Decision Systems Research developing decision tools

IRI Underpinning Activity

Climate Prediction Applications WorkshopFlorida State University

9-11 March 2004

N. WardAnd

C. F. Ropelewski

Centrality of exploring and influencing sectoral decisions,based on information that draws on sound biophysical science

IRI’s mission is to enhance society's capability to understand, anticipate and manage the impacts of seasonal climate fluctuations, in order to improve human welfare and the environment, especially in

developing countries.

The Discussion

•Scope of the Underpinning Activity

•Illustrations of its presence in Regional Project Settings- NE Brazil and Philippines Water management- Farm level agriculture - Support at regional level for agriculture issue

•Role of training / capacity building

Components of the Work

•Development of Decision Strategies and Tools (DST)

•Methodologies to extract relevant environmental information to feed DST

•Testing of DST based on forecasts/information over past years

•Experimental implementationDeveloping decision support informationEvaluating and learning through implementation

Simulating the Expected Improvements in Reservoir Management

Example for Reservoir in Ceara, NE Brazil

(collaboration led by Assis de Souza Filho, FUNCEMEand Upmanu Lall, IRI

additional contributions, especially Sankar Arumugam, IRI)

Reliability – Yield Curve

Reliability-Yield Curve

0

200

400

600

800

1000

1200

20 30 40 50 60 70 80 90 100

Reliability

Yie

ld (

hm

3)

Forecast 1989Forecast 1993Climatology

-40.0

-35.0

-30.0

-25.0

-20.0

-15.0

-10.0

-5.0

0.0

5.0

1950 1960 1970 1980 1990

Year

Sp

ill/

Sto

rag

e M

ax (

in %

)

0

1000

2000

3000

4000

5000

6000

7000

Ob

serv

ed F

low

s (m

3 /s)

Forecast - Climatology (Reliability = 0.9)

Observed Flows

Spill (Reliability = 0.9)Spill (Reliability = 0.9)

Simulating the benefits of using climate forecast informationin the operation of a reservoir in Ceara over 1950-2000

SEASONAL WATER ALLOCATION AND RESERVOIR OPERATION UTILIZING CLIMATE

INFORMATION BASED STREAMFLOW FORECASTS

A aerial view of the Angat Hydroelectric Plant

Courtesy of Mr. Rodolfo German (Angat dam)

Hydroclimatology

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%

Reservoir Operation Strategy

ANGAT H.E. PLANT

150

160

170

180

190

200

210

220

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

EL

EVA

TIO

N (

m)

1996 1997 1998 1999 2000 2001 UPPER LOWER

StatisticalDownscalingResults forSri Lanka, 1951-80 Verification

Map shows correlationskill (shading) alongwith contours of elevation

Need for cautionin regions of complex terrain

Mechanism operating in Sri Lanka?

0

1

2

3

4

5

Gra

in yie

ld, M

g/h

a

1970 1975 1980 1985 1990 1995Year

observed hindcast

0

20

40

60

Pre

cip

ita

tio

n,

cm

a

b

r = 0.50

r = 0.62

Statistical Downscaling from General Circulation Model (GCM) output to(a) Predict Oct-Dec seasonal rainfall total(b) Predict Crop yield

daily weather generator conditioned on GCM predicted wind field –

resulting daily weather sequences used to drive a crop model

Example for a Site in Kenya

Examples of Decisions Represented in Farm-level Models

Field-scale crop management decisions:

Cultivar selectionPlanting datePlanting densityAmount and timing of nitrogen fertilizer applicationLivestock stocking rates

Farm-scale management decisions:

Land allocation among cropsFeed management (pasture planting and fodder purchase)Borrowing for production inputs (planned)Allocation of household labor among farm vs. non-farm activities (planned)

Objective 2: Tailored ProductsProblem area User(s) Requirement Application

1) Areas of high Rift Valley Fever (RVF) outbreak risk

Red Sea Livestock Trade Commission

Predict RVF risk areas 3-6 months in advance

Identify and treat RVF outbreaks before regional trade barriers are imposed

2) Livestock fodder availability

Pastoral communities in northern Kenya and southern Ethiopia

Narrow the confidence interval of the current Livestock Early Warning System (LEWS) 90 day fodder outlook using a seasonal forecast

Provide improved 90 day early warning to nomadic pastoralists and sedentary agro-pastoralists of expected fodder conditions

3) Livestock fodder availability

Organization of African Union/Inter-African Bureau of Animal Resources (OAU/IBAR)

Provide 3-6 month early warning of major regional fodder shortages

Support IBAR livestock purchase programs 

4) Pastoralist livelihood system stress

USAID, other donors and international emergency assistance organizations

Simulate climate shock impacts on pastoralist livelihood systems and food security

Contingency and operational assistance planning 

Greater Horn of Africa Project

0

0.5

1

1.5

81 83 85 87 89 91 93 95 97

Year

ind

ex predicted

observed

Using Global Climate Model Output to Predict Vegetation

The index is the satellite estimated Normalized Difference Vegetation Index (NDVI)Index shown is the October-December average for Northeastern Kenya

Positive values indicate enhanced greenness in vegetation

Correlation between predicted and observed = 0.84

Using Global Climate Model Output to Predict NDVI on a 1 degree lat x 1 degree lon grid across Kenya

Skill (indicated by shading) is very good in most parts of the domain,less to the NE of Lake Victoria in region of complex orography

(to be further investigated)

Shading Indicates Correlation between predicted and observed NDVI time-series over 1981-98

Contours are elevationon 1 lat x 1 lon grid

Example of pageFrom online course

Collaborationwith CCNMTL

IRI is exploring the potential of online courses

http://iri.columbia.edu/outreach

Advanced Training Institute on Climatic Variability and Food Security

Palisades, New York, USA

8 - 26 July 2002

Dr. James Hansen, Training Institute Director

The Advanced Training Institute on Security is designed to equip young developing country professionals with expertise in agriculture and food security to apply advances in climate prediction to their home institutions' ongoing efforts to address climate-sensitive aspects of agricultural production, food insecurity and rural poverty

•Examples of the underpinning activity within emergingend-to-end regional projects

• Methodological advances to contribute to fasterimplementation in other regions

00

10001000

20002000

30003000

40004000

50005000

60006000

70007000

2929 55 1212 1919 2626 22 99 1616 2323 22 99 1616 2323 3030 66 1313 2020 2727 44 1111 1818 2525

Date

Cas

es

JanuaryDec. February March April May

The forecasting principle Early warning for changes in the incidence of the

meningococcal meningitis disease(Example for West African Sahel region)

not preventable

Vaccination starts

Early_______________ Peak_________________Late______________

+Dust

-Rain______-Rain______

Feb-Apr Precipitation and Near-surface wind Predicted by a Regional Climate Model at 10km Resolution

across Taiwan and surrounding ocean

For case study years 1983 (generally WET) MINUS 1971 (generally DRY).

Rainfall difference in mm per dayas predictedby the model for 1983relative to 1971

Illustrates theNeed for cautionIn regions of Complex terrain