Linking Seasonal Forecast To A Crop Yield Model
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Linking Seasonal Forecast To A Crop Yield Model SIMONE SIEVERT DA COSTA DSA/CPTEC/INPE – CNPq [email protected] HOMERO BERGAMASCHI UFRGS First EUROBRISA Workshop 17-19 March 2008 –Paraty, RJ - Brazil
description
Linking Seasonal Forecast To A Crop Yield Model. SIMONE SIEVERT DA COSTA DSA/CPTEC/INPE – CNPq [email protected] HOMERO BERGAMASCHI UFRGS First EUROBRISA Workshop 17-19 March 2008 –Paraty, RJ - Brazil. Talk Outline. Aim Study region Motivation - PowerPoint PPT Presentation
Transcript of Linking Seasonal Forecast To A Crop Yield Model
Linking Seasonal Forecast To A Crop Yield Model
SIMONE SIEVERT DA COSTADSA/CPTEC/INPE – CNPq
[email protected] BERGAMASCHI
UFRGS
First EUROBRISA Workshop 17-19 March 2008 –Paraty, RJ - Brazil
Talk Outline
AimStudy regionMotivationCrop model calibrated for South AmericaMethods (Bias Correction) Results Future Direction
AimProduce maize crop yield prediction based on climate information (seasonal forecasts).
Crop yield model
Climate Forecast
1000
1500
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1989 1991 1993 1995 1997 1999 2001 2003 2005
year
mai
ze g
rain
yie
ld (
kg h
a-1) National Statistics
Maize Grain YieldSource: IBGE
The Area of Study
Rio Grande Do Sul State (RS) “Long River of South”
27.2°- 29.8°S/51.2°- 56.0°W
About Maize in RS…After USA and China, Brazil is the main maize producer in the entire world, and RS is the second
greatest producer nationally (IBGE, 2006).
Sowing Date: Sep/OctHarvest: FebCrop cycle ~130 days
Santa Rosa Passo Fundo
Main producer region(all shaded area in the map)
Bergamaschi et al. 2008
Motivation: Crop and Climate RelationshipCorrelation btw obs.maize yield and rainfall
Data Source:crop yield (IBGE)rainfall (INMET, FEPAGRO)
-2.50
-2.00
-1.50
-1.00
-0.50
0.00
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2.50
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90
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year
star
dis
ed a
no
mal
y
seasonal rainfallrainfall 30 days after tasselingyield
Standardised rainfall and yield anom.
• Rainfall in all cycle (~130-140 days) 0.56
• Rainfall in 0-30 days after tasselling 0.72
http://w3.ufsm.br/solos/boletim3.php
Bergamaschi et al. 2008
1000
1500
2000
2500
3000
3500
4000
1989 1991 1993 1995 1997 1999 2001 2003 2005
year
mai
ze g
rain
yie
ld (k
g ha
-1)
Can crop model to reproduce the interannual variability of maize yield?
Source: IBGE
SOIL WATER
TRANSPIRATION
BIOMASS
LEAF CANOPY
ROOT SYSTEM
Water Stress
Transpiration Efficiency
YIELDYIELD
DevelopmentStage
Yield is a time varying fraction of Biomass
Outputs
Yield GapParameter
Schematic diagram of GLAM (adapted from crop and climate group webpage-Reading)
Crop model: General Large Area Model Challinor et al., 2003 GLAM is a processed
based crop model, which simulates soil water budget, crop plant phenology, canopy growth, root growth, aerial dry mass and grain yield
Crop model: GLAM adapted to RS GLAM were initially tested for
groundnut yield across India (Challinor et al., 2003) , and it was adapted to simulate maize yield for RS (Bergamaschi et.al., 2008 in preparation.).
Calibration GLAM were based on observational data (soil and crop phenology). UFRGS, Eldorado do Sul Site, Brazil.
0
1
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0 600 1200 1800 2400
degree-days
leaf
are
a in
dex
1993/94
1994/95
1995/96
1996/97
1997/98
1998/99
Muller et al., 2005
Input data to GLAM:
SOIL WATER
TRANSPIRATION
BIOMASS
LEAF CANOPY
ROOT SYSTEM
Water Stress
Transpiration Efficiency
YIELDYIELD
DevelopmentStage
Yield is a time varying fraction of Biomass
Outputs
Yield GapParameter
Daily data required:
Solar Radiation
Min. Temperature
Max. Temperature
Rainfall
Schematic diagram of GLAM (adapted from crop and climate group webpage-Reading)
Maize Grain yield Estimative using GLAM and observed weather data
Observed weather data from meteorological site (P, T, Rad.) FEPAGRO, INMET
GLAM
GLAM OBS (IBGE)
• Can GLAM be used to do crop prediction with daily seasonal forecast data?
Seasonal weather data:11 ensemble member ECMWF (single grid point)
0.00E+00
5.00E+03
1.00E+04
1.50E+04
2.00E+04
2.50E+04
0 50 100 150 200 250
days from first day of forecast
Acc
um
ula
ted
rai
nfa
l (m
m)
Seasonal weather data into crop model:
Forecast issued Sep. 1997
Daily Precipitation (a grid point) - 11 ensemble member from ECMWF model- first month of each forecasts initialized in Sep, Oct, Nov, Dec, Jan, Feb. (RS crop cycle)
Rad. & Temp.ObservationDaily mean climatologyfor wet and dry days
(1998 – 2005)
ECMWF Daily Climatology (1998-2005) for crop cycle
Mean Rainfall (mm/day)
sep oct nov dec jan fev month
obs Ensemble mean Indiv. Member
• Monthly Mean Rainfall R :
R(mm d-1) = I(mm wd-1) x f(wd d-1)
intensity frequency
d=daywd=wet day
(Ines and Hansen, 2006)
Rainfall decomposition:
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0 1 2 3 4 5 6 7
inte
nsi
ty (
mm
/wet
day
)
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0.9
1
0 1 2 3 4 5 6 7
Intensity(mm/wd)
Frequency(wd/d)
sep oct nov dec jan fev
month
sep oct nov dec jan fev
month
Obs.Indiv. Member Emsemble Mean
Methods - Bias Correction of daily GCM: -Frequency (wd day-1) – wd = wet day
))~((~ 1 pFFp obsgcmgcm
F(pgcm=0)
F(pobs=0)
c)
P0 – used to truncate the GCM distribution=meanfrequency of rainfall abv p0
matches the obsv. Rainfall.
a)
b)
Daily rainfall (mm)
observation
GCM
Ines and Hansen (2006)Cum
ulat
ive
dist
ribut
ion
func
tion
F(pi)
b)
pgcm p’gcm
pp
pppFFp
i
iimcgobs
i ~0
~)((1
'
Methods - Bias Correction of daily GCM: -intensity (mm wd-1)
Ines and Hansen (2006)
observationGCM
Methods - Bias Correction of daily GCM: -Multiplicative Shift
mcg
obsmcgii p
ppp ,
'
sep oct nov dec jan fev
month
3.5
4.5
5.5
6.5
7.5
8.5
0 1 2 3 4 5 6 7
3.5
4.5
5.5
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0 1 2 3 4 5 6 7
3.5
4.5
5.5
6.5
7.5
8.5
0 1 2 3 4 5 6 7sep oct nov dec jan fev
month
sep oct nov dec jan fev
month
Mean Rainfall(mm/day)
mult. shift
uncorrected
Bias correction
Obs.Indiv. MemberEnsemble Mean
sep oct nov dec jan fev
month
sep oct nov dec jan fev
month
sep oct nov dec jan fev
month
Intensity(mm/wd)
mult. shift
uncorrected
Bias correction
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0 1 2 3 4 5 6 7
Obs.Indiv. MemberEnsemble Mean
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7
9
11
0 1 2 3 4 5 6 7
sep oct nov dec jan fev
month
sep oct nov dec jan fev
month
sep oct nov dec jan fev
month
frequency(wd/d)
mult. shift
uncorrected
Bias correction
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Obs.11 GCM Mem. Mean GCM
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0 1 2 3 4 5 6 7
Hindcast of Yield– Main producer region
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5000
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006
year
yiel
d (
kg h
a-1)
GLAM(OBS) GLAM(Unc).
GLAM(MS) GLAM(IF)
2 std -GLAM(GG transf) 2std+GLAM(GG transf.)
DATA INPUT FORECAST:OBS = observed weather data UNC = uncorrected seasonal forecastMS = seasonal forecast corrected using multiplicative shiftIF = seasonal forecast, intensity & frequency correction method
Future Direction• Statistical downscaling – to take advantage of regional of
seasonal forecast skill (spatial calibration).
• Use of weather generator – to reproduce daily data (temporal disaggregation).
• Use of space-temporal downscaled daily prediction into crop model.
• Compared skill of different crop yield forecast approach (grid point and spatial downscaled data).
Thanks:
• Andrew Challinor (The University of Leeds-UK)
• Caio Coelho (CPTEC- Brazil)
• Homero Bergamaschi (UFRGS, Brazil)
• Tim Wheeler (The University of Reading-UK)
• Jim Hansen (IRI – USA)
• Walter Baethgen (IRI – USA)
