INTERNATIONAL JOURNAL OF CLIMATOLOGY

Int. J. Climatol. 24: 907–922 (2004)

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/joc.1038

TREND ANALYSIS OF PRECIPITATION AND DROUGHT IN BASILICATAFROM 1923 TO 2000 WITHIN A SOUTHERN ITALY CONTEXT

MARCO PICCARRETA, DOMENICO CAPOLONGO* and FEDERICO BOENZI

Department of Geology and Geophysics, University of Study of Bari, Bari, Italy

Received 12 April 2003Revised 4 February 2004

Accepted 12 February 2004

ABSTRACT

Series of annual, monthly and seasonal precipitation from 50 stations, distributed all over the Basilicata territory (southernItaly), were studied for the period 1923–2000. All the series were checked for homogeneity using MASH v. 1.0.1 softwareand the time series analysis was performed with the Mann–Kendall nonparametric test in order to detect possible trends.The results show that the annual total rainfall decreased by about 156 mm during the period investigated; the decreasebecomes stronger in the last 30 years. There are considerably different trends for the different seasons. In particular, onlythe winter trend, which is downward, is statistically significant. In this season the total rainfall has decreased by about133 mm. For this same period the standardized precipitation index (SPI) for multiple time scales of 12, 24 and 48 monthshas been computed. It appears that periods of drought have been quite frequent starting from 1975, with SPI rangingfrom about −1 to about −3. Copyright 2004 Royal Meteorological Society.

KEY WORDS: precipitation; homogeneity; trend analysis; drought; standardized precipitation index; Basilicata; southern Italy

1. INTRODUCTION

In recent years, several studies have been devoted to the interannual and decadal variability of precipitationin Mediterranean areas at regional, national and international scales. Different methodological approacheshave been used, such as rainfall temporal analysis (e.g. Serrano et al., 1999; De Luıs et al., 2000; Estrelaet al., 2000; Agnese et al., 2002; Turkes and Erlat, 2003), extreme events in regional daily precipitation seriesanalysis (e.g. Brunetti et al., 2002), and the standardized precipitation index (e.g. Delitala et al., 2000; Lanaet al., 2001; Bordi et al., 2001; Lloyd-Hughes and Saunders, 2002). It emerges that the Mediterranean regionhas been affected by severe and more or less prolonged periods of drought in the last 50 years.

In southern Italy, water scarcity has become increasingly problematic. The critical situation involves allindustries, especially agriculture.

According to Brunetti et al. (2004), the data from several rain gauges distributed all over Italy during along span of time (1866–1995) indicate a strong decrease in precipitation, especially in the last 50 years:61 mm in northern Italy; 135 mm in central and southern Italy. The trend is negative in spring, summer andautumn, whereas in winter it is positive in northern Italy and statistically nonsignificant for the rest of Italy.

The present study is part of a wider research effort aimed at defining the relationships betweenmeteorological conditions, nature of cropping out terrains and landscape evolution. Starting from the collectionof precipitation data from 50 pluviometric stations, this paper deals with their homogenization and analysis,in order to detect possible trends and to define drought conditions in Basilicata (southern Italy) up to the endof the 20th century. This region is 9992.24 km2 in size and is characterized by widespread forms of landdegradation (badlands), due to rainfall erosivity, including rills, gullies, debris flows and loss of soil fertility.

* Correspondence to: Domenico Capolongo, Dipartimento di Geologia e Geofisica, Universita degli Studi di Bari, Via Orabona, 4,Bari, Italy; e-mail: capolongo@geo.uniba.it

Copyright 2004 Royal Meteorological Society

908 M. PICCARRETA, D. CAPOLONGO AND F. BOENZI

The results obtained from this study can be expected to extend at least partially to the contiguous regions.A comparison with the conditions derived for other insular and southern Italy regions (Apulia, Sardinia andSicily) will be made to give a more comprehensive picture of an area in the middle of the Mediterranean region.

2. DATA AND METHODS

The pluviometric data analysed in this paper were collected from the National Hydrographic Service (SI).Time series of 50 pluviometric stations (Figure 1) distributed over the whole region were analysed for

the period 1923–2000. Where time series have missing data, single and multiple linear regressions wereperformed using the available data to fill in the gaps (e.g. Eischeid et al., 2000).

The method followed here has been used to calculate:

1. The matrix of the correlation coefficients among the variables under study.2. A multiple regression among the most correlated variables.3. The missing climatic data.

Time series were homogenized using the multiple analysis of series for homogenization (MASH) method byadopting the cumulative model of the software MASH v. 1.0.1 (Szentimrey, 1999, 2000) in order to eliminatenatural inhomogeneity, which could affect tests for trend by introduction of systematic errors. It is a relative

Figure 1. Geographical location of the selected meteorological stations and their position in regional groups used for homogenizationof monthly precipitation totals by the MASH method. Numbers refer to the stations reported in Table I

Copyright 2004 Royal Meteorological Society Int. J. Climatol. 24: 907–922 (2004)

PRECIPITATION AND DROUGHT IN SOUTHERN ITALY 909

homogeneity test procedure that does not assume the reference series are homogeneous (Peterson et al., 1998).Possible break points and shifts can be detected and adjusted through mutual comparison of series within thesame climatic area. The candidate series is chosen from the available time series and the remaining series areconsidered as reference series. During the procedure, the role of the series changes step by step.

The monthly and annual total rainfall for each pluviometric station was calculated for the period 1923–2000(Table I); the regional averages were obtained by summing monthly and annual means during the span oftime considered (78 years).

According to the World Meteorological Organization (WMO), the normal precipitation at a given stationcan be assumed as the mean of the annual precipitation over a 30 year period; accordingly, the series understudy have been subdivided into three periods 1923–52, 1947–76, 1971–2000, in order to consider the whole

Table I. List of the stations, their mean precipitation values for the period 1923–2000 and climate normals from 1931to 2000

Mean precipitationfor 1923–2000 (mm)

Normal1931–60

Normal1941–70

Normal1951–80

Normal1961–90

Normal1971–2000

1 Acquafredda 1238.18 1312.22 1245.88 1231.80 1221.77 1162.972 Agromonte 1289.88 1441.29 1398.73 1351.92 1227.47 1171.413 Albano di Lucania 803.07 898.89 821.12 786.66 729.96 739.584 Aliano 713.13 718.55 718.72 745.18 731.73 727.725 Armento 835.39 882.86 883.83 905.78 849.86 822.446 Bernalda 614.58 713.02 697.33 662.28 578.98 533.627 Calciano 622.82 715.58 651.58 639.87 597.49 549.488 Calvello 937.48 950.92 878.90 889.94 868.47 852.869 Cancellara 633.93 731.73 711.97 674.07 546.79 466.31

10 Carbone 1049.05 1096.08 1055.56 1050.91 1028.90 1026.8511 Cersosimo 1012.30 1088.02 1067.05 1095.50 1028.77 980.5512 Cogliandrino 1502.71 1649.56 1600.06 1573.66 1513.82 1391.0313 Cognato 886.34 966.96 897.83 900.23 860.68 825.6814 Ferrandina 568.98 638.16 569.07 570.01 519.34 513.9015 Grancia 838.48 817.66 807.14 856.25 879.18 904.4416 Grassano 621.33 695.34 686.91 675.00 550.93 521.0017 Grottole 572.54 647.38 616.03 619.43 513.78 521.2918 Irsina 582.27 567.77 559.15 626.03 593.79 581.5119 Lagonegro 1845.32 1840.75 1802.07 1821.99 1748.23 1633.7020 Lauria Inferiore 1641.82 1713.84 1726.39 1738.83 1597.85 1476.0621 Maratea 1357.83 1520.55 1399.05 1340.62 1289.62 1224.5122 Matera 543.50 570.67 555.50 596.92 533.71 503.9323 Metaponto 508.26 611.85 573.61 590.57 471.94 462.7224 Mezzana di Lucania 1289.45 1376.12 1512.18 1496.13 1301.26 1113.0025 Missanello 820.60 740.77 746.28 801.14 819.55 878.4626 Montalbano Jonico 643.69 731.34 660.33 666.54 601.87 581.8027 Noepoli 760.20 875.65 877.70 839.07 716.44 639.8028 Nova Siri 744.39 847.90 826.75 777.59 670.65 622.0329 Nova Siri Scalo 562.97 595.16 568.74 579.80 546.10 535.6830 Palazzo San Gervasio 595.72 666.36 621.54 657.09 575.82 547.5931 Pisticci 644.76 738.18 720.47 661.21 569.52 540.9432 Pomarico 601.43 762.86 729.61 669.81 513.41 418.3833 Policoro 571.62 625.25 577.46 573.34 555.74 542.2334 Potenza 756.44 735.43 736.20 767.32 747.39 697.3635 Roccanova 723.68 757.41 733.87 743.59 719.40 706.53

(continued overleaf )

Copyright 2004 Royal Meteorological Society Int. J. Climatol. 24: 907–922 (2004)

910 M. PICCARRETA, D. CAPOLONGO AND F. BOENZI

Table I. (Continued )

Mean precipitationfor 1923–2000 (mm)

Normal1931–60

Normal1941–70

Normal1951–80

Normal1961–90

Normal1971–2000

36 Salandra 692.95 758.65 739.31 773.30 654.56 625.6637 San Giorgio lucano 788.66 867.73 821.52 850.27 783.48 738.1838 San Nicola d’Avigliano 699.58 731.66 696.30 709.54 669.25 671.2139 San Martino d’Agri 760.77 811.29 808.17 806.12 775.94 753.2640 San Mauro forte 691.96 685.77 674.99 720.38 682.68 684.8641 San Severino lucano 1383.03 1687.04 1716.42 1726.93 1614.65 1487.3242 Senise 774.88 867.43 815.27 755.37 719.24 688.3043 Stigliano 751.85 825.49 780.81 774.84 737.49 694.4044 Tolve 628.89 650.51 612.82 632.44 604.93 605.0645 Tramutola 1159.47 1188.35 1180.05 1186.99 1164.63 1132.0746 Trecchina 1975.94 2180.47 2067.22 2027.27 1900.44 1695.2447 Tricarico 623.16 652.76 631.28 666.38 620.68 601.2848 Tursi 753.48 793.24 781.90 738.50 701.28 725.5949 Vaglio di Lucania 757.64 710.01 670.63 726.76 784.56 826.8750 Valsinni 758.17 774.01 729.08 733.56 737.54 734.86

Basilicata 862.22 922.67 894.17 895.14 836.28 800.52

data set. The 30-year averages for 1923–52, 1947–76, and 1971–2000 were calculated in order to analysethe rainfall anomalies.

A climate normal is defined, by convention, as the arithmetic mean of a climatological element computedover three consecutive decades (WMO, 1989). So, to make this work more compliant with the WMO definitionof a climate normal, we have considered five climate normals: 1931–60, 1941–70, 1951–80, 1961–90,1971–2000 (Table I).

A seasonal amount was obtained by summing rainfall quantity during the quarters, starting from winter(seasons were defined using the standard meteorological definition, i.e. winter is December, Januaryand February).

For all the parameters considered, i.e. seasonal, annual and monthly average precipitation from 1923 to2000, the Mann–Kendall nonparametric test, as described by Sneyers (1990), was applied in order to detectpossible trends. The Mann–Kendall test is preferable to other tests (Pettitt, Wald–Wolfowitz, etc.), because ithas been used in several papers concerning both Italy (e.g. Brunetti et al., 2000a,b) and other Mediterraneanareas (e.g. Serrano et al., 1999); thus, all the data can be compared more easily. The slopes of the trends werecalculated by least-squares linear fitting. For a detailed description of this method the reader should refer toSneyers et al. (1997) and Brunetti et al. (2000a,b).

Drought is a climatic event that is difficult to determine and is generally considered as a relative variable.Many methods of approach have been employed to quantify the drought of a given area. This paper considersthe standardized precipitation index (SPI) proposed by Mckee et al. (1993), since it is simple and commonlyused, taking only rainfall into account. The SPI was developed to quantify precipitation deficits on multipletime scales (1, 3, 6, 12, 24, 48 months) in order to allow drought conditions to be described for a range ofmeteorological, agricultural and hydrological applications.

The SPI is a standardized normal distribution (z-distribution) of the precipitation time series. Accordingto several authors (e.g. Hayes et al., 1999; Lana et al., 2001; Lloyd-Hughes and Saunders, 2002), the SPIpresents the following advantages: (a) different monthly pluviometric regimes can be compared with a commonpluviometric index; (b) it can be easily computed at annual or seasonal scales; and (c) it ensures that thefrequencies of extreme events at any location and on any time scale are consistent.

The SPI is computed by fitting a gamma probability density function to a given frequency distribution ofprecipitation data at a location for a specified time period. Then, the cumulative distribution of precipitationis determined and, for each value of precipitation, it is transformed into the standard normal random variable

Copyright 2004 Royal Meteorological Society Int. J. Climatol. 24: 907–922 (2004)

PRECIPITATION AND DROUGHT IN SOUTHERN ITALY 911

Table II. SPI classification

SPI value Class

>2.0 Extremely wetFrom 1.5 to 1.99 Very wetFrom 1.0 to 1.49 Moderately wetFrom −0.99 to 0.99 Near normalFrom −1.0 to −1.99 Moderately dryFrom −1.5 to −1.99 Severely dry<2.0 Extremely dry

Z with a mean of zero and a variance of one, which is the value of the SPI (Seiler et al., 2002). Table IIreports the SPI values, ranging from extremely wet records (>2) to extremely dry records (<2). In this paper,the multiple time scales of 12, 24, 48 months has been considered.

3. TIME SERIES HOMOGENIZATION

Since the 1980s the problem of homogenization of time series has assumed great relevance in climatology. Ahomogeneous climate time series is defined as one where variations are caused by variations in weather andclimate (Peterson et al., 1998 and references cited therein). Many climatological series have been affected bya number of nonclimatic factors, such as a change in instruments, observing practices and station environment,which can bias the data and lead to misinterpretation of the climate being studied (Peterson et al., 1998).It is important, therefore, to remove the inhomogeneities or, at least, to determine the possible error theymay cause.

Years

20001993198619721965195819511944193719301923-1.2

-1

-0.8

-0.6

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0

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Years

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(d)

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Series 4Series 3Series 2Series 1

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

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0.5

(c)

Series 4Series 3Series 2Series 1

Series 4Series 3Series 2Series 1

Series 4Series 3Series 2Series 1

Figure 2. The different series and the shift values identified by MASH for (a) Grancia, (b) Lagonegro, (c) San Martino d’Agri and(d) San Mauro Forte

Copyright 2004 Royal Meteorological Society Int. J. Climatol. 24: 907–922 (2004)

912 M. PICCARRETA, D. CAPOLONGO AND F. BOENZI

In this paper, data were homogenized using the MASH method, developed by T. Szentimrey at theHungarian Meteorological Service. The software can compute up to 10 stations at the same time and onlyone of them is tested. The software algorithm chooses a combination of up to four of the other series asreference series.

The homogenization has been carried out by adopting the approach used by Kveton and Nemec (2000) forthe Czech Republic by the MASH method. All 50 stations were used for the homogenization and a cumulativemodel with a critical value of 21–40 was used for the monthly pluviometric data. The stations were dividedinto six groups (Figure 1) of nine stations; apart from the group placed along the Ionian coast, all groupsinclude stations principally located in the same river catchment. For each group an artificial series was made,representing the average of the data of the nine stations, year by year. Four stations (Ferrandina, Pomarico,Roccanova and Nova Siri) were included in more than one group.

As proposed by Kveton and Nemec (2000), a homogeneity test of annual data was first carried out. Oncethe software judged the series as homogeneous, the series was recorded as homogeneous without furthercorrection. Otherwise, tests of quarterly totals were effected. The corrections made for a season were alsoused for each single month of the season. In the homogeneity process, the metadata that have been collectedin ‘Hydrological Annales’ have been used. Eventually, only 10 stations were judged as inhomogeneous andrequired adjustment. An example of the graphical results is given in Figure 2.

Figure 3. Annual precipitation in Basilicata from 1923 to 2000

Table III. Results of the application of the Mann–Kendall test and of least-squares linear fitting to the mean yearly andseasonal precipitation for Basilicata over the period 1923–2000a

Winter(mm/10 years)

Spring(mm/10 years)

Summer(mm/10 years)

Autumn(mm/10 years)

Year(mm/10 years)

u b ± σ u b ± σ x b ± σ u b ± σ x b ± σ

−3.31b −11.5 ± 5 −1.3 −3.4 ± 3 0.4 0.9 ± 2 −0.8 −2.4 ± 4 −3.0b −12.2 ± 7

a b (percentage contribution/10 years) is the linear regression coefficient, σ is the associated error, u is the Mann–Kendall test value.b Significance level greater than 99%.

Copyright 2004 Royal Meteorological Society Int. J. Climatol. 24: 907–922 (2004)

PRECIPITATION AND DROUGHT IN SOUTHERN ITALY 913

4. RESULTS

The annual total rainfall was analysed and the results are shown in Figure 3. They reveal high variabilityfrom one year to another, with a standard deviation of 142 mm where the average annual precipitation is littlehigher than 862 mm. The graphs clearly show a significant decrease of annual average rainfall, especially from1970 onward. This finding is in agreement with Mann–Kendall test results (Table III), where the negativetrend is confirmed at the 99% confidence level. The slope of the yearly series, calculated by least-squareslinear fitting, is −20 mm/10 years, giving an estimated decrease of 156 mm in the period 1923–2000, whichcorresponds to 18% of the yearly mean value.

Seasonal average values were calculated (Figure 4). The results show a negative trend for autumn, springand, especially, winter, and a weak positive trend for summer. These results are confirmed by a Mann–Kendalltest. The slope of the winter series, calculated by least-squares linear fitting, is −16 mm/10 years. In orderto point out the pattern of the trend further, the series was subdivided into three 30 year periods: 1923–52,

800

mm

700

600

500

400

300

200

100

01923 1930 1937 1944 1951 1958 1965 1972 1979 1979

19791979

1986 1993 2000

Years

(a)

(c) (d)

400

300

200

100

0

mm

500

400

300

200

100

0

mm

1923 1930 1937 1944 1951 1958 1965 1972 1986 1993 2000

Years

(b)

300

200

100

0

mm

1923 1930 1937 1944 1951 1958 1965 1972 1986 1993 2000

Years

1923 1930 1937 1944 1951 1958 1965 1972 1986 1993 2000

Years

Figure 4. Seasonal mean precipitation in Basilicata from 1923 to 2000: (a) winter; (b) spring; (c) summer; (d) autumn

Table IV. Comparison of mean annual and seasonal precipitation in Basilicata for the periods 1923–52, 1947–76 and1971–2000a

1923–52 period 1947–76 period 1971–2000 period

P mean (mm) u P mean (mm) u x P mean (mm) u x y

Winter 352.91 −0.1 321.05 0.1 −9.0 267.64 −1.8 −24.2 −16.6Spring 205.59 −0.6 203.65 0.3 −0.9 195.15 −1.0 −5.1 −4.2Summer 90.72 0.1 102.68 0.9 +13.2 94.53 −0.3 +4.2 −7.9Autumn 257.73 1.5 279.23 −0.7 +8.3 242.96 −0.3 −5.7 −13.0Year 895.95 0.3 896.08 0.3 0.0 800.52 −1.9 −10.7 −10.7

a u: Mann–Kendall test value; x : percentage variation in mean precipitation with respect to the 1923–52 period; y : percentage variationin mean precipitation with respect to the 1947–76 period.

Copyright 2004 Royal Meteorological Society Int. J. Climatol. 24: 907–922 (2004)

914 M. PICCARRETA, D. CAPOLONGO AND F. BOENZI

Table V. Comparison of mean monthly precipitation in Basilicata for the periods 1923–52, 1947–76 and 1971–2000a

Period 1923–1952 period 1947–76 period 1971–2000 period

P mean (mm) u P mean (mm) u x P mean (mm) u x y

January 123.05 0.4 112.46 −0.7 −8.6 89.07 −1.6 −27.6 −20.8February 94.69 −0.8 90.02 1.2 −4.9 83.60 −1.9 −11.7 −7.1March 81.27 0.1 78.66 0.5 −3.2 77.91 −1.0 −4.1 −0.9April 62.93 −0.9 64.96 0.5 3.2 67.60 −0.4 7.4 4.1May 58.85 −0.1 56.87 −1.1 −3.4 49.51 0.0 −15.9 −12.9June 38.87 −1.0 34.77 −0.1 −10.5 28.55 −0.2 −26.5 −17.9July 24.53 2.7b 32.37 0.0 32 30.67 0.3 25.1 −5.2August 25.51 0.8 33.84 1.0 32.7 35.21 −0.5 38.1 4.1September 55.91 0.5 62.39 −0.2 11.6 52.18 0.7 −6.7 −16.4October 83.90 1.4 94.13 0.3 12.2 85.00 −0.4 1.3 −9.7November 114.91 0.4 119.95 −0.5 4.4 105.69 0.4 −8.0 −11.9December 135.37 0.0 119.10 1.2 −12 96.39 0.0 −28.8 −19.1

a u: Mann–Kendall test value; x : percentage variation in mean precipitation with respect to the 1923–52 period; y : percentage variationin mean precipitation with respect to the 1947–76 period.b Significance level greater than 99%.

4550

000 4550000

4480000

560000 630000

560000 630000

4480

000

no trendsdownward trendsupward trends

Figure 5. Trends in annual total precipitation in Basilicata for the period 1923–2000

1947–76, 1971–2000. The averages have been compared and the results, reported in Table IV, point outa significant downward trend in rainfall regime starting from 1970, as clearly confirmed by the values ofthe different normals (Table I). They also confirm the decrease in annual and winter average rainfall. Thedownward trend also prevails in spring and autumn, whereas in summer a weak upward trend has been

Copyright 2004 Royal Meteorological Society Int. J. Climatol. 24: 907–922 (2004)

PRECIPITATION AND DROUGHT IN SOUTHERN ITALY 915

Tabl

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6±

10−1

.5−7

±5

−0.2

−1±

30.

10

±3

−0.2

−1±

3−0

.4−2

±2

−2.0

∗−4

±2

Ferr

andi

na−3

.8∗∗

−28

±8

−1.3

−5±

3−0

.7−1

±2

−1.1

−3±

2−0

.7−1

±2

−1.4

−2±

1−1

.8−3

±1

Gra

ncia

2.0∗

15±

80.

0−1

±3

0.8

2±

21.

02

±2

0.9

3±

20.

81

±2

−0.3

−1±

2G

rass

ano

−2.4

∗−2

8±

9−0

.3−2

±3

−1.2

−3±

2−1

.7−5

±2

−2.3

∗−3

±2

−0.4

−1±

2−1

.2−2

±2

Gro

ttol

e−1

.3−1

5±

80.

1−2

±3

0.0

1±

2−1

.5−3

±2

−0.5

−1±

2−0

.3−1

±2

−1.1

−2±

2Ir

sina

−1.0

−4±

7−0

.8−2

±2

−0.2

0±

2−0

.20

±2

1.3

1±

11.

02

±1

0.1

0±

1L

agon

egro

−3.9

∗∗−9

9±

21−2

.8∗∗

−20

±7

−2.4

∗−2

0±

7−1

.4−1

2±

6−1

.0−8

±5

−1.9

−9±

4−1

.6−3

±2

Lau

ria

Infe

rior

e−2

.8∗∗

−50

±17

−2.6

∗∗−1

7±

6−1

.2−1

0±

6−0

.9−4

±5

1.0

3±

4−1

.7−6

±3

−1.0

−2±

2M

arat

ea−3

.1∗∗

−48

±15

−2.1

∗−1

1±

5−1

.1−5

±4

−0.4

−1±

40.

62

±3

−0.7

−5±

3−1

.8−4

±2

Mat

era

−3.4

∗∗−1

7±

6−0

.8−3

±2

−0.3

−2±

2−1

.1−3

±2

−1.0

−1±

1−0

.5−1

±1

−0.3

−1±

1M

etap

onto

−2.4

∗−1

7±

8−1

.4−4

±3

0.1

−2±

20.

0−1

±2

−0.4

−1±

10.

3−1

±1

−0.1

−2±

1

(con

tinu

edov

erle

af)

Copyright 2004 Royal Meteorological Society Int. J. Climatol. 24: 907–922 (2004)

916 M. PICCARRETA, D. CAPOLONGO AND F. BOENZI

Tabl

eV

I.(C

onti

nued

)

Yea

rJa

nuar

yFe

brua

ryM

arch

Apr

ilM

ayJu

ne

ub

±σ

ub

±σ

ub

±σ

ub

±σ

ub

±σ

ub

±σ

ub

±σ

Mez

zana

diL

uc.

−0.9

−24

±20

−0.4

−3±

5−0

.5−4

±5

−0.3

−2±

40.

11

±4

−0.7

−3±

3−2

.8∗∗

−4±

1M

issa

nell

o2.

0∗15

±12

−0.4

−3±

30.

21

±3

1.5

4±

20.

41

±2

0.0

−3±

3−0

.6−4

±3

Mon

talb

ano

Jon.

−3.1

∗∗−2

8±

9−1

.5−7

±4

−0.9

−3±

3−0

.5−2

±2

−1.0

−2±

2−1

.2−3

±2

−0.7

−2±

2N

oepo

li−2

.6∗∗

−29

±10

−2.2

∗−8

±4

−1.6

−4±

3−0

.4−1

±3

−1.9

−4±

2−1

.0−3

±2

−2.6

∗∗−3

±1

Nov

aSi

ri−3

.8∗∗

−41

±11

−2.1

∗−1

1±

5−1

.8−5

±4

−1.2

−5±

3−3

.0∗∗

−5±

2−0

.1−2

±2

−2.0

∗−2

±1

Nov

aSi

riSc

alo

−2.2

∗−1

6±

9−1

.0−5

±3

−0.9

−4±

2−0

.3−1

±2

−1.8

−2±

10.

20

±1

−2.0

∗−2

±1

P.S.

Ger

vasi

o−2

.4∗

−17

±7

−1.6

−3±

2−1

.1−3

±2

−1.4

−3±

2−0

.4−1

±2

−0.9

−2±

20.

30

±2

Pist

icci

−2.2

∗−2

1±

11−1

.27

±5

−0.4

−1±

2−0

.6−2

±2

−0.7

−1±

2−0

.5−2

±2

−1.8

−2±

2Po

licor

o−1

.7−1

3±

7−0

.8−4

±3

−0.5

−3±

20.

40

±2

−1.0

−2±

1−0

.1−1

±1

−0.5

−2±

1Po

mar

ico

−5.6

∗∗−5

7±

10−2

.3∗

−7±

3−2

.1∗

−6±

3−1

.6−6

±3

−2.4

∗−4

±2

−1.7

−3±

2−2

.3∗

−4±

1Po

tenz

a−3

.4∗∗

−47

±11

−2.2

∗−6

±3

−0.6

−2±

2−0

.10

±2

0.9

2±

2−1

.1−2

±1

−1.2

−3±

2R

occa

nova

−0.5

−5±

8−1

.1−5

±3

−0.1

−1±

30.

31

±2

0.1

0±

2−1

.0−2

±2

−2.1

∗−2

±1

Sala

ndra

−2.6

∗∗−2

1±

9−0

.8−5

±3

−0.5

−2±

2−0

.8−3

±3

−0.3

−1±

20.

00

±2

−1.8

−3±

1S.

G.

Luc

ano

−1.8

−19

±11

−1.8

−8±

5−1

.0−4

±3

0.0

0±

3−0

.6−1

±2

−0.2

−3±

2−2

.7∗∗

−4±

2S.

M.

d’A

gri

0.9

18±

10−0

.7−4

±3

−0.3

−3±

31.

02

±2

1.7

2±

21.

21

±2

−0.4

0±

1S.

Mau

roFo

rte

0.4

5±

80.

1−1

±4

0.5

1±

20.

1−1

±3

0.1

0±

20.

00

±2

−0.5

−2±

1S.

N.

d’A

vigl

iano

−1.4

−10

±7

−1.2

−3±

2−0

.8−3

±2

−0.3

0±

20.

92

±2

−0.6

−2±

2−0

.1−2

±1

S.S.

Luc

ano

−1.1

−18

±18

−1.1

−8±

60.

51

±5

0.0

−1±

51.

14

±3

−0.6

−3±

3−1

.3−2

±2

Seni

se−3

.5∗∗

−33

±9

−2.5

∗−8

±3

−1.5

−5±

3−0

.2−1

±2

−0.8

−2±

2−1

.2−3

±2

−2.3

∗−4

±2

Stig

liano

−2.6

∗∗−2

2±

9−1

.5−7

±4

−1.1

−4±

3−0

.2−3

±4

−0.6

−1±

2−0

.2−1

±2

−0.7

−3±

2To

lve

−1.8

−14

±6

−0.9

−4±

2−1

.3−2

±2

−0.5

−1±

20.

00

±2

−0.7

−2±

2−1

.1−2

±1

Tra

mut

ola

0.1

−1±

12−1

.0−5

±4

−0.8

−6±

40.

62

±3

2.0∗

5±

30.

40

±2

−0.7

0±

2T

recc

hina

−3.5

∗∗−9

3±

24−2

.6∗∗

−23

±8

−2.1

∗−1

9±

8−0

.8−5

±6

0.1

1±

5−1

.0−6

±4

−2.1

∗−5

±2

Tri

cari

co−1

.7−1

2±

8−1

.2−3

±3

−0.8

−2±

2−1

.6−3

±2

0.3

0±

2−0

.4−1

±2

−2.7

∗∗−4

±1

Tur

si−0

.5−6

±11

−1.2

−7±

5−0

.1−1

±3

0.0

−1±

3−2

.4∗

−3±

2−0

.5−1

±2

−1.8

−2±

2V

agli

odi

Luc

ania

2.6∗∗

19±

80.

0−1

±3

0.3

0±

30.

71

±2

1.2

4±

20.

70

±2

−0.2

−1±

2V

alsi

nni

−2.0

∗−2

0±

10−1

.5−7

±4

−1.0

−3±

31.

13

±3

−1.1

−2±

2−0

.9−3

±2

−1.9

−4±

2

Bas

ilica

ta−3

.0∗∗

−21

±7

−2.6

∗∗−7

±3

−1.4

−4±

2−0

.6−1

±2

0.0

0±

2−0

.8−2

±2

−2.0

∗−2

±1

Copyright 2004 Royal Meteorological Society Int. J. Climatol. 24: 907–922 (2004)

PRECIPITATION AND DROUGHT IN SOUTHERN ITALY 917

Tabl

eV

I.(C

onti

nued

)

Yea

rJu

lyA

ugus

tSe

ptem

ber

Oct

ober

Nov

embe

rD

ecem

ber

ub

±σ

ub

±σ

ub

±σ

ub

±σ

ub

±σ

ub

±σ

ub

±σ

Acq

uafr

edda

−2.0

∗−2

8±

141.

3−1

±1

0.6

0±

2−0

.1−4

±4

−0.5

−2±

40.

10

±4

−2.0

∗−1

1±

5A

grom

onte

−1.9

−33

±17

0.2

0±

11.

62

±2

1.1

1±

3−0

.6−3

±4

−0.3

−2±

5−1

.0−1

0±

6A

lban

odi

Luc

.−2

.0∗

−22

±10

1.1

1±

11.

32

±2

−0.7

−3±

20.

30

±2

−1.0

−3±

3−1

.2−4

±3

Alia

no1.

28

±8

0.9

3±

12.

8∗∗4

±2

1.3

1±

21.

74

±2

0.5

1±

30.

11

±4

Arm

ento

0.0

5±

101.

54

±2

1.8

3±

21.

21

±2

1.8

5±

20.

60

±3

−0.1

0±

4B

erna

lda

−2.4

∗−2

4±

111.

73

±2

1.4

2±

10.

30

±2

−1.3

−4±

3−1

.4−6

±4

−1.5

−4±

4C

alci

ano

−3.6

∗∗−3

9±

90.

31

±2

1.3

−1±

1−1

.2−2

±2

−0.1

0±

2−2

.0∗

−6±

3−2

.2∗

−9±

3C

alve

llo−3

.8∗∗

−46

±11

0.6

−1±

21.

73

±2

−1.2

−5±

20.

31

±3

−0.7

−4±

4−1

.9−1

3±

4C

ance

llara

−4.3

∗∗−4

1±

8−0

.40

±1

−0.2

−1±

1−1

.0−3

±2

−1.0

−2±

2−1

.0−2

±2

−2.4

∗−6

±3

Car

bone

−0.3

−4±

100.

72

±1

2.9∗∗

4±

11.

73

±2

0.0

1±

30.

63

±4

−0.3

−4±

4C

erso

sim

o−0

.4−8

±13

0.7

2±

12.

4∗3

±1

0.9

4±

31.

45

±4

−0.7

−5±

6−1

.0−6

±6

Cog

liand

rino

−1.8

−36

±17

0.7

1±

21.

63

±2

−0.1

−2±

3−1

.2−5

±4

−0.2

−2±

5−0

.5−7

±7

Cog

nato

−2.4

∗−2

6±

100.

01

±1

−0.1

−1±

2−0

.5−1

±2

0.7

1±

2−0

.5−3

±4

−1.2

−7±

5Fe

rran

dina

−3.8

∗∗−2

8±

8−0

.11

±1

0.9

1±

1−0

.1−1

±1

−0.7

−1±

2−1

.5−4

±3

−2.1

∗−8

±3

Gra

ncia

2.0∗

15±

81.

22

±2

2.1∗

4±

21.

22

±2

2.0∗

4±

2−0

.1−1

±3

−0.6

−2±

3G

rass

ano

−2.4

∗−2

8±

90.

42

±2

0.8

0±

2−0

.9−2

±2

−0.6

−1±

2−1

.8−4

±3

−2.4

∗−7

±3

Gro

ttol

e−1

.3−1

5±

80.

92

±2

1.7

2±

1−0

.1−1

±2

−0.7

−1±

2−1

.7−4

±3

−1.9

−5±

3Ir

sina

−1.0

−4±

71.

2−1

±2

1.5

3±

2−0

.20

±2

−0.3

0±

2−0

.8−2

±2

−0.9

−4±

2L

agon

egro

−3.9

∗∗−9

9±

211.

61

±2

1.3

2±

20.

3−2

±5

−1.1

−7±

5−0

.5−2

±6

−1.8

−19

±9

Lau

ria

Infe

rior

e−2

.8∗∗

−50

±17

1.8

3±

11.

21

±2

−1.0

−4±

4−0

.2−2

±4

−0.3

−2±

6−1

.6−1

3±

7M

arat

ea−3

.1∗∗

−48

±15

−0.3

0±

11.

21

±2

−0.5

−5±

4−0

.6−4

±4

−0.4

−2±

5−2

.1∗

−16

±6

Mat

era

−3.4

∗∗−1

7±

61.

12

±1

1.2

2±

10.

2−1

±2

−1.2

−3±

2−1

.6−3

±3

−1.7

−5±

2M

etap

onto

−2.4

∗−1

7±

82.

6∗∗2

±1

2.8∗∗

2±

10.

5−1

±2

−1.2

−3±

2−0

.7−2

±3

−1.4

−4±

3

(con

tinu

edov

erle

af)

Copyright 2004 Royal Meteorological Society Int. J. Climatol. 24: 907–922 (2004)

918 M. PICCARRETA, D. CAPOLONGO AND F. BOENZITa

ble

VI.

(Con

tinu

ed)

Yea

rJu

lyA

ugus

tSe

ptem

ber

Oct

ober

Nov

embe

rD

ecem

ber

ub

±σ

ub

±σ

ub

±σ

ub

±σ

ub

±σ

ub

±σ

ub

±σ

Mez

zana

diL

uc.

−0.9

−24

±20

−0.6

0±

1−0

.70

±2

0.5

0±

30.

51

±3

−0.5

−4±

5−1

.0−6

±6

Mis

sane

llo

2.0∗

15±

121.

52

±3

3.1∗∗

6±

22.

0∗3

±2

1.4

6±

20.

91

±4

0.5

2±

4M

onta

lban

oJo

n.−3

.1∗∗

−28

±9

1.4

2±

12.

5∗∗2

±1

0.8

0±

20.

2−1

±3

−1.0

−3±

4−1

.4−7

±4

Noe

poli

−2.6

∗∗−2

9±

10−0

.11

±1

1.6

1±

10.

32

±2

0.6

2±

3−1

.8−6

±4

−1.4

−6±

4N

ova

Siri

−3.8

∗∗−4

1±

111.

02

±1

1.3

1±

11.

32

±2

−0.2

−1±

3−1

.1−4

±4

−1.6

−9±

5N

ova

Siri

Scal

o−2

.2∗

−16

±9

0.4

1±

11.

21

±1

0.7

0±

21.

01

±2

−0.1

1±

4−1

.4−6

±4

P.S.

Ger

vasi

o−2

.4∗

−17

±7

1.3

1±

11.

91

±1

−0.3

−1±

20.

1−1

±2

−1.6

−4±

3−1

.3−2

±2

Pist

icci

−2.2

∗−2

1±

111.

12

±1

1.5

1±

11.

22

±2

−0.5

−1±

3−1

.4−5

±4

−1.2

−5±

4Po

licor

o−1

.7−1

3±

72.

3∗2

±1

2.5∗

2±

10.

91

±2

−0.1

−1±

2−0

.60

±4

−1.6

−4±

3Po

mar

ico

−5.6

∗∗−5

7±

10−1

.0−2

±1

−0.5

0±

1−1

.8−3

±2

−1.8

−4±

2−2

.1∗

−7±

4−2

.9∗∗

−11

±4

Pote

nza

−3.4

∗∗−4

7±

110.

71

±1

0.5

0±

10.

50

±2

−1.1

−2±

2−0

.5−2

±2

−1.9

−32

±8

Roc

cano

va−0

.5−5

±8

0.9

2±

12.

0∗2

±1

0.9

1±

20.

72

±2

−0.2

−1±

3−0

.3−2

±4

Sala

ndra

−2.6

∗∗−2

1±

90.

82

±2

1.4

2±

2−0

.2−1

±3

−0.2

0±

2−0

.9−3

±3

−1.7

−6±

3S.

G.

Luc

ano

−1.8

−19

±11

0.7

1±

11.

62

±1

1.2

3±

30.

83

±3

−1.3

−4±

4−1

.2−5

±5

S.M

.d’

Agr

i0.

918

±10

1.3

2±

13.

1∗∗4

±1

2.0∗

3±

22.

0∗5

±2

1.7

5±

30.

30

±4

S.M

auro

Fort

e0.

45

±8

1.0

3±

21.

52

±1

1.7

2±

21.

53

±2

0.1

1±

4−0

.8−3

±3

S.N

.d’

Avi

glia

no−1

.4−1

0±

70.

00

±1

1.3

1±

10.

5−1

±2

0.0

0±

2−0

.2−1

±2

−0.6

−3±

2S.

S.L

ucan

o−1

.1−1

8±

18−0

.11

±2

0.5

2±

20.

82

±3

0.1

0±

40.

0−2

±6

−0.9

−11

±8

Seni

se−3

.5∗∗

−33

±9

−0.3

0±

11.

62

±1

0.3

1±

2−0

.1−1

±2

−1.4

−6±

3−1

.2−5

±4

Stig

liano

−2.6

∗∗−2

2±

92.

1∗4

±2

2.4∗

3±

20.

10

±2

0.4

1±

3−1

.2−5

±3

−1.3

−7±

4To

lve

−1.8

−14

±6

1.0

1±

11.

42

±1

0.6

0±

20.

81

±2

−1.1

−4±

3−1

.3−4

±3

Tra

mut

ola

0.1

−1±

121.

84

±2

1.6

2±

10.

0−1

±3

0.6

2±

40.

31

±4

−0.4

−4±

5T

recc

hina

−3.5

∗∗−9

3±

240.

21

±2

0.6

1±

2−0

.3−4

±5

−1.0

−7±

5−0

.8−4

±7

−2.1

∗−2

3±

9T

rica

rico

−1.7

−12

±8

1.7

3±

11.

11

±1

1.2

2±

20.

20

±2

−0.9

−2±

2−1

.1−4

±3

Tur

si−0

.5−6

±11

0.3

3±

11.

52

±2

1.1

3±

20.

44

±3

−0.6

−1±

4−1

.4−5

±5

Vag

lio

diL

ucan

ia2.

6∗∗19

±8

0.9

3±

13.

1∗∗5

±2

1.1

2±

21.

94

±2

1.5

4±

20.

00

±3

Val

sinn

i−2

.0∗

−20

±10

1.5

2±

12.

4∗3

±1

0.5

0±

21.

02

±3

−1.4

−6±

4−1

.2−5

±5

Bas

ilica

ta−3

.0∗∗

−21

±7

1.3

2±

11.

82

±1

0.3

0±

20.

10

±2

−0.7

−2±

3−1

.5−7

±3

∗ =Si

gnifi

canc

ele

vel

grea

ter

than

95%

.∗∗

=Si

gnifi

canc

ele

vel

grea

ter

than

99%

.

Copyright 2004 Royal Meteorological Society Int. J. Climatol. 24: 907–922 (2004)

PRECIPITATION AND DROUGHT IN SOUTHERN ITALY 919

recorded, probably because the extremes have become more intense. The same criteria were used for monthlyvalues (Table V).

The Mann–Kendall test has also been applied to annual and monthly precipitation of all the pluviometricstations investigated in order to verify pluviometric trends during the year (Table VI). The results show adownward trend in annual precipitation at more than half of the stations; only three stations (Vaglio di Lucania,Grancia and Missanello) show an upward trend. The other stations do not show statistically significant trends(Figure 5).

The results of total annual rainfall can be compared with the results of the three 30 year periods: January,June and December are characterized by a negative trend, whereas April, July and, especially, August showthe opposite tendency (Figure 6). The slopes of the trends were calculated by least-squares linear fitting.

Moreover, it emerges that, on a climatological basis, the months of June, July and August are very dry,whereas January and December are extremely rainy; so, January and December can certainly be consideredto play the main role in determining the yearly trend.

5. DEFINING DROUGHT BY SPI

The SPI for multiple time scales of 6 (SPI6), 12 (SPI12), and 24 months (SPI24) was applied to all pluviometricdata, for the period 1923–2000. This kind of approach is actually useful for agriculture, hydrology and watermanagement (Wu et al., 2001). The results are shown in Figure 7. It appears that the SPI values show azig-zag pattern with negative and positive spikes.

By using SPI24, nine well-defined wet cycles from the series analysed have been identified in Basilicata,with major floods associated with two cycles (1941 and 1960), with SPI >2 (extremely wet). A drasticshortage of precipitation has occurred in the last 30 years, in particular during the period 1986–97, when theSPI values show a high drought intensity (<−2; Figure 7(a)). The same features appear when using SPI12,

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(a) (b)

Figure 6. Trends in monthly total precipitation for the different months of the year for the period 1923–2000

Copyright 2004 Royal Meteorological Society Int. J. Climatol. 24: 907–922 (2004)

920 M. PICCARRETA, D. CAPOLONGO AND F. BOENZI

3

2

1

0

-1

-2

-31923 1930 1937 1944 1951 1958 1965 1972 1986 1993 2000

Years

Very wet periods

dry period

SP

I 24

(a)

3

2

1

0

-1

-2

-31923 1930 1937 1944 1951 1958 1965 1972 1979

1979

1979

1986 1993 2000

Years

SP

I 12

(b)

3

2

1

0

-1

-2

-31923 1930 1937 1944 1951 1958 1965 1972 1986 1993 2000

Years

SP

I 6

(c)

Figure 7. SPI values for Basilicata calculated from the 1923–2000 series for the periods of (a) 24 months (SPI24), (b) 12 months(SPI12) and (c) 6 months (SPI6)

although an increase in wet periods is recorded, especially in the first 50 years (Figure 7(b)). For SPI6, theSPI values very frequently fluctuate above and below zero (Figure 7(c)).

These data fit well the general picture described by Delitala et al. (2000) and Bordi et al. (2001) for otherregions of southern Italy (Sardinia, Sicily and Apulia) and, moreover, are in perfect agreement with the resultsgiven by Brunetti et al. (2002), who used a different approach.

Copyright 2004 Royal Meteorological Society Int. J. Climatol. 24: 907–922 (2004)

PRECIPITATION AND DROUGHT IN SOUTHERN ITALY 921

In particular, according to Bordi et al. (2001), on the 6 month scale, intense rainfall occurs in southern Italywith short periods of water scarcity. On the 12 month scale, rainfall becomes less intense with an increase indry conditions. Then, on the 24 month scale, moderate to severe drought is recorded, especially in Sardiniaand Apulia.

6. CONCLUSIONS

The pluviometric analysis for Basilicata (southern Italy), concerning the means of yearly, seasonal and monthlyprecipitation for 50 stations from 1923 up to the end of the 20th century, shows the following:

• There has been a decrease of about 156 mm in the mean yearly precipitation starting from about30 years ago.

• Annual trends are negative for 27 rain gauges, for three stations (Vaglio di Lucania, Grancia, Missanello)they are positive, and the remaining stations do not show significant trends.

• Among the seasonal trends, only the mean of precipitation during winter shows a significant negative trenddeviating from the scheme relative to central and southern Italy proposed by Brunetti et al. (2004) basedon data from a few rain gauges.

• On a monthly basis, significant negative trends are only found in January and June. It must be emphasized,however, that in December an important decrease in rainfall also occurs.

• Drought periods have frequently become severe to extreme in the last 30 years.

The above data are in general agreement with the characteristics of Sardinia (north of Basilicata) andSicily (south of Basilicata). Therefore, these climatic conditions affecting southern Italy can be consideredcharacteristic of the middle Mediterranean area.

Needless to say, there is a need for continuous detailed monitoring of precipitation to be carried out inorder to exploit water resources better.

Future efforts will be made to study to what extent precipitation and drought can affect badland evolution.

ACKNOWLEDGEMENTS

We would like to thank Giovanni Pacifico and Patrizia Brindisi at Servizio Idrografico for providing thepluviometric data. We are very grateful to Claudia Libiseller at Linkoping University (Sweden) for hersuggestions in the approach to the Mann–Kendall test. We thank Dr McGregor and two anonymous reviewersfor their constructive criticism.

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