Transcript Diapositiva 1

Climate indicators for assessing
sensitive areas to drought and
desertification in Sardinia (Italy)
A. Motroni, S. Canu
Agrometeorological Service of Sardinia
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
In 2000 the Agrometeorological Service of Sardinia started to develop a
Geographic Information System for assessing and monitoring Environmentally
Sensitive Areas to Desertification
Applied methodologies:
Desertification Prone Areas (Pimenta et al., 1997)
Environmentally Sensitive Areas (ESAs) to desertification
(MEDALUS Project (UE) Kosmas et al., 1997)
Results:
Map of vulnerable areas to desertification (scale 1:250.000) 2001
Map of Environmentally Sensitive Areas to desertification (scale
1:100.000) 2004
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Parent material
Soil texture
Rock fragment
Soil depth
SQI
Soil Quality
Index
Drainage
Slope gradient
Rainfall
Aridity index
Aspect
Fire risk
Erosion protection
Drought resistance
Plant cover
Land use intensity
Policy
CQI
Climate
Quality
Index
VQI
Vegetation
Quality
Index
MQI
Management
Quality
Index
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
ESAI
Rainfall
Aridity index
Aspect
CQI
Climate
Quality
Index
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
ESAs
Objective:
to show some aridity and
drought indexes useful for
assessing areas sensitive to
desertification processes
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Definition of “desertification”
____
UNCCD(1):“Land degradation in arid,
semi-arid and dry/sub-humid
_________
______________ areas,
resulting
from
various
factors,
including __________________
climatic variations and
human impacts” (UNEP, 1994)
(1): United Nations Convention to Combat Desertification
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
…, i.e. desertification is
a complex phenomenon
strictly dependent on
climate
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Causes of desertification:
Extreme climatic events: drought/floods
Pressures on the territory: overgrazing,
uncontrolled urbanization/country areas
abandonment…
Excessive exploitation of water
resources
Fires and deforestation
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Atmospheric conditions characterizing a desert climate lead to severe
water deficit, i.e. potential evapotranspiration (ETo) values higher than
precipitation values. Such conditions are calculated by several indices, the
most used one is
The bioclimatic index FAO-UNEP (1997), P/ETo.
Considering this index, the sensible areas to desertification can be
classified as follow:
a) arid and semi arid
b) dry/sub-humid
c) humid and hyper-humid
DESERTIFICATION
P/ETo<0.50
0.50<P/ETo<0.65
P/ETo>0.65
0.03 > P/ETo > 0.75
NO DESERTIFICATION
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Reference period 1961-90
Carta P/ETo
4,6% semi-arid
29,8% dry sub-humid
58,1% moist sub-humid
7,5% humid
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Aridity indexes:
Bagnouls-Gaussen Index
(meteorological deficit)

Simplified Water Balance Index
(hydrological deficit)

Drought index

Standardized Precipitation Index
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Aridity indexes - Input data
Climatic data
About 200 stations
Reference time period: 1961-90

Daily maximum and minimum temperature
Daily precipitation

Agrometeorological data
•Daily ETo (Hargraves-Samani)
•Daily ETa
Interpolation techniques
temperature -> multi-linear
regression with residuals Kriging
precipitation->Kriging/Co-kriging
Pedological data
•AWC data based on
soil type, texture, soil depth,
chemical composition
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Bagnouls-Gaussen Index
Originally, ESAs methodology
considered the Bagnouls-Gaussen
aridity index:
Number of days/year
with 2T>P
(climatic mean)
n
BGI   (2Ti  Pi) k
i 1
where
BGI = Bagnouls-Gaussen Index
Ti = Temperature of the i month (°C)
Pi = Total monthly precipitation of the month i (mm)
K = Frequency of the condition 2Ti-Pi>0 for the i month (%)
In this way, the soil component
is not considered!
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Simplified Water Balance
w
 P  ETa  S
t
ETa = f
x
ETo
w soil water content
t time
S water surplus
P precipitation
ETa actual evapotranspiration
wi  wi 1  P  fi 1 ETo
wi = current soil moisture for the i day
wi-1 = soil moisture in the previous day
w
f 
w*
f
evapotranspiration coefficient
w
soil water content in a given day
w*
soil available water content (AWC)
P = precipitation
ETo = potential evapotranspiration
f = evapotranspiration coefficient
f
i-1
= wi-1/w*= evapotranspiration coefficient
for the day i-1
(Reed et al., 1997)
w* = Available Water Capacity (AWC)
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Aridity Index
Simplified Water Balance
For each year, aridity index values have been
estimated computing the number of days in
which soil humidity values were below different
thresholds of AWC (0%, 10%,25%, 50%, 75%).
The 50% threshold was used for calculating the
aridity index in order to avoid over and
underestimates of the index and to obtain a good
spatial variability.
F.C. 100
75
t hr e shol d
50
25
lug-90
lug-89
gen-90
lug-88
gen-89
lug-87
gen-88
lug-86
gen-87
gen-86
lug-85
lug-84
gen-85
gen-84
lug-83
lug-82
gen-83
lug-81
0
gen-82
W.P.
gen-81
AWC - Available Water Content (%)
Trend of 1980-90 time period for soil water content
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
BGI vs. Simplified Water Balance
(Simplified Water Balance)
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
The concept of aridity is already included in the
definition of desertification (P/ETo)
from a static to a dynamic analysis
ESAs methodology should be integrated
with an analysis of drought events
What has been the trend of
drought in Sardinia for the
last 50 years?
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The Standardized Precipitation Index,
SPI (McKee et al., 1993)
Standardized Precipitation Index (SPI) is a probability index that considers only
precipitation.
•The SPI is computed for several time scales, ranging from 1 month to 48 months, to
capture the various scales of both short-term and long-term drought.
•These time scales reflect the impact of drought on the availability of the
different water resources.
•Positive SPI values indicate greater than median precipitation, while negative values
indicate less than median precipitation. A drought event occurs any time the SPI is
continuously negative and reaches an intensity where the SPI is -1.0 or less. The event
ends when the SPI becomes positive.
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
SPI
Advantages:
• Low input data requirement (monthly P)
• Availability of precipitation data
• Good territorial distribution of rain gauges
• Easy way to represent drought trends
• Short and long-term drought events
are considered
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
SPI calculation
- Procedure to calculate the SPI is very simple. It is calculated by taking the difference of the
precipitation from the mean for a particular time scale, and then dividing it by the standard
deviation.
- 102 rain gauges
- time period:1951-2000
- time scales:1, 3, 6, 12, 24, 48 months
Short-term drought
affect
Long-term drought
affect
soil moisture conditions
ground water,
stream flow,
reservoir storage
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
SPI - Geographic
distribution
of meteorological
stations
- Best and longer data series
- Homogeneous distribution
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
SPI classes classification
The index is negative for drought, and positive for wet conditions. (<-2 / >+2)
As the dry or wet conditions become more severe, the index becomes
more negative or positive
SPI value
Class
>2 or greater
Extremely wet
1.50 to 1.99
Very wet
1.00 to 1.49
Moderately wet
-0.99 to 0.99
Near normal
-1.49 to -1.00
Moderately dry
-1.99 to -1.50
Severely dry
-2.00 and less
Extremely dry
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Negative trend 3, 12, 24, 48 month SPI
Sindia- 3 month Standardized Precipitation Index
Sindia- 12 month Standardized Precipitation Index
y = -0,0033x + 2,9925
y = -0,0018x + 1,6243
4
5
3
4
2
12 month SPI
3 month SPI
3
2
1
0
1
0
-1
year
99
97
95
93
91
89
87
85
83
81
79
77
75
73
71
69
67
65
63
61
59
57
55
51
99
97
95
93
91
89
87
85
83
81
79
77
75
73
71
69
67
65
63
61
59
57
-3
55
-3
53
-2
51
-2
53
-1
year
Sindia- 24 month Standardized Precipitation Index
Sindia- 48 month Standardized Precipitation Index
y = -0,0048x + 4,4234
4
3
3
2
2
year
year
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
98
96
94
92
90
88
86
84
82
80
78
76
74
72
70
68
66
64
62
98
96
94
92
90
88
86
84
82
80
78
76
74
72
70
68
66
64
-3
62
-3
60
-2
58
-2
56
-1
54
-1
60
0
58
0
1
56
1
54
48 month SPI
4
52
24 month SPI
y = -0,004x + 3,6889
Positive trend 3, 12, 24, 48 month SPI
Orani- 3 month Standardized Precipitation Index
Orani- 12 month Standardized Precipitation Index
y = 0,001x - 0,9081
y = 0,0007x - 0,6352
4
3
3
2
year
99
97
95
93
91
89
87
85
83
81
79
77
75
73
71
69
67
65
63
61
99
97
95
93
91
89
87
85
83
81
79
77
75
73
71
69
67
65
63
61
59
57
-3
55
-3
53
-2
51
-2
59
-1
57
-1
0
55
0
1
53
12 month SPI
1
51
3 month SPI
2
year
Orani- 24 month Standardized Precipitation Index
Orani- 48 month Standardized Precipitation Index
y = 0,0015x - 1,3939
2
2
year
year
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
99
96
93
90
87
84
81
78
75
72
69
98
96
94
92
90
88
86
84
82
80
78
76
74
72
70
68
66
64
62
60
-3
58
-3
56
-2
54
-2
66
-1
63
-1
0
60
0
1
57
1
54
48 month SPI
3
52
24 month SPI
y = 0,0025x - 2,3176
3
In order to estimate
angular coefficients for
and for each time
calculated and spatial
(Spline techniques)
SPI trends,
each station
scale were
interpolated
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Distribution of negative and positive SPI trends
mean angular coefficients
0,001
0,0005
0
-0,0005
-0,001
-0,0015
-0,002
meteorological stations
102 meteorological stations
89% 11% +
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Year
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
99
97
95
93
91
89
87
85
83
81
79
77
75
73
71
69
67
65
63
61
59
57
55
53
Number of events
Extreme drought events
Sardinia - Number of events with SPI<-1
80
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
3,12, 24, 48 month SPI trend maps
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Mean 1951-00 rainfall total
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Results
• Negative SPI trends are found for almost all stations
• Extreme drought events are mostly concentrated in the last two decades
of 1951-00
• Short time scale (3, 6 months) SPI maps show wider areas with negative
trends than long time scale (12, 24, 48 months) ones
• 24 and 48 month SPI trend maps indicate
- Sardinian areas already characterized by drier conditions
(semi-arid and dry sub-humid) show a negative trend of precipitation
in 1951-2000
- Only in some areas (north-east and south-west of Sardinia)
precipitation trends are close to remain the same or smoothly increase
probably due to rain regimes
- More controversial is the situation in other areas
(central-eastern part of the region, for example)
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Next steps
• to rebalance ESAs desertification methodology with the SPI drought index
• to calculate an on-line SPI index (short term drought) for drought alert
taking into account also the 2000-2005 “controversial” period
• to relate SPI calculation results with atmosphere circulation models
and rain regimes
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Conclusions
• Drought study and monitoring should be included in any complex model
of desertification phenomena
• In an already defined climatic area, drought indexes give a better
representation of weather effects on desertification than aridity indexes,
because
- climate variability is considered
- their relation to vegetation biomass  fire risk, erosion resistance, etc.
• SPI is a very useful and easy-to-apply drought index for determining
possible climatic areas and weather conditions which can lead to
desertification processes
• trends derived from long-time scales (24, 48 months) SPI can be useful
tools for assessing drought-bound areas
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Environmentally Sensitive
Areas to desertification
Scale
of the study
1:100’000
mailto:[email protected]
www.sar.sardegna.it