1
00:00:09,476 --> 00:00:11,378
- A sunny July day is perfect
2
00:00:11,445 --> 00:00:13,981
for a boat ride
on Lake Mendota in Madison.
3
00:00:14,047 --> 00:00:18,352
- Dave Harring:
Okay, we might as well
move the ladder back here more.
4
00:00:18,418 --> 00:00:20,487
- But this boat
is filled with researchers,
5
00:00:20,554 --> 00:00:21,655
not sunbathers,
6
00:00:21,722 --> 00:00:24,892
and their goal
is to travel back in time.
7
00:00:24,958 --> 00:00:26,894
- Stephen Carpenter:
We know from historical accounts
8
00:00:26,960 --> 00:00:29,963
that the lake was ringed
with white beaches.
9
00:00:30,030 --> 00:00:31,164
It was just a beautiful...
10
00:00:31,231 --> 00:00:32,766
- Professor Steve Carpenter
is the Director
11
00:00:32,833 --> 00:00:37,337
of the Center for Limnology
at the University of Wisconsin.
12
00:00:37,404 --> 00:00:41,608
He says white settlers first
came to Madison in the 1830s
13
00:00:41,675 --> 00:00:43,510
and their written reports
indicate
14
00:00:43,577 --> 00:00:46,046
the lakes were not brown
and filled with green algae,
15
00:00:46,113 --> 00:00:47,748
as they are today.
16
00:00:47,814 --> 00:00:49,416
- Stephen Carpenter:
One of them is dated
17
00:00:49,483 --> 00:00:51,952
early July and talks about
18
00:00:52,686 --> 00:00:55,155
being able to see to the bottom
19
00:00:55,222 --> 00:00:56,590
in very deep water
20
00:00:56,657 --> 00:00:58,125
and at the bottom it's white.
21
00:00:58,192 --> 00:01:00,861
- Harring:
And we're going to add ice.
22
00:01:00,928 --> 00:01:03,163
Ethanol, ice, ethanol.
23
00:01:03,931 --> 00:01:05,732
- Carpenter and colleague,
Dave Harring,
24
00:01:05,799 --> 00:01:08,068
will use a freeze corer...
25
00:01:08,135 --> 00:01:10,737
- Harring: If you look
down here, you can see
26
00:01:10,804 --> 00:01:12,773
where it's already
starting to supercool.
27
00:01:12,840 --> 00:01:15,409
- ...to bring some of that
white bottom to the surface.
28
00:01:15,475 --> 00:01:17,611
- Carpenter:
What we're going to do
29
00:01:17,678 --> 00:01:20,280
is freeze a layer of sediment
30
00:01:20,347 --> 00:01:23,317
on the outside of that cylinder.
31
00:01:23,383 --> 00:01:26,386
- Harring: Okay, you guys ready?
32
00:01:27,254 --> 00:01:28,655
- Carpenter:
The reason to core lakes
33
00:01:28,722 --> 00:01:31,024
is to learn about
the history of the lakes.
34
00:01:31,091 --> 00:01:32,893
Each year,
35
00:01:32,960 --> 00:01:33,994
about a millimeter of sediment
36
00:01:34,061 --> 00:01:36,864
will be laid down in this lake.
37
00:01:38,165 --> 00:01:40,467
Previous core samples have shown
the lake bottom
38
00:01:40,534 --> 00:01:42,469
changed from white to black
39
00:01:42,536 --> 00:01:44,738
between 1850 and 1870,
40
00:01:44,805 --> 00:01:47,441
a time when most of the land
around the lakes
41
00:01:47,508 --> 00:01:50,244
was plowed and farmed
for the first time.
42
00:01:50,310 --> 00:01:53,046
- Harring: See up here, though,
the really soft stuff.
43
00:01:53,113 --> 00:01:54,815
- Carpenter:
Right here is the boundary
44
00:01:54,882 --> 00:01:58,385
between the glop and the harder,
lighter-colored sediment.
45
00:01:58,452 --> 00:02:02,055
- Harring:
The dark stuff here is algae
46
00:02:02,122 --> 00:02:05,692
and pretty much
from 'settlement-on' runoff.
47
00:02:05,759 --> 00:02:08,896
- Today, the original
white bottom of Lake Mendota
48
00:02:08,962 --> 00:02:11,231
is covered in
about a foot of black goop,
49
00:02:11,298 --> 00:02:15,235
the product of 150 years
of agricultural runoff.
50
00:02:15,302 --> 00:02:17,504
Carpenter says it's important
51
00:02:17,571 --> 00:02:19,840
for people to know
the lakes weren't always green,
52
00:02:19,907 --> 00:02:21,341
and that the whole region
53
00:02:21,408 --> 00:02:24,711
continues to be
in a state of transition.
54
00:02:24,778 --> 00:02:27,848
- Carpenter: There's a tendency
to extrapolate from the present,
55
00:02:27,915 --> 00:02:30,651
both to the past
and the future,
56
00:02:30,717 --> 00:02:33,287
and the sediments show us
57
00:02:33,353 --> 00:02:36,523
that this has been
a place of dramatic change.
58
00:02:36,590 --> 00:02:38,258
? ?
59
00:02:46,266 --> 00:02:49,203
- Funding for Yahara Watershed:
A Place of Change
60
00:02:49,269 --> 00:02:50,470
was provided by
61
00:02:50,537 --> 00:02:52,940
the Water Sustainability
and Climate program
62
00:02:53,006 --> 00:02:55,209
of the
National Science Foundation.
63
00:02:55,275 --> 00:02:56,944
? ?
64
00:02:58,278 --> 00:03:02,115
- As if turning clear lakes
green wasn't enough...
65
00:03:02,182 --> 00:03:05,319
there may be even more dramatic
changes to state watersheds
66
00:03:05,385 --> 00:03:06,820
in the future.
67
00:03:06,887 --> 00:03:08,889
Steve Carpenter is one
of a handful
68
00:03:08,956 --> 00:03:11,091
of UW Professors and students
69
00:03:11,158 --> 00:03:14,962
working on a research project
called Yahara 2070 .
70
00:03:15,028 --> 00:03:19,733
- What we want to do is
create a long-term perspective
71
00:03:19,800 --> 00:03:22,069
on change
in the Yahara Lakes.
72
00:03:22,135 --> 00:03:24,705
- Yahara 2070 is funded by
73
00:03:24,771 --> 00:03:27,107
a grant from
the National Science Foundation
74
00:03:27,174 --> 00:03:28,208
to look at the impact
75
00:03:28,275 --> 00:03:30,277
of climate change
and human decisions
76
00:03:30,344 --> 00:03:31,945
on the Yahara watershed.
77
00:03:32,012 --> 00:03:35,716
The watershed
is the 536 square miles
78
00:03:35,782 --> 00:03:37,718
that drain into the
Yahara Chain of Lakes
79
00:03:37,784 --> 00:03:39,820
and eventually flows
into the Rock River,
80
00:03:39,887 --> 00:03:45,325
which, in turn, eventually
flows into the Mississippi.
81
00:03:45,392 --> 00:03:47,995
2070 refers to the future.
82
00:03:48,061 --> 00:03:49,897
- Chris Kucharik:
The time frame of using
83
00:03:49,963 --> 00:03:53,800
2070 I hope is helpful
to people to think about
84
00:03:53,867 --> 00:03:58,572
"Okay, that is my grandkid's
or it is my children's future."
85
00:03:58,639 --> 00:04:01,875
- Chris Kucharik is a Professor
in the Department of Agronomy
86
00:04:01,942 --> 00:04:04,244
at the University of Wisconsin.
87
00:04:04,311 --> 00:04:06,580
He says most people
consider long-term
88
00:04:06,647 --> 00:04:08,849
to be five or ten years.
89
00:04:08,916 --> 00:04:12,586
But environmental changes
occur on a different scale.
90
00:04:12,653 --> 00:04:17,658
- Kucharik: They're embedded in
5, 10, 15, 20, 50-year, 100-year
91
00:04:17,724 --> 00:04:21,695
type of transitions
that we make as a society.
92
00:04:23,397 --> 00:04:25,532
- A trip to the bottom
of the lake shows
93
00:04:25,599 --> 00:04:27,534
what the last 150 years
94
00:04:27,601 --> 00:04:30,070
of environmental changes
looks like.
95
00:04:30,137 --> 00:04:31,905
Yahara 2070 asks
96
00:04:31,972 --> 00:04:34,708
what the watershed could
look like in the future,
97
00:04:34,775 --> 00:04:38,011
and it combines
storytelling and science
98
00:04:38,078 --> 00:04:40,414
to paint a vivid picture.
99
00:04:40,714 --> 00:04:43,550
- Carpenter: Thanks to all of
you for coming and joining us
100
00:04:43,617 --> 00:04:45,686
in what's really an experiment.
101
00:04:45,752 --> 00:04:48,055
- A group of clean water
advocates and activists
102
00:04:48,121 --> 00:04:50,224
are eager to learn
about scenarios
103
00:04:50,290 --> 00:04:52,860
created to depict
four possible futures
104
00:04:52,926 --> 00:04:54,294
in the year 2070.
105
00:04:54,361 --> 00:04:57,331
The researchers
interviewed dozens of people
106
00:04:57,397 --> 00:04:58,532
to ask what they thought
107
00:04:58,599 --> 00:05:00,968
the future might hold
for the watershed.
108
00:05:01,034 --> 00:05:02,169
- Carpenter: We gather
109
00:05:02,236 --> 00:05:04,104
beliefs about the future.
110
00:05:04,171 --> 00:05:05,572
So, what are people's hopes?
111
00:05:05,639 --> 00:05:07,641
What are their fears?
112
00:05:08,175 --> 00:05:09,543
- Professor Carpenter
and his team
113
00:05:09,610 --> 00:05:11,545
took those hopes and fears
114
00:05:11,612 --> 00:05:13,514
and created four stories...
115
00:05:13,580 --> 00:05:15,015
four different scenarios
116
00:05:15,082 --> 00:05:17,851
that tell what it's like
in the year 2070.
117
00:05:17,918 --> 00:05:19,820
- Carpenter: Each of
these stories is fiction
118
00:05:19,887 --> 00:05:22,756
because nobody knows
what's going to happen in 2070.
119
00:05:22,823 --> 00:05:24,057
- In each scenario,
120
00:05:24,124 --> 00:05:27,160
climate change
in the 2030s and 40s
121
00:05:27,227 --> 00:05:31,398
causes a major shift
in how society survives.
122
00:05:31,465 --> 00:05:33,000
Professor Carpenter
says the ideas
123
00:05:33,066 --> 00:05:35,169
may sound like science fiction,
124
00:05:35,235 --> 00:05:37,137
but they're entirely plausible.
125
00:05:37,204 --> 00:05:39,139
- Carpenter:
So everything exists now,
126
00:05:39,206 --> 00:05:43,277
it's just that we amplified
certain elements in each story.
127
00:05:43,343 --> 00:05:45,479
So in "Connected Communities,"
for example,
128
00:05:45,546 --> 00:05:48,549
we amplify sustainable values.
129
00:05:49,216 --> 00:05:51,285
- In a future with
"Connected Communities,"
130
00:05:51,351 --> 00:05:53,954
consumer choices
and supportive policies
131
00:05:54,021 --> 00:05:57,291
help farmers implement
more conservation practices,
132
00:05:57,357 --> 00:05:58,492
in order to reduce
133
00:05:58,559 --> 00:06:02,062
agriculture's impact
on the land and lakes.
134
00:06:02,129 --> 00:06:04,431
- Carpenter: In "Abandonment
and Renewal," we amplify
135
00:06:04,498 --> 00:06:08,368
doing nothing and complacency
about environmental problems.
136
00:06:08,435 --> 00:06:09,703
- In this scenario,
137
00:06:09,770 --> 00:06:13,540
a focus on increased farm
production leads to more runoff,
138
00:06:13,607 --> 00:06:16,577
and a toxic bacteria
invades the lakes.
139
00:06:16,643 --> 00:06:18,812
The bacteria
emits a poisonous fume
140
00:06:18,879 --> 00:06:20,214
that kills thousands
141
00:06:20,280 --> 00:06:23,717
and causes even more
to flee the area.
142
00:06:23,784 --> 00:06:28,922
By 2070, people are coming back
to find a changed watershed.
143
00:06:28,989 --> 00:06:31,458
- Carpenter: In "Accelerated
Innovation," we amplify
144
00:06:31,525 --> 00:06:32,759
technology.
145
00:06:32,826 --> 00:06:35,095
- In the "Accelerated
Innovation" scenario,
146
00:06:35,162 --> 00:06:37,831
society relies on technology
147
00:06:37,898 --> 00:06:40,033
as a way out
of environmental disaster.
148
00:06:40,100 --> 00:06:42,035
But there is a trade-off:
149
00:06:42,102 --> 00:06:43,504
more synthetic meats
150
00:06:43,570 --> 00:06:46,540
and less emphasis on livestock.
151
00:06:46,607 --> 00:06:48,742
- Carpenter: In "Nested
Watersheds," we amplify
152
00:06:48,809 --> 00:06:50,110
the role of governance.
153
00:06:50,177 --> 00:06:51,612
- In the final scenario,
154
00:06:51,678 --> 00:06:53,914
the boundaries and focus
of government
155
00:06:53,981 --> 00:06:55,082
is shifted towards water,
156
00:06:55,148 --> 00:06:57,718
with water becoming
a type of crop
157
00:06:57,784 --> 00:07:00,120
to be harvested and sold.
158
00:07:00,954 --> 00:07:04,191
The scenario stories are more
than just conversation starters.
159
00:07:04,258 --> 00:07:09,730
They are the fleshed out details
of four possible futures.
160
00:07:11,331 --> 00:07:13,700
The Yahara 2070 project
161
00:07:13,767 --> 00:07:14,868
will support that flesh
162
00:07:14,935 --> 00:07:16,870
with a skeleton of hard science,
163
00:07:16,937 --> 00:07:19,840
which comes in the form of a
state-of-the-art computer model
164
00:07:19,907 --> 00:07:21,141
that allows the researchers
165
00:07:21,208 --> 00:07:24,344
to answer
all sorts of questions.
166
00:07:24,745 --> 00:07:27,881
- Kucharik: What would
water quality look like
167
00:07:27,948 --> 00:07:30,117
50 years or 60 years from now?
168
00:07:30,184 --> 00:07:32,152
What might crop yields
look like?
169
00:07:32,219 --> 00:07:33,387
How might we be able
170
00:07:33,453 --> 00:07:35,455
to mitigate flooding
171
00:07:36,223 --> 00:07:39,626
with better
land management decision making?
172
00:07:39,693 --> 00:07:41,728
- So what is
this computer model?
173
00:07:41,795 --> 00:07:43,397
Start with the water cycle,
174
00:07:43,463 --> 00:07:44,831
the most basic concept
175
00:07:44,898 --> 00:07:47,467
of how water
moves through the environment.
176
00:07:47,534 --> 00:07:49,036
When it rains,
water can either
177
00:07:49,102 --> 00:07:52,372
flow into the ground
or into a lake or stream.
178
00:07:52,439 --> 00:07:54,074
Some of that water evaporates
179
00:07:54,141 --> 00:07:56,310
and becomes part
of the next rainstorm.
180
00:07:56,376 --> 00:07:58,378
The Yahara 2070 model
181
00:07:59,012 --> 00:08:01,815
takes an action like
rain falling on the ground
182
00:08:01,882 --> 00:08:04,218
and then uses
equations and algorithms
183
00:08:04,284 --> 00:08:08,021
to determine how much soaks in
and how much runs off.
184
00:08:08,088 --> 00:08:09,289
And the computer models
185
00:08:09,356 --> 00:08:10,991
more than just water.
186
00:08:11,058 --> 00:08:13,760
- Kucharik:
They represent cycles of how
187
00:08:13,827 --> 00:08:18,565
carbon, water, energy,
nitrogen, phosphorus
188
00:08:18,632 --> 00:08:20,501
move through the system
189
00:08:20,567 --> 00:08:22,035
from the atmosphere,
190
00:08:22,102 --> 00:08:23,370
through plants,
191
00:08:23,437 --> 00:08:24,471
through the soil,
192
00:08:24,538 --> 00:08:25,839
through the ground water system,
193
00:08:25,906 --> 00:08:28,075
and then, how those feed back
194
00:08:28,141 --> 00:08:30,611
to the plants themselves.
195
00:08:31,111 --> 00:08:33,947
- The computer model
is thousands of lines of code,
196
00:08:34,014 --> 00:08:35,516
all equations that deal with
197
00:08:35,582 --> 00:08:37,317
different variables in nature.
198
00:08:37,384 --> 00:08:39,620
- Kucharik: Where do
those equations come from?
199
00:08:39,686 --> 00:08:42,389
They came from people going out
in the field and collecting data
200
00:08:42,456 --> 00:08:44,925
and taking measurements.
201
00:08:45,459 --> 00:08:48,061
- Eric Booth is
an assistant research scientist
202
00:08:48,128 --> 00:08:49,429
on the Yahara Project.
203
00:08:49,496 --> 00:08:52,332
One of Booth's jobs
in modeling the water cycle
204
00:08:52,399 --> 00:08:54,334
is to learn
just where the water goes
205
00:08:54,401 --> 00:08:55,836
when it lands on the ground,
206
00:08:55,903 --> 00:08:59,406
and how that's impacted
by what's growing there.
207
00:08:59,473 --> 00:09:00,674
- Booth: The corn is going
208
00:09:00,741 --> 00:09:03,577
to behave a lot differently
than switchgrass
209
00:09:03,644 --> 00:09:06,847
when you have a one-inch
rain event, for instance.
210
00:09:06,914 --> 00:09:09,583
-Some of the water will be used
by the plant to grow,
211
00:09:09,650 --> 00:09:13,353
but some of that water will be
given off by the plant later on .
212
00:09:13,420 --> 00:09:17,024
- Sam Zipper: You always want
to do a sunlit part of the leaf.
213
00:09:17,090 --> 00:09:18,792
- Sam Zipper
is a research assistant.
214
00:09:18,859 --> 00:09:20,561
- Got a value of about
215
00:09:20,627 --> 00:09:24,064
200 millimoles
per square meter per second.
216
00:09:24,131 --> 00:09:25,499
- Zipper uses a porometer
217
00:09:25,566 --> 00:09:27,768
to see how much water
is leaving this corn plant
218
00:09:27,835 --> 00:09:30,504
in a process
called "evapotranspiration."
219
00:09:30,571 --> 00:09:32,072
- Zipper: Kind of looking at
220
00:09:32,139 --> 00:09:34,408
how water moves from the soil
into the atmosphere.
221
00:09:34,474 --> 00:09:37,044
There's a continuum
from the soil to the plant
222
00:09:37,110 --> 00:09:39,713
and then moving up into the sky.
223
00:09:39,780 --> 00:09:42,716
- Measuring individual
corn stalks or leaves of grass
224
00:09:42,783 --> 00:09:44,651
can be a tedious process,
225
00:09:44,718 --> 00:09:46,854
but all water needs to be
226
00:09:46,920 --> 00:09:49,022
accounted for
in the computer model.
227
00:09:49,089 --> 00:09:51,925
- This is one of the big parts
of the water balance and
228
00:09:51,992 --> 00:09:54,695
it's definitely the hardest one
to get a handle on.
229
00:09:54,761 --> 00:09:57,331
- Booth: We have several
stations around the watershed
230
00:09:57,397 --> 00:09:58,732
that are looking at
231
00:09:58,799 --> 00:09:59,933
soil moisture
232
00:10:00,000 --> 00:10:02,636
in a whole bunch of different
land cover types.
233
00:10:02,703 --> 00:10:03,637
We got forest, wetland,
234
00:10:03,704 --> 00:10:05,939
a couple of corn fields,
235
00:10:06,440 --> 00:10:07,741
grasslands,
236
00:10:07,808 --> 00:10:10,644
even some urban areas,
some turf grass.
237
00:10:10,711 --> 00:10:12,679
- How water reacts
in different environments
238
00:10:12,746 --> 00:10:14,882
and in different parts
of the growing cycle
239
00:10:14,948 --> 00:10:20,087
will impact how water is handled
by the computer model.
240
00:10:20,921 --> 00:10:22,089
Booth needs to
241
00:10:22,155 --> 00:10:26,727
map the groundcover
across the whole watershed.
242
00:10:27,361 --> 00:10:29,696
So, he takes to the air.
243
00:10:29,763 --> 00:10:33,333
There is a generalized big
picture to the Yahara Watershed.
244
00:10:33,400 --> 00:10:35,969
Dairy dominates the north.
245
00:10:36,503 --> 00:10:38,438
- Booth: We have
a lot of livestock,
246
00:10:38,505 --> 00:10:42,242
particularly in
the upper part of the watershed.
247
00:10:42,309 --> 00:10:45,078
- The lakes and the urban core
sit in the middle.
248
00:10:45,145 --> 00:10:46,680
- Booth: In the southern part,
249
00:10:46,747 --> 00:10:49,850
it's mostly
commodity grain farming.
250
00:10:49,917 --> 00:10:52,452
- Booth breaks
the Yahara Watershed
251
00:10:52,519 --> 00:10:55,289
in to grid cells
of about 12 acres,
252
00:10:55,355 --> 00:10:57,624
and then determines
what is growing in each cell.
253
00:10:57,691 --> 00:11:00,093
He even factors in
animals like cows
254
00:11:00,160 --> 00:11:02,963
and how they
impact the landscape.
255
00:11:03,030 --> 00:11:05,098
This is where reality
is first measured
256
00:11:05,165 --> 00:11:08,535
and then converted into
equations and computer code.
257
00:11:08,602 --> 00:11:10,537
- Kucharik:
It's that system of equations
258
00:11:10,604 --> 00:11:13,807
that really go into
making a more complex model
259
00:11:13,874 --> 00:11:16,009
that represents the physics,
260
00:11:16,076 --> 00:11:17,744
the chemistry,
261
00:11:18,412 --> 00:11:22,216
the interactions of how things
in an environment work together.
262
00:11:22,282 --> 00:11:24,551
- Motew: This is computer code.
263
00:11:24,618 --> 00:11:26,453
- Melissa Motew is
264
00:11:26,520 --> 00:11:28,655
a graduate research assistant
on the Yahara project.
265
00:11:28,722 --> 00:11:31,792
- Motew: I'm making lots of
graphs and animations and things
266
00:11:31,859 --> 00:11:34,394
to help get an idea
of what the numbers are saying.
267
00:11:34,461 --> 00:11:36,930
- She's part of the team working
on writing the code
268
00:11:36,997 --> 00:11:38,465
that makes up
the computer model.
269
00:11:38,532 --> 00:11:40,033
Her graphs help her understand
270
00:11:40,100 --> 00:11:42,803
if the model
is producing accurate results.
271
00:11:42,870 --> 00:11:47,741
- We're seeing what the model
is generating, checking to see:
272
00:11:47,808 --> 00:11:49,676
"Do these numbers make sense?
273
00:11:49,743 --> 00:11:52,412
"Do they agree with
what we've seen in the past?
274
00:11:52,479 --> 00:11:54,448
What we understand
from previous research?"
275
00:11:54,515 --> 00:11:56,917
- You can go out
and take fancy equipment
276
00:11:56,984 --> 00:11:59,553
and collect
a bunch of junk measurements.
277
00:11:59,620 --> 00:12:01,688
- Professor Chris Kucharik says
278
00:12:01,755 --> 00:12:04,558
the model is more than just
a sum of their measurements.
279
00:12:04,625 --> 00:12:06,760
- Researcher: 71.5...
280
00:12:07,294 --> 00:12:08,829
- Kucharik: While everything
is rooted in reality,
281
00:12:08,896 --> 00:12:10,597
once you put
all the equations together,
282
00:12:10,664 --> 00:12:13,734
there's usually
some type of uncertainty
283
00:12:13,800 --> 00:12:16,069
when things start
interacting with each other.
284
00:12:16,136 --> 00:12:19,173
- They have to make sure
the predictions of
the computer model
285
00:12:19,239 --> 00:12:20,908
matches reality.
286
00:12:21,408 --> 00:12:24,311
This process is called
"calibration and validation."
287
00:12:24,378 --> 00:12:27,948
- Motew: So we have to test them
against real observations.
288
00:12:28,015 --> 00:12:31,585
- Validation is putting
field data for past years
289
00:12:31,652 --> 00:12:32,953
into the computer model
290
00:12:33,020 --> 00:12:36,356
and seeing if the prediction
matches what actually happened.
291
00:12:36,423 --> 00:12:39,326
- We run simulations of the past
292
00:12:39,393 --> 00:12:40,594
and then we compare the two.
293
00:12:40,661 --> 00:12:43,330
- If the prediction
doesn't match reality,
294
00:12:43,397 --> 00:12:46,567
they have to calibrate
or adjust the computer code.
295
00:12:46,633 --> 00:12:48,368
- Motew: So then we go out,
296
00:12:48,435 --> 00:12:51,038
and we might take measurements
and refine our models
297
00:12:51,104 --> 00:12:55,008
so that they
better capture that process.
298
00:12:55,375 --> 00:12:57,377
- The modeling group
meets weekly
299
00:12:57,444 --> 00:13:00,013
to compare results
and troubleshoot.
300
00:13:00,080 --> 00:13:02,082
- Kucharik: We probably spend
301
00:13:02,149 --> 00:13:04,985
more of our time on calibrating
302
00:13:05,052 --> 00:13:07,955
and validating these models.
303
00:13:08,021 --> 00:13:10,724
- But even a fully
calibrated and validated model
304
00:13:10,791 --> 00:13:12,459
needs details.
305
00:13:13,126 --> 00:13:15,395
How do you get
field data from the future?
306
00:13:15,462 --> 00:13:16,997
Without the computer model,
307
00:13:17,064 --> 00:13:20,133
the scenarios are just stories.
308
00:13:20,200 --> 00:13:23,203
Without a vision of the future
provided by the scenarios,
309
00:13:23,270 --> 00:13:26,273
the computer model
is just a fancy algorithm.
310
00:13:26,340 --> 00:13:29,309
So researcher Eric Booth
is looking to the details
311
00:13:29,376 --> 00:13:31,278
written in each scenario
312
00:13:31,345 --> 00:13:34,548
to help him predict
the landscape of the future.
313
00:13:34,615 --> 00:13:37,651
- Booth: Okay, how are we
getting energy in this scenario?
314
00:13:37,718 --> 00:13:40,220
What are we eating
in this scenario?
315
00:13:40,287 --> 00:13:42,990
- To simulate the future
presented in each scenario,
316
00:13:43,056 --> 00:13:44,992
the team has to tell
the model details
317
00:13:45,058 --> 00:13:47,160
like what's being grown
318
00:13:47,227 --> 00:13:50,330
and how much has the
urban center expanded.
319
00:13:50,397 --> 00:13:53,700
Each scenario story describes
the severity of climate change,
320
00:13:53,767 --> 00:13:56,370
and gives hints at the
lifestyles of the people.
321
00:13:56,436 --> 00:13:58,438
- Booth:
We're just trying to follow
322
00:13:58,505 --> 00:14:01,708
kind of the logical consequences
323
00:14:01,775 --> 00:14:05,646
of what has already
been decided in the story line.
324
00:14:05,712 --> 00:14:09,283
- To demonstrate how lifestyle
can influence land use
325
00:14:09,349 --> 00:14:11,351
Booth turns to pizza.
326
00:14:11,752 --> 00:14:14,521
Today, the northern part
of the watershed
327
00:14:14,588 --> 00:14:16,690
is dominated by dairy farming,
328
00:14:16,757 --> 00:14:19,126
and a lot of that milk
becomes cheese.
329
00:14:19,193 --> 00:14:21,795
- Booth: A lot of the cheese
that we make in this country--
330
00:14:21,862 --> 00:14:25,699
and Wisconsin is obviously
a big player-- ends up on pizza.
331
00:14:25,766 --> 00:14:27,267
- As long as pizza remains
332
00:14:27,334 --> 00:14:30,003
one of the most popular foods
in America,
333
00:14:30,070 --> 00:14:31,605
there will be a strong
demand for cheese,
334
00:14:31,672 --> 00:14:33,340
and therefore milk,
335
00:14:33,407 --> 00:14:38,111
which means a lot of cows
producing a lot of manure.
336
00:14:38,178 --> 00:14:39,179
- Booth: That's connecting diet
337
00:14:39,246 --> 00:14:42,182
to what we see
on the landscape today.
338
00:14:42,249 --> 00:14:43,884
- The "Abandonment and Renewal"
scenario
339
00:14:43,951 --> 00:14:47,187
calls for a mass exodus
of people from the watershed
340
00:14:47,254 --> 00:14:49,189
due to poisonous algae.
341
00:14:49,256 --> 00:14:51,592
In that case, Booth predicts
demand for milk and pizza
342
00:14:51,658 --> 00:14:55,262
would go down and
Booth has fewer cows on the map.
343
00:14:55,329 --> 00:14:58,532
- Booth:
What happens on a landscape
just doesn't happen in a vacuum.
344
00:14:58,599 --> 00:15:00,200
It's a reflection
345
00:15:00,267 --> 00:15:04,838
of consumer demand
for different food products.
346
00:15:05,205 --> 00:15:08,108
- On the other hand, in the
"Connected Communities" scenario
347
00:15:08,175 --> 00:15:10,444
dairy demand remains high.
348
00:15:10,511 --> 00:15:11,678
Booth has to calculate
349
00:15:11,745 --> 00:15:13,614
the amount of manure
those cows will produce
350
00:15:13,680 --> 00:15:16,984
and the impact
of growing corn to feed them.
351
00:15:17,050 --> 00:15:18,819
- Booth:
Each animal is going to produce
352
00:15:18,886 --> 00:15:22,189
a certain amount of manure,
on average, each year,
353
00:15:22,256 --> 00:15:24,424
and in that manure
is going to have
354
00:15:24,491 --> 00:15:26,927
nutrients, phosphorus,
and nitrogen.
355
00:15:26,994 --> 00:15:29,663
- Booth even needs to estimate
in the future
356
00:15:29,730 --> 00:15:30,964
how much manure
357
00:15:31,031 --> 00:15:33,467
is likely
to be piped to digesters,
358
00:15:33,534 --> 00:15:35,836
where the phosphorus is removed,
359
00:15:35,903 --> 00:15:39,039
and how much
gets spread on fields,
360
00:15:39,106 --> 00:15:42,509
especially frozen fields,
where it is much more likely
361
00:15:42,576 --> 00:15:46,113
to run off in the lakes
during the spring melt.
362
00:15:46,180 --> 00:15:48,815
- We're certainly seeing
the effects of phosphorus runoff
363
00:15:48,882 --> 00:15:51,018
in poor water clarity.
364
00:15:51,818 --> 00:15:53,554
- Which leads us
back to Lake Mendota
365
00:15:53,620 --> 00:15:55,389
and Professor Steve Carpenter.
366
00:15:55,455 --> 00:15:57,925
- Carpenter: There is a
tremendous amount of phosphorus
367
00:15:57,991 --> 00:15:59,193
in our soils
368
00:15:59,259 --> 00:16:01,428
and in the sediments
of our streams
369
00:16:01,495 --> 00:16:04,097
and in the sediments
of our lakes.
370
00:16:04,164 --> 00:16:06,466
- Phosphorus is a big focus
of the study
371
00:16:06,533 --> 00:16:08,802
because when it runs off
in the lakes,
372
00:16:08,869 --> 00:16:10,470
it leads to
bigger algae blooms
373
00:16:10,537 --> 00:16:12,906
and poor water quality.
374
00:16:12,973 --> 00:16:14,208
- Professor Carpenter says
375
00:16:14,274 --> 00:16:16,743
algae blooms are more than
just a cosmetic issue.
376
00:16:16,810 --> 00:16:18,478
Different types of algae
377
00:16:18,545 --> 00:16:21,748
contain toxins
known as cyanobacterium .
378
00:16:21,815 --> 00:16:24,184
- Carpenter: The toxins,
when they're present,
379
00:16:24,251 --> 00:16:25,652
are quite deadly.
380
00:16:25,719 --> 00:16:27,354
They are liver toxins
381
00:16:27,421 --> 00:16:29,089
and brain toxins.
382
00:16:29,857 --> 00:16:32,492
- Professor Carpenter says
there is a connection
383
00:16:32,559 --> 00:16:34,361
between our food choices
as consumers
384
00:16:34,428 --> 00:16:36,230
and water quality.
385
00:16:36,663 --> 00:16:38,799
- Carpenter: The enabling factor
is the continued runoff.
386
00:16:38,866 --> 00:16:42,369
If these lakes were not
so polluted with phosphorus,
387
00:16:42,436 --> 00:16:47,474
we would not have to worry
about toxic algae problems.
388
00:16:47,541 --> 00:16:48,909
- While phosphorus runoff
389
00:16:48,976 --> 00:16:50,911
is the oldest
and most persistent threat
390
00:16:50,978 --> 00:16:53,080
to water quality
in the Yahara Watershed,
391
00:16:53,146 --> 00:16:55,415
other threats
like invasive species
392
00:16:55,482 --> 00:16:56,950
are emerging.
393
00:16:57,017 --> 00:16:59,086
- Carpenter: All right,
let's see what we got.
394
00:16:59,152 --> 00:17:00,420
Oh, yeah!
395
00:17:00,487 --> 00:17:02,189
There are a lot of animals
in here.
396
00:17:02,256 --> 00:17:03,824
- Professor Carpenter
is pointing
397
00:17:03,891 --> 00:17:06,894
to a native water flea
called "daphnia."
398
00:17:06,960 --> 00:17:10,430
- Carpenter: Water fleas
are really good at eating algae
399
00:17:10,497 --> 00:17:15,435
so the water fleas
actually improve water clarity.
400
00:17:15,502 --> 00:17:18,272
- With plenty of algae to eat,
daphnia should be abundant.
401
00:17:18,338 --> 00:17:19,640
- Carpenter:
Recently, this lake
402
00:17:19,706 --> 00:17:24,711
was invaded by an animal called
the "spiny water flea"
403
00:17:25,445 --> 00:17:27,481
and the spiny water flea
404
00:17:27,548 --> 00:17:29,216
eats the daphnia.
405
00:17:29,716 --> 00:17:31,051
- Recent samples have shown
406
00:17:31,118 --> 00:17:34,054
the daphnia population
has dropped 95%,
407
00:17:34,121 --> 00:17:37,791
and water clarity
has gone down as well.
408
00:17:38,625 --> 00:17:40,460
- Jiangxiao Qiu:
So that's how they move.
409
00:17:40,527 --> 00:17:43,397
See it here,
it's like a snake movement.
410
00:17:43,463 --> 00:17:44,498
- The invasive species
411
00:17:44,565 --> 00:17:46,500
that could drastically alter
the watershed
412
00:17:46,567 --> 00:17:48,068
aren't just in the water.
413
00:17:48,135 --> 00:17:50,170
- Monica Turner: They're known
as the "Asian jumping worms"
414
00:17:50,237 --> 00:17:51,972
or "Asian crazy worms."
415
00:17:52,039 --> 00:17:54,041
- Professor Monica Turner
416
00:17:54,107 --> 00:17:56,376
and her student assistant,
Jiangxiao Qui,
417
00:17:56,443 --> 00:17:58,478
are looking at how a new invader
418
00:17:58,545 --> 00:18:00,614
can change the forest floor.
419
00:18:00,681 --> 00:18:02,850
Their research shows crazy worms
420
00:18:02,916 --> 00:18:05,619
eat through
the top layer of the soil
421
00:18:05,686 --> 00:18:07,921
twice as fast
as the common earthworm.
422
00:18:07,988 --> 00:18:11,658
Most of these invasive
species are new to Wisconsin,
423
00:18:11,725 --> 00:18:13,594
and no one knows
what new species will come
424
00:18:13,660 --> 00:18:16,463
between now and the year 2070.
425
00:18:17,097 --> 00:18:20,234
- When we have a new invasive
species show up in our area,
426
00:18:20,300 --> 00:18:21,635
that may change things
427
00:18:21,702 --> 00:18:24,404
in ways we don't even
necessarily anticipate.
428
00:18:24,471 --> 00:18:27,374
- The impact of invasive species
429
00:18:27,441 --> 00:18:28,609
like crazy worms
430
00:18:28,675 --> 00:18:31,078
will likely factor
into a future computer model.
431
00:18:31,144 --> 00:18:33,480
- Motew: And here we go...
432
00:18:34,815 --> 00:18:36,984
- Meanwhile, the model created
by the Yahara 2070 team
433
00:18:37,050 --> 00:18:38,852
is ready to run now.
434
00:18:39,520 --> 00:18:41,855
The team has estimated details
like the climate,
435
00:18:41,922 --> 00:18:43,056
ground cover,
436
00:18:43,123 --> 00:18:44,291
and population growth
437
00:18:44,358 --> 00:18:45,893
for each of the four scenarios,
438
00:18:45,959 --> 00:18:47,394
and they've plugged
439
00:18:47,461 --> 00:18:49,897
that hypothesized field data
from the future
440
00:18:49,963 --> 00:18:51,198
into the computer model.
441
00:18:51,265 --> 00:18:52,633
- Motew: It is going to
442
00:18:52,699 --> 00:18:56,036
now simulate
every hour of every day
443
00:18:56,803 --> 00:19:00,140
from the year 2014
to the year 2070.
444
00:19:00,207 --> 00:19:04,111
We have 120 processors
in this computer.
445
00:19:04,178 --> 00:19:06,246
Each processor is simulating
446
00:19:06,313 --> 00:19:08,815
a chunk of the Yahara Watershed.
447
00:19:08,882 --> 00:19:11,051
- Each of the four scenarios
448
00:19:11,118 --> 00:19:13,654
takes two days to simulate.
449
00:19:13,720 --> 00:19:16,156
- Motew: All those pieces
will be stitched together
450
00:19:16,223 --> 00:19:18,792
into one gigantic dataset.
451
00:19:21,228 --> 00:19:22,362
- Finally, they'll have results
452
00:19:22,429 --> 00:19:24,531
and just one job left.
453
00:19:24,598 --> 00:19:26,867
- Motew:
Analyze the heck out of it.
454
00:19:26,934 --> 00:19:29,303
- Researchers like Chris
Kucharik want to make it clear
455
00:19:29,369 --> 00:19:31,338
the results
of the computer modeling
456
00:19:31,405 --> 00:19:34,341
are closer to a forecast
than a prediction.
457
00:19:34,408 --> 00:19:36,076
- We're not trying
to predict things.
458
00:19:36,143 --> 00:19:38,846
We're trying
to offer perspectives on,
459
00:19:38,912 --> 00:19:41,048
"What are the plausible outcomes
460
00:19:41,114 --> 00:19:43,016
"that might happen as a result
461
00:19:43,083 --> 00:19:45,919
of these decisions
that might be made?"
462
00:19:45,986 --> 00:19:48,889
- The idea is to add data
to the stories,
463
00:19:48,956 --> 00:19:51,592
so the tradeoffs
in the different scenarios
464
00:19:51,658 --> 00:19:54,428
can be discussed through
science, not speculation.
465
00:19:54,494 --> 00:19:56,029
- Kucharik:
If we go in this direction,
466
00:19:56,096 --> 00:19:58,565
"What are the types of responses
that we're likely to see?"
467
00:19:58,632 --> 00:20:00,234
If we go
in a different direction,
468
00:20:00,300 --> 00:20:03,837
"What do the models suggest
things might be like?"
469
00:20:03,904 --> 00:20:05,873
- They might learn
the stories in the scenarios
470
00:20:05,939 --> 00:20:07,474
contain false assumptions.
471
00:20:07,541 --> 00:20:09,743
- Motew: We don't actually know
472
00:20:09,810 --> 00:20:11,578
if what's in the scenarios
will play out.
473
00:20:11,645 --> 00:20:14,815
So that's kind of
one of our questions:
474
00:20:14,882 --> 00:20:18,051
"How good are
the assumptions there?"
475
00:20:18,118 --> 00:20:19,853
- In fact, that ends up being
476
00:20:19,920 --> 00:20:22,956
one of the major takeaways
from the modeling results.
477
00:20:23,023 --> 00:20:25,192
- One of these things doesn't
look like the other, right?
478
00:20:25,259 --> 00:20:27,794
- Melissa Motew presented
the results on phosphorus
479
00:20:27,861 --> 00:20:29,830
at a research symposium
480
00:20:29,897 --> 00:20:30,998
for all the project members.
481
00:20:31,064 --> 00:20:32,699
The models predict that
482
00:20:32,766 --> 00:20:34,334
in all four scenarios
483
00:20:34,401 --> 00:20:37,437
increasing climate change
in the 2030s and 40s
484
00:20:37,504 --> 00:20:40,140
will dramatically increase
the levels of phosphorus
485
00:20:40,207 --> 00:20:42,743
in the landscape
and in the lakes.
486
00:20:42,809 --> 00:20:46,280
In theory, as societal changes
occur in the scenarios
487
00:20:46,346 --> 00:20:48,749
leading up to the year 2070,
488
00:20:48,815 --> 00:20:50,684
soil phosphorus should decrease.
489
00:20:50,751 --> 00:20:52,386
- Motew:
Three of these scenarios
490
00:20:52,452 --> 00:20:54,054
are able to turn the corner,
491
00:20:54,121 --> 00:20:56,056
they've reached
their thresholds,
492
00:20:56,123 --> 00:20:58,091
they're starting
to come back down,
493
00:20:58,158 --> 00:21:00,394
or at least to level off.
494
00:21:01,395 --> 00:21:04,031
- The outlier is
"Connected Communities."
495
00:21:04,097 --> 00:21:07,067
That's the scenario
that emphasized societal values
496
00:21:07,134 --> 00:21:09,102
to improve the water quality.
497
00:21:09,169 --> 00:21:10,838
The computer model says in 2070,
498
00:21:10,904 --> 00:21:14,308
it has the worst
phosphorus problem.
499
00:21:14,374 --> 00:21:15,742
- Motew: We have
"Connected Communities"
500
00:21:15,809 --> 00:21:17,578
which is kind of running away.
501
00:21:17,644 --> 00:21:20,113
- In the "Connected Communities"
scenario,
502
00:21:20,180 --> 00:21:22,049
people don't plant
many cornfields
503
00:21:22,115 --> 00:21:24,551
because they
are prone to erosion.
504
00:21:24,618 --> 00:21:26,954
This is an example
of conventional wisdom
505
00:21:27,020 --> 00:21:29,990
leading to unexpected results.
506
00:21:30,057 --> 00:21:32,226
With corn,
there is too much erosion
507
00:21:32,292 --> 00:21:34,361
which leads to phosphorus
in the lakes.
508
00:21:34,428 --> 00:21:35,729
However, corn plants
509
00:21:35,796 --> 00:21:38,165
take the most phosphorus
out of the soil.
510
00:21:38,232 --> 00:21:41,969
So without corn, too much
phosphorus remains in the soil
511
00:21:42,035 --> 00:21:44,438
and any erosion from flooding
512
00:21:44,505 --> 00:21:47,341
leads to phosphorus
in the lakes.
513
00:21:47,407 --> 00:21:50,444
- Kucharik:
Surprised, a little bit.
514
00:21:50,511 --> 00:21:52,746
- Even more surprising is that
515
00:21:52,813 --> 00:21:54,515
none of the big changes
in the scenarios
516
00:21:54,581 --> 00:21:57,384
made the water quality
better than it is today.
517
00:21:57,451 --> 00:21:58,719
- Kucharik:
Even though the scenarios
518
00:21:58,785 --> 00:22:03,023
were not designed to solve
the water quality problems,
519
00:22:03,090 --> 00:22:05,158
you'd like to think
that those large-scale changes
520
00:22:05,225 --> 00:22:07,427
would have had
some immediate impact.
521
00:22:07,494 --> 00:22:11,198
- Carpenter: I had expected
that some of the scenarios
522
00:22:11,265 --> 00:22:16,436
would show improvements and some
would show things getting worse.
523
00:22:16,503 --> 00:22:17,871
And in fact,
524
00:22:17,938 --> 00:22:21,842
all of the scenarios
are worse than today.
525
00:22:22,409 --> 00:22:23,744
- The scenarios
tackled the problem
526
00:22:23,810 --> 00:22:24,912
through government,
527
00:22:24,978 --> 00:22:26,079
technology,
528
00:22:26,146 --> 00:22:27,080
abandonment,
529
00:22:27,147 --> 00:22:28,949
and sustainable values.
530
00:22:29,016 --> 00:22:30,350
Despite the differences,
531
00:22:30,417 --> 00:22:33,554
the end result
is nearly the same.
532
00:22:35,189 --> 00:22:37,524
- Kucharik:
There's just a long legacy
533
00:22:37,591 --> 00:22:39,960
of buildup of soil phosphorus,
534
00:22:40,027 --> 00:22:43,530
that seems to be
overwhelming the system.
535
00:22:43,597 --> 00:22:45,499
And when you superimpose
changes in climate
536
00:22:45,566 --> 00:22:47,234
on top of that,
537
00:22:47,501 --> 00:22:49,269
it's kind of a one-two punch.
538
00:22:49,336 --> 00:22:53,207
- Almost no matter how
you manage phosphorus
539
00:22:53,273 --> 00:22:54,942
in, say, 2050,
540
00:22:55,776 --> 00:22:58,312
you've got big rainstorms
541
00:22:58,378 --> 00:23:01,982
and all this phosphorus
in the soil already,
542
00:23:02,049 --> 00:23:06,887
and it's coming into the lakes
and causing problems.
543
00:23:06,954 --> 00:23:10,490
- This is more than just
bad news for the year 2070.
544
00:23:10,557 --> 00:23:11,825
While the scenarios assumed
545
00:23:11,892 --> 00:23:15,162
the worst climate change
would take place in the 2030s,
546
00:23:15,229 --> 00:23:16,430
in reality
547
00:23:16,496 --> 00:23:21,134
scientists say climate change
is already underway.
548
00:23:21,201 --> 00:23:24,004
- Kucharik: I think all of
the best management practices
549
00:23:24,071 --> 00:23:25,772
that have been going on
in the watershed
550
00:23:25,839 --> 00:23:27,875
in the last 30 years or so
551
00:23:27,941 --> 00:23:30,711
have sort of held their own
against the changes in climate,
552
00:23:30,777 --> 00:23:34,481
but I'm worried we might
be reaching a tipping point
553
00:23:34,548 --> 00:23:37,551
where we sort of
wear those effects out.
554
00:23:37,618 --> 00:23:41,154
- Most efforts to battle
phosphorus in the water
555
00:23:41,221 --> 00:23:43,824
depend on activists
and volunteers
556
00:23:43,891 --> 00:23:46,426
who believe their efforts
are making a difference.
557
00:23:46,493 --> 00:23:48,295
- Carpenter: These projections
558
00:23:48,362 --> 00:23:51,064
of water quality changes are bad
559
00:23:51,131 --> 00:23:55,035
and potentially discouraging
to people.
560
00:23:56,436 --> 00:23:59,940
We're going to end up
in a worse place pretty quickly.
561
00:24:00,007 --> 00:24:02,042
- They hope the activists
won't give up,
562
00:24:02,109 --> 00:24:04,378
because despite
the unexpected results,
563
00:24:04,444 --> 00:24:06,180
Professors Kucharik
and Carpenter
564
00:24:06,246 --> 00:24:08,115
can see a silver lining.
565
00:24:08,182 --> 00:24:09,950
- As a result of this project,
566
00:24:10,017 --> 00:24:12,886
we have created
a new generation of models
567
00:24:12,953 --> 00:24:14,888
for understanding water quality
568
00:24:14,955 --> 00:24:17,925
that are better
than what we had before.
569
00:24:17,991 --> 00:24:20,227
- Kucharik: Now we can start to
use the modeling tools
570
00:24:20,294 --> 00:24:24,164
to maybe design, come up with
alternative scenarios
571
00:24:24,231 --> 00:24:25,532
that might actually show
572
00:24:25,599 --> 00:24:29,136
what you have to do
to get to the point
573
00:24:29,203 --> 00:24:32,339
that we want to be at
in the future.
574
00:24:33,006 --> 00:24:34,575
- The researchers say
the next step
575
00:24:34,641 --> 00:24:38,045
is to get the public
and policymakers on board.
576
00:24:38,111 --> 00:24:41,181
- Steve: I would like to see
land use planning
577
00:24:41,248 --> 00:24:43,050
taking a long view,
578
00:24:44,451 --> 00:24:47,754
longer than
the usual decade or so.
579
00:24:47,821 --> 00:24:49,156
- Kucharik:
There's a lot of things
580
00:24:49,223 --> 00:24:50,390
that have happened in history
581
00:24:50,457 --> 00:24:52,559
that you could never
have thought would have happened
582
00:24:52,626 --> 00:24:54,528
50 years into the future.
583
00:24:54,595 --> 00:24:58,532
- Carpenter: Economic incentives
could change.
584
00:24:58,599 --> 00:25:01,768
The value of water could change.
585
00:25:01,835 --> 00:25:03,904
- Now that they've developed
the computer model,
586
00:25:03,971 --> 00:25:05,405
they'd like to see what results
587
00:25:05,472 --> 00:25:07,774
different scenarios
could produce.
588
00:25:07,841 --> 00:25:09,109
- Kucharik:
A bigger question is:
589
00:25:09,176 --> 00:25:13,614
"Is there a mosaic
of land use, land cover that
590
00:25:13,680 --> 00:25:14,882
"sort of gets us
591
00:25:14,948 --> 00:25:17,684
our biggest bang for the buck?"
592
00:25:17,751 --> 00:25:21,088
- The water quality problems
took decades to build up,
593
00:25:21,154 --> 00:25:23,390
and will likely
take decades to solve.
594
00:25:23,457 --> 00:25:24,791
- Carpenter: It'll be slow,
595
00:25:24,858 --> 00:25:29,229
but over a generation or two,
there could be big changes.
596
00:25:29,296 --> 00:25:32,332
All right,
let's see what we got.
597
00:25:32,399 --> 00:25:34,334
- Professor Carpenter
has dedicated his life
598
00:25:34,401 --> 00:25:36,170
to studying lakes,
599
00:25:36,236 --> 00:25:38,539
but over time,
he's learned to expand his view
600
00:25:38,605 --> 00:25:40,274
beyond the shore.
601
00:25:41,008 --> 00:25:42,376
- You can't understand
602
00:25:42,442 --> 00:25:44,545
the biology and ecology
of a lake
603
00:25:44,611 --> 00:25:45,779
without understanding
604
00:25:45,846 --> 00:25:49,149
the priorities
and values and activities
605
00:25:49,216 --> 00:25:52,352
of the human beings
around a lake.
606
00:25:53,086 --> 00:25:56,190
- He hopes this project will
help with that understanding.
607
00:25:56,256 --> 00:25:58,225
- Carpenter: I think
the efforts of this project
608
00:25:58,292 --> 00:26:02,196
really will have
long-lasting benefits.
609
00:26:08,101 --> 00:26:09,770
? ?
610
00:26:34,995 --> 00:26:37,931
- Funding for Yahara Watershed:
A Place of Change
611
00:26:37,998 --> 00:26:39,199
was provided by
612
00:26:39,266 --> 00:26:41,668
the Water Sustainability
and Climate program
613
00:26:41,735 --> 00:26:45,205
of the
National Science Foundation.