39
Models for Managing Climate Risk in Water Management Policy Input from Casey Brown and Assis Francisco F. IRI
Models for Managing Climate Riskin Water Management Policy
Input from Casey Brown and Assis Francisco F. IRI
Application of Seasonal Climate Forecasts to Water Management
Managing The Full Range of Variability
FOR
EFI
TED
O
PPO
RTU
NIT
Y
CR
ISIS
HA
RD
SH
IP
commonassumption of a static policy
storage level)
SAHEL
1930 1940 1950 1960 1970 1980 1990 200080
100
120
140
160
180
200
ANO
Pre
cipi
taçã
o JA
S (
mm
)
Sen declividade = 0.64Mann-Kendall Tau Test
=23 mmmm2
1930 1940 1950 1960 1970 1980 1990 20000
20
40
60
80
100
120
140
160
180
200
Ano
Pre
cipi
taçã
o (m
m/a
no)
Tendência=13.20mm
34% Variância
1930 1940 1950 1960 1970 1980 1990 2000-25
-20
-15
-10
-5
0
5
10
15
20
25
ANO
Pre
cip
itacão (
mm
)
=11.21 mm
BaixaFreqüência24% Variância
1930 1940 1950 1960 1970 1980 1990 2000-40
-30
-20
-10
0
10
20
30
40
ANO
Pre
cipi
tacã
o (m
m)
=14.71
AltaFreqüência42% Variância
Sometimes policy is based on a sample that isnot representative of the true expectation. From Meko
Colorado River, western U.S.
From Connie Woodhouse
Vazão do Rio Colorado em Lees Ferry
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950-4
-2
0
2
4
Time (year)
INF
LOW
(af)
Time (year)
Per
iod
(yea
rs)
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950
2
4
8
16
32
64
128
2560 5 10
x 107Power (AF2)
Precipitação em Fortaleza 1849-2006
0
500
1000
1500
2000
2500
3000
1840 1860 1880 1900 1920 1940 1960 1980 2000
Pre
cipi
tatio
n (m
m)
Fortaleza
10 years moving average
Seca 1877
Fortaleza, Brazil
Afluência ao Reservatório Orós
0
20
40
60
80
100
120
140
160
180
200
1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
An
nu
al I
nfl
ow
(c
ms
)
Fortaleza, Brazil
Correlação das Vazões Afluentes ao Oros e a Temperatura da Superfície do Mar
A variabilidade hidrológica esta associada a fenômenos climáticos em escala planetária.
Fortaleza, Brazil
Zero Flow
Perfect Knowledge Climatology Forecasting
Forecasting-Zero
(ZF) (PK) (C-Median) (F-Median) (FZ-Median)
Average Yield (hm3/year) 480.0 530.9 531.1 545.7 545.1 System Reliability (Vol) % 80.0 88.5 88.5 91.0 90.8 System Reliability (freq) % 60.3 73.5 74.7 75.9 74.7 System Maximum Shortfall(%) 83 74 85 85 85 Shortfall (VOL) LOW PRIORITY 26.9 16.2 14.7 11.4 10.9 Shortfall (VOL) HIGH PRIORITY 3.8 0.6 4.0 3.5
5.03
Shortfall (freq) LOW PRIORITY 36.2 24.1 21.7 20.5 18.1 Shortfall (freq) HIGH PRIORITY 20.5
7.2 9.7 9.7 16.9
Maximum Shortfall HIGH PRIORITY (%) 44 13 50 50 50
System Risk Perception
Zero Flow
Perfect Knowledge Climatology Forecasting
Forecasting-Zero
(ZF) (PK) (C-Median) (F-Median)
(FZ-Median)
Prob (V<50) 0% 4% 9% 7% 7%
Prob (V<100) 0% 7% 12% 9% 9%
Prob (V<200) 0% 14% 18% 14% 13%
Prob (V<400) 8% 25% 26% 22% 19%
Reservoir Storage (V) in hm3
System Regret in Relation to Perfect Knowledge
Zero Flow
Perfect Knowledge Climatology Forecasting
Forecasting-Zero
(ZF) (PK) (C-Median)
(F-Median) (FZ-Median)
Average REGRET System (hm3/Year) 52.65 0 15.45 26.97 30.14
Average REGRET High Priority(hm3/Year) 5.98 0 6.67 6.5 8.86
Average REGRET Low Priority(hm3/Year) 46.69 0 8.77 20.47 22.7
1910 1920 1930 1940 1950 1960 1970 1980 1990 20000
200
400
600
800
1000
1200
1400
1600
1800
2000
Sto
rage
(hm
3)
1910 1920 1930 1940 1950 1960 1970 1980 1990 20000
200
400
600
800
1000
1200
1400
1600
1800
2000
Sto
rage
(hm
3)
1910 1920 1930 1940 1950 1960 1970 1980 1990 20000
200
400
600
800
1000
1200
1400
1600
1800
2000
Sto
rage(h
m3)
1910 1920 1930 1940 1950 1960 1970 1980 1990 20000
200
400
600
800
1000
1200
1400
1600
1800
2000
Sto
rage
(hm
3)
(a): zero flow(b): climatology
(d): forecast(c): perfect knowledge
(e) forecast – zero flow
Reservoir Storage: (a) “Zero Fllow”, (b)”Climatology”, (c)”Perfect Knowledge”, (d)”Forecast”, (e) “forecast-Zero”
1910 1920 1930 1940 1950 1960 1970 1980 1990 20000
200
400
600
800
1000
1200
1400
1600
1800
2000
Plots show storage,from 1912 to 1995
1920 1930 1940 1950 1960 1970 1980 19900
100
200
300
400
500
600
700
year
Dem
and
sup
ple
d (h
m3/
yea
r)
Total
Agriculture
Urban
1920 1930 1940 1950 1960 1970 1980 19900
100
200
300
400
500
600
700
year
Dem
and
supp
led
(hm
3/ye
ar)
Total
AgricultureUrban
1920 1930 1940 1950 1960 1970 1980 19900
100
200
300
400
500
600
700
year
Dem
and
supp
led
(hm
3/ye
ar)
Total
AgricultureUrban
1920 1930 1940 1950 1960 1970 1980 19900
100
200
300
400
500
600
700
year
Dem
and
supp
led
(hm
3/ye
ar)
Total
Agriculture
Urban
1920 1930 1940 1950 1960 1970 1980 19900
100
200
300
400
500
600
700
yearD
eman
d su
pple
d (h
m3/
year
)
Total
AgricultureUrban
(a): zero flow (b): climatology
(c): perfect knowledge (d): forecast
(e): forecast – zero flow
Demand Suplly for High and Low Priority and for the system simulated in: (a) “Zero Fllow”, (b)”Climatology”,(c)”Perfect Knowledge”, (d)”Forecast”,(e) “forecast-Zero”
totalagric (low)urban (high)
m3/year
RESERVEOIR STORAGE JULY
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
200
400
600
800
1000
1200
1400
1600
1800
2000
Probability
Vol
ume
July
(hm
3)
Zero Flow
Perfect Knowled
Permanence Curve of Reservoir Storage in July for “Zero Flow”, “Climatology”, “Perfect
Knowledge” and “Forecast”
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
200
400
600
800
1000
1200
1400
1600
1800
2000
Probability
Vol
ume
July
(hm
3)
Zero Flow
Perfect Knowled
Climatology 0.5
KNN 0.5
Probability of Shortfall will be less than some value in the system. Using the forecast provides the possibility that the
shortfall will be less than the shortfall using climatology
0 100 200 300 400 500 6000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Pro
babi
lity
less
tha
n
Shortfall (hm3/year)
ZERO
PERFECT KWOLEDGE
CLIMA
FORECAST
Relation between the storage in July (hm3) and Volume release between July and December (hm3) for “Zero Flow”, “Climatology”,
“Perfect Knowledge” and “Forecast”.
0 200 400 600 800 1000 1200 1400 1600 1800 20000
100
200
300
400
500
600
vol July (hm3)
Yie
ld J
ul-D
ec (
hm3)
Zero Flow
Perfect Knowled
Climate 0.5
KNN 0.5
CLIMATE DYNAMICAL DOWNSCALING FORECAST SYSTEM FOR NORDESTE
PERSISTED GLOBAL SST ANOMALIES
ECHAM4.5 AGCM (T42)
AGCM INITIAL CONDITIONS
UPDATED ENSEMBLES (10+)WITH OBSERVED SSTs
Persisted SSTA ensembles 1 Mo. lead
Predicted SSTA ensembles 1-4 Mo. lead
10
10
PostProcessing
RSM97 (60km)RAMS (40km)
HISTORICAL DATA•Extended Simulations•Observations
PREDICTED SST ANOMALIES
Tropical Pacific Ocean(LDEO Dynamical Model)(NCEP Dynamical Model) (NCEP Statistical CA Model)Tropical Altantic Ocean(CPTEC Statistical CCA Model)Tropical Indian Ocean(IRI Statistical CCA Model)Extratropical Oceans(Damped Persistence)
IRI FUNCEME
CPTECGCM (T42)
Hydrologic Models
Downscaling(Modo Simulação)
Esquema de Previsão Climática de Vazões: Propoagação de Incertezas “END to END”
Temperatura Superfície do Mar
Modelos de Circulação Geral
Modelos Climáticos Regionais
Correção Estatística
“Weather Generation”
Modelos Hidrológicos
Combinação de Multi-Modelos
Previsão de Vazão
Calibração/Validação (incerteza parâmetros)
Estrutura do Modelo
Condições Iniciais
Estrutura do Modelo Condições Iniciais
Condições IniciaisEstrutura do Modelo
Inflow to Angat Reservoir
0
50
100
150
200
250
300
350
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
Str
ea
mfl
ow
(in
hm
3)
0
50
100
150
200
250
300
350
400
450
500
Ra
infa
ll (
mm
)
StreamflowRainfall
3-months lag correlation
(Nino3.4,QJJAS) = -0.20
(Nino3.4,QOND) = -0.51
JJAS – 30%
OND – 46%
(Arumugam et al., submitted)
Another Setting: Near Manilla, Philippines
Seasonal Climate Forecast: Expected skill for a 3-month season
Current Reservoir Contents
Remaining Water:Agriculture and Hydropower
First Priority: Manila Water
Urban Centers
Low Inflow
“Business as Usual”
Reservoir Management
Hydropower
Water Delivery
Storage
Spill
Inflows0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12
Dynamic Rule Curve
Inflow
Flood
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6
More Inflow
Greater Flood Risk
More Release Possible
Wet Forecast
Increased Hydropower
0
200
400
600
800
1000
1200
1400
1987 1989 1991 1993 1995 1997 1999 2001
Year
Hyd
rop
ow
er
Ge
ne
rate
d (
in G
WH
)
0
50
100
150
200
250
300
350
400
Ob
se
rve
d In
flo
w
ActualUpdated ForecastOctober ForecastObserved
Irrigation Improvement
0
50
100
150
200
250
1987 1989 1991 1993 1994 1997Year
Alloca
tion
for
Irri
gati
on
(in
hm
3)
DecemberNovemberOctober
Dry Forecast
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6
Less Inflow
Less Flood Risk
More Storage Possible - but not sufficient
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2
1996 1997 1998 1999 2000 2001 2002 2003 2004
Pro
du
ctio
n/H
arve
sted
Are
a
Production (M T) Area Harvested (ha)
Irrigated Palay Production in AMRIS
1 – First Semester Harvest (Nov – Mar cropping season/dry) 2 – Second Semester Harvest (Jun – Oct cropping season/wet)
1998 (1) - 86.60 %
1998 (2) - 43.94 %
Impacts on Irrigation
Current Reservoir Contents
Remaining Water:Agriculture and Hydropower
First Priority: Manila Water
Urban Centers
Low Inflow
“Business as Usual”
Current ReservoirContents
Probabilistic Inflow Forecast
Dry Year Option Contracts
Contractsw/
Dry Year Option
Insurance + Contracts
Option Exercise Decision
np ?
nppp + nipi
Observe preseason flows
Decide preseason options to exercise
TotalCost
Observe In-season flows
Water Supply Costs
PP=2.35, PI=2.93
0
200
400
600
800
1000
1200
19
68
19
70
19
72
19
74
19
76
19
78
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
Cos
ts in
Mill
ion
Pes
os
Contracts
Insured
