A. Motroni, S. CanuA. Motroni, S. Canu
Climate indicators for assessing Climate indicators for assessing sensitive areas to drought and sensitive areas to drought and
desertification in Sardinia (Italy)desertification in Sardinia (Italy)
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Agrometeorological Service of SardiniaAgrometeorological Service of Sardinia
Applied methodologies:Applied methodologies: Desertification Prone Areas (Pimenta Desertification Prone Areas (Pimenta et alet al., 1997)., 1997)
Environmentally Sensitive Areas (ESAs) to desertificationEnvironmentally Sensitive Areas (ESAs) to desertification ((MEDALUS Project (UE)MEDALUS Project (UE) Kosmas Kosmas et alet al., 1997)., 1997)
Results: Results:
Map of vulnerable areas to desertification (scale 1:250.000)Map of vulnerable areas to desertification (scale 1:250.000) 2001 2001
Map of Environmentally Sensitive Areas to desertification (scale Map of Environmentally Sensitive Areas to desertification (scale 1:100.000) 2004 1:100.000) 2004
In 2000 the Agrometeorological Service of Sardinia started to develop a In 2000 the Agrometeorological Service of Sardinia started to develop a Geographic Information System for assessing and monitoring Environmentally Geographic Information System for assessing and monitoring Environmentally Sensitive Areas to DesertificationSensitive Areas to Desertification
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Parent materialParent material
Soil textureSoil texture
Rock fragmentRock fragment
Soil depthSoil depth
DrainageDrainage
Slope gradientSlope gradient
RainfallRainfall
Aridity indexAridity index
AspectAspect
Fire riskFire risk
Erosion protectionErosion protection
Drought resistanceDrought resistance
Plant coverPlant cover
Land use intensityLand use intensity
Policy Policy
VQIVegetation
Quality Index
SQISoil Quality
Index
CQIClimate
Quality Index
MQIManagement Quality Index
ESAI
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
RainfallRainfall
AspectAspect
CQIClimate
Quality Index
ESAs
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Aridity Aridity indexindex
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Objective: Objective: to show some to show some aridity and aridity and droughtdrought indexes useful for indexes useful for assessing areas sensitive to assessing areas sensitive to desertification processes desertification processes
UNCCDUNCCD(1)(1):“:“Land degradation in arid, Land degradation in arid, semi-arid and dry/sub-humid areas, semi-arid and dry/sub-humid areas, resulting from various factors, resulting from various factors, including climatic variations and including climatic variations and human impacts” (UNEP, 1994)human impacts” (UNEP, 1994)
Definition of “desertification”Definition of “desertification”
(1): United Nations Convention to Combat DesertificationUnited Nations Convention to Combat Desertification
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
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“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
……, i.e. , i.e. desertificationdesertification is is a complex phenomenon a complex phenomenon
strictlystrictly dependent on dependent on climateclimate
Causes of desertification:Causes of desertification:Extreme climatic events: drought/floodsExtreme climatic events: drought/floods
Pressures on the territory: overgrazing, Pressures on the territory: overgrazing, uncontrolled urbanization/country areas uncontrolled urbanization/country areas abandonment…abandonment…
Excessive exploitation of water Excessive exploitation of water resourcesresources
Fires and deforestationFires 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 P/ETo<0.50b) dry/sub-humid 0.50<P/ETo<0.65c) humid and hyper-humid P/ETo>0.65
DESERTIFICATION 0.03 > P/ETo > 0.75 NO DESERTIFICATION
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Carta P/EToCarta P/ETo 4,6% semi-arid4,6% semi-arid
29,8% dry sub-humid29,8% dry sub-humid
7,5% humid7,5% humid
58,1% moist sub-humid58,1% moist sub-humid
Reference period 1961-90Reference period 1961-90
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
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
Climatic dataAbout 200 stations Reference time period: 1961-90
Daily maximum and minimum temperature Daily precipitation
Aridity indexes - Input data
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
Agrometeorological data
•Daily ETo (Hargraves-Samani) •Daily ETa
Bagnouls-Gaussen Index
Originally, ESAs methodologyconsidered the Bagnouls-Gaussen aridity index:
where
BGI = Bagnouls-Gaussen IndexTi = 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!
kPiTiBGIn
i)2(
1
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Number of days/yearwith 2T>P
(climatic mean)
SETaPt
w
Simplified Water Balance
S water surplus
ETa actual evapotranspiration
P precipitation
w soil water content
t time
(Reed et al., 1997)
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
ETofPww iii 11
wi = current soil moisture for the i day
wi-1 = soil moisture in the previous day P = precipitation
ETo = potential evapotranspiration
f = evapotranspiration coefficient f i-1 = wi-1/w*= evapotranspiration coefficient
for the day i-1
w* = Available Water Capacity (AWC)
ETa = f x ETo
*w
wf
evapotranspiration coefficient f
soil water content in a given dayw*w soil available water content (AWC)
Trend of 1980-90 time period for soil water content
0
25
50
75
100
gen-
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lug-
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gen-
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gen-
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lug-
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AW
C -
Ava
ilab
le W
ater
Co
nte
nt
(%)
F.C.
W.P.
threshold
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
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.
Aridity IndexSimplified Water Balance
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
(Simplified Water Balance)
BGI vs. Simplified Water BalanceBGI vs. Simplified Water Balance
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
What has been the trend of What has been the trend of drought in Sardinia for the drought in Sardinia for the last 50 years?last 50 years?
from a static to a dynamic analysisfrom a static to a dynamic analysis
ESAs methodology should be integrated ESAs methodology should be integrated with an analysis of drought eventswith an analysis of drought events
The concept of aridity is already included in the The concept of aridity is already included in the definition of desertification (P/ETo)definition of desertification (P/ETo)
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
The Standardized Precipitation Index,The Standardized Precipitation Index,SPISPI (McKee (McKee et alet al., 1993) ., 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
SPISPI
• Low input data requirement (monthly P)Low input data requirement (monthly P)
Advantages:Advantages:
• Availability of precipitation dataAvailability of precipitation data
• Good territorial distribution of rain gaugesGood territorial distribution of rain gauges
• Easy way to represent drought trendsEasy way to represent drought trends
• Short and long-term drought events Short and long-term drought events are consideredare considered
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
SPI calculationSPI calculation
- 102 rain gauges- 102 rain gauges
- time period:1951-2000- time period:1951-2000
- time scales:1, 3, 6, 12, 24, 48 months- time scales:1, 3, 6, 12, 24, 48 months
Short-term drought Long-term drought
soil moisture conditionsground water, stream flow,reservoir storage
- Procedure to calculate the SPI is very simple. It is calculated by taking the difference of theprecipitation from the mean for a particular time scale, and then dividing it by the standard deviation.
affectaffect affectaffect
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
SPI - Geographic SPI - Geographic distribution distribution
of meteorological of meteorological stationsstations
- Best and longer data series- Best and longer data series
- Homogeneous distribution- Homogeneous distribution
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
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
SPI classes classificationSPI classes classificationThe 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
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Negative trend 3, 12, 24, 48 month SPINegative trend 3, 12, 24, 48 month SPI
Sindia- 48 month Standardized Precipitation Indexy = -0,0048x + 4,4234
-3
-2
-1
0
1
2
3
4
54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98
year
48 m
onth
SPI
Sindia- 24 month Standardized Precipitation Index
y = -0,004x + 3,6889
-3
-2
-1
0
1
2
3
4
52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98
year
24 m
onth
SP
I
Sindia- 12 month Standardized Precipitation Indexy = -0,0033x + 2,9925
-3
-2
-1
0
1
2
3
4
51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99
year
12 m
on
th S
PI
Sindia- 3 month Standardized Precipitation Index
y = -0,0018x + 1,6243
-3
-2
-1
0
1
2
3
4
5
51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99
year
3 m
on
th S
PI
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Positive trend 3, 12, 24, 48 month SPIPositive trend 3, 12, 24, 48 month SPI
Orani- 48 month Standardized Precipitation Index
y = 0,0025x - 2,3176
-3
-2
-1
0
1
2
3
54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99
year
48 m
on
th S
PI
Orani- 24 month Standardized Precipitation Index
y = 0,0015x - 1,3939
-3
-2
-1
0
1
2
3
52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98
year
24 m
onth
SPI
Orani- 12 month Standardized Precipitation Index
y = 0,001x - 0,9081
-3
-2
-1
0
1
2
3
51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99
year
12 m
onth
SPI
Orani- 3 month Standardized Precipitation Index
y = 0,0007x - 0,6352
-3
-2
-1
0
1
2
3
4
51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99year
3 m
onth
SPI
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
In order to estimate SPI trends, In order to estimate SPI trends, angular coefficients for each station angular coefficients for each station and for each time scale were and for each time scale were calculated and spatial interpolated calculated and spatial interpolated (Spline techniques)(Spline techniques)
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Distribution of negative and positive SPI trends
-0,002
-0,0015
-0,001
-0,0005
0
0,0005
0,001
meteorological stations
me
an
an
gu
lar
co
eff
icie
nts
89% -89% -
11% +11% +102 meteorological stations102 meteorological stations
Sardinia - Number of events with SPI<-1
05
101520253035404550556065707580
1953
1955
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
Year
Nu
mb
er
of
ev
en
tsExtreme drought eventsExtreme drought events
3,12, 24, 48 month SPI trend maps3,12, 24, 48 month SPI trend maps
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
“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
ResultsResults
• Negative SPI trends are found for almost all stationsNegative SPI trends are found for almost all stations
• Short time scale (3, 6 months) SPI maps show wider areas with negative Short time scale (3, 6 months) SPI maps show wider areas with negative trends than long time scale (12, 24, 48 months) onestrends than long time scale (12, 24, 48 months) ones
• 24 and 48 month SPI trend maps indicate24 and 48 month SPI trend maps indicate
- Sardinian areas already characterized by drier conditions - Sardinian areas already characterized by drier conditions (semi-arid and dry sub-humid) show a negative trend of precipitation (semi-arid and dry sub-humid) show a negative trend of precipitation in 1951-2000in 1951-2000- Only in some areas (north-east and south-west of Sardinia)Only in some areas (north-east and south-west of Sardinia)precipitation trends are close to remain the same or smoothly increaseprecipitation trends are close to remain the same or smoothly increaseprobably due to rain regimesprobably due to rain regimes
• Extreme drought events are mostly concentrated in the last two decades Extreme drought events are mostly concentrated in the last two decades of 1951-00of 1951-00
- More controversial is the situation in other areasMore controversial is the situation in other areas(central-eastern part of the region, for example)(central-eastern part of the region, for example)
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
Next stepsNext steps
• to calculate an on-line SPI index (short term drought) for drought alert to calculate an on-line SPI index (short term drought) for drought alert taking into account also the 2000-2005 “controversial” periodtaking into account also the 2000-2005 “controversial” period
• to relate SPI calculation results with atmosphere circulation models to relate SPI calculation results with atmosphere circulation models and rain regimesand rain regimes
• to rebalance ESAs desertification methodology with the SPI drought indexto rebalance ESAs desertification methodology with the SPI drought index
“CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE” – Bologna 14-17 June 2005
ConclusionsConclusions
• Drought study and monitoring should be included in any complex model Drought study and monitoring should be included in any complex model of desertification phenomenaof desertification phenomena
• In an already defined climatic area, drought indexes give a better In an already defined climatic area, drought indexes give a better representation of weather effects on desertification than aridity indexes,representation of weather effects on desertification than aridity indexes,because because - climate variability is considered- climate variability is considered- their relation to vegetation biomass - their relation to vegetation biomass fire risk, erosion resistance, etc. fire risk, erosion resistance, etc.
• SPI is a very useful and easy-to-apply drought index for determining SPI is a very useful and easy-to-apply drought index for determining possible climatic areas and weather conditions which can lead to possible climatic areas and weather conditions which can lead to desertification processesdesertification processes
• trends derived from long-time scales (24, 48 months) SPI can be useful trends derived from long-time scales (24, 48 months) SPI can be useful tools for assessing drought-bound areastools for assessing drought-bound areas
Scale Scale of the studyof the study1:100’0001:100’000
Environmentally Sensitive Environmentally Sensitive Areas to desertificationAreas to desertification
GraziGrazi
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