Decision Systems Research developing decision tools IRI Underpinning Activity

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Decision Systems Research developing decision
toolsIRI Underpinning Activity
N. Ward And C. F. Ropelewski
Climate Prediction Applications Workshop Florida
State University 9-11 March 2004
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IRIs 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.
Centrality of exploring and influencing sectoral
decisions, based on information that draws on
sound biophysical science
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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

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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 implementation
• Developing decision support information
• Evaluating and learning through implementation

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Simulating the Expected Improvements in
Reservoir Management Example for Reservoir in
Ceara, NE Brazil (collaboration led by Assis de
Souza Filho, FUNCEME and Upmanu Lall,
IRI additional contributions, especially Sankar
Arumugam, IRI)
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Reliability Yield Curve
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Simulating the benefits of using climate forecast
information in the operation of a reservoir in
Ceara over 1950-2000
Spill (Reliability 0.9)
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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)
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Hydroclimatology
JJAS 30 OND 46

3-months lag correlation ?(Nino3.4,QJJAS)
-0.20 ?(Nino3.4,QOND) -0.51
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Reservoir Operation Strategy
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Need for caution in regions of complex terrain
Statistical Downscaling Results for Sri Lanka,
1951-80 Verification Map shows
correlation skill (shading) along with contours
of elevation
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Mechanism operating in Sri Lanka?
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Example for a Site in Kenya
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
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Examples of Decisions Represented in Farm-level
Models Field-scale crop management
decisions Cultivar selection Planting
date Planting density Amount and timing of
nitrogen fertilizer application Livestock
stocking rates Farm-scale management
decisions Land allocation among crops Feed
management (pasture planting and fodder
purchase) Borrowing for production inputs
(planned) Allocation of household labor among
farm vs. non-farm activities (planned)
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Greater Horn of Africa Project
Objective 2 Tailored Products
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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 Correla
tion between predicted and observed 0.84
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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 elevation on 1 lat x 1 lon grid
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(No Transcript)
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http//iri.columbia.edu/outreach
IRI is exploring the potential of online courses
Example of page From online course Collaboration
with CCNMTL
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Advanced Training Institute on Climatic
Variability and Food Security Palisades, New
York, USA 8 - 26 July 2002
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
Dr. James Hansen, Training Institute Director
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• Examples of the underpinning activity within
  emerging
• end-to-end regional projects
• Methodological advances to contribute to faster
• implementation in other regions

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The forecasting principle Early warning for
changes in the incidence of the meningococcal
meningitis disease (Example for West African
Sahel region)
Early_______________ Peak_________________Late____
__________
7000
6000
5000
4000
Cases
-Rain______
3000
-Rain______
2000
Dust
1000
0
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9
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2
9
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30
6
13
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January
Dec.
February
March
April
May
Date
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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 day as
predicted by the model for 1983 relative to 1971
Illustrates the Need for caution In regions of
Complex terrain