure is relatively limited.
25:52
So think San Francisco and not, say, Ocean Beach, San Francisco.
25:58
Okay. So we analyzed this data all around the U.S.
26:05
And here I’m showing a little bit of these GEV parameters.
26:09
And we performed a clustering analysis to try to identify particular
26:13
groups or clusters that had a very consistent behavior.
26:18
And this clustering was done on the GEV parameters, but interestingly,
26:21
you see a very strong latitudinal dependence of these clusters.
26:26
So the red clusters generally occur at low latitudes.
26:32
The green clusters generally occur at mid-latitudes. And so the
26:36
yellow clusters at mid-latitudes and the green clusters at high latitudes.
26:40
And these blue clusters are very interesting.
26:43
Those are tide gauge stations with high values of the shape parameter
26:48
which correspond to tide gauges that were really –
26:50
you see large tropical storm events in that data.
26:57
Okay. So what we did with this data was this is an example for Boston.
27:02
We generate these return period curves.
27:05
And then what we want to look at, we want to look at these two
27:07
different scenarios. One is, we want to find the difference
27:11
between the 50-year water level event, where the infrastructure is going to
27:15
be designed, and the water level event that’s exceeded every year.
27:20
And, in Boston, that’s about 30 centimeters of difference
27:25
between those two scenarios.
27:28
So, if you took sea level, and you raised it by 30 centimeters,
27:33
then you’d exceed this 50-year threshold essentially every year.
27:37
So the difference between the water levels is directly analogous
27:40
to how much sea level rise you would need in order have
27:43
a regime shift from one scenario to the other.
27:46
And likewise, we tried to find the water level difference between
27:49
the 50-year water level event and the mean high or high water, which is the
27:54
high tide level that occurs every day. So you can see, for Boston, the 50-year
28:00
water level event, which is sort of the extreme, is only about
28:04
a meter difference from mean high or high water.
28:08
So you can imagine a 1-meter sea level rise would cause your extreme level
28:14
of all your infrastructure to be exceeded every day at high tide.
28:18
So a pretty consequential thing.
28:23
So this is looking at, for all the tide gauge stations that we analyzed,
28:27
and with labels for a few important locations, the difference between the
28:34
50-year water level and the 1-year water level event, and the difference
28:37
between the 50-year water level event and the mean
28:39
high or high water tide gauge – tide level.
28:44
So looking for Scenario 1. For most stations around the U.S.,
28:51
the difference between the 50-year water level and
28:52
the 1-year water level is less than 1/2 a meter.
28:57
And maybe a little bit more scary, the difference between the 50-year
29:00
water level and mean high or high water around the U.S. for most of
29:03
the stations is less than 1.25 meters. So these values are really comparable
29:09
to how much sea level rise we expect in the next several decades.
29:15
So, if you take this plot, and instead of looking at it in terms of the difference
29:22
in water level, you apply a sea level curve with time and then try to ask,
29:27
okay, well, when, in the future, is this scenario going to take place,
29:32
where the 50-year flood regime transitions to an annual flood regime?
29:38
So now, instead of looking at it with sea level rise, we look at,
29:41
on the Y axis, is a year in the future.
29:46
And we see here that, well, 2100 is a very common timeframe
29:52
far away from now, but it’s used in sea level rise.
29:56
And you can see here, in this Scenario #2, that, at about 90%
30:03
of the tide stations in the U.S., they transition from – or, they experience
30:09
today’s 50-year extreme coastal flood – they will experience that 50-year
30:15
extreme coastal flood every day at highest tide before 2100.
30:22
And you can see, a lot of stations transition even before 2050 –
30:27
these highly vulnerable sites, which are generally associated
30:30
with low latitudes in red here.
30:36
So next, we wanted to look at the growth rate in looking at the odds
30:45
of exceeding the 50-year water level, how that increases steadily with
30:51
small amounts of sea level rise. And this is a sort of spaghetti diagram,
30:55
where each of these lines corresponds to a single tide gauge station. Okay?
31:00
So, for the most vulnerable sites, the odds of exceeding the
31:05
50-year threshold essentially doubles almost with every centimeter
31:11
of sea level rise that you have.
31:13
And this is a doubling with every 5 centimeters of sea level rise
31:18
and a doubling with every 25 centimeters of sea level rise.
31:21
And if you keep this doubling scale as a parameter, you can take all of
31:26
these different tide gauge stations, and you can sort of collapse them
31:32
onto this one relationship which looks like this.
31:35
So the odds of increased flooding at a extreme threshold look like
31:42
2 to the power of sea level rise divided by this doubling parameter.
31:46
So, for instance, say your doubling parameter was 10 centimeters of
31:50
sea level rise. You have 20 centimeters of sea level rise.
31:54
20 divided by 10 is 2. 2 to the 2 is 4, so you get a factor
31:59
of 4 increase in sea level rise, which is two doubling periods.
32:05
And so you can imagine, if you only have a doubling period – or, a doubling
32:12
amount of sea level as 10 centimeters of sea level rise, and you have 1 meter,
32:20
so the doubling – you’re doubling amount of sea level is 10 centimeters,
32:23
and your amount of sea level is 1 centimeter – or, sorry, 1 meter
32:27
of sea level rise, then you essentially have 10 doubling periods.
32:31
So 2 to the 10, which is a factor of 1,000.
32:34
So a pretty serious increase in flooding.
32:38
And taking this same approach, we can apply a sea level projection,
32:42
and we can look at, rather than an amount of sea level rise that you’d need,
32:46
amount of time that you would need to, say,
32:49
double the odds of having extreme flooding.
32:52
And looking at it in this way, you see that, well, for different
32:55
sea level projections, which are shown here, but focusing on some of
32:58
the more high-end projections, you see that, for the most vulnerable
33:03
sites, the odds of exceeding extreme flooding thresholds
33:08
doubles approximately every five years.
33:12
Okay. So the summary of this section, getting back to the relation to the movie
33:18
Point Break, so the reason that this is related to the Point Break, if you can
33:24
remember, is – well, this guy, Patrick Swayze, is a bank robber, slash, surfer.
33:32
[laughter] And Keanu Reaves is a undercover FBI agent who pretends to
33:37
be a surfer to go and catch him. And without trying to spoil too much
33:41
of the movie [laughter], Keanu Reaves, at the very end of the movie, ends up
33:46
catching Patrick Swayze because he – because Keanu Reaves knows that
33:51
Patrick Swayze is chasing the 50-year storm. [laughter]
33:56
And so that’s where he knows where he’s going to be, so he ends up catching
33:59
him because of the 50-year storm. So prominently featured in the movie,
34:04
and a interesting fact about this, is looking at the odds of this,
34:08
well, you’re going to have a lot more 50-year storms
34:12
that exceed the extreme water level in the future.
34:17
So that’s the relation, in that case, to the movie Point Break. [laughter]
34:21
So a summary here is just the odds of exceeding the 50-year extreme
34:24
water level event, we’re finding, doubles approximately every
34:28
five years due to sea level rise.
34:30
Okay. The next section we’re going to talk about is coastal erosion.
34:35
And talking about a data-assimilated model of coastal change in California.
34:40
And now think about, as I’m going through,
34:43
how this might relate to the movie True Lies.
34:47
Okay. So California’s beaches and sediment supply.
34:53
Most of the beaches in California get their sediment
34:55
from three different sources.
34:57
One is fluvial sediment input, which is sediment delivered from rivers.
35:03
Here you see the Santa Clara River in Ventura – a satellite image of a ton of
35:08
sand that’s basically being dumped in the coast from the Santa Clara River.
35:13
Another source of sediment for California’s beaches
35:16
is from eroding cliffs and dunes.
35:20
And another one, particularly in highly developed areas like southern
35:24
California, a lot of the sand comes from artificial beach nourishments.
35:30
And this is a California-centric view of sediment supply, but if you go to
35:34
a tropical setting, another big source of sand is from coral reefs.
35:41
So here’s a few examples. Some really impressive examples
35:45
of these different processes. Here is fluvial sediment.
35:49
Fluvial sediment plume that was delivered to the coastal area
35:53
from the removal of the Elwha Dam in Washington.
35:57
Just a huge signal of sediment being delivered from the coast –
36:01
the sediment that was previously impounded by this dam.
36:04
And if you remember a couple of years ago, the Big Sur landslide –
36:10
so this is sort of the before shot. And this is the after.
36:15
So you can see, like, a lot of material was sort of delivered to the coastline.
36:19
And I’m not sure if, in several decades, you’re going to have, like, a really nice
36:23
beach here from all this stuff that was dumped there, but maybe.
36:28
Interestingly, we have a guy in our office, John Warrick, who was really
36:34
instrumental in monitoring both the Elwha and this landslide.
36:41
So here’s another really impressive video of cliff erosion in Pacifica.
36:48
And certainly you would not want to be living in
36:52
these little condos while this is going on.
36:57
I am almost positive that all of these structures have since been removed.
37:02
[laughs] As you can imagine, they certainly need to be.
37:07
Another – probably the most famous example of a beach nourishment
37:12
is this thing called a sand engine in the Netherlands.
37:16
And essentially, this is a big beach nourishment, which was
37:20
essentially a research project by the Dutch government.
37:24
So what they did was, they built out this gigantic little sand lobe
37:31
as a way to – thinking, well, maybe we should just nourish the beach
37:35
in one location and then let it naturally spread down the coast rather than
37:40
nourish the beach everywhere. So this thing is slowly spreading down
37:44
the coast, both ways, and providing additional sand to these areas over here.
37:49
So if you can get a scale of this thing, this is – they built it out a kilometer
37:54
from the existing shoreline, and it’s about 3 kilometers long.
37:57
So it’s pretty massive.
37:59
Biggest nourishment project in the world, by far.
38:02
Okay. So those were sediment supply processes.
38:07
And now we’re going to look at factors that contribute to erosion.
38:12
So one factor that contributes to erosion is waves, of course.
38:16
Another is sea level rise.
38:19
And also river damming and shoreline armoring.
38:23
Essentially, anything that’s getting in the way of a natural sediment supply.
38:27
For instance, say, the river is giving you a lot of fluvial sediment supply to your
38:32
beaches. If you dam that river, reduce that sediment, well,
38:35
the beaches are going to be affected. If those eroding cliffs are – if those
38:38
eroding cliffs are providing sand to the beach, well, if you armor all those
38:42
cliffs, you’re going to reduce the sediment supply from the cliffs.
38:44
And waves, of course, move everything around.
38:48
So looking at the wave-driven components in particular, so for wave-
38:54
driven transport, you essentially have what’s called longshore transport.
38:59
So transport from one area of the beach – beach sand over here to –
39:03
in a different location. So here’s just a little example of longshore sand
39:08
transport. So here, there are a couple of idealized headlands.
39:15
And the sand – the yellow sand here – essentially moves in concert with the
39:22
wave angle. So when the waves are coming from more the north, it pushes
39:25
sand on one side of the headland. And when the waves are coming
39:28
more from the south, it pushes sand on the other side of the headland.
39:31
So that’s essentially longshore transport.
39:34
Another transport mechanism is called cross-shore transport, what’s also called
39:39
equilibrium shoreline transport, or equilibrium shoreline change.
39:45
And essentially, the behavior of that process looks like this where you have
39:51
a normal beach profile, where you have some dunes, and you have a beach,
39:55
and you have sort of small waves. When the waves become larger,
40:01
it erodes sand from the dry beach and deposits that offshore.
40:06
So the beach profile is becoming more into equilibrium with a large wave state,
40:11
which favors having big sand bars that dissipate wave energy more offshore.
40:19
And when the waves get a little bit smaller, it favors on-shore transport of
40:24
the building of that beach back out. So, if you go to the beach in California,
40:27
that’s probably going to be the biggest seasonal change that you’re going to
40:31
see is erosion of the beach in the wintertime, when the waves are big, and
40:35
then that beach will slowly be built out when the waves are little bit smaller.
40:39
Okay. Now sea level rise.
40:42
So sea level rise changes beaches in a particular way that was first
40:52
sort of described by Bruun, so it’s called the Bruun Rule.
40:59
And the idea behind this is that the beach maintains
41:02
an equilibrium profile, or shape. So this shape.
41:07
It won’t – the beach wants to keep that shape to be the same.
41:12
And so, as sea level rise – as sea level goes up, it wants to
41:17
maintain this same beach profile at a – at a higher elevation.
41:21
So the consequences of that, generally, is you have erosion from the
41:25
dry beach and deposition offshore. So the whole profile migrates both
41:32
upward due to sea level rise but also landward.
41:37
Okay. There are a variety of different models to predict shoreline change.
41:48
One approach that you can imagine, which is a very nice approach, which
41:52
is physics-based numerical models. They essentially solve conservation
41:56
of mass and momentum of fluid, being water, and sediment.
42:02
And so they can – they can move it around in different ways.
42:06
Very much like a model that predicts the weather
42:10
or a model that predicts behavior of the ocean.
42:15
So there are more simplified approaches, which are called
42:17
data-driven approaches or process-based models, where they
42:21
don’t try to directly simulate the physics of the model, but they try to
42:25
represent processes with data or in a much more simplified way.
42:30
And one nice example of this approach is the USGS National
42:36
Assessment of Shoreline Change.
42:40
So USGS National Assessment of Shoreline Change was a
42:42
very large project where they tried to determine long-term rates
42:46
of shoreline change around the entire United States.
42:51
And the concept behind this was, okay, if you have a bunch of aerial photos
42:57
going back in time, well, you can digitize the location of the shoreline
43:04
for each of those photos, and then you can – based on those little shape files,
43:09
you can fit a relationship to see how they might change over time.
43:15
So, for a given location, you generate these little spaghetti lines,
43:18
which represent a shoreline at a given time.
43:21
You can shoot some transects through it.
43:25
And you can look at the long-term change of the shoreline position
43:29
on that transect over time. And so this is the approach for
43:34
the USGS National Assessment of Shoreline Change.
43:37
It’s a very nice way to look at long-term shoreline change,
43:40
but there are some drawbacks. One drawback is that, if you only
43:44
have a limited amount of data – a limited amount of shoreline photos,
43:50
some of those photos can have large erosion events or accretion
43:53
events or different things happening. The shorelines can be highly variable,
43:58
so depending on when that photo was taken, you can have different behavior.
44:03
So some colleagues that I’ve worked with when I was at the University of
44:06
Hawaii have come up with ways of improving that, which has sort of been
44:11
really important to improving my understanding of the problem.
44:17
So if you wanted to come up with a better long-term rate, you have to
44:20
account for these short-term variability due to storms.
44:24
And finally, if you – if you want to address things like long-term sea level
44:29
rise, well, that’s going to inevitably accelerate the erosion
44:32
that’s going to be happening. So if you’re looking at past data
44:36
and trends, well, those probably will accelerate due to sea level rise.
44:41
Because, during this period, sea level rise was very minimal.
44:45
But, as you get to higher and higher sea level projections going farther
44:50
into the future, you can see a lot more – a lot more sea level
44:55
in the future period than you saw in the past.
45:00
So another really nice method was developed by Joe Long and Nathaniel
45:08
Plant, who were – at the time, were at the USGS in St. Petersburg.
45:15
They developed this model that combines a model for a long-term
45:18
erosion combined with a wave-driven short-term equilibrium model.
45:24
And they combined these two techniques with a method called
45:26
data assimilation. And data assimilation is just a way where
45:30
you can combine some type of forward model with data to
45:35
sort of calibrate the behavior to a individual location.
45:39
And interestingly, the data assimilation is maybe the primary technique that
45:47
was developed to really improve simulation of weather.
45:51
So the reason that you probably believe your weather forecast is not only
45:55
because the models are good, but the models use a lot of data
45:58
collected all over the place. And that data assimilation piece
46:02
has really improved the predictability of the weather.
46:04
So, in case of sediment transport, these guys had the great idea of, well,
46:09
let’s use data assimilation to try to improve our predictive capabilities
46:12
of shoreline change. So we took that approach, and we tried
46:16
to develop it a little bit further and apply it to southern California.
46:22
So here is just an example of a model that we developed for
46:26
about 500 kilometers of coastal area in southern California.
46:32
The model that we developed includes longshore sediment transport,
46:36
cross-shore sediment transport, the effects of sea level rise,
46:39
and sediment supply from natural and anthropogenic sources.
46:42
And it uses data assimilation to estimate some of the parameters
46:46
that are involved.
46:48
So I don’t want to get too much into the details of the equation, but essentially,
46:53
the model looks at – solves a differential equation, which looks at
46:59
the rate of shoreline change. Y is the shoreline position.
47:03
But it’s driven by gradients in longshore sediment transport,
47:07
cross-shore shoreline change due to waves and due to sea level rise.
47:13
This is a model that was developed by some folks at Scripps.
47:17
This is a model that looks like the Bruun Rule.
47:19
And this is a residual term that sort of represents everything that
47:24
we’re not resolving in these dynamical terms, which we
47:27
estimate from data assimilation. And if you cross off these dynamical
47:32
terms – if you just had this piece and this piece, well, that looks very much
47:36
like the USGS National Assessment of Shoreline Change. And we wanted
47:40
to improve upon that by adding some more dynamical processes
47:45
related to waves and sea level.
47:48
And so we used data assimilation, and the data generally comes from
47:53
Lidar surveys. Lidar surveys is you have a plane, and you have
47:58
a plane that flies over with a laser. And that laser records basically the
48:03
elevation all – of the whole coastal area. So the USGS, you know, digital
48:10
elevation models are a very important product.
48:13
And we’re very fortunate to have a number of Lidar surveys that are
48:19
essentially twice a year going back pretty far to really assess coastal change
48:27
in a – in a highly accurate way. So we’re lucky that the application
48:31
was southern California where all this data exists.
48:34
So each of these spaghetti lines represents, say, the mean high water
48:38
contour on that elevation model at a particular time.
48:43
And so the model works by – well, the intersection of those
48:47
spaghetti shorelines and an individual transect basically gives you these
48:53
blue dots, which are the observational data. And the model
48:58
takes wave height and wave direction and period and produces
49:02
a prediction of how the shoreline evolves.
49:06
Which you can see here is, generally looks like, well,
49:09
whenever you have big waves, you have a lot of erosion.
49:11
Whenever you have small waves, you have a lot of accretion.
49:14
But, as the model runs, whenever there’s a time where you have both
49:19
a model prediction and you have a data point, it uses that opportunity
49:23
to adjust the model parameters to best calibrate the behavior for
49:28
that particular location of interest. So this is essentially a data assimilation
49:33
process applied to this model. And so, once you come up with a good
49:38
parameter estimate for your particular location of interest, you can run that
49:42
model in a forecast mode and try to predict what might happen on
49:46
a particular section of beach. So this is a section of beach in La Jolla.
49:52
This is Scripps Pier. And you can see the lines here
49:55
correspond to the initial shoreline position in green, the final shoreline –
50:00
predicted shoreline position in red, with the uncertainty band in yellow.
50:04
And another uncertainty band associated with a year of particularly
50:09
large waves, which would cause additional erosion.
50:12
And for many locations, as you run this model to 2100, they sort of look
50:17
like this section in the bottom here, where essentially you have the
50:21
predicted shoreline position is right at the edge of the infrastructure.
50:26
So in this simulation right here, the beach is entirely gone. Okay?
50:31
So if you look at that across the whole section of California coastline,
50:36
you run that to 2100, you’ll find that about 1/3 to about 2/3 of the beaches
50:41
in southern California become completely eroded by 2100
50:45
under these sea level rise scenarios. So completely gone.
50:49
So this finding was prominently featured in California’s Fourth Climate
50:54
Change Assessment, where – you know, warning that, you know, losing 2/3 of
51:01
the beaches in California could certainly make the place very different. [laughter]
51:14
Okay, so getting back to a summary and my connection to the movie True Lies.
51:19
So think in your mind about how that might connect, okay?
51:23
So this section was talking about a data-assimilated model of
51:27
coastal change in California. And there’s a real famous quote that
51:31
I think might illustrate why [laughter] I think we’re getting towards
51:36
the movie True Lies. So there’s this famous quote
51:39
from a real famous mathematician, George Box, that says that
51:43
all models are wrong. Some models are useful.
51:46
And the essence of this quote is essentially that the California coastline
51:52
is far more complex than my little model can possibly hope to resolve.
52:00
So it’s making a lot of assumptions and a lot of approximations, but we’re really
52:06
trying to capture the most important processes, and we’re trying to use data
52:10
to improve that as much as we can. So although the model is not right,
52:15
it can have some aspects of it where
52:17
we really learn more about the system through that whole exercise.
52:23
So getting to the title True Lies. So if you see here, data-assimilated
52:29
model – well, to me, essentially that’s true lies. [laughter]
52:37
And you also might think that, okay, well, maybe this section might relate
52:43
to the movie True Lies because it’s looking at coastal change in California.
52:49
And, as governor of California, Arnold Schwarzenegger, you know,
52:55
fought to address climate change very much like, in the movie True Lies,
52:59
he fought terrorists. So I think it might be
53:03
a nice tribute to his actions as the governor of California.
53:09
So that essentially concludes my talk. I’ll just give you a few summary points.
53:16
So how bad could it possibly be? Well, if sea level rise projections hold,
53:21
then we’re going to have some pretty serious consequences.
53:24
Today’s 50-year water levels may be exceeded every high tide before 2100.
53:31
And the odds of exceeding extreme flood thresholds due to sea level rise
53:35
will double approximately every five years going forward into the future.
53:41
Modeling coastal change is still very hard.
53:43
But data assimilation and the acquisition of more and more data
53:48
is really starting to enable us to achieve reliable quantitative
53:52
predictions in a very complicated system.
53:55
And that is basically what I’m going to continue to work on at the USGS and
54:00
hopefully have better predictions of what might happen to the beaches.
54:05
So that’s it. Thank you very much.
54:07
[Applause]
54:14
- Thank you, Sean. So if you’d like to ask a question,
54:17
we ask that you please step up to the microphone.
54:20
And Sean will be thrilled to answer your question.
54:25
[Silence]
54:31
- Okay. It’s been a long time since I had calculus or anything [laughter]
54:35
other than measuring and stuff. So a lot of that went way over my head.
54:39
However, the big question I have is, you had that map down there
54:46
by La Jolla. - Mm-hmm.
54:48
- Are there maps available for other coastline areas of California
54:52
that show the same thing? Or, show …
54:54
- Yes. So, as part of a project called the Coastal Storm Modeling System,
55:01
we finished southern California. I’ve also finished the modeling results
55:06
for central California, which go from Point Conception to the Golden Gate.
55:12
The shoreline projections are done for that.
55:15
There’s accompanying flooding projections associated with those
55:18
coastal change and storm scenarios, which are being finished right now.
55:21
Those should be available very soon.
55:24
And then now we’re going to be working on northern California.
55:27
We’ve also got some Hurricane Florence supplemental funding to try to
55:31
make these projections on the East Coast. So we’re starting to get over in
55:34
that location. I can give you a specific website, or if you
55:38
Google “our coast our future,” you’ll have access to all of those projections
55:43
for both flooding and coastal change. So it’s Our Coast, Our Future,
55:48
and I can give you specifically where you’d want to go to look at that.
55:51
But it is available. - Okay.
55:56
- My name is Chuck Hakkarinen. I’m long-time retired from the
55:59
Electric Power Research Institute, where I managed research on this
56:02
and other areas. Could you comment on the influences –
56:07
perhaps not in California, but other regions you study,
56:10
of groundwater extraction in coastal regions and isostatic rebound of
56:16
the land masses in glacial regions on sea level rise?
56:20
- Yeah. Absolutely. So very important processes.
56:25
Most of the processes that I focused on have to do with sort of the
56:29
global average of sea level rise – more water being added to the system.
56:33
But, like you mentioned, there’s an extremely important component,
56:36
which I didn’t talk about, which is the tectonics. Okay?
56:40
So if you look at, for instance, areas on the East Coast, particularly in the
56:44
Gulf Coast, like New Orleans, although global sea level rise is rising
56:49
at about 3 or 4 centimeters per year, you look at tide gauges in New Orleans,
56:53
and it’s almost 1 centimeter per year. So triple the global average.
56:59
And that is a combination of a little bit of sea level rise and a lot of subsidence.
57:04
So groundwater extraction can certainly contribute to that.
57:08
But the subsidence of that whole delta, just because it’s sinking,
57:13
is also a pretty important factor. And rebound.
57:17
Rebound is another really important thing.
57:20
Ironically, for areas in Greenland, where you think you might get
57:25
a lot of sea level rise because, well, the water’s melting right there,
57:29
you have a process called rebound.
57:31
So all that ice mass is melting, so that whole thing weighs less now.
57:38
So you’re not going to have sea level rise in a area like Greenland.
57:43
You’re going to have sea level fall.
57:45
So there’s this interesting process called sea level fingerprinting, by,
57:49
based on where the ice is melting and how the crust is rebounding,
57:55
you might have little localized hot spots of high amounts of sea level rise.
58:00
In particular, low latitude areas, like in Hawaii, are really particularly
58:05
hit very bad. So all those tectonic processes, which are extremely
58:10
important to the U.S. Geological Survey, are very important to consider.
58:14
So thank you for your comment on that.
58:19
[Silence]
58:22
- Your 280 million people displaced by 2100, versus what I saw happening
58:29
in California – California doesn’t look that bad.
58:32
So we lose the southern California beaches. [laughter]
58:34
- Yeah. - I assume that’s Florida and
58:37
Bangladesh and places like that. - Yep.
58:39
- Can you speak to a little more global distribution?
58:43
- Where those things are. I think, exactly like you’re talking about.
58:46
They’re low-lying estuaries. So, you know, Bangladesh,
58:52
you know, Mumbai, some low-lying areas in China are hit very, very hard.
59:00
We’re not hit nearly as hard as they are. Another real important area is
59:05
low-lying Pacific island atolls. Many of them don’t have very large
59:09
populations, but they’re going to be hit particularly hard
59:13
because of these processes.
59:16
In California, we’re going to lose beaches, and we’re going to
59:18
lose property, and we’re going to try to make solutions.
59:23
But we’re not the ones that are going to be hit the hardest.
59:26
I would say the people that are going to be hit the hardest are the low-lying
59:28
Pacific island nations, where their country might be underwater. [laughs]
59:34
- What about Florida? - Florida is going to be hit pretty hard.
59:37
Absolutely.
59:39
- [inaudible] - You know, they’re going to be doing a
59:42
lot of nourishing and a lot of pumping. But absolutely, Florida is going to
59:46
be hit much harder than California, for instance.
59:50
You know, California – or, sorry, Florida and New Jersey spend the
59:53
most amount of money on beach nourishments, and that certainly
59:56
will continue, to try to preserve sort of the infrastructure there.
60:01
Yeah? - In one of your first slides, you had
60:06
the variation in sea level going back … - Yep.
60:11
- … half a million years or so. - Mm-hmm.
60:13
- There are two things about that that struck me.
60:17
One is that the sea level rise after the – after the glacial periods is much faster
60:24
than sea level fall … - Yeah.
60:26
- … when it’s – when it’s starting. - Mm-hmm. Mm-hmm.
60:28
- And the second is that the – that the peak has always ended up being
60:33
around zero, where we are today. - Yep.
60:35
- And never got much higher than that. - Yep.
60:37
- Even though we still have a lot of ice surface on the – on the planet now.
60:46
Is it likely that even more – okay. - Yep, that one.
60:51
- Things over – yeah, thanks. - Yeah. So like you were saying,
60:55
there’s some pretty interesting sea level high stands here.
61:00
So you have these low stands associated with these glacial periods.
61:03
But you also have these high stands. And these are about 10 meters
61:08
higher than present. So partially associated with,
61:14
you know, more ice being melted than there is ice right now.
61:19
But we think we’re going to go way past that.
61:22
I couldn’t specifically tell you, what are the geologic reasons why the high stand
61:29
looks particularly at this level, but that’s a very important thing to
61:33
sort of note that it sort of peaks around this level with these little high stands.
61:39
I think just sort of – it’s just an equilibrium concept of, you know,
61:43
the position of the sun and the Earth and their orbit are just such that
61:49
this is about the maximum sea level that we’re sort of getting.
61:55
But the projections are, well, right now, we’re probably
61:57
a little bit below these high stands. I think these are around 10 meters.
62:03
So we could – but you can see sometimes those high stands have
62:05
been reached during the interglacials, and sometimes they haven’t.
62:10
So we’re – right now, we’re sort of over in the – in the orbital cycle,
62:15
we’re sort of – should be over that hump and going back down,
62:19
but we’re not seeing that. And CO2 … - Years ago, people were
62:25
predicting a new Ice Age. - Yeah. [laughter]
62:28
- And the speed at which they go up. That’s another thing.
62:31
- Yep. Yeah. The speed which they go up, I believe, is entirely related to
62:36
the orbital motions. That the orbital sort of procession
62:39
of the Earth will describe this behavior of rapid rise and then a –
62:44
then a slow decline. Milankovitch cycles, I believe, will describe that.
62:50
Question?
62:52
- Yeah. I was looking at your model results where you take your data,
62:56
and you – and you work with the data until you run out.
62:59
- Mm-hmm. Mm-hmm. - And then you just have a model result.
63:01
And I noticed that, when you have data, you have this really large dynamic
63:06
range. You have a rebuilding, and then you have erosion.
63:10
- Yep. - And that dynamic range totally
63:12
evaporates when you run out of data. - Yeah, yeah.
63:15
- And so I’m curious if you could speak to that too.
63:16
- Yeah, yeah. - How do you feel about this
63:18
model based on that characteristic? - Absolutely. You know,
63:20
that’s an extremely important thing. And the problem with that,
63:24
that you totally saw in this, is related to this end period.
63:29
That actually – let’s see here. This behavior, right, where there’s not
63:35
as much variability in this portion … - Yeah. That’s right.
63:38
Right after your black line. Uh-huh. - Unfortunately, that is not related to
63:43
the behavior of the model parameters. That is entirely related to the fact that
63:47
we’re terrible at forecasting waves in contrast to hindcasting.
63:53
The variance of this hindcast is extremely low compared to
63:58
the historical period. So that’s really what’s driving
64:01
the limited variance. This is a … - So you’d say it’s more like
64:04
an average? - Yeah, so …
64:05
- And maybe an accurate average, but more like an average that
64:08
doesn’t capture the entire range. - This behavior improves vastly
64:14
as you go beyond this initial transition from hindcast to forecast.
64:20
The variance of the wave height improves much better to a realistic
64:27
range as you go longer into the – into the future.
64:30
But unfortunately, in this case, the forecast is not very good for the waves.
64:35
And that’s really what’s driving the behavior.
64:38
So this is something that we’ve sort of really been looking at in the future.
64:43
If I can skip ahead a little bit – my computer
64:47
is not quite catching up with me here.
64:51
But that behavior is something that we’re very, very much interested in,
64:55
trying to understand – okay. Well, if we’re trying to make a future
65:01
prediction of how the system is going to behave, and we’re trying to make a
65:07
prediction in the future, well, we really need to know what
65:10
the waves are going to be like. And you don’t always know what the
65:13
waves are going to be like in the future. So we’ve tried to do these different
65:16
scenarios where, well, instead of running known wave conditions,
65:22
we try to run an ensemble of potential future wave conditions.
65:26
And you can see, in this case, well, the variability of the modeled shoreline
65:30
condition with the uniform forcing and with the ensemble forcing,
65:34
which is 100 different possible combinations of what the waves
65:38
might be, can look very different. So that’s an extremely important thing
65:42
that we’re trying to look at in the future is, how can we come up with the best
65:46
wave projections, and not just use a single wave projection, but a whole
65:50
range of projections, to try to understand the variability of the
65:53
shoreline as a consequence of the waves. So thank you for noticing that.
65:58
Yeah. A very important thing. - Yeah. Can I ask a bonus question?
66:01
- Yeah. Please. [laughter]
66:03
- Can you – can you speak to – I know this isn’t really your
66:06
wheelhouse, but what do you – I’d just say, what do you think
66:09
we should do about it? [laughter]
66:13
- Great line. - [inaudible] [laughter]
66:17
- So what should we do about it? [laughter]
66:20
- Wait. Can I actually just add to that? - Yeah, please.
66:23
- Just related? What are your thoughts on armament versus nourishment,
66:27
since that’s in the what-do- you-do-about-it category?
66:28
- Yeah, absolutely.
66:31
At the end of the day, it really comes down to sea level rise.
66:36
All of coastal hazards ultimately are linked with, what is global
66:41
sea level rise in the future. If we get a half a meter of sea level
66:45
rise, we’re going to be really lucky [laughs] by 2100.
66:48
If we get 2-1/2 meters of sea level rise, well, we might not be that unlucky,
66:53
but we’ll be pretty unlucky. If you get more than 2-1/2 meters
66:56
of sea level rise, we’re going to be really unlucky.
66:58
But even with 2 meters of sea level rise, we’re going to be really unlucky.
67:02
So the best thing to do about it would be to reduce carbon emissions and pray
67:09
that sea level rise is not going to get as high as it will.
67:14
But, as we’re getting more down the line of really high carbon emissions,
67:20
if we’re locked into that scenario where we’re going to get 2 meters
67:23
of sea level rise, well, essentially we’re going to do what the Dutch do.
67:30
Because their whole country is pretty much, you know, under sea level.
67:32
So what we’re going to do is, we’re going to build walls.
67:36
We’re either going to build walls out of concrete or we’re going to
67:38
build walls out of sand. Either way, they’re building walls.
67:42
And we’re going to pump. You know, we’re going to pump
67:44
a lot because those walls might not necessarily stop the groundwater –
67:50
you know, higher water table from coming in through the groundwater.
67:54
So the way that you solve that problem is by pumping.
67:56
So you’re constantly pumping, dumping that water into the ocean,
68:00
it comes back, you pump it out. [laughs]
68:02
Because the groundwater – the movement of water through
68:05
the groundwater is so much slower, right, you can do that process of
68:10
continuously pumping even though it wants to come back.
68:15
So, in a place like the Netherlands, they have the material to do those
68:17
large beach nourishments. On the East Coast, they have the
68:21
material to do those large beach nourishments, and we’re going to
68:23
continue do to those beach nourishments.
68:25
On the West Coast, we could probably find the material to do large beach
68:29
nourishments, and I think that’s probably what we will do.
68:32
But, you know, there are a lot of areas where they’re marine-protected, so
68:36
you’re not necessarily allowed to do beach nourishments in those areas.
68:40
So that might be a particular challenge in those certain areas
68:44
where you essentially can’t do beach nourishments.
68:49
So, I mean, that’s what I think we’re going to do is be building
68:52
a lot of walls out of sand, out of concrete, and doing a lot of pumping.
68:58
You know, I think sea level rise and coastal engineering is very much
69:01
a growth industry. [laughter] So if you have kids that are going to
69:05
college, then encourage them to work on this.
69:08
And pluming. Plumbers. [laughter]
69:10
We’re going to need a lot more plumbers in the future, so …
69:13
[laughs] So, thanks.
69:19
- Would you go back to the introductory picture with that
69:24
exciting wave coming in? - Mm-hmm.
69:29
[Silence]
69:40
This one? - Yeah.
69:44
Is that a 100-year – that’s exciting. I mean … [laughter]
69:48
- Yeah. Very exciting, right? Well, this is – this is a El Niño year.
69:53
2015-2016 was a rather large El Niño year.
69:57
We had ’82-83, ’97-98, 2015-2016. So I couldn’t exactly tell you,
70:04
you know, if this is – this is a 50-year wave or this is a 100-year wave,
70:09
but no doubt, you know, if they design the pier
70:14
[laughs], you know, which they did. Somebody designed the pier so that
70:19
it’s going to be situated above the 50- or 100-year wave.
70:24
You can see, well, you know, not quite large enough.
70:27
Whether that’s sea level rise or from experiencing a larger-than-50-year
70:31
wave, could be one or the other. But I’m guessing that, you know,
70:36
this wave is probably very close to the design condition
70:40
for that – for that – for that pier. [laughs]
70:43
- [inaudible] flag come from? - Yeah. The flag is on
70:46
the end of the pier. - Oh. [laughs]
70:47
- Yeah. So this pier keeps going. [laughter]
70:50
Yep. It keeps going. You can’t see the end of it,
70:53
but this is on the end of the pier. - The pier is behind that wave.
70:55
- Yep. [laughter]
70:59
[inaudible background conversations]
71:03
So pretty impressive picture. - On the groundwater that you’re
71:07
going to pump out … - Mm-hmm.
71:09
- … is the salt going to cause troubles? - Yeah. absolutely.
71:14
[laughter] Yep. [laughs]
71:16
So agriculture, I would say, is the main impact.
71:19
Of course, drinking water is another big impact.
71:22
A guy at our office, Curt Storlazzi, has done a lot of – and Cliff Voss
71:26
here at the USGS has done a lot of interesting work on trying to
71:30
understand – associated with sea level rise, you have that saltwater intrusion.
71:34
You also have flooding. You know, flooding from that saltwater.
71:39
But also groundwater intrusion that can affect agriculture and
71:43
drinking water supply. And they’re basically showing that,
71:46
because of sea level rise and those processes, you may have
71:49
some atolls in the Pacific that are essentially uninhabitable in the
71:52
next few decades because of those processes.
71:55
So pretty important. Salinas Valley is also an area that
71:58
might be an interesting thing going on where there’s a lot of agriculture there.
72:04
And, you know, as they pump, the saltwater table wants to come in.
72:07
As sea level goes up, the water wants to come in.
72:09
So interesting to see what would happen there.
72:15
- Second round of questions. - Yep.
72:17
- I’ve heard that there’s a differential between what’s happening in the
72:20
Atlantic – the East Coast of the United States – versus the West Coast
72:24
of the United States and the Pacific in terms of sea level rise.
72:27
That’s the first part of the question. The other is, I didn’t see anything
72:30
in your sea level predictions that accounts for thermal expansion
72:34
of the water. - Yeah. So thermal expansion
72:37
is all included in these projections. Very fortunately …
72:46
… thermal expansion is sort of the easiest part to project.
72:52
Because you have a temperature projection, you can make a calculation
72:55
of the volumetric expansion. That gives you the amount of sea level rise.
72:59
So that’s kind of the easy part. And so those projections really
73:03
go into these scenarios here. And if you look at historical sea level
73:12
rise in the past, they’ve done these nice studies where the sea level rise
73:18
that we have gotten, 50% of it comes from thermal expansion.
73:22
50% comes from eustatic sea level, which is, you’re adding more water.
73:29
But the uncertainty going forward into the future is all driven by the
73:34
eustatic part – almost all of it. And then, going back to your other
73:38
comment about East Coast versus West Coast – well, the big difference
73:43
between East Coast and West Coast gets back to the tectonics
73:46
on sort of an obvious level. If you look at the tide gauges
73:49
around here, Monterey tide gauges going up at about 1 millimeter per year.
73:58
So not as much. And the reason that it’s only going up
74:00
at 1 millimeter per year is, well, say you’re getting 3 centimeters
74:04
of sea level rise. But the tectonic area around here
74:07
is uplifting at about 2 millimeters, so you only get 1 meter – 1 millimeter
74:11
per year. San Francisco is going up at about 2 millimeters per year,
74:15
so maybe you have about 1 millimeter of uplift.
74:17
The East Coast, it’s swinging the other way.
74:20
You know, it’s subsiding, so you have very high localized
74:23
relative rates of sea level rise. If you look at a – at a plot of the rate of
74:30
sea level rise predicted from satellites, the East Coast is, like, so nice and
74:37
predictable at about 3 millimeters per year.
74:40
The West Coast is all over the place. Because you have these really big
74:44
patterns associated with El Niño, where you have the whole water in the
74:49
basin sort of sloshing back and forth. And that behavior can completely offset
74:57
the amount of sea level rise that you’d be getting over decades
74:59
at the current rate. So it’s much harder to detect sea level
75:03
in the Pacific than it is in the Atlantic. But, again, on the East Coast, it’s so
75:07
much dominated by the subsidence. and that’s certainly going to continue.
75:13
There are also a lot of interesting properties about the Gulf Stream –
75:18
the acceleration and the relaxation of the Gulf Stream, which can sort of
75:22
cause local variations in sea level. And it’d be very interesting to see
75:26
what happens with the Gulf Stream. Another thing about the East Coast,
75:29
which I’ve taken kind of a California- Hawaii-centric view of things.
75:36
But the East Coast is really dominated by hurricanes.
75:40
Those are the most important hazards, by far.
75:43
And, you know, with climate change, we think that those processes are
75:48
going to be sort of accelerating. The extratropical patterns in the north
75:53
Pacific that are causing large waves and extreme flooding events, for most
75:59
climate ensembles for wave models, which I’m working on a little bit right
76:02
now with some collaborators in Australia, those extratropical patterns
76:08
in the northern hemisphere look like they’re actually going down
76:12
a little bit with climate. Not very much, but going down a little bit.
76:16
So kind of an interesting behavior. The southern ocean is really ramping up
76:21
with CO2 and with climate. So it’s going to be a very, very
76:25
interesting coastal hazard picture down under and also on the East Coast
76:29
with what the climate might do to hurricane storm tracks.
76:33
So a very big differences between the West Coast and the East Coast –
76:37
the hurricane hazard.
76:41
[Silence]
76:52
- I’m glad you only took this up to the year 2100. [laughter]
76:55
But if you look back in historical terms, in 100,000 years, we’ll be
77:00
up 100 meters, not 10 meters. - Oh, yeah?
77:02
- What’s going to happen then? [laughs]
77:05
- Yeah. I mean, if you look at some of the climate projections sort of in the
77:10
long-term, a lot of the climate models show there’s not a ton of ice in these
77:21
big land masses as you look very, very long into the future
77:24
with high-end sea level projections. So, in those cases where, if those things
77:30
really come true, you know, we’re looking at about, you know –
77:34
say, I don’t know, 10% of this melted. 7 meters of sea level rise.
77:40
10% plus this 1 meter, you know, we’re looking at about 8 meters of
77:44
sea level rise in the next 300 years or something like that.
77:48
And that is kind of about the upper range of – well, no, I mean,
77:53
it could – it could keep going. [laughter]
77:55
But … - [inaudible]
77:57
- You know, yeah. - It won’t be worse than 100 meters.
77:59
- Yeah. You know, this is – this is what we would – you know,
78:05
if we could somehow accelerate our transition – you know, this. [laughs]
78:16
Go back down to minus 100 meters, you know, we’d have a lot more
78:19
coastal property that we could sell, right? [laughter] So …
78:24
But, you know, that is going to be sort of a, you know, interesting
78:29
potential thing is, like, what are going to be the solutions to these things?
78:34
Generally speaking, the cheapest option for all this is with solar geoengineering.
78:40
I don’t know if you’ve ever heard of what that is.
78:43
But solar geoengineering is essentially you’re spraying aerosol particles in
78:48
the atmosphere to increase reflection. [laughs]
78:52
And that, you know, may help you from, you know, global warming and
78:58
things like that. But who knows what that’s going to do to the climate?
79:02
So, you know, it would be pretty scary if we’re on the trajectory where our
79:08
only option, sort of, out of climate change is some pretty drastic solutions
79:13
like solar geoengineering. Carbon geoengineering is also
79:16
potentially another very, very interesting solution that hopefully
79:19
we do more and more, which is we actively remove CO2
79:23
from the atmosphere. You suck it out, and you inject it
79:26
into these deep saline wells. And I’m sure tons of folks at the
79:32
USGS have really looked at that a lot. And it seems like a fairly interesting
79:36
proposition to reduce greenhouse gases while still relying on fossil fuels and
79:44
emissions and that sort of energy sector and things like that.
79:47
You could – you could be sort of carbon neutral in the atmosphere
79:50
because you’re sucking it out. Kind of the concept of, you know,
79:54
the Dutch, where, right, you just pump the water.
79:58
Like, if the water’s getting too high, you just pump it out. [laughs]
80:01
That’s carbon geoengineering. But we may – we may get
80:04
down that road. We’ll see.
80:09
[Silence]
80:13
- Could you say more about sea level? I mean … [laughter]
80:18
Well, if – how do you measure it? I mean, if the – if the East Coast is
80:22
sinking and the West Coast is rising … - Mm-hmm.
80:26
- … how do you know – maybe the whole thing is sinking
80:29
or rising, you know, faster – I … - Yeah.
80:32
So I haven’t seen this data set yet, but NASA is producing, I think,
80:38
a pretty cool data set, which is vertical land motion for all the
80:43
way around the U.S., maybe potentially around the world.
80:47
The best way to really keep track of it is if you have a tide gauge, which is
80:52
sort of measuring the water level, but you also a nice GPS.
80:57
So you can really get an absolute sense of how the sea level is rising.
81:01
If that GPS is sinking over time, you can – you can assess that,
81:06
oh, it’s not – the GPS position is stable, and sea level is rising.
81:10
But you actually are detecting an absolute, oh, it’s actually sinking.
81:14
So the – to measure all these things, it’s a combination of GPS stations and
81:20
satellite altimetry and sea level stations and a lot of interesting stuff.
81:25
Definitely not entirely my field. But they do a lot of really interesting
81:32
stuff to really assess, you know, is it subsidence?
81:36
It is sea level rise? Which one it is.
81:39
And they can usually do a very good job of assessing how much is associated
81:43
with subsidence, how much is associated with actual rise in sea level.
81:47
So I think that problem is usually well-solved.
81:50
The more active GPS stations we put on tide gauges, the easier it
81:54
will be to address that problem. Only a few stations have those.
81:57
- But what does – how do the GPS guys know where they’re [inaudible]?
82:00
- [laughs] Relative to all the other GPSs and all the other cool sensors that they
82:07
have out there, they can – they can really come up with a really nice sort of
82:10
baseline of how these things evolve. But it’s a very complicated process
82:15
about how these things evolve over time.
82:22
- Well, thank you, Sean. - Yeah. Thank you very much.
82:24
[Applause]
82:26
- And thank you all for coming. I hope to see you January 23rd, 2020.
82:33
[inaudible background conversations]
82:38
[Silence]