Megan gave a fascinating talk showcasing her work on network analysis of virtual water trade. We discussed water and food security in the context of population growth, economic development and climate change.
3. Global
DistribuKon
of
Water
on
Earth
SURFACE
WATER
0.34%
UNDERGROUND
30%
FRESH
WATER
3%
FROZEN
70%
OCEANS
97%
Water
available
for
plant
use
is
less
than
0.34%
of
all
fresh
water
4. Water
for
Food
Water
and
food
security
are
inextricably
linked.
Water
and
food
systems
face
increasing
pressures
from
global
populaKon
growth,
economic
development,
and
climate
change.
How
are
water
and
food
systems
linked
through
internaKonal
trade?
5. Sources
of
Water
The
vast
majority
(80%)
of
irrigaKon
(i.e.
“blue”)
water
resources
goes
to
agriculture.
Most
food
(60-‐70%)
is
actually
rainfed,
or
produced
using
“green”
water
resources.
Green
Water
Blue
Water
8. Crop
Water
Use
Varies
In
Both
Space
and
Time
Virtual
Water
Content
[-‐]
Hanasaki,
2010
H08
Global
Hydrologic
Model
½₀
x
½₀
SpaKal
ResoluKon
9. Water,
Food,
and
Trade
The
water
embodied
in
the
internaKonal
food
trade
is
referred
to
as
“virtual
water
trade”.
Virtual
water
trade
is
driven
by
underlying
economic
system
and
climaKc
factors.
10. Water,
Food,
and
Trade
The
water
embodied
in
the
internaKonal
food
trade
is
referred
to
as
“virtual
water
trade”.
Virtual
water
trade
is
driven
by
underlying
economic
system
and
climaKc
factors.
This
complex
system
can
be
represented
as
a
network.
NODES:
Countries
parKcipaKng
in
internaKonal
food
trade.
LINKS:
Weighted
by
water
volumes,
directed
by
trade.
16. Regional
Networks
Crops
and
Livestock
Crops
Livestock
All
Water
Green
Water
Blue
Water
17. Regional
Networks
Crops
and
Livestock
Crops
Livestock
All
Water
Green
Water
Blue
Water
18. Regional
Networks
Crops
and
Livestock
Crops
Livestock
All
Water
EnKre
network
is
Green
driven
by
the
trade
Water
of
rain-‐fed
crops
Blue
Water
19. Network
ProperKes
Node
Degree
k out = 0
k in = 1
ExponenKal
export
degree
distribuKon
Node
Strength
Data
4 1
Fit
s out = 3
2
s in = 10
6
Stretched-‐exponenKal
strength
distribuKon
20. Power
Law
RelaKonship
Volume
of
virtual
water
traded
vs.
number
of
trade
partners
follows
a
power
law
relaKonship
Data
Fit
This
relaKonship
is
parKcularly
strong
for
food
import,
indicaKng
that
the
more
import
trade
partners
a
country
has,
the
much
more
virtual
water
it
imports.
21. Evidence
of
Rich
Club
Nearest-‐Neighbor
Degree
Clustering
Coefficient
Unweighted
Unweighted
Weighted
Weighted
Weights
break
down
disassortaKve
structure
22. Network
Dynamics
Over
Time
1986
2007
%
Change
from
1986
Green
Water
Blue
Water
Volume
of
virtual
water
embodied
in
trade
has
more
than
doubled
from
1986-‐2007,
parKcularly
for
green
water.
Dalin
et
al,
2012;
Konar
et
al,
In
Review
23. Global
Water
Savings
from
Trade
Savings
=
238
km3
yr-‐1
Equivalent
to
9%
of
global
agricultural
water
use
Dalin
et
al,
2012
24. Blue
and
Green
Networks
Save
Water
.
Pos.
Links
[%]
.
Blue
Water
Green
Water
Global
Water
Savings
[m3]
<VWC
Gap>
Crop
Trade
[%]
What
is
driving
this
increased
water
saving
over
Kme?