1
00:00:00,000 --> 00:00:02,025
Hopefully

2
00:00:02,025 --> 00:00:05,294
that is visible and
you're not seeing.

3
00:00:05,294 --> 00:00:06,750
Okay. Good.

4
00:00:06,750 --> 00:00:07,859
That's good. Yeah.

5
00:00:07,859 --> 00:00:10,019
Well, thanks for
having me, everyone.

6
00:00:10,019 --> 00:00:12,329
I appreciate
getting the invite.

7
00:00:12,329 --> 00:00:14,249
I was wondering
if it has more to

8
00:00:14,249 --> 00:00:15,899
do to see if

9
00:00:15,899 --> 00:00:18,120
my computer survives
to talk this time?

10
00:00:18,120 --> 00:00:20,240
Does it Hopefully died

11
00:00:20,240 --> 00:00:22,254
in the middle of
my talk last time.

12
00:00:22,254 --> 00:00:25,909
So I have a pretty
long title there.

13
00:00:25,909 --> 00:00:28,039
I'd have alternate title.

14
00:00:28,039 --> 00:00:29,855
That's basically,
you know,

15
00:00:29,855 --> 00:00:31,340
cities are hot,
but they're

16
00:00:31,340 --> 00:00:32,840
not high equally across.

17
00:00:32,840 --> 00:00:34,580
So let's measure
some stuff.

18
00:00:34,580 --> 00:00:37,100
And I guess the
caveat here is,

19
00:00:37,100 --> 00:00:38,360
I'm not a climate
scientist,

20
00:00:38,360 --> 00:00:39,664
I'm a hydrologist,

21
00:00:39,664 --> 00:00:41,780
but I do have

22
00:00:41,780 --> 00:00:43,159
some expertise in talking

23
00:00:43,159 --> 00:00:44,825
about depressing stuff,

24
00:00:44,825 --> 00:00:46,669
which is a lot of
time unfortunately,

25
00:00:46,669 --> 00:00:48,680
where climate goes.

26
00:00:48,680 --> 00:00:52,595
To frame up this
conversation,

27
00:00:52,595 --> 00:00:54,260
kinda have this image here

28
00:00:54,260 --> 00:00:55,910
where we can look at kind

29
00:00:55,910 --> 00:01:00,080
of a city in South Africa.

30
00:01:00,080 --> 00:01:01,400
So as to Harrisburg,

31
00:01:01,400 --> 00:01:02,945
what you see is, you know,

32
00:01:02,945 --> 00:01:04,759
as a strong man
example a little bit.

33
00:01:04,759 --> 00:01:07,099
It's that cities aren't

34
00:01:07,099 --> 00:01:08,450
exactly the same across.

35
00:01:08,450 --> 00:01:10,130
They're quite
different depending

36
00:01:10,130 --> 00:01:10,879
on where you're at,

37
00:01:10,879 --> 00:01:13,519
you can be feeling very
different effects.

38
00:01:13,519 --> 00:01:15,620
And why this is

39
00:01:15,620 --> 00:01:18,829
important is because if
you think about this,

40
00:01:18,829 --> 00:01:20,990
so now we're moving
to New York City.

41
00:01:20,990 --> 00:01:22,940
If you're in a
certain part of

42
00:01:22,940 --> 00:01:24,349
a single neighborhood
in your city

43
00:01:24,349 --> 00:01:25,790
might look like this.

44
00:01:25,790 --> 00:01:26,990
Versus if you're in

45
00:01:26,990 --> 00:01:28,130
the exact same
neighborhood,

46
00:01:28,130 --> 00:01:29,540
but a different part,

47
00:01:29,540 --> 00:01:30,815
it could look like this.

48
00:01:30,815 --> 00:01:33,230
And what this means is,

49
00:01:33,230 --> 00:01:35,404
depending on
where you're at,

50
00:01:35,404 --> 00:01:38,060
you could be experiencing
temperatures,

51
00:01:38,060 --> 00:01:40,384
air temperatures that
are about could be

52
00:01:40,384 --> 00:01:42,889
up to ten degrees hotter.

53
00:01:42,889 --> 00:01:44,884
End likely if you're

54
00:01:44,884 --> 00:01:47,929
in these areas that
are less screen,

55
00:01:47,929 --> 00:01:51,260
you probably are
experiencing

56
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less resource
availability in

57
00:01:53,720 --> 00:01:55,025
terms of having an EC,

58
00:01:55,025 --> 00:01:57,019
having a car to escape,

59
00:01:57,019 --> 00:01:59,569
just to go somewhere
where it's cooler.

60
00:01:59,569 --> 00:02:03,139
And you're also
probably about twice as

61
00:02:03,139 --> 00:02:07,910
likely to suffer severe
consequences for me,

62
00:02:07,910 --> 00:02:09,590
including death,
particularly

63
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for people of color.

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00:02:11,749 --> 00:02:14,884
So this kind of
begs the question,

65
00:02:14,884 --> 00:02:17,075
okay, this is happening.

66
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What we really need to
understand that is,

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where is it occurring,

68
00:02:20,749 --> 00:02:22,025
how severe is it,

69
00:02:22,025 --> 00:02:23,689
and what are the
underlying mechanisms?

70
00:02:23,689 --> 00:02:26,309
So we could work
to address this.

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00:02:26,670 --> 00:02:30,040
And basically
the outline of

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this talk that
is effectively

73
00:02:31,659 --> 00:02:32,755
trying to get at that.

74
00:02:32,755 --> 00:02:35,619
So I'll spend a good
bit of time talking

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about what are

76
00:02:37,210 --> 00:02:40,045
the actual challenges
with urban heat.

77
00:02:40,045 --> 00:02:44,020
You know, how we would go

78
00:02:44,020 --> 00:02:48,699
about measuring it
in different ways.

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00:02:48,699 --> 00:02:49,900
What we would need to know

80
00:02:49,900 --> 00:02:51,070
to measure it and then not

81
00:02:51,070 --> 00:02:53,305
then kind of like the
through line on it.

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00:02:53,305 --> 00:02:55,120
Like, you know, what?
If we have this data,

83
00:02:55,120 --> 00:02:57,535
what can we do with
it? How can we help?

84
00:02:57,535 --> 00:03:00,985
Alright, so
starting off with

85
00:03:00,985 --> 00:03:02,469
the challenges and what's

86
00:03:02,469 --> 00:03:04,615
the issue with urban heat?

87
00:03:04,615 --> 00:03:09,284
So why is it a big deal?

88
00:03:09,284 --> 00:03:11,704
Well, primarily it has

89
00:03:11,704 --> 00:03:14,839
several effects
on the human body

90
00:03:14,839 --> 00:03:17,074
when we're faced
with high heat,

91
00:03:17,074 --> 00:03:19,895
a lot of the big
organs in the body,

92
00:03:19,895 --> 00:03:23,135
heart, lungs, kidneys
are affected.

93
00:03:23,135 --> 00:03:26,030
Maybe less commonly known

94
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is it also has some
mental health effects.

95
00:03:29,029 --> 00:03:33,244
It's been shown to
impact learning,

96
00:03:33,244 --> 00:03:36,500
work performance,
particularly outdoors.

97
00:03:36,500 --> 00:03:39,469
And also can increase
things like aggression.

98
00:03:39,469 --> 00:03:40,880
Primarily because
what people

99
00:03:40,880 --> 00:03:42,259
are hot, they're angry.

100
00:03:42,259 --> 00:03:44,150
If it stays hotter
longer at night,

101
00:03:44,150 --> 00:03:45,320
there's not a whole
lot of relief,

102
00:03:45,320 --> 00:03:47,869
so it has a lot of
different effects.

103
00:03:47,869 --> 00:03:49,924
What this leads
to is the fact

104
00:03:49,924 --> 00:03:53,045
that heat is kind of

105
00:03:53,045 --> 00:03:55,340
the number one cause of

106
00:03:55,340 --> 00:03:58,879
weather fatalities
in the US.

107
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So in the US it's
quite a bit more

108
00:04:01,340 --> 00:04:02,360
than if you look at

109
00:04:02,360 --> 00:04:04,400
things like
hurricanes, tornadoes,

110
00:04:04,400 --> 00:04:06,335
floods, things that we

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00:04:06,335 --> 00:04:07,790
are commonly
associated with,

112
00:04:07,790 --> 00:04:09,019
weather-related
fatalities,

113
00:04:09,019 --> 00:04:10,370
heats greater
than all of them.

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00:04:10,370 --> 00:04:13,370
This averaging
about what's

115
00:04:13,370 --> 00:04:17,479
estimated by CDC at
700 deaths per year.

116
00:04:17,479 --> 00:04:19,280
There's been some
other studies that say

117
00:04:19,280 --> 00:04:20,779
that figure is
probably closer

118
00:04:20,779 --> 00:04:23,750
to 6,000 to 12,000
because there's

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00:04:23,750 --> 00:04:25,039
a lot of indirect
ways people

120
00:04:25,039 --> 00:04:26,884
could die from drowning,

121
00:04:26,884 --> 00:04:29,480
trying to swim to
escape heat, etc.

122
00:04:29,480 --> 00:04:31,175
And globally it could be

123
00:04:31,175 --> 00:04:32,434
somewhere on the
magnitude of

124
00:04:32,434 --> 00:04:36,565
170,000 people that
would die per year.

125
00:04:36,565 --> 00:04:42,230
So just want to quickly
look into something.

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00:04:42,230 --> 00:04:44,209
So this leads to a lot of

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00:04:44,209 --> 00:04:45,380
things that have been

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00:04:45,380 --> 00:04:47,059
developed like heat.gov,

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00:04:47,059 --> 00:04:49,700
which is a federal
government webpage.

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00:04:49,700 --> 00:04:51,959
And hopefully you
can all see that.

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And what we see
with that is they

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provide these
alerts because

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this is such a
severe issue.

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00:04:59,929 --> 00:05:00,950
And you know, they have

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00:05:00,950 --> 00:05:01,999
this helpful little map

136
00:05:01,999 --> 00:05:03,050
here where you can look

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00:05:03,050 --> 00:05:04,939
around and see what areas

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00:05:04,939 --> 00:05:07,229
are under heat advisories.

139
00:05:09,520 --> 00:05:17,400
So don't force them
to listen to. This.

140
00:05:18,850 --> 00:05:21,170
Is somebody's
got an error.

141
00:05:21,170 --> 00:05:23,135
Can somebody take their
speaker off, please?

142
00:05:23,135 --> 00:05:24,839
Thank you.

143
00:05:26,350 --> 00:05:28,234
No worries.

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00:05:28,234 --> 00:05:30,320
Some added commentary.

145
00:05:30,320 --> 00:05:31,459
They are very upset

146
00:05:31,459 --> 00:05:33,110
by those heat
figures, I'm sure.

147
00:05:33,110 --> 00:05:37,400
So it affects health.

148
00:05:37,400 --> 00:05:40,235
But the question is,

149
00:05:40,235 --> 00:05:41,465
or I guess, what's,

150
00:05:41,465 --> 00:05:42,650
what's a bigger impact

151
00:05:42,650 --> 00:05:43,670
of what we're
talking about here.

152
00:05:43,670 --> 00:05:47,300
Cities, NY is
common in cities

153
00:05:47,300 --> 00:05:49,025
is that they're actually

154
00:05:49,025 --> 00:05:51,260
decently hotter than
surrounding areas.

155
00:05:51,260 --> 00:05:52,879
And this is an effect
that's referred

156
00:05:52,879 --> 00:05:54,874
to commonly as the
urban heat island.

157
00:05:54,874 --> 00:05:58,309
And this is where the
city in general can

158
00:05:58,309 --> 00:06:01,340
be three to ten degrees

159
00:06:01,340 --> 00:06:04,160
Fahrenheit hotter than
the surrounding areas.

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00:06:04,160 --> 00:06:06,049
And the reason for

161
00:06:06,049 --> 00:06:07,910
this is just due
to the properties,

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00:06:07,910 --> 00:06:09,169
surface properties or

163
00:06:09,169 --> 00:06:10,865
the characteristics
of a city.

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00:06:10,865 --> 00:06:13,039
Lot of artificial
materials.

165
00:06:13,039 --> 00:06:15,380
So these different
materials

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00:06:15,380 --> 00:06:17,419
like asphalt or concrete,

167
00:06:17,419 --> 00:06:19,384
you know, store more heat.

168
00:06:19,384 --> 00:06:20,959
They reflect less,

169
00:06:20,959 --> 00:06:22,670
and then they slowly
release it as it

170
00:06:22,670 --> 00:06:24,320
is stored so that
it keeps cities it

171
00:06:24,320 --> 00:06:27,000
next to these higher
and keeps them hotter.

172
00:06:27,730 --> 00:06:30,184
Now when we add in

173
00:06:30,184 --> 00:06:33,170
complicating factors
like climate change,

174
00:06:33,170 --> 00:06:35,479
we see something like this

175
00:06:35,479 --> 00:06:39,005
where our climate is
shifting to be warmer.

176
00:06:39,005 --> 00:06:41,270
Probably not news
to anybody here.

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00:06:41,270 --> 00:06:43,310
But what this does is

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00:06:43,310 --> 00:06:45,665
it shifts this
distribution

179
00:06:45,665 --> 00:06:47,569
such that it makes

180
00:06:47,569 --> 00:06:50,404
really high heat events
more likely as well.

181
00:06:50,404 --> 00:06:52,069
So not only is it
getting harder overall,

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00:06:52,069 --> 00:06:54,904
but do you see extreme
heat events become

183
00:06:54,904 --> 00:06:56,360
more likely as
you're seeing

184
00:06:56,360 --> 00:06:58,070
here in this figure,

185
00:06:58,070 --> 00:06:59,749
as it's moving
over to the right.

186
00:06:59,749 --> 00:07:04,460
They're just sort of
this idea, right?

187
00:07:04,460 --> 00:07:05,959
So we're, we're faced

188
00:07:05,959 --> 00:07:07,099
with these issues
and they can

189
00:07:07,099 --> 00:07:08,359
become more severe because

190
00:07:08,359 --> 00:07:10,279
the heat is more severe.

191
00:07:10,279 --> 00:07:16,190
In since 22,005,
excuse me,

192
00:07:16,190 --> 00:07:18,619
we've had the ten
warmest years on record,

193
00:07:18,619 --> 00:07:20,180
so now we're really

194
00:07:20,180 --> 00:07:22,099
getting into the
depressing climate stuff.

195
00:07:22,099 --> 00:07:24,245
2022, summer

196
00:07:24,245 --> 00:07:27,589
was the third hottest
year on record.

197
00:07:27,589 --> 00:07:29,735
So top three.

198
00:07:29,735 --> 00:07:33,650
And if we're looking
in the areas

199
00:07:33,650 --> 00:07:34,909
that maybe people on

200
00:07:34,909 --> 00:07:37,369
this call are
interested in the West,

201
00:07:37,369 --> 00:07:39,110
we see that these
temperatures

202
00:07:39,110 --> 00:07:40,745
have been increasing
quite a bit.

203
00:07:40,745 --> 00:07:44,569
If we're looking
even just 1971-2002,

204
00:07:44,569 --> 00:07:48,889
the current person
years on top of this.

205
00:07:48,889 --> 00:07:50,630
It's becoming more
of an issue because

206
00:07:50,630 --> 00:07:52,250
more people are
living in cities.

207
00:07:52,250 --> 00:07:55,054
So in 2007,

208
00:07:55,054 --> 00:07:58,085
half the world's
population were in,

209
00:07:58,085 --> 00:08:00,275
were classified
as urban areas.

210
00:08:00,275 --> 00:08:03,019
It's about 55% now,

211
00:08:03,019 --> 00:08:06,034
it should be about 68% by

212
00:08:06,034 --> 00:08:08,014
2050, according to the UN.

213
00:08:08,014 --> 00:08:09,319
And so that's globally.

214
00:08:09,319 --> 00:08:10,730
And a lot of that
growth is going to

215
00:08:10,730 --> 00:08:13,025
be in Africa and Asia.

216
00:08:13,025 --> 00:08:15,770
In the figure here, this
is the us and we can

217
00:08:15,770 --> 00:08:17,945
see the US is already

218
00:08:17,945 --> 00:08:20,209
maybe 80% people living in

219
00:08:20,209 --> 00:08:21,619
urban areas and
that's expected to

220
00:08:21,619 --> 00:08:23,659
grow to about 90%.

221
00:08:23,659 --> 00:08:25,189
So more people
are going to be

222
00:08:25,189 --> 00:08:27,740
faced with these
harder cities.

223
00:08:27,740 --> 00:08:29,300
And so it's
something that we

224
00:08:29,300 --> 00:08:31,259
really need to
be aware of.

225
00:08:32,470 --> 00:08:35,630
And again, to make
it even more worse,

226
00:08:35,630 --> 00:08:37,969
these heat waves
when we're moving,

227
00:08:37,969 --> 00:08:40,009
our temperature
distribution to allow for

228
00:08:40,009 --> 00:08:42,829
these more extreme
events are going to

229
00:08:42,829 --> 00:08:44,960
increase and
they're going to

230
00:08:44,960 --> 00:08:48,064
increase both and
how hot they are,

231
00:08:48,064 --> 00:08:51,139
how often they occur
and how long they last.

232
00:08:51,139 --> 00:08:54,260
So you could see some
of these graphs.

233
00:08:54,260 --> 00:08:56,359
The bigger areas are,

234
00:08:56,359 --> 00:08:59,449
but it is pretty
pervasive across the US.

235
00:08:59,449 --> 00:09:02,009
So that's a problem.

236
00:09:02,290 --> 00:09:05,030
And as an example of all

237
00:09:05,030 --> 00:09:06,979
of these factors
coming together,

238
00:09:06,979 --> 00:09:09,380
we can look at the
heat wave that was

239
00:09:09,380 --> 00:09:10,430
experienced in the

240
00:09:10,430 --> 00:09:13,055
Pacific Northwest
last summer,

241
00:09:13,055 --> 00:09:14,749
which I'm sure many of us

242
00:09:14,749 --> 00:09:16,835
are all too familiar with.

243
00:09:16,835 --> 00:09:19,699
And what we saw was
temperatures that

244
00:09:19,699 --> 00:09:22,580
were extremely
abnormal so far end of

245
00:09:22,580 --> 00:09:23,690
that distribution
I was just

246
00:09:23,690 --> 00:09:25,670
showing and we were
seeing temperatures

247
00:09:25,670 --> 00:09:27,559
maybe 40 degrees
Fahrenheit

248
00:09:27,559 --> 00:09:30,659
hotter than an
average year.

249
00:09:31,000 --> 00:09:34,609
This figure here is

250
00:09:34,609 --> 00:09:36,259
just showing how
abnormal this is.

251
00:09:36,259 --> 00:09:38,855
These little dots are
all the temperatures

252
00:09:38,855 --> 00:09:42,019
since 1979 in
those three days.

253
00:09:42,019 --> 00:09:43,279
So that heat
wave or up here,

254
00:09:43,279 --> 00:09:44,960
so very much
off the charts.

255
00:09:44,960 --> 00:09:48,890
It was estimated as
like 1,000 year event.

256
00:09:48,890 --> 00:09:52,339
And maybe some other
estimates have placed

257
00:09:52,339 --> 00:09:53,914
climate change making that

258
00:09:53,914 --> 00:09:57,019
about 150 times more
likely to happen.

259
00:09:57,019 --> 00:09:59,930
So unfortunately,
what this led to was

260
00:09:59,930 --> 00:10:02,315
about 100 times
the amount of

261
00:10:02,315 --> 00:10:03,949
ER visits when we compare

262
00:10:03,949 --> 00:10:08,969
2000 2021 versus
two years previous.

263
00:10:08,980 --> 00:10:11,149
And of course, this led

264
00:10:11,149 --> 00:10:13,910
to quite a significant
amount of deaths.

265
00:10:13,910 --> 00:10:16,160
Unfortunately, the loss of

266
00:10:16,160 --> 00:10:18,199
life and Oregon and
Washington combine.

267
00:10:18,199 --> 00:10:20,104
It was about 600 people,

268
00:10:20,104 --> 00:10:22,579
is about 800
people in Canada.

269
00:10:22,579 --> 00:10:25,009
When we look at
sort of, you know,

270
00:10:25,009 --> 00:10:26,524
where a lot of
these deaths

271
00:10:26,524 --> 00:10:28,704
occurred in
around Portland.

272
00:10:28,704 --> 00:10:30,935
We overlay that
with the heat map

273
00:10:30,935 --> 00:10:32,870
where these red areas are,

274
00:10:32,870 --> 00:10:34,850
the hotter areas, we see,

275
00:10:34,850 --> 00:10:36,499
most of them are

276
00:10:36,499 --> 00:10:38,944
occurring and
fairly hot areas,

277
00:10:38,944 --> 00:10:40,280
which is, shouldn't be

278
00:10:40,280 --> 00:10:42,709
really surprising,
I suppose.

279
00:10:42,709 --> 00:10:45,035
And then, you know.

280
00:10:45,035 --> 00:10:46,729
A big reason why

281
00:10:46,729 --> 00:10:48,169
a lot of this was
occurring is that

282
00:10:48,169 --> 00:10:50,345
the Northwest
traditionally isn't

283
00:10:50,345 --> 00:10:52,490
well-equipped to
handle heat like this.

284
00:10:52,490 --> 00:10:54,544
And if you think
of things like AC,

285
00:10:54,544 --> 00:10:56,480
portland has the third

286
00:10:56,480 --> 00:10:58,334
lowest percentage
of households with

287
00:10:58,334 --> 00:11:00,499
AAC in these areas

288
00:11:00,499 --> 00:11:01,520
where it's hotter
like we were

289
00:11:01,520 --> 00:11:02,794
talking about at
the beginning,

290
00:11:02,794 --> 00:11:05,599
they tend to have
less resources,

291
00:11:05,599 --> 00:11:08,225
so they have even
less AAC than this.

292
00:11:08,225 --> 00:11:10,880
And it has actually

293
00:11:10,880 --> 00:11:12,710
led to one of
the outcomes of

294
00:11:12,710 --> 00:11:13,820
this heat wave
was that there

295
00:11:13,820 --> 00:11:14,840
was a law passed and

296
00:11:14,840 --> 00:11:15,859
Oregon that actually

297
00:11:15,859 --> 00:11:18,560
requires new
housing after,

298
00:11:18,560 --> 00:11:21,125
I think April in 2024

299
00:11:21,125 --> 00:11:23,269
to have at least
one room with AAC.

300
00:11:23,269 --> 00:11:25,699
So just this UF is causing

301
00:11:25,699 --> 00:11:28,565
big changes. But
not to be left out.

302
00:11:28,565 --> 00:11:29,930
You know, 2022 is pretty

303
00:11:29,930 --> 00:11:31,850
hot to, in this case,

304
00:11:31,850 --> 00:11:33,139
didn't have
quite the peaks,

305
00:11:33,139 --> 00:11:35,794
but it had more
of the longevity.

306
00:11:35,794 --> 00:11:37,340
And so that was
the first time

307
00:11:37,340 --> 00:11:38,629
we saw seven days that

308
00:11:38,629 --> 00:11:41,584
were greater than 95
Fahrenheit in a row.

309
00:11:41,584 --> 00:11:45,080
So different ways of
experiencing heat,

310
00:11:45,080 --> 00:11:48,784
but both fairly severe
for human health.

311
00:11:48,784 --> 00:11:51,619
Okay, So sort of
hinted at this,

312
00:11:51,619 --> 00:11:53,389
but what is actually

313
00:11:53,389 --> 00:11:54,499
making cities hotter

314
00:11:54,499 --> 00:11:56,225
than the
surrounding areas?

315
00:11:56,225 --> 00:11:59,645
And again, if we're
looking at this end,

316
00:11:59,645 --> 00:12:01,159
I noticed this
after the fact,

317
00:12:01,159 --> 00:12:02,209
but I don't know what this

318
00:12:02,209 --> 00:12:03,455
guy is here and around it,

319
00:12:03,455 --> 00:12:04,684
it might be a
mouse or something

320
00:12:04,684 --> 00:12:05,795
escaping the city,

321
00:12:05,795 --> 00:12:07,489
which is maybe
not a bad idea.

322
00:12:07,489 --> 00:12:10,959
But basically what this

323
00:12:10,959 --> 00:12:12,339
comes down to is looking

324
00:12:12,339 --> 00:12:14,080
at the radiation balance.

325
00:12:14,080 --> 00:12:16,914
And what I mean by
that is how does

326
00:12:16,914 --> 00:12:18,489
the surface of the Earth

327
00:12:18,489 --> 00:12:20,800
handle incoming
solar radiation?

328
00:12:20,800 --> 00:12:22,389
So the sun is

329
00:12:22,389 --> 00:12:24,174
providing radiation
to the earth.

330
00:12:24,174 --> 00:12:26,530
What happens to that
is very much based

331
00:12:26,530 --> 00:12:27,670
on what's on
the ground and

332
00:12:27,670 --> 00:12:28,870
the characteristics
of that.

333
00:12:28,870 --> 00:12:31,839
So again, it's when
that radiation hits,

334
00:12:31,839 --> 00:12:33,909
how much of it is
reflected back?

335
00:12:33,909 --> 00:12:35,904
How much of it is stored,

336
00:12:35,904 --> 00:12:37,779
then absorbed
or released in.

337
00:12:37,779 --> 00:12:39,219
Are there
characteristics of

338
00:12:39,219 --> 00:12:41,439
that land surface
that can take

339
00:12:41,439 --> 00:12:42,894
that radiation
and you use it

340
00:12:42,894 --> 00:12:43,959
for something other

341
00:12:43,959 --> 00:12:45,265
than just
transmitting heat.

342
00:12:45,265 --> 00:12:47,109
So if you think
of like a tree,

343
00:12:47,109 --> 00:12:49,660
uses the radiation
for photosynthesis,

344
00:12:49,660 --> 00:12:52,574
which creates
evapotranspiration,

345
00:12:52,574 --> 00:12:54,200
which is a
cooling process.

346
00:12:54,200 --> 00:12:56,120
So all of this has to

347
00:12:56,120 --> 00:12:58,669
do with how that
radiation is handled.

348
00:12:58,669 --> 00:13:01,234
When we're talking
about cities.

349
00:13:01,234 --> 00:13:03,364
They have these materials

350
00:13:03,364 --> 00:13:04,909
that are going to
store it absorb

351
00:13:04,909 --> 00:13:07,429
heat at a much higher
rate than what

352
00:13:07,429 --> 00:13:10,579
we'd call a natural or
less developed area.

353
00:13:10,579 --> 00:13:12,439
They have less trees
so you don't get

354
00:13:12,439 --> 00:13:14,270
that evapotranspiration
of cooling.

355
00:13:14,270 --> 00:13:16,144
And they also are
more what we say is

356
00:13:16,144 --> 00:13:17,614
impervious so
they don't allow

357
00:13:17,614 --> 00:13:19,414
water to enter the soil,

358
00:13:19,414 --> 00:13:20,720
which reduces
the amount of

359
00:13:20,720 --> 00:13:22,970
water available for
evapotranspiration.

360
00:13:22,970 --> 00:13:24,289
So we're also
then reducing

361
00:13:24,289 --> 00:13:26,999
those ability to cool.

362
00:13:29,170 --> 00:13:31,129
The issue with this.

363
00:13:31,129 --> 00:13:33,019
That is, cities
are harder.

364
00:13:33,019 --> 00:13:35,300
We know this, but they're

365
00:13:35,300 --> 00:13:37,639
not harder equally across.

366
00:13:37,639 --> 00:13:39,785
And if you just
think of Portland,

367
00:13:39,785 --> 00:13:41,389
if you look at
these two pictures,

368
00:13:41,389 --> 00:13:42,634
these are both
Portland, right?

369
00:13:42,634 --> 00:13:43,699
But you could imagine

370
00:13:43,699 --> 00:13:44,510
and you're going
to experience

371
00:13:44,510 --> 00:13:46,070
very different
heat depending on

372
00:13:46,070 --> 00:13:48,455
where you're at in
one versus the other.

373
00:13:48,455 --> 00:13:50,405
And in fact, if
we map this,

374
00:13:50,405 --> 00:13:52,849
we very much see
that heat can

375
00:13:52,849 --> 00:13:55,835
vary quite a lot
within a city.

376
00:13:55,835 --> 00:13:58,940
And in fact,
the differences

377
00:13:58,940 --> 00:14:00,259
within a city can be

378
00:14:00,259 --> 00:14:02,869
as high as the differences

379
00:14:02,869 --> 00:14:04,760
between a city in
a natural area.

380
00:14:04,760 --> 00:14:07,670
So the urban heat
island effect is big.

381
00:14:07,670 --> 00:14:09,229
But within the
city it can be

382
00:14:09,229 --> 00:14:11,910
equally as big depending
on where you're at.

383
00:14:13,000 --> 00:14:15,529
It's also, so
it's not equal,

384
00:14:15,529 --> 00:14:17,254
but it's also
not equitable.

385
00:14:17,254 --> 00:14:18,620
This kind of
comes into some

386
00:14:18,620 --> 00:14:20,015
of the social issues,

387
00:14:20,015 --> 00:14:21,440
which I'll just
touch on briefly,

388
00:14:21,440 --> 00:14:22,759
but are super,

389
00:14:22,759 --> 00:14:23,959
super important is really

390
00:14:23,959 --> 00:14:25,250
a motivating
factor in a lot of

391
00:14:25,250 --> 00:14:28,219
this work is that you

392
00:14:28,219 --> 00:14:30,694
find in cities that

393
00:14:30,694 --> 00:14:33,035
different racial groups

394
00:14:33,035 --> 00:14:34,730
experienced heat
differently.

395
00:14:34,730 --> 00:14:37,670
So, for instance,
people of color tend to

396
00:14:37,670 --> 00:14:39,020
experience heat at a much

397
00:14:39,020 --> 00:14:41,570
increased rate
than others.

398
00:14:41,570 --> 00:14:44,780
Income plays a role in
this to some extent.

399
00:14:44,780 --> 00:14:46,520
But you can see from
this other maps.

400
00:14:46,520 --> 00:14:49,609
So lower income areas
tend to be hotter.

401
00:14:49,609 --> 00:14:51,319
They tend to have
less resources

402
00:14:51,319 --> 00:14:53,540
invested in them
by the city.

403
00:14:53,540 --> 00:14:55,070
And in fact, there's

404
00:14:55,070 --> 00:14:57,319
a really
interesting article

405
00:14:57,319 --> 00:14:59,460
in The New York Times
that addresses this.

406
00:14:59,460 --> 00:15:01,749
Just as an example.

407
00:15:01,749 --> 00:15:04,149
There's this old policy

408
00:15:04,149 --> 00:15:05,665
that was called redlining,

409
00:15:05,665 --> 00:15:06,970
where the federal
government

410
00:15:06,970 --> 00:15:09,520
decided where to
give loans out.

411
00:15:09,520 --> 00:15:11,334
And it marked off

412
00:15:11,334 --> 00:15:13,180
risky areas so
they were less

413
00:15:13,180 --> 00:15:14,889
likely to provide
bones and

414
00:15:14,889 --> 00:15:17,050
housing and investment
in these areas.

415
00:15:17,050 --> 00:15:18,759
And not surprisingly,

416
00:15:18,759 --> 00:15:21,220
these tended to be
black neighborhoods.

417
00:15:21,220 --> 00:15:23,214
And what we see today

418
00:15:23,214 --> 00:15:25,179
are the legacy
effects of that,

419
00:15:25,179 --> 00:15:26,800
that these, these
areas are actually

420
00:15:26,800 --> 00:15:27,849
the hardest parts of

421
00:15:27,849 --> 00:15:29,965
cities for the most part.

422
00:15:29,965 --> 00:15:32,230
A study that looked at
100 cities in the US

423
00:15:32,230 --> 00:15:34,269
found to redlined for

424
00:15:34,269 --> 00:15:35,530
formerly redlined areas

425
00:15:35,530 --> 00:15:36,580
that are about
five degrees

426
00:15:36,580 --> 00:15:40,615
Fahrenheit hotter than
non redlined areas.

427
00:15:40,615 --> 00:15:42,859
So, you know, kind of

428
00:15:42,859 --> 00:15:45,379
just shows you the
importance of this

429
00:15:45,379 --> 00:15:47,299
and why this is
a challenge and

430
00:15:47,299 --> 00:15:50,164
really wild land cover.

431
00:15:50,164 --> 00:15:51,320
What's on the
ground is playing

432
00:15:51,320 --> 00:15:52,070
a big difference in

433
00:15:52,070 --> 00:15:53,270
how people
experience heat.

434
00:15:53,270 --> 00:15:54,289
And within a
city, which in

435
00:15:54,289 --> 00:15:55,790
itself is really hot.

436
00:15:55,790 --> 00:15:57,320
And really what
it comes down to,

437
00:15:57,320 --> 00:15:58,850
you know, is, is
it a green space?

438
00:15:58,850 --> 00:16:01,404
Is there impervious
cover or not?

439
00:16:01,404 --> 00:16:03,725
If we have a lot of
impervious cover,

440
00:16:03,725 --> 00:16:05,300
we're seeing
amplification of

441
00:16:05,300 --> 00:16:07,370
temperatures versus if
we see green space,

442
00:16:07,370 --> 00:16:08,540
we could see
some cooling if

443
00:16:08,540 --> 00:16:10,520
temperatures, right?

444
00:16:10,520 --> 00:16:12,649
So it kinda gets
at this idea

445
00:16:12,649 --> 00:16:15,469
where they really need

446
00:16:15,469 --> 00:16:17,780
to focus in on what's
on the ground,

447
00:16:17,780 --> 00:16:19,430
in how much is that
going to increase

448
00:16:19,430 --> 00:16:20,915
or potentially decrease

449
00:16:20,915 --> 00:16:22,940
temperature for
some studying

450
00:16:22,940 --> 00:16:24,870
some of these
relevant issues.

451
00:16:24,870 --> 00:16:27,580
Indeed affecting where you

452
00:16:27,580 --> 00:16:29,635
want to be right
in the city or

453
00:16:29,635 --> 00:16:31,975
improving areas
and finding out

454
00:16:31,975 --> 00:16:35,215
where is our that
need to be improved.

455
00:16:35,215 --> 00:16:37,134
So that leads me
into, you know,

456
00:16:37,134 --> 00:16:39,474
okay, we understand
the problem.

457
00:16:39,474 --> 00:16:41,500
What do we actually
need to know

458
00:16:41,500 --> 00:16:42,910
to address this?

459
00:16:42,910 --> 00:16:45,429
And how are we
going to know it?

460
00:16:45,429 --> 00:16:47,889
So the brief,

461
00:16:47,889 --> 00:16:50,590
in brief, you know
what, I need data.

462
00:16:50,590 --> 00:16:54,295
And that's primarily
a challenge

463
00:16:54,295 --> 00:16:57,670
because temperature
is very

464
00:16:57,670 --> 00:16:59,335
different even at
what we would say,

465
00:16:59,335 --> 00:17:01,944
like microclimate scale,
very small scale.

466
00:17:01,944 --> 00:17:03,550
If we look at
it, it changes

467
00:17:03,550 --> 00:17:06,620
quite a lot in
space and time.

468
00:17:06,790 --> 00:17:09,919
So what that means

469
00:17:09,919 --> 00:17:11,389
is we need
representative data.

470
00:17:11,389 --> 00:17:14,914
So we need a lot
of data over time.

471
00:17:14,914 --> 00:17:16,370
But we really
need to capture

472
00:17:16,370 --> 00:17:17,479
that spatial variability

473
00:17:17,479 --> 00:17:18,650
because there's so much

474
00:17:18,650 --> 00:17:19,730
of a difference across

475
00:17:19,730 --> 00:17:21,139
land cover types in

476
00:17:21,139 --> 00:17:23,329
relatively short distance.

477
00:17:23,329 --> 00:17:25,309
The best way that
we could measure

478
00:17:25,309 --> 00:17:27,770
temperature traditionally
is with a sensor.

479
00:17:27,770 --> 00:17:30,890
So you

480
00:17:30,890 --> 00:17:31,234
have

481
00:17:31,234 --> 00:17:32,960
a direct relevant
measure of temperature.

482
00:17:32,960 --> 00:17:34,819
The problem is, if
you're looking at

483
00:17:34,819 --> 00:17:35,990
New York City where you

484
00:17:35,990 --> 00:17:37,130
only have a few sensors,

485
00:17:37,130 --> 00:17:38,660
where you're going
to put them to

486
00:17:38,660 --> 00:17:40,369
capture that variability.

487
00:17:40,369 --> 00:17:41,510
How are you going
to capture all

488
00:17:41,510 --> 00:17:42,649
that? We can cover?

489
00:17:42,649 --> 00:17:44,749
You know, you'd imagine
if you put one in

490
00:17:44,749 --> 00:17:47,600
Central Park versus
outside of Central Park,

491
00:17:47,600 --> 00:17:47,899
you can get

492
00:17:47,899 --> 00:17:49,370
a really different
temperature measurement.

493
00:17:49,370 --> 00:17:52,445
So the challenge
with sensors is

494
00:17:52,445 --> 00:17:54,110
it's really hard
to have enough of

495
00:17:54,110 --> 00:17:55,969
them to cover all
these different areas.

496
00:17:55,969 --> 00:17:57,155
They're pretty sparse.

497
00:17:57,155 --> 00:17:58,669
And, you know,
it's related to

498
00:17:58,669 --> 00:17:59,900
just cost and access

499
00:17:59,900 --> 00:18:01,069
and a lot of
different things.

500
00:18:01,069 --> 00:18:02,959
And if you think you know,

501
00:18:02,959 --> 00:18:07,535
more or less
advantaged areas

502
00:18:07,535 --> 00:18:09,050
or cities globally,

503
00:18:09,050 --> 00:18:10,669
probably don't have
resources to be

504
00:18:10,669 --> 00:18:12,350
deploying like
thousands of sensors.

505
00:18:12,350 --> 00:18:14,705
It's really
isn't feasible.

506
00:18:14,705 --> 00:18:17,750
Alternatively, what
we could do is

507
00:18:17,750 --> 00:18:21,485
use satellite imagery,
remote sensing.

508
00:18:21,485 --> 00:18:24,275
So we're just in
this case, you know,

509
00:18:24,275 --> 00:18:25,760
remote sensing
very generally

510
00:18:25,760 --> 00:18:26,734
is the idea that

511
00:18:26,734 --> 00:18:29,644
we can take the
electromagnetic spectrum.

512
00:18:29,644 --> 00:18:32,719
And if we sense energy
in different regions,

513
00:18:32,719 --> 00:18:35,194
we can detect different
processes on Earth.

514
00:18:35,194 --> 00:18:38,540
Very general high-level
kind of definition.

515
00:18:38,540 --> 00:18:40,819
But really we're
talking about

516
00:18:40,819 --> 00:18:44,480
is taking something
like a camera,

517
00:18:44,480 --> 00:18:46,295
let's say as an analogy

518
00:18:46,295 --> 00:18:47,360
and taking
pictures, right?

519
00:18:47,360 --> 00:18:49,219
And in processing
that information,

520
00:18:49,219 --> 00:18:51,109
the advantage
of this is that

521
00:18:51,109 --> 00:18:54,484
particularly for
spaceborne satellites,

522
00:18:54,484 --> 00:18:56,420
we can get this
information

523
00:18:56,420 --> 00:18:58,519
globally and
repeated over time.

524
00:18:58,519 --> 00:19:01,200
So that's a pretty
big advantage.

525
00:19:01,240 --> 00:19:03,394
The challenges to that,

526
00:19:03,394 --> 00:19:04,850
or that we're not

527
00:19:04,850 --> 00:19:06,350
measuring air temperature
in this case.

528
00:19:06,350 --> 00:19:07,550
So air temperature
is what's most

529
00:19:07,550 --> 00:19:09,920
relevant for thinking
of human experience.

530
00:19:09,920 --> 00:19:11,359
For satellites are
actually measuring

531
00:19:11,359 --> 00:19:12,620
the surface temperature.

532
00:19:12,620 --> 00:19:15,469
Primarily. There's a
bit of a trade-off

533
00:19:15,469 --> 00:19:16,850
between how often
would we get

534
00:19:16,850 --> 00:19:19,249
that data and out
what spatial scale.

535
00:19:19,249 --> 00:19:21,589
The other
challenge specific

536
00:19:21,589 --> 00:19:23,719
to temperature sensing
is if there's clouds,

537
00:19:23,719 --> 00:19:24,829
we're sensing the clouds

538
00:19:24,829 --> 00:19:25,700
and not the ground, right?

539
00:19:25,700 --> 00:19:27,859
Because it's a
surface measurement.

540
00:19:27,859 --> 00:19:31,880
So there's several
satellites

541
00:19:31,880 --> 00:19:33,980
that are in our
orbit right now that

542
00:19:33,980 --> 00:19:35,510
are called Earth
observation

543
00:19:35,510 --> 00:19:37,280
satellites and nasa and

544
00:19:37,280 --> 00:19:38,989
European Space
Agency and other

545
00:19:38,989 --> 00:19:41,404
agencies globally
control these.

546
00:19:41,404 --> 00:19:43,850
And I've circled a few

547
00:19:43,850 --> 00:19:46,849
here that are used for
surface temperature.

548
00:19:46,849 --> 00:19:49,759
So don't have a lot of
time to go into this,

549
00:19:49,759 --> 00:19:52,085
but there's one
called Modus,

550
00:19:52,085 --> 00:19:53,419
there's one
called Landsat,

551
00:19:53,419 --> 00:19:55,490
and they have
thermal cameras on

552
00:19:55,490 --> 00:19:56,689
them and we can use that

553
00:19:56,689 --> 00:19:57,784
to predict temperature.

554
00:19:57,784 --> 00:19:59,165
There's also a sensor on

555
00:19:59,165 --> 00:20:00,559
the International
Space Station

556
00:20:00,559 --> 00:20:02,704
that's doing
something similar.

557
00:20:02,704 --> 00:20:05,690
But what I think is
probably more interesting

558
00:20:05,690 --> 00:20:07,985
is what does this
data look like?

559
00:20:07,985 --> 00:20:12,334
So if I just pop open
this figure here,

560
00:20:12,334 --> 00:20:15,334
so this is just
a nasa Platform.

561
00:20:15,334 --> 00:20:17,059
And this is what some of

562
00:20:17,059 --> 00:20:18,875
that data might
look like, right?

563
00:20:18,875 --> 00:20:20,554
And I do encourage
you that, you know,

564
00:20:20,554 --> 00:20:21,590
if you have
time check this

565
00:20:21,590 --> 00:20:22,699
out called worldview.

566
00:20:22,699 --> 00:20:25,055
It's pretty nice platform.

567
00:20:25,055 --> 00:20:27,664
So you can see we have

568
00:20:27,664 --> 00:20:30,215
our surface
temperatures globally.

569
00:20:30,215 --> 00:20:32,554
We can zoom in and
look around at this.

570
00:20:32,554 --> 00:20:34,070
We can change it over

571
00:20:34,070 --> 00:20:37,860
time, plot
different things.

572
00:20:37,900 --> 00:20:42,680
So we get a pretty
good global idea

573
00:20:42,680 --> 00:20:44,269
of what temperature
is looking

574
00:20:44,269 --> 00:20:45,530
like on the surface

575
00:20:45,530 --> 00:20:47,540
pretty regularly
and routinely.

576
00:20:47,540 --> 00:20:49,790
Again, this
platform has a lot

577
00:20:49,790 --> 00:20:52,414
of different
things to look at.

578
00:20:52,414 --> 00:20:54,395
A few typing
something like

579
00:20:54,395 --> 00:20:59,780
urban heat has
urban heat islands,

580
00:20:59,780 --> 00:21:02,190
which we are
talking about,

581
00:21:03,700 --> 00:21:05,719
which many disappeared.

582
00:21:05,719 --> 00:21:07,159
But there's a lot of
interesting info.

583
00:21:07,159 --> 00:21:08,569
But the bigger
point being we

584
00:21:08,569 --> 00:21:11,009
can get this
data does exist.

585
00:21:13,320 --> 00:21:16,180
Okay, here we go.

586
00:21:16,180 --> 00:21:20,049
Okay, so because of

587
00:21:20,049 --> 00:21:23,799
these challenges with
sensing and sensors,

588
00:21:23,799 --> 00:21:25,750
where they're too
sparse, remote sensing

589
00:21:25,750 --> 00:21:27,580
is only at the surface.

590
00:21:27,580 --> 00:21:30,730
A big idea that's
usually use is can we

591
00:21:30,730 --> 00:21:32,170
use remote sensing to

592
00:21:32,170 --> 00:21:33,744
then predict air
temperature?

593
00:21:33,744 --> 00:21:35,094
So you're kind of using

594
00:21:35,094 --> 00:21:38,410
the global spatial
availability of

595
00:21:38,410 --> 00:21:40,165
remote sensing to predict

596
00:21:40,165 --> 00:21:43,300
air temperature at that
same availability.

597
00:21:43,300 --> 00:21:44,919
And then you can use
some of the sensors to

598
00:21:44,919 --> 00:21:48,429
inform that the
challenges.

599
00:21:48,429 --> 00:21:49,870
So how do you
do that, right?

600
00:21:49,870 --> 00:21:51,009
So there's
different ways you

601
00:21:51,009 --> 00:21:54,199
can use advanced models.

602
00:21:54,220 --> 00:21:57,680
Different kind of
combinations of data.

603
00:21:57,680 --> 00:21:59,540
Or it could do
what would be

604
00:21:59,540 --> 00:22:01,399
considered more of a
statistical approach,

605
00:22:01,399 --> 00:22:02,809
which is the most
common because you

606
00:22:02,809 --> 00:22:05,060
don't need a lot of
data to do this.

607
00:22:05,060 --> 00:22:07,595
The challenge with this is

608
00:22:07,595 --> 00:22:10,430
it's fairly
empirical approach.

609
00:22:10,430 --> 00:22:11,449
And so we don't
really know

610
00:22:11,449 --> 00:22:14,569
why temperature
has changed.

611
00:22:14,569 --> 00:22:15,289
Surface temperature and

612
00:22:15,289 --> 00:22:17,045
air temperature are
changing together.

613
00:22:17,045 --> 00:22:19,129
If we're thinking
about what are

614
00:22:19,129 --> 00:22:20,300
the mechanisms causing

615
00:22:20,300 --> 00:22:21,440
heat and how do
we address them?

616
00:22:21,440 --> 00:22:22,489
Not having that
information

617
00:22:22,489 --> 00:22:24,200
is a little bit limiting.

618
00:22:24,200 --> 00:22:27,709
So these are
the main issues

619
00:22:27,709 --> 00:22:29,435
when we're thinking
about predicting

620
00:22:29,435 --> 00:22:30,860
air temperature
from surface

621
00:22:30,860 --> 00:22:32,675
temperatures is that

622
00:22:32,675 --> 00:22:34,189
often we don't have

623
00:22:34,189 --> 00:22:35,539
enough air
temperature data

624
00:22:35,539 --> 00:22:37,740
to validate this approach.

625
00:22:37,740 --> 00:22:40,390
We can interrogate
these factors

626
00:22:40,390 --> 00:22:43,239
to see what's
causing high heat.

627
00:22:43,239 --> 00:22:44,380
We're also,

628
00:22:44,380 --> 00:22:45,699
because we don't
have a lot of data,

629
00:22:45,699 --> 00:22:47,350
can't make predictions by

630
00:22:47,350 --> 00:22:48,460
different land
cover classes,

631
00:22:48,460 --> 00:22:50,934
so just have a
generalized prediction.

632
00:22:50,934 --> 00:22:54,700
So all of this
now leads into

633
00:22:54,700 --> 00:22:56,169
what I would
call AS now the

634
00:22:56,169 --> 00:22:58,089
technical part of
this presentation,

635
00:22:58,089 --> 00:22:59,800
which is an approach

636
00:22:59,800 --> 00:23:03,879
that I used with my
supervisors to look at.

637
00:23:03,879 --> 00:23:05,709
Can we make these
predictions

638
00:23:05,709 --> 00:23:07,630
in a more meaningful way?

639
00:23:07,630 --> 00:23:09,520
And so the
advantage we had

640
00:23:09,520 --> 00:23:11,185
is we had a couple of

641
00:23:11,185 --> 00:23:14,155
spatially dense air
temperature data sets

642
00:23:14,155 --> 00:23:16,240
from which to build from.

643
00:23:16,240 --> 00:23:18,744
And so because
we had that,

644
00:23:18,744 --> 00:23:19,930
we didn't have that

645
00:23:19,930 --> 00:23:21,350
limit air temperature data

646
00:23:21,350 --> 00:23:23,330
to build these
relationships.

647
00:23:23,330 --> 00:23:25,519
This is where it's
probably going to get

648
00:23:25,519 --> 00:23:27,349
a little too complicated.

649
00:23:27,349 --> 00:23:28,909
So I'm not going to
spend a lot of time,

650
00:23:28,909 --> 00:23:30,500
but basically what

651
00:23:30,500 --> 00:23:31,579
we did instead
of just doing

652
00:23:31,579 --> 00:23:33,349
a statistical
relationship between

653
00:23:33,349 --> 00:23:35,735
surface temperature
and air temperature,

654
00:23:35,735 --> 00:23:37,309
we still wanted
to use surface

655
00:23:37,309 --> 00:23:38,599
temperature to predict it.

656
00:23:38,599 --> 00:23:40,460
So we don't need a lot
of data to do this,

657
00:23:40,460 --> 00:23:42,514
so you can do it anywhere.

658
00:23:42,514 --> 00:23:45,394
But we tried to base
that prediction

659
00:23:45,394 --> 00:23:47,810
off of these
radiation balances.

660
00:23:47,810 --> 00:23:49,340
So again, I won't
really go into this,

661
00:23:49,340 --> 00:23:52,639
but basically
we could build

662
00:23:52,639 --> 00:23:56,209
this sort of coefficients
out of physical,

663
00:23:56,209 --> 00:23:58,399
physically based
processes so

664
00:23:58,399 --> 00:23:59,389
that there's
actually something

665
00:23:59,389 --> 00:24:00,410
to interpret what these,

666
00:24:00,410 --> 00:24:01,880
that it's not
just like an MX

667
00:24:01,880 --> 00:24:03,604
plus B type scenario.

668
00:24:03,604 --> 00:24:06,320
So we built that and
we can compare that to

669
00:24:06,320 --> 00:24:07,415
just a simple like

670
00:24:07,415 --> 00:24:09,379
uninformed
statistical approach.

671
00:24:09,379 --> 00:24:11,270
So that helps us
cross off this.

672
00:24:11,270 --> 00:24:12,679
So then we can
interpret some of

673
00:24:12,679 --> 00:24:14,644
these key factors
hopefully.

674
00:24:14,644 --> 00:24:19,040
Then lastly, we can
use land cover data in

675
00:24:19,040 --> 00:24:20,120
these predictions and make

676
00:24:20,120 --> 00:24:21,260
separate predictions by

677
00:24:21,260 --> 00:24:22,519
landcover class to see if

678
00:24:22,519 --> 00:24:23,929
that helps improve
our predictions.

679
00:24:23,929 --> 00:24:25,340
Since we know landcover is

680
00:24:25,340 --> 00:24:26,630
really changing some

681
00:24:26,630 --> 00:24:28,204
of these heat
relationships.

682
00:24:28,204 --> 00:24:29,660
So again, that
helps us cross off

683
00:24:29,660 --> 00:24:33,199
that third limitation
and sort of allows

684
00:24:33,199 --> 00:24:35,435
for this more
robust evaluation

685
00:24:35,435 --> 00:24:36,650
and these different
land cover

686
00:24:36,650 --> 00:24:38,660
models that we can test.

687
00:24:38,660 --> 00:24:40,669
All of this leads to

688
00:24:40,669 --> 00:24:42,545
these really long
research questions

689
00:24:42,545 --> 00:24:44,690
which are
basically asking,

690
00:24:44,690 --> 00:24:47,135
are there differences
between land covers?

691
00:24:47,135 --> 00:24:48,440
Pretty confident there is,

692
00:24:48,440 --> 00:24:52,250
but we still test that
you individually land

693
00:24:52,250 --> 00:24:54,230
cover models help versus

694
00:24:54,230 --> 00:24:56,345
just one big model
for the whole city.

695
00:24:56,345 --> 00:24:57,784
And then lastly, you know,

696
00:24:57,784 --> 00:24:59,300
kinda get coefficients

697
00:24:59,300 --> 00:25:00,469
of these models that are

698
00:25:00,469 --> 00:25:02,030
actually informative
about what's

699
00:25:02,030 --> 00:25:03,320
happening on the ground.

700
00:25:03,320 --> 00:25:05,090
And if we can,
then we can use

701
00:25:05,090 --> 00:25:07,910
that information to help
mitigate it, right?

702
00:25:07,910 --> 00:25:13,129
So what this looks
like then is we took

703
00:25:13,129 --> 00:25:14,630
five cities and these

704
00:25:14,630 --> 00:25:15,800
are based off of
where we have

705
00:25:15,800 --> 00:25:16,880
that high density air

706
00:25:16,880 --> 00:25:18,995
temperature data
from which to build.

707
00:25:18,995 --> 00:25:21,784
Take Landsat surface
temperature data,

708
00:25:21,784 --> 00:25:23,869
that high density air
temperature date,

709
00:25:23,869 --> 00:25:27,180
excuse me, then
land cover data.

710
00:25:27,180 --> 00:25:30,309
And if we take our
surface temperature data,

711
00:25:30,309 --> 00:25:33,565
we can take some
of which can

712
00:25:33,565 --> 00:25:35,214
hold some of our link or

713
00:25:35,214 --> 00:25:37,150
air temperature data
for the prediction,

714
00:25:37,150 --> 00:25:38,904
then some of it
to test later on,

715
00:25:38,904 --> 00:25:41,440
we can predict air
temperature from

716
00:25:41,440 --> 00:25:42,999
our surface
temperature data by

717
00:25:42,999 --> 00:25:44,739
land cover so that

718
00:25:44,739 --> 00:25:46,374
we have a predicted
air temperature.

719
00:25:46,374 --> 00:25:47,649
So this is what we want.

720
00:25:47,649 --> 00:25:48,939
We have surface
temperature, we

721
00:25:48,939 --> 00:25:50,365
can get air temperature,

722
00:25:50,365 --> 00:25:51,939
and then we can do
some comparisons

723
00:25:51,939 --> 00:25:53,664
to see how
accurate this is.

724
00:25:53,664 --> 00:25:55,630
So this is roughly
what we're doing.

725
00:25:55,630 --> 00:25:58,089
So again, to try
to keep it simple,

726
00:25:58,089 --> 00:25:59,964
let's just take surface
temperature data,

727
00:25:59,964 --> 00:26:01,960
predict air
temperature data.

728
00:26:01,960 --> 00:26:04,045
The key to this study
is that we have this

729
00:26:04,045 --> 00:26:05,980
air temperature data
from which we can

730
00:26:05,980 --> 00:26:07,810
test across different
land covers

731
00:26:07,810 --> 00:26:10,580
and see if land cover
is actually helping.

732
00:26:10,830 --> 00:26:13,779
Okay, so again, these are

733
00:26:13,779 --> 00:26:14,829
the five cities in

734
00:26:14,829 --> 00:26:17,319
case you want to know
where they're at.

735
00:26:17,319 --> 00:26:19,105
I'm assuming
everyone does.

736
00:26:19,105 --> 00:26:20,199
And then this is
what some of the

737
00:26:20,199 --> 00:26:21,250
data looks like, right?

738
00:26:21,250 --> 00:26:24,744
So these different
columns are little small,

739
00:26:24,744 --> 00:26:26,860
but these are air
temperature maps that

740
00:26:26,860 --> 00:26:29,079
we're building from
using for testing.

741
00:26:29,079 --> 00:26:30,369
So we're just cutting
out pieces of

742
00:26:30,369 --> 00:26:31,959
this to help build
those models.

743
00:26:31,959 --> 00:26:33,625
This is our surface
temperature data,

744
00:26:33,625 --> 00:26:35,040
which we're using
to predict.

745
00:26:35,040 --> 00:26:37,405
This third column is
the land cover data.

746
00:26:37,405 --> 00:26:38,229
So for each of

747
00:26:38,229 --> 00:26:39,925
these unique land
cover classes,

748
00:26:39,925 --> 00:26:41,875
we can build an
individual prediction

749
00:26:41,875 --> 00:26:44,889
of surface temperature
to air temperature.

750
00:26:44,889 --> 00:26:48,219
So just spending
a little bit of

751
00:26:48,219 --> 00:26:49,239
time talking about each of

752
00:26:49,239 --> 00:26:51,250
these individual
data products,

753
00:26:51,250 --> 00:26:53,410
just because I think
it's probably worth it.

754
00:26:53,410 --> 00:26:56,289
So the key one that I
keep talking about is

755
00:26:56,289 --> 00:26:57,490
this high spacial

756
00:26:57,490 --> 00:26:59,260
density air
temperature data.

757
00:26:59,260 --> 00:27:02,649
And this was collected
by vivax jaundice and

758
00:27:02,649 --> 00:27:04,284
others using

759
00:27:04,284 --> 00:27:06,070
what are called
mobile transects.

760
00:27:06,070 --> 00:27:07,630
So they have these
little sensors

761
00:27:07,630 --> 00:27:08,079
to put them on

762
00:27:08,079 --> 00:27:10,570
a card and they drive
the car around.

763
00:27:10,570 --> 00:27:12,010
In the car is
measuring temperature.

764
00:27:12,010 --> 00:27:13,405
There's a GPS on it.

765
00:27:13,405 --> 00:27:14,844
So we know where it's at.

766
00:27:14,844 --> 00:27:16,989
And it's very
intentional process

767
00:27:16,989 --> 00:27:19,270
where the goal is to
drive these transects to

768
00:27:19,270 --> 00:27:21,070
cover as much
differences in

769
00:27:21,070 --> 00:27:22,795
land cover types
as possible,

770
00:27:22,795 --> 00:27:24,175
like within an hour.

771
00:27:24,175 --> 00:27:25,540
And from that,
you get about

772
00:27:25,540 --> 00:27:27,069
60,000 data points.

773
00:27:27,069 --> 00:27:28,390
And we can generate these

774
00:27:28,390 --> 00:27:29,560
maps across the city.

775
00:27:29,560 --> 00:27:30,040
So that's where

776
00:27:30,040 --> 00:27:31,974
this air temperature
data is coming from.

777
00:27:31,974 --> 00:27:33,670
And they do this
multiple times a day.

778
00:27:33,670 --> 00:27:36,769
So we have multiple air
temperature products.

779
00:27:37,030 --> 00:27:39,320
The remote sensing
data. So that's

780
00:27:39,320 --> 00:27:40,549
surface temperature
data that we're

781
00:27:40,549 --> 00:27:42,739
using as a predictor.

782
00:27:42,739 --> 00:27:44,960
Is this coming
from Landsat?

783
00:27:44,960 --> 00:27:46,280
Again, in this
case, let's thermal

784
00:27:46,280 --> 00:27:47,930
infrared sensing.

785
00:27:47,930 --> 00:27:50,089
It's about that. I
haven't really mentioned

786
00:27:50,089 --> 00:27:51,770
this to this point,
but that's about it.

787
00:27:51,770 --> 00:27:53,630
30-meter spatial
resolution or

788
00:27:53,630 --> 00:27:55,205
air temperature
data is at ten.

789
00:27:55,205 --> 00:27:57,409
So we're making our
predictions at 10 m. So

790
00:27:57,409 --> 00:27:59,179
it's like a 10 m
squared pixel.

791
00:27:59,179 --> 00:28:00,800
You can think of.

792
00:28:00,800 --> 00:28:04,130
The challenge with Landsat

793
00:28:04,130 --> 00:28:07,819
is the hats pretty good
spatial resolution.

794
00:28:07,819 --> 00:28:09,665
But it only
overpasses like

795
00:28:09,665 --> 00:28:12,749
a single location
maybe every 16 days.

796
00:28:13,030 --> 00:28:15,754
That can be a
problem because if

797
00:28:15,754 --> 00:28:18,859
that overpass is a
cloudy day, you know,

798
00:28:18,859 --> 00:28:20,059
there's never
a problem for

799
00:28:20,059 --> 00:28:21,575
the Northwest,
but if it were,

800
00:28:21,575 --> 00:28:22,970
you know, you
don't actually

801
00:28:22,970 --> 00:28:24,410
have a lot of
days of data.

802
00:28:24,410 --> 00:28:25,850
So trying to find days

803
00:28:25,850 --> 00:28:27,290
that lineup can
be a challenge.

804
00:28:27,290 --> 00:28:29,689
So just worth mentioning.

805
00:28:29,689 --> 00:28:30,710
Right.

806
00:28:30,710 --> 00:28:32,210
And it's passing at 10:00

807
00:28:32,210 --> 00:28:33,905
A.M. every time
it comes over.

808
00:28:33,905 --> 00:28:35,570
So that's also a
little challenge

809
00:28:35,570 --> 00:28:36,769
in terms of lining
up the time.

810
00:28:36,769 --> 00:28:38,780
So these datasets,
alright,

811
00:28:38,780 --> 00:28:43,534
the third primary
dataset is land cover.

812
00:28:43,534 --> 00:28:45,605
And you know, so again,

813
00:28:45,605 --> 00:28:48,199
each land cover has a
unique energy balance.

814
00:28:48,199 --> 00:28:49,730
So we're breaking
these out for

815
00:28:49,730 --> 00:28:51,455
individual models
by land cover.

816
00:28:51,455 --> 00:28:53,059
And there's about
16 that are

817
00:28:53,059 --> 00:28:55,459
provided by what's
called the LCD,

818
00:28:55,459 --> 00:28:57,350
which is a national
land cover database.

819
00:28:57,350 --> 00:29:00,060
And they update this
every few years.

820
00:29:00,100 --> 00:29:02,600
Alright, so talk
the whole lot

821
00:29:02,600 --> 00:29:04,520
about this process
and everything.

822
00:29:04,520 --> 00:29:07,145
You know, how are
we evaluating this?

823
00:29:07,145 --> 00:29:09,439
Is this useful
at all right?

824
00:29:09,439 --> 00:29:12,320
So we can test

825
00:29:12,320 --> 00:29:14,945
for different things
with our approach.

826
00:29:14,945 --> 00:29:16,474
And primarily
we're testing,

827
00:29:16,474 --> 00:29:17,419
Are there differences

828
00:29:17,419 --> 00:29:18,695
between land cover types?

829
00:29:18,695 --> 00:29:20,060
Is it significant?

830
00:29:20,060 --> 00:29:21,755
In a moment
we're modelling

831
00:29:21,755 --> 00:29:24,320
air temperature from
surface temperature.

832
00:29:24,320 --> 00:29:25,955
Are we seeing

833
00:29:25,955 --> 00:29:27,950
similar or better
performance from

834
00:29:27,950 --> 00:29:29,390
a physically based
approach with

835
00:29:29,390 --> 00:29:30,979
interpretable
coefficients versus

836
00:29:30,979 --> 00:29:33,079
just a statistical
benchmark.

837
00:29:33,079 --> 00:29:36,530
Then does land covers

838
00:29:36,530 --> 00:29:38,389
specific models
show improvement

839
00:29:38,389 --> 00:29:40,640
versus just a
generalized model?

840
00:29:40,640 --> 00:29:43,609
And then again,
are there value in

841
00:29:43,609 --> 00:29:44,720
these coefficients
that we could

842
00:29:44,720 --> 00:29:46,745
use to inform mitigation?

843
00:29:46,745 --> 00:29:48,650
Okay, so I'll have

844
00:29:48,650 --> 00:29:50,419
three key findings that

845
00:29:50,419 --> 00:29:52,204
we'll talk about a
little bit here.

846
00:29:52,204 --> 00:29:53,479
So this is the first

847
00:29:53,479 --> 00:29:55,250
and they're probably
all going to be

848
00:29:55,250 --> 00:29:56,990
crazy figures that
there's a lot

849
00:29:56,990 --> 00:29:58,609
going on and on
first glance.

850
00:29:58,609 --> 00:30:01,879
But what we're
looking at here is by

851
00:30:01,879 --> 00:30:03,440
land cover class,

852
00:30:03,440 --> 00:30:05,360
the temperature
distribution.

853
00:30:05,360 --> 00:30:06,499
So this isn't processing.

854
00:30:06,499 --> 00:30:07,955
This is just the raw data.

855
00:30:07,955 --> 00:30:10,459
And what we're
looking at is,

856
00:30:10,459 --> 00:30:12,439
are there significant
differences

857
00:30:12,439 --> 00:30:14,360
between land cover
classes because

858
00:30:14,360 --> 00:30:16,159
it's sort of justifies
the approach of

859
00:30:16,159 --> 00:30:18,004
using individual
landcover models, right?

860
00:30:18,004 --> 00:30:18,860
If they're not different

861
00:30:18,860 --> 00:30:20,104
than what's the point?

862
00:30:20,104 --> 00:30:24,230
And not surprisingly,
probably to anyone.

863
00:30:24,230 --> 00:30:26,179
If you look at
the endpoints is

864
00:30:26,179 --> 00:30:29,375
here being developed
and forest,

865
00:30:29,375 --> 00:30:31,325
we see that there
is a difference.

866
00:30:31,325 --> 00:30:32,974
In fact,

867
00:30:32,974 --> 00:30:35,375
surface temperature is
a little bit bigger.

868
00:30:35,375 --> 00:30:37,549
Surface temperatures
tend to be hotter,

869
00:30:37,549 --> 00:30:39,439
has about ten
degree difference.

870
00:30:39,439 --> 00:30:39,950
And then there's about

871
00:30:39,950 --> 00:30:42,440
a one degree difference
in air temperature,

872
00:30:42,440 --> 00:30:45,270
which is fairly
significant.

873
00:30:45,520 --> 00:30:47,510
The reason we're seeing

874
00:30:47,510 --> 00:30:48,710
these differences
again goes

875
00:30:48,710 --> 00:30:49,969
back to the
characteristics

876
00:30:49,969 --> 00:30:51,199
of these land
cover is right.

877
00:30:51,199 --> 00:30:53,600
So these developed areas

878
00:30:53,600 --> 00:30:55,384
of storing and
absorbing more heat,

879
00:30:55,384 --> 00:30:57,439
the vegetated
areas are able

880
00:30:57,439 --> 00:30:59,584
to shade the
ground for one,

881
00:30:59,584 --> 00:31:00,800
but also a cooling

882
00:31:00,800 --> 00:31:02,225
it with evaporative
cooling.

883
00:31:02,225 --> 00:31:04,400
So this is really
where we're seeing

884
00:31:04,400 --> 00:31:06,589
the effects of these
processes and our data.

885
00:31:06,589 --> 00:31:07,325
This is good.

886
00:31:07,325 --> 00:31:09,304
So landcover is
different, that's great.

887
00:31:09,304 --> 00:31:10,640
We can go forward with

888
00:31:10,640 --> 00:31:13,400
modeling individually
land cover types.

889
00:31:13,400 --> 00:31:17,254
Which leads us
to finding two,

890
00:31:17,254 --> 00:31:19,790
which is the
modelling results.

891
00:31:19,790 --> 00:31:21,889
And these are pictures
of usually when I

892
00:31:21,889 --> 00:31:24,350
talk modelling
to my family,

893
00:31:24,350 --> 00:31:26,044
what they're probably
thinking of.

894
00:31:26,044 --> 00:31:27,575
But in this case,
we're talking,

895
00:31:27,575 --> 00:31:28,579
unfortunately, I guess,

896
00:31:28,579 --> 00:31:30,154
about temperature models.

897
00:31:30,154 --> 00:31:33,005
And again, a lot going
on in this figure.

898
00:31:33,005 --> 00:31:35,870
We still have our land
cover types here.

899
00:31:35,870 --> 00:31:38,960
And along our x-axis
we have what's RMSE.

900
00:31:38,960 --> 00:31:40,294
So it's basically error.

901
00:31:40,294 --> 00:31:41,600
So as you go further,

902
00:31:41,600 --> 00:31:43,235
right, you have
more error.

903
00:31:43,235 --> 00:31:43,880
You want to see

904
00:31:43,880 --> 00:31:46,024
less error for these
different ones.

905
00:31:46,024 --> 00:31:49,175
This highlighted here is

906
00:31:49,175 --> 00:31:51,359
a land cover
independent model.

907
00:31:51,359 --> 00:31:53,510
So we just put all
the data together.

908
00:31:53,510 --> 00:31:55,039
What does it show?

909
00:31:55,039 --> 00:31:57,244
And so what you'd
want to see

910
00:31:57,244 --> 00:31:59,180
is individual land
cover models,

911
00:31:59,180 --> 00:32:00,500
which are the
rest of these

912
00:32:00,500 --> 00:32:02,284
performing better
than that.

913
00:32:02,284 --> 00:32:04,505
There's some other
stuff going on here.

914
00:32:04,505 --> 00:32:07,969
So there's four
different lines.

915
00:32:07,969 --> 00:32:10,984
When this linear lines

916
00:32:10,984 --> 00:32:12,110
are the benchmark model,

917
00:32:12,110 --> 00:32:13,850
that linear
statistical model,

918
00:32:13,850 --> 00:32:15,499
what's called
the hybrid here

919
00:32:15,499 --> 00:32:17,824
is a physically
based model.

920
00:32:17,824 --> 00:32:20,270
We're also comparing
that benchmark

921
00:32:20,270 --> 00:32:21,560
to the physically-based.

922
00:32:21,560 --> 00:32:26,149
It very briefly, when
we're looking at that,

923
00:32:26,149 --> 00:32:28,580
we see similar
performance.

924
00:32:28,580 --> 00:32:30,499
Overall, it's about
a half a degree

925
00:32:30,499 --> 00:32:31,804
of error in temperature,

926
00:32:31,804 --> 00:32:33,290
air temperature
predictions which

927
00:32:33,290 --> 00:32:34,564
are pretty happy with

928
00:32:34,564 --> 00:32:36,710
compared to some other

929
00:32:36,710 --> 00:32:38,614
studies that
are out there,

930
00:32:38,614 --> 00:32:40,099
which I would be happy to

931
00:32:40,099 --> 00:32:41,689
talk about in the
questions part.

932
00:32:41,689 --> 00:32:44,180
But the key there is,

933
00:32:44,180 --> 00:32:45,469
we get good performance is

934
00:32:45,469 --> 00:32:47,209
not performing
much better.

935
00:32:47,209 --> 00:32:48,815
It's very slightly better

936
00:32:48,815 --> 00:32:49,910
that physically
based model

937
00:32:49,910 --> 00:32:50,690
than the linear model,

938
00:32:50,690 --> 00:32:53,044
but it's not worse.
So that's great.

939
00:32:53,044 --> 00:32:55,639
There's also this
differentiation

940
00:32:55,639 --> 00:32:57,440
between all data
and subset data.

941
00:32:57,440 --> 00:32:58,714
So I didn't really
talk about this.

942
00:32:58,714 --> 00:33:00,620
I don't have time to
you, but we also ran

943
00:33:00,620 --> 00:33:02,210
a separate simulation
where if we didn't

944
00:33:02,210 --> 00:33:04,040
have that entire air
temperature dataset,

945
00:33:04,040 --> 00:33:05,779
we only had like
100 data points.

946
00:33:05,779 --> 00:33:07,714
How does that change
our findings?

947
00:33:07,714 --> 00:33:09,424
So that's also
plotted here.

948
00:33:09,424 --> 00:33:10,640
But again.

949
00:33:10,640 --> 00:33:12,934
The key thing that we
really want to focus

950
00:33:12,934 --> 00:33:16,175
on is that the majority of

951
00:33:16,175 --> 00:33:18,815
individually and
cognitive models

952
00:33:18,815 --> 00:33:21,770
outperformed just a
generalized model,

953
00:33:21,770 --> 00:33:22,970
which we're happy to see,

954
00:33:22,970 --> 00:33:24,380
which demonstrates
the importance

955
00:33:24,380 --> 00:33:26,224
of land cover
in determining

956
00:33:26,224 --> 00:33:27,500
air temperature and

957
00:33:27,500 --> 00:33:29,000
that relationship
from surface to

958
00:33:29,000 --> 00:33:30,650
air of transferring heat

959
00:33:30,650 --> 00:33:32,599
from the surface
into the air.

960
00:33:32,599 --> 00:33:35,495
So it was 8.14.

961
00:33:35,495 --> 00:33:37,280
Land cover types
were better than

962
00:33:37,280 --> 00:33:39,110
the all the ones

963
00:33:39,110 --> 00:33:40,399
that weren't. We're
pretty close.

964
00:33:40,399 --> 00:33:42,230
So you'd want to
see is this is

965
00:33:42,230 --> 00:33:44,239
the line where the
all land cover areas.

966
00:33:44,239 --> 00:33:45,319
So anything to
the left, it

967
00:33:45,319 --> 00:33:46,820
is performing better.

968
00:33:46,820 --> 00:33:48,440
Anything to the right
is performing worse.

969
00:33:48,440 --> 00:33:52,200
So you can see the
majority outperformed it.

970
00:33:52,940 --> 00:33:57,260
Last finding in kind of
thing that we looked

971
00:33:57,260 --> 00:33:59,600
into then was
those coefficients

972
00:33:59,600 --> 00:34:00,679
of the model, right?

973
00:34:00,679 --> 00:34:04,730
So how different are

974
00:34:04,730 --> 00:34:06,049
those between land cover

975
00:34:06,049 --> 00:34:07,490
classes and is there
something there,

976
00:34:07,490 --> 00:34:08,960
is there something
interpretable that

977
00:34:08,960 --> 00:34:10,549
we could use like tie

978
00:34:10,549 --> 00:34:12,649
this coefficient to
a specific process

979
00:34:12,649 --> 00:34:14,450
so we know if we're
changing that process,

980
00:34:14,450 --> 00:34:16,159
we could model
that difference.

981
00:34:16,159 --> 00:34:19,790
So this linear or
benchmark model is,

982
00:34:19,790 --> 00:34:22,010
it doesn't take that
into consideration,

983
00:34:22,010 --> 00:34:24,559
is just purely
empirical statistical.

984
00:34:24,559 --> 00:34:27,470
And so what we'd want
to see are we have

985
00:34:27,470 --> 00:34:30,289
a coefficient one and
a coefficient two.

986
00:34:30,289 --> 00:34:31,429
So coefficient one is like

987
00:34:31,429 --> 00:34:33,739
your intercept
and coefficients

988
00:34:33,739 --> 00:34:35,240
who is like your slope.

989
00:34:35,240 --> 00:34:37,894
And when we plot them
against each other,

990
00:34:37,894 --> 00:34:39,920
we'd want to see
if there was

991
00:34:39,920 --> 00:34:42,259
actual differences
in terms of what

992
00:34:42,259 --> 00:34:44,389
being meaningful
is that similarly

993
00:34:44,389 --> 00:34:47,944
uncovers would group
on these plots.

994
00:34:47,944 --> 00:34:49,939
So you'd see all
the trees together,

995
00:34:49,939 --> 00:34:52,115
all the urban
areas together.

996
00:34:52,115 --> 00:34:54,169
For the linear model,

997
00:34:54,169 --> 00:34:55,115
we don't really see that,

998
00:34:55,115 --> 00:34:56,600
which is what we expect.

999
00:34:56,600 --> 00:34:58,055
They're all over.

1000
00:34:58,055 --> 00:35:01,009
There's an urban
developed area here,

1001
00:35:01,009 --> 00:35:02,719
really close to trees.

1002
00:35:02,719 --> 00:35:04,370
And I should say
these points are

1003
00:35:04,370 --> 00:35:06,139
weighted across
those five cities.

1004
00:35:06,139 --> 00:35:06,949
And so these lines

1005
00:35:06,949 --> 00:35:08,015
that you're seeing
that it kinda

1006
00:35:08,015 --> 00:35:08,929
extending out is

1007
00:35:08,929 --> 00:35:11,345
the range between
the cities.

1008
00:35:11,345 --> 00:35:13,834
We move to that
physically based model.

1009
00:35:13,834 --> 00:35:16,624
We now have an intercept

1010
00:35:16,624 --> 00:35:18,079
and we have two
slope term.

1011
00:35:18,079 --> 00:35:20,000
So there's, there's
two plots here.

1012
00:35:20,000 --> 00:35:22,204
What we see
that's different

1013
00:35:22,204 --> 00:35:23,884
from our linear model,

1014
00:35:23,884 --> 00:35:26,599
that we do see
a grouping of

1015
00:35:26,599 --> 00:35:29,579
like the trees versus
the urban areas.

1016
00:35:29,579 --> 00:35:32,179
In fact, if you look at
this a little closer,

1017
00:35:32,179 --> 00:35:34,759
what you find is these,

1018
00:35:34,759 --> 00:35:37,190
for this physically
based model,

1019
00:35:37,190 --> 00:35:40,370
the slope term tends

1020
00:35:40,370 --> 00:35:42,799
to be higher or I'm sorry,

1021
00:35:42,799 --> 00:35:44,389
excuse me, the
intercept tends to be

1022
00:35:44,389 --> 00:35:46,445
higher and the slope
tends to be lower.

1023
00:35:46,445 --> 00:35:49,714
For developed, for trees

1024
00:35:49,714 --> 00:35:50,900
or the vegetated areas,

1025
00:35:50,900 --> 00:35:52,039
it's the inverse, so we

1026
00:35:52,039 --> 00:35:55,040
see a lower intercept
and a higher slope.

1027
00:35:55,040 --> 00:35:57,300
What does it
actually mean?

1028
00:35:57,700 --> 00:36:01,415
Or developed areas
that higher intercept,

1029
00:36:01,415 --> 00:36:02,689
if you're
thinking of this,

1030
00:36:02,689 --> 00:36:03,709
means that it starts

1031
00:36:03,709 --> 00:36:05,239
off at a higher
temperature and

1032
00:36:05,239 --> 00:36:06,799
a lower slope means

1033
00:36:06,799 --> 00:36:09,155
it's approximating
more of a flat line,

1034
00:36:09,155 --> 00:36:11,660
meaning it starts high
and it stays high.

1035
00:36:11,660 --> 00:36:14,420
Versus a vegetated
area has

1036
00:36:14,420 --> 00:36:15,859
lower intercepts
or starting

1037
00:36:15,859 --> 00:36:17,180
a lower temperature and

1038
00:36:17,180 --> 00:36:18,364
it has a higher slope,

1039
00:36:18,364 --> 00:36:19,954
is there's more kind of

1040
00:36:19,954 --> 00:36:21,559
fungibility in
terms of how

1041
00:36:21,559 --> 00:36:22,879
much it's able to cool.

1042
00:36:22,879 --> 00:36:25,309
So we see that it's
sort of buffering heat,

1043
00:36:25,309 --> 00:36:26,855
which is what we'd expect.

1044
00:36:26,855 --> 00:36:27,995
Um, so that's great.

1045
00:36:27,995 --> 00:36:30,440
So this sort of
lens evidence,

1046
00:36:30,440 --> 00:36:31,669
the idea that
these coefficients

1047
00:36:31,669 --> 00:36:32,569
do have some meeting,

1048
00:36:32,569 --> 00:36:34,475
which is what
we're hoping for.

1049
00:36:34,475 --> 00:36:36,260
Challenged that
we've looked at

1050
00:36:36,260 --> 00:36:37,520
when we will solve
this across cities,

1051
00:36:37,520 --> 00:36:37,730
there's

1052
00:36:37,730 --> 00:36:40,175
pretty high variability
between cities.

1053
00:36:40,175 --> 00:36:41,719
Ideally, you'd
like to have

1054
00:36:41,719 --> 00:36:43,594
a consistent set of this.

1055
00:36:43,594 --> 00:36:45,170
And we didn't
really find that.

1056
00:36:45,170 --> 00:36:46,340
So that's sort of
indicates to us

1057
00:36:46,340 --> 00:36:47,719
that there's still
some work to be

1058
00:36:47,719 --> 00:36:49,850
done looking at how maybe

1059
00:36:49,850 --> 00:36:50,509
we're formulating

1060
00:36:50,509 --> 00:36:52,985
those coefficients
going forward.

1061
00:36:52,985 --> 00:36:55,955
All right, so you survived

1062
00:36:55,955 --> 00:36:58,040
all of these graphs
and figures.

1063
00:36:58,040 --> 00:36:59,434
What does this mean?

1064
00:36:59,434 --> 00:37:00,439
How does this tie back

1065
00:37:00,439 --> 00:37:01,640
into what I was
talking about at

1066
00:37:01,640 --> 00:37:02,719
the beginning with

1067
00:37:02,719 --> 00:37:04,535
urban heat being
a challenge.

1068
00:37:04,535 --> 00:37:08,570
Well, first, let's just
to reiterate this,

1069
00:37:08,570 --> 00:37:11,839
we're able to make
these predictions with

1070
00:37:11,839 --> 00:37:13,339
our models are
in general we

1071
00:37:13,339 --> 00:37:15,019
can the performance
first of all,

1072
00:37:15,019 --> 00:37:16,640
we get fairly
accurate results.

1073
00:37:16,640 --> 00:37:19,835
We find that by land
cover is important.

1074
00:37:19,835 --> 00:37:21,680
It isn't really
surprising when we think

1075
00:37:21,680 --> 00:37:23,509
about CD is experiencing

1076
00:37:23,509 --> 00:37:25,804
these differing
levels of heat.

1077
00:37:25,804 --> 00:37:28,250
We also see
that we can get

1078
00:37:28,250 --> 00:37:30,455
some information out
these coefficients.

1079
00:37:30,455 --> 00:37:31,850
But there's still
maybe a little bit of

1080
00:37:31,850 --> 00:37:33,455
work that needs
to be done.

1081
00:37:33,455 --> 00:37:35,330
But really, if we

1082
00:37:35,330 --> 00:37:37,355
go back to where we
started with this,

1083
00:37:37,355 --> 00:37:40,504
can we understand
where heat occurs?

1084
00:37:40,504 --> 00:37:41,435
Yes.

1085
00:37:41,435 --> 00:37:44,854
Either a surface
temperatures

1086
00:37:44,854 --> 00:37:46,339
or air temperatures.

1087
00:37:46,339 --> 00:37:47,734
There's there's
different ways.

1088
00:37:47,734 --> 00:37:49,415
Can we understand
the magnitude?

1089
00:37:49,415 --> 00:37:50,089
Well, in this case,

1090
00:37:50,089 --> 00:37:51,289
we need those air
temperatures.

1091
00:37:51,289 --> 00:37:52,249
So what's our predictions?

1092
00:37:52,249 --> 00:37:54,109
We can do that. Then.

1093
00:37:54,109 --> 00:37:55,789
Can we understand
the mechanisms

1094
00:37:55,789 --> 00:37:57,605
which is maybe the
biggest challenge?

1095
00:37:57,605 --> 00:37:59,870
And the answer is
kind of right?

1096
00:37:59,870 --> 00:38:00,950
So what their coefficients

1097
00:38:00,950 --> 00:38:02,209
we sort of get at that.

1098
00:38:02,209 --> 00:38:02,494
So there's,

1099
00:38:02,494 --> 00:38:04,220
these different coefficients
are representing

1100
00:38:04,220 --> 00:38:05,600
different physical

1101
00:38:05,600 --> 00:38:06,890
processes that
are happening.

1102
00:38:06,890 --> 00:38:08,329
So we're sort of getting
at that a little

1103
00:38:08,329 --> 00:38:10,475
bit, which is encouraging.

1104
00:38:10,475 --> 00:38:13,010
But what all of this
allows for then is

1105
00:38:13,010 --> 00:38:14,764
if we have these
coefficients,

1106
00:38:14,764 --> 00:38:15,995
we have this data.

1107
00:38:15,995 --> 00:38:18,560
We can run kind of
different analyses in

1108
00:38:18,560 --> 00:38:21,605
terms of urban heat
is really bad.

1109
00:38:21,605 --> 00:38:23,584
What if we change
the land cover?

1110
00:38:23,584 --> 00:38:25,249
Well, we have an equation

1111
00:38:25,249 --> 00:38:26,599
that predicts
that if we change

1112
00:38:26,599 --> 00:38:30,140
this area from developed
to trees or two.

1113
00:38:30,140 --> 00:38:32,345
You know, grass
field or wherever

1114
00:38:32,345 --> 00:38:34,729
we can see what we
can predict that.

1115
00:38:34,729 --> 00:38:37,384
Or if we're not using
the whole land cover,

1116
00:38:37,384 --> 00:38:38,060
let's just say we're

1117
00:38:38,060 --> 00:38:39,409
changing part of
the land cover.

1118
00:38:39,409 --> 00:38:41,704
We're changing how
much it reflects.

1119
00:38:41,704 --> 00:38:43,685
Well, we can change
our coefficients

1120
00:38:43,685 --> 00:38:45,349
and see what
the outcome is.

1121
00:38:45,349 --> 00:38:47,450
So this is really helpful

1122
00:38:47,450 --> 00:38:50,225
in terms of being able to

1123
00:38:50,225 --> 00:38:53,149
test or think about
different ways to address

1124
00:38:53,149 --> 00:38:54,559
urban heat in

1125
00:38:54,559 --> 00:38:56,210
a much more comprehensive

1126
00:38:56,210 --> 00:38:57,529
way across the city.

1127
00:38:57,529 --> 00:38:58,969
Not just, you know, very

1128
00:38:58,969 --> 00:39:00,259
limited or generalized

1129
00:39:00,259 --> 00:39:01,490
so that we're
making sure that

1130
00:39:01,490 --> 00:39:03,110
it's an equitable
approach.

1131
00:39:03,110 --> 00:39:04,789
We're not just
improving things in

1132
00:39:04,789 --> 00:39:07,295
little small areas that
don't help anyone.

1133
00:39:07,295 --> 00:39:10,159
So on that note,

1134
00:39:10,159 --> 00:39:12,334
these data are helpful

1135
00:39:12,334 --> 00:39:13,700
because we have

1136
00:39:13,700 --> 00:39:15,440
surface temperature
everywhere.

1137
00:39:15,440 --> 00:39:16,939
If we can really refine

1138
00:39:16,939 --> 00:39:19,205
these methods to predict
air temperature,

1139
00:39:19,205 --> 00:39:20,525
air temperature
everywhere,

1140
00:39:20,525 --> 00:39:21,485
That's a huge help.

1141
00:39:21,485 --> 00:39:23,059
And just knowing where

1142
00:39:23,059 --> 00:39:24,559
these problems are and how

1143
00:39:24,559 --> 00:39:28,564
severe they are is half
the battle. Right?

1144
00:39:28,564 --> 00:39:31,475
Then the real
thing then is

1145
00:39:31,475 --> 00:39:34,130
using this to inform
mitigation efforts.

1146
00:39:34,130 --> 00:39:35,959
So I keep mentioning this.

1147
00:39:35,959 --> 00:39:38,359
What are some of the
mitigation methods?

1148
00:39:38,359 --> 00:39:44,030
So usually what you'll
find is people say,

1149
00:39:44,030 --> 00:39:47,434
add more trees, which
is entirely true.

1150
00:39:47,434 --> 00:39:51,320
That's the primary
suggestion

1151
00:39:51,320 --> 00:39:52,144
for a good reason.

1152
00:39:52,144 --> 00:39:54,289
So just add trees,
it'll cool it down.

1153
00:39:54,289 --> 00:39:55,925
Um, which of course is

1154
00:39:55,925 --> 00:39:58,534
good solution if
it's feasible,

1155
00:39:58,534 --> 00:40:01,654
especially if you're
planting new green space

1156
00:40:01,654 --> 00:40:03,260
equitably across the city

1157
00:40:03,260 --> 00:40:05,390
or putting it in areas
that need it most.

1158
00:40:05,390 --> 00:40:08,104
This is of course, She's
are providing shade,

1159
00:40:08,104 --> 00:40:10,085
the providing
evaporative cooling.

1160
00:40:10,085 --> 00:40:11,629
So that's great.
That's not

1161
00:40:11,629 --> 00:40:12,965
the only solution.

1162
00:40:12,965 --> 00:40:14,794
There are several others

1163
00:40:14,794 --> 00:40:17,269
which I'll just sort of
mentioned in passing,

1164
00:40:17,269 --> 00:40:18,440
but are pictured here.

1165
00:40:18,440 --> 00:40:19,939
So we can also change

1166
00:40:19,939 --> 00:40:22,204
how impervious
ground covers are.

1167
00:40:22,204 --> 00:40:23,960
So things like
porous pavement

1168
00:40:23,960 --> 00:40:25,129
where it allows some of

1169
00:40:25,129 --> 00:40:26,269
the water to enter into

1170
00:40:26,269 --> 00:40:27,439
the soil column
so it can be

1171
00:40:27,439 --> 00:40:29,699
evaporated and
cool the air.

1172
00:40:29,770 --> 00:40:33,094
You can change
sort of roofs

1173
00:40:33,094 --> 00:40:35,794
or construction
materials and building.

1174
00:40:35,794 --> 00:40:37,640
So you might have
heard of things

1175
00:40:37,640 --> 00:40:40,565
like cool roof, a
white roof where they,

1176
00:40:40,565 --> 00:40:42,350
they'll paint roofs or

1177
00:40:42,350 --> 00:40:44,869
sometimes even streets
or sidewalks or

1178
00:40:44,869 --> 00:40:46,909
things like that to be

1179
00:40:46,909 --> 00:40:47,900
lighter in color so it

1180
00:40:47,900 --> 00:40:49,189
reflects more heat back,

1181
00:40:49,189 --> 00:40:51,080
so it's not
absorbing as much.

1182
00:40:51,080 --> 00:40:54,470
You can turn roofs
into green roofs.

1183
00:40:54,470 --> 00:40:57,139
There's lots of
different solutions

1184
00:40:57,139 --> 00:40:59,160
that are available.

1185
00:40:59,380 --> 00:41:02,614
But a key part of it
also is just awareness,

1186
00:41:02,614 --> 00:41:04,310
making people
more aware of it,

1187
00:41:04,310 --> 00:41:05,810
making them more engaged

1188
00:41:05,810 --> 00:41:07,775
in things like heat.gov.

1189
00:41:07,775 --> 00:41:10,040
And then finally,
of course,

1190
00:41:10,040 --> 00:41:12,379
reducing the effects
of climate change,

1191
00:41:12,379 --> 00:41:14,539
which are going to
continually push these,

1192
00:41:14,539 --> 00:41:17,640
these heat events to
being hotter and hotter.

1193
00:41:18,180 --> 00:41:21,055
And so, you know,

1194
00:41:21,055 --> 00:41:23,050
with all this knowledge
and information,

1195
00:41:23,050 --> 00:41:25,584
we can use this
to work for

1196
00:41:25,584 --> 00:41:27,159
mitigation and
solutions that

1197
00:41:27,159 --> 00:41:29,754
keep places cool
for everyone,

1198
00:41:29,754 --> 00:41:31,404
including my dog, who

1199
00:41:31,404 --> 00:41:33,160
really hated the heatwave.

1200
00:41:33,160 --> 00:41:35,889
So with that, I'll
end here and say,

1201
00:41:35,889 --> 00:41:37,210
thank you very much
for your time.

1202
00:41:37,210 --> 00:41:38,319
Thanks for listening to

1203
00:41:38,319 --> 00:41:40,014
my talk and inviting me.

1204
00:41:40,014 --> 00:41:42,444
And I'll gladly take
some questions.

1205
00:41:42,444 --> 00:41:44,739
First Garret, thank
you so much and let's

1206
00:41:44,739 --> 00:41:46,795
everybody give him
a silent hand.

1207
00:41:46,795 --> 00:41:48,985
Great Talk.

1208
00:41:48,985 --> 00:41:51,130
Go ahead and answer
questions and if

1209
00:41:51,130 --> 00:41:53,409
there's too many or
moderate for you, okay?

1210
00:41:53,409 --> 00:41:56,179
Yeah. Sounds good.

1211
00:41:56,340 --> 00:41:59,479
And who has questions?

1212
00:42:01,300 --> 00:42:03,049
Well, I can start

1213
00:42:03,049 --> 00:42:04,354
with mine if
you don't mind.

1214
00:42:04,354 --> 00:42:05,059
Sure.

1215
00:42:05,059 --> 00:42:06,439
I realized this
wouldn't work

1216
00:42:06,439 --> 00:42:07,940
for heavy city areas,

1217
00:42:07,940 --> 00:42:11,734
but has anybody looked
at soils on this?

1218
00:42:11,734 --> 00:42:18,660
You're talking about
cell types a bit.

1219
00:42:18,760 --> 00:42:21,514
I mean, when I
think of soils,

1220
00:42:21,514 --> 00:42:23,479
one of the key
components of

1221
00:42:23,479 --> 00:42:25,400
soils is how much
water they can hold

1222
00:42:25,400 --> 00:42:28,190
is a big aspect
of it since

1223
00:42:28,190 --> 00:42:30,110
I'm pretty biased because

1224
00:42:30,110 --> 00:42:31,639
I did a lot of work
with soil moisture,

1225
00:42:31,639 --> 00:42:33,679
but that's pretty it's

1226
00:42:33,679 --> 00:42:35,329
a pretty key component.

1227
00:42:35,329 --> 00:42:38,029
The issue with, I think
why there hasn't been

1228
00:42:38,029 --> 00:42:39,259
a lot of research
into that

1229
00:42:39,259 --> 00:42:40,985
is a lot of
times in cities,

1230
00:42:40,985 --> 00:42:44,015
it's hard for word,
even get to the soil.

1231
00:42:44,015 --> 00:42:45,860
And there's so many
things that are

1232
00:42:45,860 --> 00:42:47,329
happening above
the soil that

1233
00:42:47,329 --> 00:42:49,609
are controlling
that has asphalt

1234
00:42:49,609 --> 00:42:50,750
over it or there's grass

1235
00:42:50,750 --> 00:42:51,949
growing on it and
things like that.

1236
00:42:51,949 --> 00:42:53,705
But yeah.

1237
00:42:53,705 --> 00:42:58,504
Thanks. I got a question.

1238
00:42:58,504 --> 00:43:00,185
So first of all,

1239
00:43:00,185 --> 00:43:01,280
the reasons I
live in Portland

1240
00:43:01,280 --> 00:43:02,090
is because when
you look at

1241
00:43:02,090 --> 00:43:03,169
that national weather map,

1242
00:43:03,169 --> 00:43:03,920
it's in the summer,

1243
00:43:03,920 --> 00:43:04,879
it's often the only place.

1244
00:43:04,879 --> 00:43:06,380
It's your greenish, right?

1245
00:43:06,380 --> 00:43:07,700
And I love the
weather here.

1246
00:43:07,700 --> 00:43:08,930
It looks like I
need to move to

1247
00:43:08,930 --> 00:43:11,015
North Dakota or
South Dakota.

1248
00:43:11,015 --> 00:43:13,730
Have a little
disappointed, Garrett.

1249
00:43:13,730 --> 00:43:17,030
I know. It's a tough deal.

1250
00:43:17,030 --> 00:43:20,300
It also is your UPN
w here in Portland.

1251
00:43:20,300 --> 00:43:23,089
I'm in Corvallis. Okay.

1252
00:43:23,089 --> 00:43:24,364
Excellent. Yeah.

1253
00:43:24,364 --> 00:43:26,299
My question was and

1254
00:43:26,299 --> 00:43:27,410
this might be a little

1255
00:43:27,410 --> 00:43:28,610
bit peripheral
to your talk.

1256
00:43:28,610 --> 00:43:30,649
You talked about
the deaths from

1257
00:43:30,649 --> 00:43:34,009
the heatwave. What 2021.

1258
00:43:34,009 --> 00:43:36,380
If you said 600
deaths and they were

1259
00:43:36,380 --> 00:43:38,390
centered in areas
that were lower,

1260
00:43:38,390 --> 00:43:40,790
that had the higher
heat or heat

1261
00:43:40,790 --> 00:43:43,340
indexes at some point,

1262
00:43:43,340 --> 00:43:44,600
isn't it true that

1263
00:43:44,600 --> 00:43:45,949
it doesn't matter
where you are,

1264
00:43:45,949 --> 00:43:47,570
the temperature is
going to get to

1265
00:43:47,570 --> 00:43:49,279
a point that even

1266
00:43:49,279 --> 00:43:50,794
if you live in a
nice shady area,

1267
00:43:50,794 --> 00:43:51,619
you can still run into

1268
00:43:51,619 --> 00:43:54,305
those same lethal effects.

1269
00:43:54,305 --> 00:43:57,120
And do we know what
that level is?

1270
00:43:57,910 --> 00:44:01,234
Well, yeah, that's
a great question.

1271
00:44:01,234 --> 00:44:03,319
So I'll answer the
last part first.

1272
00:44:03,319 --> 00:44:05,434
So do we know what
that level is?

1273
00:44:05,434 --> 00:44:07,504
This is a very
science answer.

1274
00:44:07,504 --> 00:44:08,344
It depends.

1275
00:44:08,344 --> 00:44:09,709
It depends on the person.

1276
00:44:09,709 --> 00:44:12,079
So a lot of times,
yeah, of course.

1277
00:44:12,079 --> 00:44:13,550
Right. So a lot of times

1278
00:44:13,550 --> 00:44:15,905
it's people who
are most at risk.

1279
00:44:15,905 --> 00:44:18,394
I have underlying
health conditions.

1280
00:44:18,394 --> 00:44:21,139
So the level of

1281
00:44:21,139 --> 00:44:23,129
heat that can cause
some severe issues

1282
00:44:23,129 --> 00:44:25,489
is probably a good bit
lower than what you'd

1283
00:44:25,489 --> 00:44:28,594
say is maybe the average
healthier person.

1284
00:44:28,594 --> 00:44:31,835
That being said,
we do know this.

1285
00:44:31,835 --> 00:44:34,609
Also, I didn't talk
about this much,

1286
00:44:34,609 --> 00:44:36,515
but humidity also
plays a role in this,

1287
00:44:36,515 --> 00:44:38,629
is that the
combined effect

1288
00:44:38,629 --> 00:44:40,280
of temperature
plus humidity.

1289
00:44:40,280 --> 00:44:40,745
Well,

1290
00:44:40,745 --> 00:44:42,829
it's making these
things livable or not.

1291
00:44:42,829 --> 00:44:44,135
And so there's been
a lot of studies.

1292
00:44:44,135 --> 00:44:46,939
You'll see this
called wet, wet bulb,

1293
00:44:46,939 --> 00:44:48,080
global temperature like

1294
00:44:48,080 --> 00:44:50,075
virtual temperature,
something like that.

1295
00:44:50,075 --> 00:44:52,340
Is the humidity
sort of dictates

1296
00:44:52,340 --> 00:44:54,890
our ability to sweat
down and cool ourselves.

1297
00:44:54,890 --> 00:44:56,449
So these levels are

1298
00:44:56,449 --> 00:44:58,309
fairly well-studied
and they've looked

1299
00:44:58,309 --> 00:45:00,290
at places like India in

1300
00:45:00,290 --> 00:45:02,360
the future will
consistently

1301
00:45:02,360 --> 00:45:04,625
be over what's a livable

1302
00:45:04,625 --> 00:45:07,620
global wet bulb
temperature?

1303
00:45:07,990 --> 00:45:10,834
Yeah, so that's
challenging.

1304
00:45:10,834 --> 00:45:12,455
I mean, the other
aspect of all this

1305
00:45:12,455 --> 00:45:14,810
is access to cooling.

1306
00:45:14,810 --> 00:45:16,819
So yeah, it's gonna be

1307
00:45:16,819 --> 00:45:17,840
hot everywhere
at a certain

1308
00:45:17,840 --> 00:45:19,220
point for everyone.

1309
00:45:19,220 --> 00:45:21,290
I sort of vaguely

1310
00:45:21,290 --> 00:45:23,179
hinted at this with
talking about AC,

1311
00:45:23,179 --> 00:45:25,489
but having access to
cooling resources.

1312
00:45:25,489 --> 00:45:27,380
And that's sort of
like raising awareness

1313
00:45:27,380 --> 00:45:28,640
about these are places

1314
00:45:28,640 --> 00:45:30,094
you can go to get cool.

1315
00:45:30,094 --> 00:45:31,639
Or here's different
strategies is

1316
00:45:31,639 --> 00:45:33,199
really helpful for
a lot of people.

1317
00:45:33,199 --> 00:45:35,150
The sad part of that, a
lot of the deaths was

1318
00:45:35,150 --> 00:45:36,409
that most of

1319
00:45:36,409 --> 00:45:38,344
those people were
fairly isolated.

1320
00:45:38,344 --> 00:45:39,739
And a lot of cases
they didn't have

1321
00:45:39,739 --> 00:45:41,389
someone checking on
them, things like that.

1322
00:45:41,389 --> 00:45:43,790
So I sort of an
aside, but yeah.

1323
00:45:43,790 --> 00:45:44,659
To answer your
question, yeah.

1324
00:45:44,659 --> 00:45:45,349
Eventually it does hit

1325
00:45:45,349 --> 00:45:46,039
a point where it's just

1326
00:45:46,039 --> 00:45:47,554
super hard for everyone.

1327
00:45:47,554 --> 00:45:48,739
I don't know if
the answer is

1328
00:45:48,739 --> 00:45:50,210
moved into North Dakota.

1329
00:45:50,210 --> 00:45:51,829
No offensive so
much from there.

1330
00:45:51,829 --> 00:45:53,299
I'm not sure if I'm
willing to do that,

1331
00:45:53,299 --> 00:45:56,524
but yeah, that might be
the only saves them.

1332
00:45:56,524 --> 00:45:57,845
All right. Thank you.

1333
00:45:57,845 --> 00:45:59,510
Yeah. I see.

1334
00:45:59,510 --> 00:46:01,619
Maria has a question.

1335
00:46:01,750 --> 00:46:05,315
Thanks. Thanks so much
for this presentation.

1336
00:46:05,315 --> 00:46:08,059
I was so my training is in

1337
00:46:08,059 --> 00:46:09,410
land use planning
in the network at

1338
00:46:09,410 --> 00:46:11,030
Portland Bureau of
environmental services.

1339
00:46:11,030 --> 00:46:12,110
So I'm always thinking

1340
00:46:12,110 --> 00:46:13,669
about how the, you know,

1341
00:46:13,669 --> 00:46:14,750
the environmental side and

1342
00:46:14,750 --> 00:46:16,520
the land-use side connect.

1343
00:46:16,520 --> 00:46:19,909
And I think the science is

1344
00:46:19,909 --> 00:46:21,469
happening and

1345
00:46:21,469 --> 00:46:23,494
there are climate
resilience folks

1346
00:46:23,494 --> 00:46:26,060
are working in one area

1347
00:46:26,060 --> 00:46:27,514
and our planners
are sort of in the

1348
00:46:27,514 --> 00:46:29,749
other trying to think
about how do we

1349
00:46:29,749 --> 00:46:30,920
manage growth and prevent

1350
00:46:30,920 --> 00:46:32,059
sprawl and all of that.

1351
00:46:32,059 --> 00:46:33,379
And it feels like there's

1352
00:46:33,379 --> 00:46:35,930
not enough connection

1353
00:46:35,930 --> 00:46:37,940
between those disciplines.

1354
00:46:37,940 --> 00:46:39,545
So I'm just wondering

1355
00:46:39,545 --> 00:46:41,750
if you have heard
from or I've

1356
00:46:41,750 --> 00:46:44,255
had involvement with any

1357
00:46:44,255 --> 00:46:46,984
of the local
planning agencies.

1358
00:46:46,984 --> 00:46:49,609
And if not, this

1359
00:46:49,609 --> 00:46:50,884
might be a question
for a lorry to,

1360
00:46:50,884 --> 00:46:52,219
because you work at Metro,

1361
00:46:52,219 --> 00:46:55,729
what can be done to
help our planners in

1362
00:46:55,729 --> 00:46:58,550
our resilience
officers work

1363
00:46:58,550 --> 00:46:59,749
together so that as

1364
00:46:59,749 --> 00:47:01,759
we're planning
new communities,

1365
00:47:01,759 --> 00:47:03,829
they're more habitable or

1366
00:47:03,829 --> 00:47:05,329
redeveloping communities
that they're

1367
00:47:05,329 --> 00:47:06,799
more habitable in
the long term,

1368
00:47:06,799 --> 00:47:08,299
not just in the near term.

1369
00:47:08,299 --> 00:47:09,154
Yeah.

1370
00:47:09,154 --> 00:47:11,164
That's a really
excellent question.

1371
00:47:11,164 --> 00:47:15,665
There's a lot to that.

1372
00:47:15,665 --> 00:47:17,779
It's gonna be
difficult for me

1373
00:47:17,779 --> 00:47:19,549
to address some of it,

1374
00:47:19,549 --> 00:47:21,379
but I mean,

1375
00:47:21,379 --> 00:47:22,865
you really hit the nail
on the head width.

1376
00:47:22,865 --> 00:47:24,049
Okay. Yeah, we
do all this,

1377
00:47:24,049 --> 00:47:26,015
but how do we actually
effectively use it?

1378
00:47:26,015 --> 00:47:27,889
I mean, not getting

1379
00:47:27,889 --> 00:47:30,109
into the bureaucracy
of all this,

1380
00:47:30,109 --> 00:47:33,410
but there is
political aspects

1381
00:47:33,410 --> 00:47:34,729
to some of this
in terms of

1382
00:47:34,729 --> 00:47:36,889
willingness to do things
and being funded.

1383
00:47:36,889 --> 00:47:40,009
But in terms of
communicating

1384
00:47:40,009 --> 00:47:41,299
enough between land-use

1385
00:47:41,299 --> 00:47:42,619
planners and
climate scientists,

1386
00:47:42,619 --> 00:47:44,480
I would totally agree that

1387
00:47:44,480 --> 00:47:46,714
that's a huge challenge.

1388
00:47:46,714 --> 00:47:48,349
I honestly don't know

1389
00:47:48,349 --> 00:47:51,229
what the answers so
that all I can speak to

1390
00:47:51,229 --> 00:47:52,654
is that I've seen

1391
00:47:52,654 --> 00:47:54,830
more concerted efforts to

1392
00:47:54,830 --> 00:47:56,300
go and address that,

1393
00:47:56,300 --> 00:47:57,995
to share this information.

1394
00:47:57,995 --> 00:48:01,699
There was a couple of
scientists who recently

1395
00:48:01,699 --> 00:48:06,169
publish this like heat
adaptation approach.

1396
00:48:06,169 --> 00:48:10,280
Those were more useful,

1397
00:48:10,280 --> 00:48:11,329
I would say like it wasn't

1398
00:48:11,329 --> 00:48:12,410
just a paper right there,

1399
00:48:12,410 --> 00:48:13,189
not just reading a paper.

1400
00:48:13,189 --> 00:48:14,765
They're reading a
document that's

1401
00:48:14,765 --> 00:48:17,810
intended to be
used by planning.

1402
00:48:17,810 --> 00:48:20,539
Their names were
Sarah mural

1403
00:48:20,539 --> 00:48:23,000
and let Keith, if you
want to look that up.

1404
00:48:23,000 --> 00:48:24,290
But basically,

1405
00:48:24,290 --> 00:48:25,189
that's sort of what I've

1406
00:48:25,189 --> 00:48:26,705
seen so far happening.

1407
00:48:26,705 --> 00:48:30,034
And how do we make
that happen even more?

1408
00:48:30,034 --> 00:48:31,549
I'm not sure that
I could answer.

1409
00:48:31,549 --> 00:48:33,845
That's probably a question
that's beyond me.

1410
00:48:33,845 --> 00:48:35,899
But it's an
excellent, excellent

1411
00:48:35,899 --> 00:48:38,854
thought and I
really hope that it

1412
00:48:38,854 --> 00:48:40,700
continues to have
more integration

1413
00:48:40,700 --> 00:48:45,680
for sure. All right.

1414
00:48:45,680 --> 00:48:48,330
It looks like I'm
next in line.

1415
00:48:49,930 --> 00:48:53,459
I'm going to take
just one moment.

1416
00:48:53,830 --> 00:48:56,689
Maybe Laurie, you
could take it just

1417
00:48:56,689 --> 00:48:59,074
another break
for a second.

1418
00:48:59,074 --> 00:49:00,649
On the recording.

1419
00:49:00,649 --> 00:49:02,389
Lori? Yes.

1420
00:49:02,389 --> 00:49:03,259
Thanks.

1421
00:49:03,259 --> 00:49:05,300
Yeah, Thanks.

1422
00:49:05,300 --> 00:49:06,259
Yeah.

1423
00:49:06,259 --> 00:49:07,730
And I really appreciate

1424
00:49:07,730 --> 00:49:09,485
the presentation, Garrett.

1425
00:49:09,485 --> 00:49:11,105
And I feel like it's

1426
00:49:11,105 --> 00:49:13,219
a lot and I'm asked worry

1427
00:49:13,219 --> 00:49:14,975
if maybe you can

1428
00:49:14,975 --> 00:49:16,024
share it so we could

1429
00:49:16,024 --> 00:49:17,644
absorb it a
little bit more.

1430
00:49:17,644 --> 00:49:19,414
That'll be awesome.

1431
00:49:19,414 --> 00:49:21,680
I also had a
question and I

1432
00:49:21,680 --> 00:49:23,869
feel like you've covered
this a little bit.

1433
00:49:23,869 --> 00:49:25,969
I wonder if you
could expound.

1434
00:49:25,969 --> 00:49:28,249
On kinda maybe some

1435
00:49:28,249 --> 00:49:29,959
of the differences between

1436
00:49:29,959 --> 00:49:31,880
the different ground
covers in terms

1437
00:49:31,880 --> 00:49:35,344
of like trees
versus same lawn.

1438
00:49:35,344 --> 00:49:38,255
And there were some night

1439
00:49:38,255 --> 00:49:39,815
when I was looking
at the graph.

1440
00:49:39,815 --> 00:49:45,364
There's one that stretched
out differently,

1441
00:49:45,364 --> 00:49:47,389
like the hey, I
think it was hey,

1442
00:49:47,389 --> 00:49:49,580
cover or something like
that that was down by

1443
00:49:49,580 --> 00:49:52,399
the the like
impervious area.

1444
00:49:52,399 --> 00:49:53,750
So I was curious
if you if you

1445
00:49:53,750 --> 00:49:55,939
could touch on that.

1446
00:49:55,939 --> 00:49:59,119
And also the difference
between like trees

1447
00:49:59,119 --> 00:50:03,395
and grasses would be
great. Yeah, absolutely.

1448
00:50:03,395 --> 00:50:04,895
Yeah, thanks for
the question.

1449
00:50:04,895 --> 00:50:09,094
So what that's
really getting at is

1450
00:50:09,094 --> 00:50:11,929
these different
characteristics of

1451
00:50:11,929 --> 00:50:13,669
these land cover
types in terms

1452
00:50:13,669 --> 00:50:15,140
of when we're talking

1453
00:50:15,140 --> 00:50:16,640
about the
radiation balance,

1454
00:50:16,640 --> 00:50:18,514
how they're
processing that.

1455
00:50:18,514 --> 00:50:20,345
And so if you're
looking at,

1456
00:50:20,345 --> 00:50:23,254
there's a few
components to that.

1457
00:50:23,254 --> 00:50:24,890
But basically if you're

1458
00:50:24,890 --> 00:50:27,364
thinking about like
trees versus grass,

1459
00:50:27,364 --> 00:50:30,244
one of the big ones
is going to be

1460
00:50:30,244 --> 00:50:32,120
how much
evaporative cooling

1461
00:50:32,120 --> 00:50:33,529
is happening are possible.

1462
00:50:33,529 --> 00:50:34,639
So trees are able to do

1463
00:50:34,639 --> 00:50:35,720
that at a much
greater rate.

1464
00:50:35,720 --> 00:50:37,460
So you're going to
see that there's one.

1465
00:50:37,460 --> 00:50:39,080
The second potentially

1466
00:50:39,080 --> 00:50:40,129
bigger thing with trees

1467
00:50:40,129 --> 00:50:42,740
is that in terms of
ground temperature,

1468
00:50:42,740 --> 00:50:44,569
at least you're
going to see

1469
00:50:44,569 --> 00:50:47,209
a lot more shading
of the ground.

1470
00:50:47,209 --> 00:50:48,499
So you're actually
preventing

1471
00:50:48,499 --> 00:50:49,879
a lot of that radiation

1472
00:50:49,879 --> 00:50:53,789
from being absorbed
into the surface.

1473
00:50:53,920 --> 00:50:57,289
So that's kind of a
quick answer to that.

1474
00:50:57,289 --> 00:50:58,834
But yeah,

1475
00:50:58,834 --> 00:51:00,455
then when you're looking
at something like,

1476
00:51:00,455 --> 00:51:04,070
hey, that gets
into something,

1477
00:51:04,070 --> 00:51:06,079
I can't speak
100% of that.

1478
00:51:06,079 --> 00:51:07,580
But what I suspect
is happening is

1479
00:51:07,580 --> 00:51:09,890
that another big component

1480
00:51:09,890 --> 00:51:11,419
of how much radiation

1481
00:51:11,419 --> 00:51:13,489
is being reflected
or absorbed

1482
00:51:13,489 --> 00:51:16,834
is related to what's
called the albedo,

1483
00:51:16,834 --> 00:51:18,739
which is basically
the color.

1484
00:51:18,739 --> 00:51:19,879
And you can think
of this in terms

1485
00:51:19,879 --> 00:51:21,245
of like if you were

1486
00:51:21,245 --> 00:51:22,880
a black t-shirt
outside versus

1487
00:51:22,880 --> 00:51:24,500
a white t-shirt, you
feel a lot hotter.

1488
00:51:24,500 --> 00:51:25,519
The black t-shirt absorbs

1489
00:51:25,519 --> 00:51:26,689
more white t-shirt

1490
00:51:26,689 --> 00:51:28,250
reflects more
of the light.

1491
00:51:28,250 --> 00:51:33,545
Hey, is going to have
a different albedo.

1492
00:51:33,545 --> 00:51:35,914
I think it also

1493
00:51:35,914 --> 00:51:38,749
it tends to be drive
for more of the time.

1494
00:51:38,749 --> 00:51:41,134
It's not really
cooling as much.

1495
00:51:41,134 --> 00:51:42,995
I think a lot of the year

1496
00:51:42,995 --> 00:51:44,780
I suspect it's probably

1497
00:51:44,780 --> 00:51:47,449
cleared versus like a tree

1498
00:51:47,449 --> 00:51:49,324
or grass is probably
there year round.

1499
00:51:49,324 --> 00:51:50,690
So a lot of the
times the k is

1500
00:51:50,690 --> 00:51:52,280
probably like bare ground.

1501
00:51:52,280 --> 00:51:53,990
And there was a bare
ground category.

1502
00:51:53,990 --> 00:51:56,015
And what you find
is bare ground

1503
00:51:56,015 --> 00:51:58,639
can somewhat behave like

1504
00:51:58,639 --> 00:51:59,719
a developed area a lot of

1505
00:51:59,719 --> 00:52:01,159
times in terms
of just like

1506
00:52:01,159 --> 00:52:02,570
sucking in a
lot of heat and

1507
00:52:02,570 --> 00:52:04,684
not being able
to release it.

1508
00:52:04,684 --> 00:52:05,779
So that's what I

1509
00:52:05,779 --> 00:52:07,129
suspect it was
happening with.

1510
00:52:07,129 --> 00:52:11,909
Hey, hopefully that
answers question.

1511
00:52:14,200 --> 00:52:17,750
I also see there's
a question in

1512
00:52:17,750 --> 00:52:23,855
the chat from Lia
which is saying,

1513
00:52:23,855 --> 00:52:27,784
you know, some of the
mitigation actions

1514
00:52:27,784 --> 00:52:29,840
include creating
more green space or

1515
00:52:29,840 --> 00:52:32,359
access to water
cooling areas.

1516
00:52:32,359 --> 00:52:35,329
Have you or others
looked at whether it was

1517
00:52:35,329 --> 00:52:37,160
land types not
only stay cooler

1518
00:52:37,160 --> 00:52:39,214
but also cool
more quickly.

1519
00:52:39,214 --> 00:52:41,255
Usually notice
patented staying hot

1520
00:52:41,255 --> 00:52:43,339
until late at night
and vegetated areas

1521
00:52:43,339 --> 00:52:45,319
cooling more
quickly come 06:00

1522
00:52:45,319 --> 00:52:46,309
P.M. so I'm wondering if

1523
00:52:46,309 --> 00:52:47,405
that's an important
component.

1524
00:52:47,405 --> 00:52:51,499
So yeah, that that's
a great question.

1525
00:52:51,499 --> 00:52:53,059
It's a kind of a follow

1526
00:52:53,059 --> 00:52:54,964
along to what I was
just talking about.

1527
00:52:54,964 --> 00:52:57,289
So it's a different
property and it relates,

1528
00:52:57,289 --> 00:52:58,760
so we would say
is like thermal

1529
00:52:58,760 --> 00:53:01,430
inertia in terms of

1530
00:53:01,430 --> 00:53:05,539
how quickly
different materials

1531
00:53:05,539 --> 00:53:07,384
absorb or release heat.

1532
00:53:07,384 --> 00:53:10,970
So something like asphalt

1533
00:53:10,970 --> 00:53:12,154
or pavement or concrete,

1534
00:53:12,154 --> 00:53:14,150
actually very much more

1535
00:53:14,150 --> 00:53:16,175
slowly release that
heat over time,

1536
00:53:16,175 --> 00:53:18,230
which is why a lot
of times you'll even

1537
00:53:18,230 --> 00:53:22,249
see sort of the
developed areas being,

1538
00:53:22,249 --> 00:53:23,960
having even more of
a heat effect at

1539
00:53:23,960 --> 00:53:25,369
night because it very

1540
00:53:25,369 --> 00:53:26,854
slowly release that heat.

1541
00:53:26,854 --> 00:53:28,429
Um, so yeah, that's

1542
00:53:28,429 --> 00:53:30,829
a very insightful
question in commentary,

1543
00:53:30,829 --> 00:53:32,089
but yeah, so it
is related to

1544
00:53:32,089 --> 00:53:33,050
those properties in here,

1545
00:53:33,050 --> 00:53:34,190
right on the money there.

1546
00:53:34,190 --> 00:53:35,930
That's exactly
what's happening.

1547
00:53:35,930 --> 00:53:36,770
So yeah,

1548
00:53:36,770 --> 00:53:38,600
these greener spaces
are occurring quicker.

1549
00:53:38,600 --> 00:53:40,169
Yes.

1550
00:53:43,810 --> 00:53:46,130
You have any
other questions?

1551
00:53:46,130 --> 00:53:53,210
Forget. Wow, great
colic and timely.

1552
00:53:53,210 --> 00:53:53,899
Garrett, go ahead and say

1553
00:53:53,899 --> 00:53:54,905
what you were
going to say.

1554
00:53:54,905 --> 00:53:55,190
No.

1555
00:53:55,190 --> 00:53:56,659
I was just going to say
feel free to shoot me

1556
00:53:56,659 --> 00:53:58,430
an email if
anything comes up.

1557
00:53:58,430 --> 00:54:01,294
Always happy to
correspond that way.

1558
00:54:01,294 --> 00:54:03,240
Thank you.