Expert meeting on the application of climate forecasts for agriculture 1 The application of climate forecasts and agrometeorological information for agriculture,

Expert meeting on the application of climate forecasts for agriculture

1

The application of climate forecasts and

agrometeorological information for agriculture,

food security, forestry, livestock and fisheries

G. Maracchi, F. Meneguzzo, M. PaganiniBanjul, Gambia, 9-13 December, 2002


2

Information needs ofFOOD SECURITY

Availability of input data

Appropriate location

Appropriate spatial resolution

Timely information


3

Existing Food Security Systems

Name ParameterAGRHYMET/SISP Prediction of the current crop yieldUSAID/FEWS Identification of vulnerable groupsAGRHYMET/DHC Prediction of the current crop yieldWFP/VAM Mapping disaster in order to mitigate itFAO/GIEWS Warnings on food shortages


4

Existing Food Security Systems - AGRHYMET SISP

Base parameters•statistical analysis procedures on rainfall for ecological zoning;

•a millet simulation model to estimate millet crop conditions and the effect of rainfall distribution;

•statistical analysis of the yields.


5


Varieties Phases length InitialKc

Growth at five days interval

Growing FloweringGrain filling75 days 50 15 15 0.2 0.0890 days 65 15 15 0.15 0.06538120 days 80 15 20 0.1 0.0562120 daysphot.

JFL - JSEM 15 20 0.1 (1-0.1)/(IPFL-IPSEM)


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-12 -10 -8 -6 -4 -2 0 2 410

12

14

16

18

20

22

24

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

Variability of SISP yield index

-12 -10 -8 -6 -4 -2 0 2 410

12

14

16

18

20

22

24

5101520253035404550556065707580859095

Average yield index 1961-90


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8

Existing Food Security Systems - USAID FEWS

The analysis is organised in three sections:

• Vulnerability/Baseline Information

• Hazard/Shock Information

• Risk/Outcome Analysis


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10


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Existing Food Security Systems - DHC-Champs pluviaux

The CCD are used in the crop water diagnostic (DHC) in order to produce:

• maps of the crop water satisfaction• maps of the crop water needs• maps of crop yields


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Existing Food Security Systems - DHC-Champs pluviaux


13

Existing Food Security Systems - World Food Programme -Vulnerability Analysis & MappingWFP has produced vulnerability assessment maps in 3 stages:

identifying the income sources for each relevant groupanalysing the causal structure of vulnerabilityreconciling the analysis of risk and coping capacity


14


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Existing Food Security Systems - FAO GIEWS

-monitors food supply and demand

-analyses information on production stocks, trade and food aid

-monitors export prices

-reacts to natural disasters

-issues Special Alerts and up-to-date reports


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-web pages on the Internet

-develops new approaches for early warning

-cultivates and maintains information-sharing between governmental and private actors

-depends on the free exchange of information


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Solving the problem

• Information available on Internet• More appropriate to the decision

makers information needs

• Improved survey methods and operations for monitoring actual and potential outbreak areas

• Create interaction between producers of information

FOOD SECURITY INFORMATION

CLIMATE PREDICTION INFORMATION


19

The local CLIMATE

• Decreasing annual pluviometry S-N

• Alternation of dry season (9-5 months) and rainy season

• The monsoon is the main defining factor

• Unimodal distribution of the rain


20

Link between climate and teleconnections

The average of the weather over periods

The effects of changes in sea surface temperatures in the Pacific Ocean on temperature and rainfall patterns in regions that are far away from the Pacific

CLIMATE DEFINITION

TELECONNECTIONS DEFINITION


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22


23

Teleconne-ctions in Sahel


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Sim

ulta

neou

s C

orre

latio

n o

f S

ah

el R

ain

fall w

ith S

ST (Ju

ne,

July

)


25

Sim

ulta

neou

s C

orre

latio

n o

f S

ah

el R

ain

fall w

ith S

ST (A

ug

ust,

Sep

tem

ber)


26

Corre

latio

n o

f Sah

el R

ain

fall in

Ju

ne a

nd

July

with

SS

T in

May


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INTERTROPICAL CONVERGENCE ZONE - location

Drought years are associated with the ITCZ being south of its normal position, while wet years are associated with the ITCZ north of normal

Warmer SST in Guinea Gulf lead to higher precipitation over Guinea coast (increased moisture) and lesser over

Sahel (northerly flow, sinking at low levels)


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INTERTROPICAL CONVERGENCE ZONE - location

Rapidly increasing SST in May over Guinea cause

delayed monsoon in

Sahel (June and July)


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Synthetic descriptions of atmospheric teleconnection patterns

Can be found at following addresses:

The Climate Diagnostics Center (NOAA)http://www.cdc.noaa.gov/TeleconnectionsClimate Precition Center (NOAA): http://www.cpc.noaa.gov/data/teledoc/telecontents.html


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Existing climate predictions

Amount of rainfall• IRI Net assessments• PRESAO outlook• CLIMAG WA enhanced methodology

Onset of the growing season• IBIMET methodology (Maracchi/Pini)• Omotosho method• CLIMAG WA enhanced methodology


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Applications for 2001 & 2002

•Comparison of results per single zone for each year

BUTBUT

•Each methodology has its own spatial resolution

•Each methodology has its own temporal resolution


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Data formats

Spatial scale Time scale Methodology

Amount of rainfall

IRI Net assessments 2,8° of resolution Three months Coupled dynamicmodels andstatistical models

PRESAO outlook Regional Once a year, inMay

Dynamical andstatistical models

CLIMAG WAenhancedmethodology

Punctual -spatialised

Once a year, inApril

Statistical

Onset of the growingseason

Maracchi methodology Punctual -spatialised

Five days SISPmethodology

Omotosho method Punctual -spatialised

Three weeks Omotosho fieldmethodology

CLIMAG WAenhancedmethodology

Punctual -spatialised

Once a year, inApril

AP3Amethodology


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The IRI Forecast Process (1)

• Forecasting the tropical SST anomalies using dynamical and statistical models

• Using the predicted SST for atmospheric general circulation models (GCMs)

• Estimating the expected skill


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• Statistical postprocessing of model output

• Putting all the indications together a final IRI forecast called net assessment

• issued in the form of maps that show regions having homogeneous forecast probabilities for the below, near and above normal terciles

The IRI Forecast Process (2)


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Examples of Net Assessments


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Omotosho methodology

Onset of the growing seasonThe method is empirical/dynamical and uses the following requirements:

•Difference between the U-component of the wind at 3000 m and at the surface must be between –20 m/s and –5 m/s Difference between the U-component of the wind at 7500 m and at 3000 m must be between 0 and 10 m/s


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Omotosho methodology

Onset of the growing season


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• Predict the seeding decades for the different zones in order to produce advises to peasants

• The philosophy is to utilise the information already available on INTERNET (NOAA, IGES COLA, ADDS)

Onset of the growing seasonIBIMET method


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1 – Rainfall Forecasting sectionNOAA - Climate Prediction Center, Prediction of the rainfall quantity at 24-96 hours2 – Rainfall Estimation sectionADDS - Africa Data Dissemination Service, Decadal rainfall estimation images3 – Field data sectionReal sowing dates in different areas in Mali collected by local institutions

Exercise for the agricultural season 2001


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Forecasting Section

Total rainfall of the decade

Daily forecast images =

Through the daily images it is possible to forecast the amount of rainfall expected in the decade and give the advise of the sowing date to farmers


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Estimation Section

Precipitation Estimate based on

GPI, SSM/I, AMSU and GTS

The image has been utilised to validate the information prepared by the forecasting information


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Field Observation Data Section

Field observation areas

Data collected by local institutions

The collected information are related to the real sowing date


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The information of the different three sections has been compared in order to evaluate the process

REAL SOWING FORECASTED ESTIMATED DATE

DEPARTMENT DATE 2001 SOWING DATE SOWING DATE

Gao-Central 5/7 15/7 25/6

Gossi 5/7 5/7 25/6

Nara-Central 25/6 25/6 25/6

Douentzan-Centr 5/7 25/6 15/6

Kayes-Central 5/6 15/6 5/6

Ambidedi 5/6 15/6 5/6

Same 5/6 15/6 5/6

Kolokani-Centr 5/6 5/6 5/6

Mahina 5/6 5/6 5/6

Djidian 5/6 5/6 5/6

Kita-Central 5/6 5/6 5/6

Koutiala-Centr 15/5 15/5

Bougouni-Centr 15/5 15/5

Sikasso-Central 15/5 15/5

relation between forecasted and real sowing date

15/7

5/7

25/6

5/65/65/65/6

25/615/6

R2 = 0.7948

real sowing decade 2001

fore

cast

ed s

ow

ing

dat

e 20

01

Results-2001relation between forecasted and real sowing date

R2 = 0.7948

6/5

16/5

26/5

5/6

15/6

25/6

5/7

15/7

25/7

6/5 16/5 26/5 5/6 15/6 25/6 5/7 15/7 25/7


fore

cast

ed s

ow

ing

dat

e 20

01

relation between estimated and real sowing date

R2 = 0.9162

6/5

16/5

26/5

5/6

15/6

25/6

5/7

15/7

25/7

6/5 16/5 26/5 5/6 15/6 25/6 5/7 15/7 25/7


esti

mat

ed s

ow

ing

dat

e 20

01

.

42A


44

Results-2002


R2 = 0.5343

0

5

10

15

20

25

15.5 16 16.5 17 17.5 18 18.5 19 19.5


fore

cas

ted

so

win

g d

ecad

e 2

002


R2 = 0.5343

0

5

10

15

20

25

15.5 16 16.5 17 17.5 18 18.5 19 19.5


fore

cas

ted

so

win

g d

ecad

e 2

002

42B

DEPARTMENT

REAL SOWING DECADE 2002

FORECASTED SOWING DECADE

ESTIMATED SOWING DECADE

Banamba 17 17 16Banco 17 16 15Bankass 19 18 18Dioro 19 18 17Gao 19 20 20Kangaba 17 16 15Kayes 17 18 17Kita 17 18 17Kolokani 17 16 17Mopti 19 18 20Niono 18 18 18Ouelessebougou 17 16 15San 17 18 17Segou 18 17 16Sikasso 16 15 15Soufouroulaye 19 18 19Tombouctou 19 no 20


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Comparison between predictions

ACTUAL STATUS CLIMAG WA FORECAST MARACCHI NCEP IRI PRESAO2001 wet season (terciles) (terciles)

Nature of the wet season

on the whole, normal to Zone 1 Very below average 40/35/25 25/40/35above normal Zone 2 Below average 25/35/40 25/40/35

Zone 3 Below average 20/30/50 25/40/35

Date on onset Zone 1 Late Average Late

average Zone 2 Average Average AverageZone 3 Very late Average Average

Date of termination

Zone 1 LateZone 2 EarlyZone 3 Average

2002 wet season

Nature of the wet season

Zone 1 Below average Average 20/35/45Zone 2 Average Average 30/45/25Zone 3 Average Average 30/45/25

Date on onset Zone 1 Average Very earlyZone 2 Average AverageZone 3 Late Late

Date of termination

Zone 1 AverageZone 2 LateZone 3 Very late

below normal to far below normal in most of the country’s agricultural


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Examples of comparison2002 wet season

Zone 3

Zone 2

Zone 1

Zone 1

Zone 2

Zone 3

Actual situation

CLIMAG WA FORECAST

PRESAO (tercile)

Zone 1 Below average 20/35/45Zone 2 Average 30/45/25Zone 3 Average 30/45/25

Below normal to far below normal

CLIMAG WA FORECAST

PRESAO FORECAST


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MONITORING ACTIVITIES - an added value

• Allows to evaluate the conditions of the wet season on the agricultural and food situation

• Allows to evaluate the conditions and the effectiveness of the Early Warning Systems and of the mechanisms of crisis management


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MONITORING ACTIVITIES - operational tools

• Ex. AGRHYMET Regional Centre Bulletins


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• Satellite images (METEOSAT, NOAA,...)Space-borne information


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• Warnings diffused by Internet

INTERNET


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CONCLUSIONS

• Improving and merging food security systems

• Improve interactions and combinations between food security and seasonal prediction systems

• Using seasonal prediction to define specific inputs (e.g. onset of the growing season)

Documents

Expert meeting on the application of climate forecasts for agriculture 1 The application of climate forecasts and agrometeorological information for agriculture,