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Vulnerability, Uncertainties &
Adaptation to Future Scenarios

Henrique Marinho Leite Chaves

EFL
-

FT / Univ. de Brasília
-
UnB

2
nd

DIALOG

OF

THE

SINERGIA

PROJECT

ABOUT

WATER

MANAGEMENT

AND

GLOBAL

WARMING
:

MAKING

RESEARCH

ACTIONS

CONVERGE

IN

THE

PARAGUAY

RIVER

BASIN”

Outline


The watershed as a system


Watershed vulnerability


Potential impacts


Mitigation & Adaptation


P.E.S. as a tool to reduce vulnerability


Uncertainty, failure, & risk


From paradigm
-
block to watershed
sustainability


Conclusion

The Watershed as a System

Precipitation

(
input
)

Streamflow

(
output
)

O(t,s)

=
Ω

I(t,s)

Watershed Vulnerability

Schroter et al. (2004)

Exposure:


External conditions


Impacts to surface & GW water
quantity & quality, biota, land
productivity


Combined with
Sensitivity
,
yields
Potential Impact




Examples of Exposure


Climate change


Deforestation


Rainfall
erosivity


Water pollution


Etc





Example of Exposure:

Deforestation

Example of Exposure:

P & Q erosivity

I
s
o
i
e
t
a
.
s
h
p
1
2
5
0
<
i
s
o
<
=
1
3
5
0
1
3
5
0
<
i
s
o
<
=
1
4
5
0
1
4
5
0
<
i
s
o
<
=
1
5
5
0
1
5
5
0
<
i
s
o
<
=
1
6
5
0
0
1
0
K
i
l
o
m
e
t
e
r
s
N
Pipiripau river basin (DF
-
Brazil)

Sensitivity:


System sensitivity to
natural & human impacts


Depends on physical, biotic
conditions; resilience


Watersheds have different
sensitivities in different
areas, periods




Examples of Sensitivity:

Soil erodibility

E
r
o
d
i
b
.
s
h
p
0
.
0
1
2
0
.
0
1
2

-

0
.
0
1
5
0
.
0
1
5

-

0
.
0
2
9
0
.
0
2
9

-

0
.
0
3
3
0
.
0
3
3

-

0
.
0
4
1
H
i
d
r
o
.
s
h
p
0
1
0
K
i
l
o
m
e
t
e
r
s
N
Chaves & Piau (2008)

U
s
o
.
s
h
p
A
g
r
i
c
u
l
t
u
r
a

E
x
t
e
n
s
i
v
a
Á
r
e
a
s

A
g
r
í
c
o
l
a
s

-

C
u
l
t
i
v
o

d
e

H
o
r
t
i
f
r
u
t
i
g
r
a
n
j
e
i
r
o
s
Á
r
e
a
s

d
e

E
m
p
r
é
s
t
i
m
o
Á
r
e
a
s

Ú
m
i
d
a
s
Á
r
e
a
s

U
r
b
a
n
a
s
C
o
r
p
o
s

d
'
á
g
u
a
C
u
l
t
u
r
a

T
e
m
p
o
r
á
r
i
a

I
r
r
i
g
a
d
a
G
a
l
p
õ
e
s
P
a
s
t
a
g
e
m
S
e
d
e

d
e

N
ú
c
l
e
o

R
u
r
a
l
B
o
s
q
u
e
s

e

P
o
m
a
r
e
s
V
e
g
e
t
a
ç
ã
o

N
a
t
i
v
a
0
1
0
K
i
l
o
m
e
t
e
r
s
N
E
r
o
d
i
b
.
s
h
p
0
.
0
1
2
0
.
0
1
2

-

0
.
0
1
5
0
.
0
1
5

-

0
.
0
2
9
0
.
0
2
9

-

0
.
0
3
3
0
.
0
3
3

-

0
.
0
4
1
H
i
d
r
o
.
s
h
p
0
1
0
K
i
l
o
m
e
t
e
r
s
N
I
s
o
i
e
t
a
.
s
h
p
1
2
5
0
<
i
s
o
<
=
1
3
5
0
1
3
5
0
<
i
s
o
<
=
1
4
5
0
1
4
5
0
<
i
s
o
<
=
1
5
5
0
1
5
5
0
<
i
s
o
<
=
1
6
5
0
0
1
0
K
i
l
o
m
e
t
e
r
s
N
Erosivity

Potential Impact:

Sedimentation

D
e
c
l
i
v
%
0

-

2
2

-

4
4

-

8
8

-

1
6
1
6

-

3
2
3
2

-

6
4
6
4

-

1
0
0
1
0
0

-

1
6
0
1
6
0

-

2
0
0
N
o

D
a
t
a
H
i
d
r
o
.
s
h
p
0
1
0
K
i
l
o
m
e
t
e
r
s
N
Erodibility

Steepness

Land cover

Potential Impact
:
River sedimentation under





conventional tillage

0
25,000
50,000
75,000
800
1,000
1,200
1,400
1,600
1,800
Y

(
ton
/
yr
)

P
(mm/
yr
)

Chaves & Piau (2008)

Conventional Tillage

Potential Impact
: Mercury movement

Tagliari & Chaves (2009)

y = 0,0737x

R² = 0,87

0
10
20
30
0
100
200
300
400
Hg

Loss

(
kg/
ha.yr
)

Soil Loss
(t/ha.yr)

Adaptive Capacity


Capacity of a system or
community to cope with
impacts


Includes both adaptation &
mitigation


Key: identify & quantify
system sensitivity &
management capacity


Uncertainty about system?
Adaptive management




Adap. Capacity
: Examples


IWRM: Supply & demand mgt.


Land
-
use suitability


BMPs: Reduction of soil &
water losses


Water biota: environmental
flows


Monitoring is crucial



No
-
till farming

Vulnerability


Balance between Pot.
Impact &
Adap
. Capacity


Depend on external
factors, intrinsic
conditions & human
management


On
-
site
&
off
-
site
vulnerabilities




Watershed Vulnerab. to Sedimentation

0
20,000
40,000
60,000
80,000
800
1000
1200
1400
1600
1800
Y

(t/
yr
)

P
(
mm/
yr
)

Conv.
No-Till
Cerr
High

Low

Low/Med

Watershed Vulnerab. to Sedimentation

0
20,000
40,000
60,000
80,000
800
1000
1200
1400
1600
1800
Y

(t/
yr
)

P
(
mm/
yr
)

Conv.
No-Till
Cerr
Support Capacity?
Adaptive mgt.

Reducing Vulnerability
: P.E.S.

Conserv
.
Effort

Cost

or

Revenue

($)





0

I

II

III

Fixed cost

Fixed +Var. C

Revenue

P.E.S.
could

be

the

answer


System Uncertainty


Arises from the limitations of
understanding & measuring
processes


Understanding: models


Measurements: indicators, variables


Uncertainty should be incorporated
in decision
-
making process!




Uncertainty in Models



Calibration

Error

Structural

Error

Input Error


Troutman (1985)

Model Error

Modeling Uncertainty
: MUSLE






Y = 89,5 (Q
q
p
)
0,56

K L S C P








=

Random Variables

Random

Variable

Quantifying Uncertainty
: Methods


Monte
-
Carlo Simulation
: non
-
trivial,
requires
a priori
knowledge of
distribution & correlation of model
input variables


P.E.M.
: simple, robust, requires only
information about input distribution
moments & correlations


Quantifying uncertainty is crucial for
the decision
-
making process!





1 S.D.:

P = 67%

2 S.D.:

P = 90%

Uncertainty
: Model error propagation

C.V.

(%)

System Uncertainty
: p
f

& risk

P

Demand

Capacity

p
f

“Risk is the cost of failure”

C, D

The Question of Scale

Mgt
.
Unit

Area


(km
2
)

Main

Intervention

Actor

Mgt
.
Focus

Micro
watershed

0,15
-
2,5

Farmer
(owner)

BMPs

Sub
-

basin

2,5
-
25

Local govt.

Land use
classification

Small
Basin


25
-
250

Local/
region.

govt
.

Basin


zoning

Medium

Basin

250
-
2.500

Local, state

Basin plans

Large

basin

> 2.500

State,
federal

Basin

plans

Schueler, 1995

Science

Mgt
.

Paradigm block

Watershed Sustainability

Science

&
Mgt
.

Watershed Sustainability

Watershed Sustain. Index


Watershed
-
based (up to 2,500 km
2
)


Uses Unesco
HELP

philosophy


Dynamic


Incorporates cause
-
effect relationships
(
Pressure
-
State
-
Response
)


Parameters are mostly quantitative, off
-
the
-
shelf


Allows for demographic & climate change
impacts


Mathematically robust


Easy to use

Watershed Sustain. Index

H = Hydrology indicator (0
-
1)

E = Environmental indicator (0
-
1)

L = Human Life indicator (0
-
1)

P = Policy indicator (0
-
1)


WSI =

_____________

4

H+E+L+P

Watershed Sustain. Index

P
olicy


-

Variation in the
basin HDI
-
Ed in
the period


-

Basin institutional
capacity in WRM


-

Evolution in the
basin’s WRM
expenditures in
the period





Pressure


State


Response


Indicators



Parameters



H
ydrology


-
Variation in the
basin’s
per capita

water availability in
the period;

-
Variation in the
basin BOD (or other
limiting parameter)

-

Basin
per capita

water availability



-

Basin BOD5
(yearly average)


-

Improvement in
water
-
use efficiency
in the period;

-

Improvement in
sewage treatment/
disposal in the
period


E
nvironm.


-

Basin EPI
(Rural & urban)




-

% of basin area
with natural
vegetation


-

Evolution in basin
conservation (Prot.
areas, BMPs)


L
ife


-

Variation in the
basin per capita
GDP in the period


-

Basin HDI
(weighed by county
pop.)


-

Evolution in the
basin HDI in the
period


WSI
: Panamá Canal Basin





CHCP


Panamá

(
2003
-
07)

Indicator

Pressure

State

Response

WSI

Level

Score

Level

Score

Level

Score

Hydro
-
Quant
.

85%

1,00

4.384

0,50


Bueno

0,75

Hydro
-
Qual
.

0%

0,75

0,39

1,00


Bueno

0,75

0,79

Environment

11,9%

0,25

57,9%

1,00

142,70%

1,00

0,75

Life

1,53%

0,75

0,727

0,50

0,83%

0,50

0,58

Policy

1,45%

0,75

Bueno

0,75

25%

1,00

0,83



0,66



0,75



0,81

0,74

WSI
: Latin American basins





WSI
: Future Scenarios


Temp.





26,9
26
,
9
26,9
28,4
28,4
28,4
29,5
30,0
30,5
24
,
0
26,0
28,0
30,0
32,0
Optimista
Tendencial
Pesimista
T
(
o
C)
Presente
2050
2100
Best
-
Case B2
Average

Worst
-
case A2

WSI
: Future Scenarios
-

Rainfall





1.907
1.907
1.907
1.924
1
.
924
1.924
1.940
1.946
1.953
1
.
880
1.900
1.920
1.940
1.960
Optimista
Tendencial
Pesimista
P
(mm/año)
Presente
2050
2100
Best
-
Case B2
Average

Worst
-
case A2

WSI
: Future Scenarios


Water Vol.





819
819
819
814
814
814
811
808
806
800
805
810
815
820
825
Optimista
Tendencial
Pesimista
Q
(mm/año)
Presente
2050
2100
Best
-
Case B2
Average

Worst
-
case A2

WSI
: Future Scenarios


Land use





WSI
: Adaptation to Pop. Growth &


Climate Change





Water

Avail
.

(m3/
per.yr
)

Basin Sustainability in 2050





Present


Best
-
Case B.A.U. Worst
-
case

Conclusions






Vulnerability is the balance between
potential impact & adaptive capacity


Vulnerability can be reduced with
socioeconomic & environmental
gains: P.E.S.


Uncertainty ought to be
incorporated in the decision
-
making
process


Sustainability indices are useful to
assess IRWR effectiveness under
future scenarios





hchaves@unb.br

Modeling is the art of
making good assumptions