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REVIEW OF CURRENT AGROMETEOROLOGICAL APPLICATIONSREVIEW OF CURRENT AGROMETEOROLOGICAL APPLICATIONS AND FUTURE POTENTIAL IN URUGUAYAND FUTURE POTENTIAL IN URUGUAY
(MSc) Mario BidegainDirección Nacional de Meteorología
Uruguay
Regional Technical Meeting on CLIPS and Agrometeorological Applications for the Mercosur Countries
13 - 16 July 2005, Campinas – Sao Paulo - BRAZIL
Country background
•Uruguay is located in the south-eastern part of South America, located between 30° to 35° South and 53° to 58° West. •It is bordered on the west by Argentina, on the north and north-east by Brazil, on the south by the Río de la Plata, and on the east by the Atlantic Ocean, covering 176,000 km2. •Montevideo, the capital city, and most of Uruguay’s population are located on the coast of the Río de la Plata.
Topography
•The country consists mostly (75%) of gently rolling plateau, interrupted at two points by low hilly ridges (maximum heigth 500 mts). The remainder of the country is fertile coastal lowlands, including a narrow coastal plain and the somewhat broader littorals of the Río de la Plata and Río Uruguay.
Cultures and productions http://www.mgap.gub.uy/Diea/Anuario2004/indice_mapas.htm
Red: agriculture (maize, soybean, wheat and barley)
Green: grassland and meat production
Grey: Dairy production
Purple: Fruits and vegetables
Blue: Rice
Yellow: Citrics
Brown: Forest
National meteorological network
Agrometeorological products produced by D.N.M.
(Dirección Nacional de Meteorología)(http://www.meteorologia.com.uy)
• Daily rainfall bulletin (web page)
• Decade, monthly and annual climate statistics.
• Climate maps (rainfall, mean and extreme temperatures, rainfall days, anomalies, etc.) (web page)
• Users tailored reports• Water balance and soil
moisture monitoring.• Diagnosis of climate anomalies• Forecasting of seasonal climate
(web page)• Routinely weather forecasting
(web page)
Agrometeorological products produced by I.N.I.A.(National Institute for Agriculture Research)
http://www.inia.org.uy/disciplinas/agroclima/index.html
• Climate monthly maps of accumulated rainfall, and anomalies. (web page)
• Water balance and soil moisture monitoring. (web page)
• NDVI monthly vegetation monitoring (web page)
Regional and National research related to agrometeorology
1. Projects funded by the Inter-American Institute for Global Change (IAI)
ISP II 1997 – 2000
Climate Variability and Agriculture in Argentina and Uruguay: Assessment of ENSO Effects and Climate Variability and Agriculture in Argentina and Uruguay: Assessment of ENSO Effects and Perspectives for the use of Climate Forecast. Perspectives for the use of Climate Forecast.
Countries: Argentina – Uruguay – USA
CRN 2000-2005
Development of a Collaborative Research Network for the Study of Regional Climate Variability, Development of a Collaborative Research Network for the Study of Regional Climate Variability, Changes and Prediction in the MERCOSUR Area.Changes and Prediction in the MERCOSUR Area.
Countries: Argentina – Brazil - Paraguay - Uruguay – USA
SAAC: An International Consortium for the Study of Global and Climate Changes in the western SAAC: An International Consortium for the Study of Global and Climate Changes in the western south Atlantic.south Atlantic.
Countries: Argentina – Brazil - Uruguay – USA
1. Project funded by INIA (Uruguayan Government and Private Sector)FPTA 2000-2002
Applications of Climate Forecast in the agricultural SectorApplications of Climate Forecast in the agricultural Sector
3. Project funded by MVOTMA (Climate Change Unit)2004-2005
Developed of Climate Change scenarios at regional and national scalesDeveloped of Climate Change scenarios at regional and national scales
Some findings from research related to agriculture
Precipitation regional anomalies
El Niño (warm phase) La Niña (cold phase)
-75 -70 -65 -60 -55 -50 -45 -40 -35
-40
-35
-30
-25
-20
-15
-10
0
30
60
90
120
150
180
210
240
270
300
330
360
390
420
450
-75 -70 -65 -60 -55 -50 -45 -40 -35
-40
-35
-30
-25
-20
-15
-10
0
30
60
90
120
150
180
210
240
270
300
330
360
390
420
450
Reference: Dr. V.Barros (U.B.A.)
Some findings from research related to agriculture
IAI ISPII Climate Variability and Agriculture: Assessment of ENSO Effects IAI ISPII Climate Variability and Agriculture: Assessment of ENSO Effects
Precipitation anomalies Maximum mean temperature anomalies
-200
010
0
Mercedes Warm-Neutral
mm
-200
010
0
Mercedes Cold-Neutral
mm
-2-1
01
2
Mercedes Warm-Neutral
°C
-2-1
01
2
Mercedes Cold-Neutral
°C
A M J J A S O N D J F M A M J J A S A M J J A S O N D J F M A M J J A S
Some findings from research related to agriculture
IAI ISPII Climate Variability and Agriculture: Assessment of ENSO Effects IAI ISPII Climate Variability and Agriculture: Assessment of ENSO Effects
Maize (short season) in Uruguay (EELE) and ENSO:Sowing Date = 15 September (Generated weather)
Maize Yield (kg/ha)
0 2000 4000 6000 8000 10000 12000 14000
Cu
mu
lati
ve F
req
uen
cy (
%)
0
10
20
30
40
50
60
70
80
90
100
All YearsEl Niño YearsLa Niña Years
Maize Yield (kg/ha)
0 2000 4000 6000 8000 10000 12000 14000
Cu
mu
lati
ve F
req
uen
cy (
%)
0
10
20
30
40
50
60
70
80
90
100
All YearsEl Niño YearsLa Niña Years
All YearsEl Niño YearsLa Niña Years
Maize (short season) in Uruguay (EELE) and ENSO:Sowing Date = 15 December (Generated weather)
MAIZE: EELEShort Season Hybrid
Using 400 years of generated weather data per ENSO phase
Dr. W. Baethgen (IFDC)
IAI CRN N°055 “IAI CRN N°055 “Development of a Collaborative Research Network for the Development of a Collaborative Research Network for the Study of Regional Climate Variability, Changes and Prediction in the Study of Regional Climate Variability, Changes and Prediction in the
MERCOSUR Area”MERCOSUR Area”
Probabilities of MaximumTemperatures over 35º. Uruguay - Paysandú 1935-2000
0,0
0,5
1,0
1,5
2,0
2,5
44 43 42 41 40 39 38 37 36 35
Fre
qu
ency
%
NIño
Niña
Probabilities of Minimum Temperatures below to 0°C. Uruguay - Paysandú 1935-2000
0,0
0,5
1,0
1,5
2,0
2,5
-0,5 -1,5 -2,5 -3,5 -4,5 -5,5 -6,5 -7,5
Freq
uenc
y %
Niños
Niñas
• Higher maximum extremes (above 35° C) values during cold ENSO phase, produce a increase probability of temperatures over threshold, and higher chance of warm spells during summer.
• Lower maximum extremes (above 35° C) values during warm ENSO phase, reduce the probability of of warm spells during summer.
• Lower minimum extremes (below 0° C) values during cold ENSO phase, produce a increase probability of temperatures below zero (frost), and shorter frost free period and higher chance of an later frost in the year.
• Higher minimum extremes during the warm ENSO phase produce lower amount of days with frost, longer frost-free period and lower probability of early and later frosts.
IAI CRN N°055 “IAI CRN N°055 “Development of a Collaborative Research Network for the Development of a Collaborative Research Network for the Study of Regional Climate Variability, Changes and Prediction in the Study of Regional Climate Variability, Changes and Prediction in the
MERCOSUR Area”MERCOSUR Area”Numerical Weather Prediction with WRF mesoscale modelNumerical Weather Prediction with WRF mesoscale model
http://meteo.fisica.edu.uy
Project UNDP Project UNDP ““Development of climate change scenarios Development of climate change scenarios at regional and national scale”at regional and national scale”
Changes in Precipitation 2050 (%) Changes in temperature 2050 (° C) Changes in Precipitation 2050 (%) Changes in temperature 2050 (° C)
-63 -61 -59 -57 -55 -53 -51 -49 -47 -45-40
-38
-36
-34
-32
-30
-28
-26
-24
-22
-20
CAMBIO EN LA PRECIPITACION ANUAL (% )PARA LA DECADA DE 2050
SEGUN EL ESCENARIO SRES A2
-63 -61 -59 -57 -55 -53 -51 -49 -47 -45-40
-38
-36
-34
-32
-30
-28
-26
-24
-22
-20
CAMBIO EN LA TEMPERATURA MEDIA ANUAL (° C)PARA LA DECADA DE 2050
SEGUN EL ESCENARIO SRES A2
Future potential use of agrometeorology information
• Operational rainfall estimation (satellite and observed) at higher scales (4 x 4 km)
• Improved climate seasonal forecasting (better climate models and “ensemble” techniques) as input for yields models.
• Improved weather forecasting at meso and local scale (< 15 km)
• Improved grain production models (ex. DSSAT) for simulate yields
• Use of grassland models (ex. SPUR) for milk and meat production
• Improved satellite images for use in vegetation monitoring and flood areas (ex. MODIS)
• New improved climate change scenarios at mesoscale (ex. PRECIS)