Preliminary Assessment of the Climate Change on Patagonia

Preliminary Assessment of the Climate Change on Patagonia Argentina’s
Agriculture
Nidia Barbero, Cristina Rössler, Pablo Canziani
Programa de Estudio de los Procesos Atmosféricos para el Cambio Global
UCA
Pontificia Universidad Católica Argentina
Consejo Nacional de Investigaciones Cientìficas y Técnicas
Servicio Meterológico de la Armada Argentina
[email protected]
Climate Change Scenarios
Baselines Climatologies
mm
150
130
110
90
70
50
30
10
-10
BARILOCHE
Cdro. RIVADAVIA
TRELEW
Lat 41,09 Lon 71,1 840 m
L a t 4 5, 4 7 L o n 6 7 , 3 4 6 m
Lat 43,12 Lon 65,16 43 m
ºC
30
mm
50
ºC
30
mm
50
ºC
40
40
25
40
30
30
20
25
20
15
30
10
20
5
0
J A S O N D E F MA M J
20
15
20
10
10
5
10
0
0
0
-5
ESQUEL
Lat 51,37 Lon 69,17 19 m
Lat 38,57 Lon 68,08 271m
Lat 42,76 Lon 71,09 785 m
ºC
25
mm
50
20
40
15
60
30
10
40
0
0
10
-5
0
J A S O N D E F MA M J
ºC
35
mm
100
30
80
25
60
20
40
15
20
5
20
NorthEast Patagonia – 2070-2099 Autumn: MAM
-10
NEUQUEN
80
NorthWest Patagonia – 2070-2099 Autumn: MAM
0
J A S O N D E F M A M J
Río GALLEGOS
mm
100
NorthEast Patagonia – 2070-2099 Summer: JFM
10
J A S O N D E F M A M J
ºC
25
20
15
10
5
0
-5
20
10
0
5
J A S O N D E F M A M J
0
J A S O N D E F M A M J
Precipitation
Tmed
Tmax
Tmin
Mean, maximum/minimum temperatures of Patagonia stations (1961-1990)
NEUQUÉN
precipitation
ºC
Lat 38,57 Lon 68,08 271
temp med
900
20
800
700
15
600
500
10
400
Lat 38,57 Lon 68,08 271
12
1000
temp min
10
8
South Patagonia – 2070-2099 Autumn: MAM
4
2
0
J
A
S O
N D
E
F
M A
M J
200
Anual cycle Mountly
values of heliophany for
Neuquén (1961-1990)
100
0
0
67
69
71
73
75
77
79
81
83
85
87
89
91
Historical trends of anual temperatures and
total precipitation for Neuquén
METHODOLOGY AND TOOLS
Scatterplots Temperature vs precipitation changes for
NordWest, NordEast, Nord and South Patagonia’s
zones. GCMs simulations
Historical data
In this presentation we assess the effect of significant climatic variations on agricultural production in Patagonia
and its ultimate impact on agricultural incomes.
The main steps were the:
• collection of base data and information (climatic and agricultural from 1961-1990 period)
• daily temperatures, and precipitation for Neuquén, Bariloche, Esquel, Trelew, Comodoro Rivadavia, Puerto
Gallegos (1961-1990) used.
• provision climate change scenarios for Patagonia based on selected IPCC SRES emission scenarios for 3
period time horizons (2010-2039, 2040-2069, 2070-2100)
• current focus is on climate change scenario construction using GCM output: 7 GCMs climate change
scenarios: ECHAM4, HadCM3, NCAR-PCM, CCCma, CSIRO/Mk2, CCSR/NIES, GFDL-R30 (SERES
scenario available from IPCC-DDC:
)
• 3 experimental regions (in Patagonia) studied.
• making of scatter plots to assist with scenario selection.
• analyses made for each of agricultural factors: crops productions (wheat, apple, pear, oat, fruit), wool
production, crops yield and incomes
• impacts of climate change and agricultural production are evaluated by comparing some important variables
during the two time periods using multiple regression.
• provision socio-economic scenarios for Patagonia relevant to agriculture for the 2080s
• climatic records used in conjunction with the agricultural records for the country using GIS techniques.
Neuquén
Neuquén
Oat
Oat
A popular climatological baseline period is a 30-year “normal period” as defined by the WMO. The current
WMO normal period is 1961-1990, which provides a standard reference for many impact studies
Climate Scenario; refers to a plausible future climate, and a climate change scenario, which implies the
difference between some plausible future climate and the present-day climate, through the terms are used
interchangeably in the scientific literature.
Neuquén
Apple
Tons
CNA
'60
Tons
Yield
Yield
1.200
1.200
1.200
1.000
1.000
1.000
800
800
800
600
600
600
400
400
400
200
200
200
0
Governance
Economic
Production (T)
Yield
Yield (kg/ha)
(kg/ha)
2.000.000
1.000.000
0
Neuquén
Wheat
1.000
2.500
65/66
67/68
69/70
71/72
73/74
75/76
CNA
'60
400
1.000
200
500
20.000
0
0
10.000
89/90
91/92
93/94
95/96
97/98
99/00
01/02
Neuquén
Pear
Number of plants
CNAEFRUT
EFRUT EFRUT EFRUT
EFRUT
EFRUT
'88 '90
'92
'95
'98 '00
'02
Census
2.500.000
2.000.000
60.000
1.500.000
40.000
1.000.000
30.000
500.000
0
0
59/60 61/62 63/64 65/66 67/68 69/70 71/72 73/74 75/76 77/78 79/80 81/82 83/84 85/86 87/88 89/90 91/92 93/94 95/96 97/98 99/00 01/02
Production (T)
Yield (kg/ha)
Number of plants *1000
Planted area (ha)
Gross County Product
Neuquen
Tons
3.000
2.500
2.000
1.500
1.000
500
1960-89. At constant prices of 1970
GCP ($)
8.000
6.000
6.000
4.000
4.000
2.000
2.000
0
00
60
60 6162
616263
63 64
6465
65 66
6667
67 68
6869
6970
70 7172
7172 73
7374
7475
75 76
7677
77 78
7879
79 80
80 8182
8182 83
83 84
84 85
85 86
86 87
87 88
88 89 90
90 91
91 92 93 94 95 96 97 98
98 99 00 01
0102
02
1960
1960
1962
1962
1964
1964
1966
1966
1968
1968
1970
1970
GCP-1
GCP-1
Production
Production of
of Wool
Wool (T)
(T)
1972
1972
1974
1974
1976
1976
1978
1978
1980
1980
GCP-Agriculture
GCP-Agriculture
1982
1982
1984
1984
1986
1986
1988
1988
GCP-Livestock
GCP-Livestock
Crops and sheep distribution
Arándano (Azul)
Castaño (Verde)
Damasco (Rosa)
Grosella (Amarillo)
Almendro (V. Petróleo)
Guindo (Azul)
Membrillo (Amarillo)
Frambuesa (Rojo)
Hasta 20 has
CNA02
Hasta 50 has
CNA02
Uva de Mesa (Violeta)
Avellano (Rojo)
Nogal (Celeste)
Pelón (Negro)
Cerezo (Amarillo)
Hasta 250 has
CNA02
Pear
Apple
Wool
GCP
GCP-1
GCP-Agri
Adjusted R²
Adjusted R²
(with year)
(within year)
0,90
0,75
0,93
0,83 *
0,60
0,65 *
0,18
0,96
0,78
0,50
0,68
0,60
0,89
0,98*
0,33
0,65*
0,18
0,64
0,57
0,52
α=0.05
Manzano (Rojo)
Pera (Amarillo)
Uva Vinífera (Violeta)
Durazno (Celeste)
Ciruelo (Verde)
storylines
Trigo-has (Amarillo)
Avena-has (Rojo)
Ovino-cabezas (Azul)
CNA02
Pear (yield) N=11
Hasta 600-15000 has
CNA02
IMPACT ON AGRICULTURE
•May alter spatial and temporal demands and supply by impacting:
•Trade
87/88
50.000
Planted area
area (ha)
(ha)
Planted
900.000
900.000
800.000
800.000
700.000
700.000
600.000
600.000
500.000
500.000
400.000
400.000
300.000
300.000
200.000
200.000
100.000
100.000
00
Expl variable
year
Mean Temp Autumn
Total Precip Winter
Annual Min Temp
Total Precip Spring
Mean Temp Summer
Total Precip Autumn
Total Precip Spring
Mean Temp Spring
Total Precip Summer
Mean Temp Autumn
Year
Annual Max Temp
N=20
N*=11
B
42,82
-406,54
-6,78
-414,36
4,14
6.729,42
-148,47
168,21
-7.501,98
-8,98
3.886,55
375,83
-1.767,51
St. Err. of B
9,00
133,36
2,80
163,73
1,80
63,28
2,35
1,59
77,93
0,81
104,73
7,79
113,95
t(11)
4,7601128
-3,0484901
-2,4256326
-2,5308542
2,3008250
106,3418611
-63,2220572
106,0870842
-96,2704029
-11,1373181
37,1099941
48,2427588
-15,5118590
p-level
0,0005900
0,0110773
0,0336692
0,0279309
0,0419710
0,0059864
0,0100687
0,0060007
0,0066126
0,0570081
0,0171508
0,0131943
0,0409841
α=0.05
Model Predictions Depend on Future Scenarios
•Profitability
85/86
Yield (kg/ha)
Planted area (ha)
CFRUT
'74
Arandano=Blueberries , Cas taño=Chestnu t, Damasco=Apricot, Grosella=Grooseberries, Alm endro=Almond, Guindo=Cranberries, Me mbrillo=Quince , Frambuesa=Raspberries, U va de Mesa=Grape, A vellano=Haselnut, Nogal=Walnu t, Pe lo n=Nectarin e, Cer ezo=Cherr ies, Manzano=Apple, Pera=P ear,
Viníf era=Grape-wine, Durazno=Peach, Cirue lo=Plu m, Trig o=Whea t, Avena=Oat, Ovin o=She ep(head). Azul= Blu e , Verde=Green , Amarillo=Ye llo w , V. Petro leo=Dark green, Rojo =Red, V iole ta=Violet, C eleste= Light blue , Negro=Black
Local
•Employment and autonomy
83/84
70.000
1.500
Evolution
Evolution of
of the
the livestock
livestock
Stock
Stock and
and Wool
Wool
Neuquen
Neuquen
81/82
80.000
2.000
Yield (kg/ha)
(kg/ha)
Yield
79/80
90.000
600
59/60 61/6263/6465/6667/6869/70
61/6263/6465/6667/6869/70 71/7273/7475/7677/7879/80
71/7273/7475/7677/7879/80 81/8283/8485/8687/8889/90
81/8283/8485/8687/8889/90 91/9293/9495/9697/98
91/9293/9495/9697/98 99/00
99/00 01/02
01/02
59/60
77/78
Production (T)
Number of plants *1000
800
Sheep
Sheep (heads)
(heads)
63/64
Tons
Yield
Tons
Production (T)
(T)
Production
61/62
Planted
Planted area
area(ha)
(ha)
Wheat (yield) N=20
•Stability and sustainability
4.000.000
0
B2
•Food production
EFRUT
'02
00
FACTOR
SRES
EFRUT EFRUT
'98
'00
50.000
Environmental
Development
A2
EFRUT
'95
100.000
FACTOR
B1
EFRUT
'92
3.000.000
Wheat
Oat
Global
EFRUT
'90
150.000
SRES emissions scenarios:
A1
Number of plants
CNA
'88
Census
200.000
59/60
0
0
CFRUT
'74
250.000
59/60 61/62 63/64
63/64 65/66
65/66 67/68
67/68 69/70
69/70 71/72
71/72 73/74
73/74 75/76
75/76 77/78
77/78 79/80
79/80 81/82
81/8283/84
83/84 85/86
85/86 87/88
87/88 89/90
89/90 91/92
91/92 93/94
93/9495/96
95/96 97/98
97/9899/00
99/00 01/02
01/02
Heads
Heads
CLIMATIC AND CLIMATE CHANGE SCENARIOS
North Patagonia – 2070-2099 Autumn: MAM
6
300
5
CCSRNIES
CCCma
CSIRO
GFDL
HADCM3
NEUQUEN
mm
temp max
25
Heliofania (Hs)
Climate change represents a potential threat for the global agricultural. Current climatic, soil and socioeconomic conditions vary widely across the world. Each crop has specific climatic tolerances and optima. A
large number of studies conducted over the past few years for many different sites across the world show few,
if any, conclusions of either the magnitude or sometime even the direction of change and impact for individual
countries or regions as for example Patagonia Argentina. In Patagonia there are few cities and many rural
communities. Farmers have limited financial resources and farming systems with few adaptive technological
opportunities available to limit or reverse adverse climate. As agriculture is one of the most weather-dependent
of all human activities, climate change can make fruit farming, forestry, horticulture and agricultural production
uncertain, posing risks to individual farmers, local, and regional economies. Temporal-spatial variations and
land-use activities exist due to changes in climatic, economic and environmental conditions. However, through
effective systems analysis and planning, the desired land use patterns for adapting to the changing climate
could be obtained. The importance developing new methodologies to evaluate the socio economic impact on
the climatic projection is well established. The goal of this work is to make an exploration of the relationship
between climate and agriculture through economic income. This work attempts to evaluate how the future
scenarios could modify the regional overall harvest, the land use, the diversity of the production and the
regional economics. Temperature and precipitation data provided by a set of Global Climatic Model (GCMs)
considering three scenarios for Southern South Argentina are analyzed to evaluate their ability to represent the
annual fields of both variables, temperature and precipitation. GCMs outputs corresponding to drier, wetter, and
without substantially variations of the scenarios than the current ones were chosen for this study. All global
climate models (GCMs) show warming in response to increased greenhouse gas concentrations, but the
sensitivity of models varies considerably. Precipitation changes occur in both directions. These values are
known as the climate sensitivity. However, they describes a highly idealized situation. Temporal and spatial
changes in present and future variables under the baseline climate and GCMs climate-change scenarios were
mapped and integrated in a Geographical Information System (GIS).They are integrated by combining various
data layers such as climatic, agricultural and socio-economic datasets.
NorthWest Patagonia – 2070-2099 Summer: JFM
CONCLUSIONS
• Different GCMs would produce different regional climate changes. No single GCM model can be taken as correct.
Until GCMs get better at evaluating regional climate, the regional estimates of future environmental resources must
be considered speculative and uncertain.
• Because of the importance of regional and small-scale processes in determining impacts on water resources and
water systems, substantial improvements in methods to downscale climate information continue to be needed.
• Improved GCMs, downscaling methods, and integrated impact assessments are important in further studies.
• The statistic information suggest that others explicative variables should be considered a well in the analyses,
such as technology and selection of species/varieties, etc…
•The study demonstrates that climate variability effects on agricultural in Patagonia are detectable hence future
agricultural activities could indeed depend on the magnitude of global warming and precipitation changes projected
by GCM used in impact analysis studies.