Changes in daily precipitation frequency and distribution in Italy over

the last 120 years

Michele Brunetti,Consiglio Nazionale delle Ricerche, Istituto di Scienze dell’Atmosfera e del Clima, Bologna, Italy

Maurizio Maugeri and Fabio MontiIstituto di Fisica Generale Applicata, Milan University, Milan, Italy

Teresa NanniConsiglio Nazionale delle Ricerche, Istituto di Scienze dell’Atmosfera e del Clima, Bologna, Italy

Received 28 October 2003; revised 9 December 2003; accepted 31 December 2003; published 5 March 2004.

[1] A new data set of 45 daily precipitation series, covering quite uniformly Italianterritory for the period 1880–2002, was recovered. The series have been homogenizedon daily basis, completed by means of statistical methods and grouped into five regionsby a Principal Component Analysis. Seasonal and yearly total precipitation, numberof wet days, and precipitation intensity were analyzed for each station record andaveraged into five regional series for a synthetic description of the results. Proportionand frequency of daily rainfall amounts, belonging to six precipitation class-intervals,defined on the basis of some percentiles of the precipitation distribution, were alsoanalyzed. The results show a negative significant trend in the number of wet days allover Italy, and a positive trend in precipitation intensity, which is significant only in thenorthern regions. The negative trend in wet days has persisted since the end of 19thcentury and is due to the marked decrease in the number of low intensity precipitationevents. An increase in the number of events belonging to the highest intensity classinterval was observed too, but only in northern regions. INDEX TERMS: 1620 Global Change:

Climate dynamics (3309); 1610 Global Change: Atmosphere (0315, 0325); 1655 Global Change: Water cycles

(1836); 1694 Global Change: Instruments and techniques; 3309 Meteorology and Atmospheric Dynamics:

Climatology (1620); KEYWORDS: daily precipitation, Italy, extreme events

Citation: Brunetti, M., M. Maugeri, F. Monti, and T. Nanni (2004), Changes in daily precipitation frequency and distribution in Italy

over the last 120 years, J. Geophys. Res., 109, D05102, doi:10.1029/2003JD004296.

1. Introduction

[2] One of the most important focuses of recent climato-logical studies is to characterize possible changes in climaticextremes, since they have the strongest impact on society.Modelling results have indicated increases in extremeprecipitation in a warmer climate [Zwiers and Kharin,1998; Kharin and Zwiers, 2000; Palmer and Raisanen,2002]. Zwiers and Kharin [1998] studied changes due toCO2 doubling in the extremes of the surface climate, assimulated by the second-generation circulation model of theCanadian Centre for Climate Modelling and Analysis in20-yr equilibrium simulations. They observed that precipi-tation extremes increase almost everywhere over the globeand return periods of extreme precipitation are shortened bya factor 2 and more in the 2XCO2 climate in many parts ofthe world. Moreover, the number of rainy days per yeargenerally increases in polar regions and decreases in mid-latitudes. Also Kharin and Zwiers [2000], by examining the

extremes of precipitation in an ensemble of three climatechange simulations for the period 1900–2100, found anincrease almost everywhere and the observed increase waslarger for larger return periods. Palmer and Raisanen[2002], by using a multimodel ensemble of 19 globalcoupled ocean-atmosphere climate models, estimated winterseasonal precipitation exceeding two standard deviationsabove normal will increase over much of central andnorthern Europe (with the probability of occurrence increas-ing to over 12%) over the next 100 years. They found asimilar increase in the probability for the Asian monsoonregion.[3] Observational studies also suggest evidence of

changes in climatic extremes. Karl et al. [1995] and Karland Knight [1998] provide evidence of a statisticallysignificant increase in precipitation greater than 50.8 mmper day in the United States from 1910s to 1990s. In arecent paper, Kunkel et al. [2003] analyzed a longer newlyavailable data set of daily precipitation series to study thetemporal variability of the frequency of short-durationextreme precipitation events for 1895–2000. They con-firmed the results of Karl et al. [1995] and Karl and Knight

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109, D05102, doi:10.1029/2003JD004296, 2004

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[1998] for the recent period, but observed that heavyprecipitation frequencies at the beginning of 20th centurywere nearly as high as during the late 20th century. Suppiahand Hennessy [1998] observed, for the period 1910–1990,an increasing trend in heavy daily rainfall (greater than the90th and 95th percentiles) in Australia, both in summer andwinter half years. Osborn et al. [2000] studied the intensitydistribution of daily precipitation amounts in the UK andshowed that it changed over the period 1961–1995, be-coming on average more intense in winter and less intensein summer. Also Fowler and Kilsby [2003a, 2003b] ob-served that the frequency of extreme rainfall changed overparts of the UK in the period 1961–2000, in particularprolonged heavy rainfall events were increasing. In theValencia region of Spain, Gonzalez Hidalgo et al. [2003]observed a positive trend in most intense events as apercentage of annual rainfall, in spite of a negative trendin their volume.[4] Results for Italy are presented in Brunetti et al.

[2001a, 2002] for the period 1951–2000: the number ofwet days has a clear and highly significant negative trend allover Italy and there is a tendency toward an increase inprecipitation intensity which, in Northern Italy, is mainlyowing to a strong increase in precipitation, which falls intothe highest class-intervals, whereas in Southern Italy, it isspread over a wider part of the distribution of amounts ofwet days.[5] On a global scale, significant increases have been

seen by Frich et al. [2002] in the extreme amount derivedfrom wet spells and number of heavy rainfall events for thesecond half of the twentieth century. Easterling et al.[2000], in a brief review of climate extreme, observed thatthe tendency in most countries that have experienced asignificant increase or decrease in total precipitation hasbeen directly related to the change in the amount ofprecipitation during the heavy and extreme events.[6] The relationship between the increase in total precip-

itation, and the frequency of heavy rain events was studiedby Groisman et al. [1999], by applying a simple statisticalmodel based on the gamma distribution to summer data ofeight countries: Canada, the United States, Mexico, theformer Soviet Union, China, Australia, Poland and Norway.The results showed that the shape parameter of the precip-itation distributions remained rather stable, independent oftotal precipitation, while the scale parameter was morevariable. If these results can be generalized and used as amodel for the future, as total precipitation increases, adisproportionate increase in heavy precipitation has to beexpected [Groisman et al., 1999].[7] The relationship between precipitation intensity and

total precipitation is, however, not general. In some places,such as Siberia in the summer season in the period 1936–1994, an increase in heavy precipitation was observedtogether with a tendency toward a decrease in total precip-itation [Groisman et al., 1999]. A similar behavior wasobserved in Italy [Brunetti et al., 2000, 2001a, 2001b].[8] One of the biggest problems in examining the climate

record for changes in the occurrence of extremes is a lack ofhigh-quality and high temporal and spatial resolution long-term data [Jones et al., 1999; Folland et al., 2000].Furthermore, daily data are often not in digital form. Infact, as evident from the above-described state of the art, for

most countries the analysis period starts from the end ofWorld War II. Only in few countries (Costal regions of NewSouth Wales and Victoria in Australia, United States, Nor-way, Natal in South Africa, and Northern Italy) analysesbegan near the start of the twentieth century [Easterling etal., 2000; Brunetti et al., 2000; Kunkel et al., 2003]. Inrecent years, many researchers have understood the neces-sity to set up new daily series data sets to extend further intothe past the study of changes in extreme events. At thepresent the most relevant results come from the UnitedStates, where 920 station series were collected for the period1895–2000, albeit with a nonuniform coverage [Kunkel etal., 2003]. As far as Europe is concerned, the most impor-tant attempt to realise a data set of daily resolution climatictime series was the European Climate Assessment (ECA)project [Klein Tank et al., 2002]. It focuses on the 20thcentury, from 1901 to 1999, and consists of about 200 seriesof minimum, maximum and/or mean temperature and dailyprecipitation amount. Unfortunately, only 50% of the seriesextends back to at least 1925. An attempt to test the seriesstatistically with respect to homogeneity was made. As aresult, a high percentage of series were labeled ‘‘doubtful’’or ‘‘suspect’’ [Wijngaard et al., 2003]. This highlights thenecessity for more effort from individual countries in settingup national homogeneous daily resolution databases toconverge into wider international projects.[9] Our goal is to do this for Italy. In this paper we will

present the results of the analysis of a new daily precipita-tion database covering the last 120 years. The originality ofthis work lies, not only in the new data here presented, butalso in the length of the series, in the wider metadataavailability, and in the homogenization procedure per-formed on a daily basis.

2. Data

[10] Italy has some of the longest meteorological series inthe world. After the observations recorded from 1654 to1667 by the Accademia del Cimento, in Italy regularmeteorological observations began in the eighteenth centuryin Bologna (1714), Padua (1725), Turin (1756), Milan(1763), Rome (1782) and Palermo (1791). In a number ofother cities, e.g., Aosta, Florence, Genoa, Mantova, Mod-ena, Naples, Parma, Pavia, Perugia, Udine, Trieste andVenice, observations started in the first half of thenineteenth century.[11] This vast heritage of observations is still almost

entirely available, even if it has been only partiallytransferred to a digitized form. Concerning precipitation,in the frame of some national and international projectswe set up a new data set of daily series, the oldest onesstarting from the second half of the 18th century. At themoment the database consists of 45 series quite uniformlydistributed all over the Italian territory (Table 1 andFigure 1), but the data collection is still in progress toimprove the area coverage. Within the same projects wealso recovered monthly data for a larger number ofstations (more than 100). This wider data set will beused in the near future to study total precipitation trendsfor the past two centuries with a higher and more uniformspatial coverage, in particular for Southern Italy wherethe density of daily series is rather low.

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[12] Besides daily and monthly data we also set up a widecollection of metadata. Research activity concerning meta-data was organized in order (1) to understand the evolutionof the Italian meteorological network and (2) to reconstructthe history of all the stations with secular series.[13] The network management displayed a very complex

evolution, with short periods of rather high standardizationand longer ones with the presence of different independentsubjects (Central Office, Hydrological Service, Air Force).However, also in the period with higher standardization,there was not a complete homogeneity between instrumentsand methods used in the different observatories, as the mostimportant ones were more autonomous because they hadtheir own instruments and people with adequate knowledgeto manage them and do experiments to check their reliabil-ity and calibrate different instruments. For these observato-ries the contribution of the Central Office in the stationmaintenance was rather marginal. In other cases, especiallyfor the smaller observatories, the support of the CentralOffice was indispensable for both instruments and methods.

The weak dependence of many observatories on the CentralOffice and the presence of different networks (CentralOffice, Hydrological Service, Air Force) for long periodsare sometimes negative, because they lead to informationlosses or great difficulties in recovering station historyinformation. On the other hand, the availability of datafrom different sources avoids the undesired problems pro-duced by the simultaneous changes in the instruments ormeasurement methods of the whole network (not identifi-able without information from different sources). An inter-esting example concerns rain gauge height above groundlevel. Even if all networks suggested the use of stations atground level, a significant part of Italian observatories (e.g.,Bologna Milan, Genoa, Rome, Palermo) used rain gaugeson the top of buildings, more than 20 meters above groundlevel, for all observation periods.[14] All the information on the evolution of the Italian

network was collected in the frame of two national projects.One, ‘‘Reconstruction of the historical climate in the Med-iterranean Area’’, funded for the period 1997–1999 by the

Table 1. List of the Daily Precipitation Series, Location and Covered Period

Station Name Station Code Lat., deg Lon., deg H, m asl Series Length

Alessandria ALE 44� 410 08� 450 98 1857–1986L’Aquila AQU 42� 210 13� 240 735 1879–2002Arezzo ARE 43� 270 11� 520 274 1879–2002Belluno BEL 46� 070 12� 130 404 1879–2002Bologna BOL 44� 290 11� 200 60 1813–2002Bolzano BOZ 46� 310 11� 210 272 1921–2002Bra BRA 44� 420 07� 520 308 1862–2002Brixen BRI 46� 430 11� 390 569 1921–2002Cagliari CAG 39� 120 09� 190 55 1879–2002Catania CAT 37� 300 15� 070 75 1901–2002Cuneo CUN 44� 240 07� 310 536 1879–1998Domodossola DOM 46� 070 08� 170 300 1872–1998Ferrara FER 44� 490 11� 360 15 1879–2002Firenze FIR 43� 470 11� 150 51 1860–2002Foggia FOG 41� 280 15� 320 80 1901–2002Genova GEN 44� 240 09� 080 53 1833–2002Livorno LIV 43� 330 10� 180 3 1876–2002Locarno LOC 46� 100 08� 470 379 1901–2000Lugano LUG 46� 000 08� 540 276 1901–2000Mantova MAN 45� 090 10� 450 20 1840–1997Messina MES 38� 120 15� 300 54 1881–2002Milano MIL 45� 280 09� 110 122 1858–2002Montemaria MMA 46� 440 10� 290 1323 1923–2002Montevergine MOV 40� 550 04� 470 270 1884–2002Napoli NAP 40� 500 14� 150 149 1866–2002Padova PAD 45� 240 11� 520 14 1877–2002Palermo PAL 38� 060 13� 210 71 1787–2002Parma PAR 44� 480 10� 180 57 1878–2002Pavia PAV 45� 100 09� 090 75 1883–1979Perugia PER 43� 060 12� 230 520 1874–2002Pesaro PES 43� 520 12� 520 11 1871–2002Piacenza PIA 45� 010 09� 430 50 1875–2002Reggio Calabria RCA 38� 060 15� 390 15 1878–2002Reggio Emilia REM 44� 420 10� 370 62 1879–2002Roma ROM 45� 520 10� 450 73 1866–2002Rovereto ROE 41� 540 12� 280 56 1921–2002Rovigo ROV 45� 520 11� 030 206 1879–2002Sassari SAS 45� 030 11� 460 9 1876–2002Siracusa SIR 40� 430 08� 360 224 1874–2002Taranto TAR 37� 030 15� 170 23 1901–2001Riva-Torbole RIV 40� 270 17� 180 22 1921–2002Torino TOR 45� 030 07� 400 275 1802–2000Trento TRE 46� 040 11� 070 199 1921–2002Vallombrosa VAL 43� 430 11� 000 955 1872–2002Venezia VEN 45� 260 12� 150 1 1900–2002

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National Research Council, and the second, ‘‘CLIMAGRI’’(www.climagri.it), financed by the Ministero delle politicheagricole e forestali, which is still in progress. At the conclu-sion of the first year (over a total of three) of the latter project,the authors produced a document containing a card for eachdata series where all the information about the station wassummarized. Each card was divided into three parts. In thefirst part all the information obtained from articles andmonographies was reported. In the second part there areabstracts from the epistolary correspondence between thestations and the Central Office. In the third part the sources ofthe data used to construct the record were summarized. Anexample of these cards is reported in the Web site of theproject (www.climagri.it/FinaleMaugeriCap4.htm).[15] Both the metadata on the evolution of the National

network and that concerning the station cards contain a lotof information about instruments, station relocation andmaintenance, changes of instruments and calibration prob-lems, changes in the number of observations per day, in theobservation time and in the people working at the station.All this information was very useful for a critical check ofthe quality of the data. At the end of the current projectsinvolving the setting up of this data set, all the series will beavailable to the scientific community.

3. Data Homogenization

[16] Many studies over the past two decades have dem-onstrated that climate variability research is not possiblewithout clear knowledge about the state of the data in termsof homogeneity. The real climate signal in original series ishidden behind nonclimatic noise caused by station reloca-tion, changes in instruments and instrument screens,changes in observation times, observers, and observingregulations, algorithms for the calculation of means, and

so on. Overviews focusing on the homogeneity problem anddifferent ways of solving it are given in Peterson et al.[1998], and in the two volumes of the proceedings of thehomogeneity seminars in Budapest [World MeteorologicalOrganization (WMO), 1997, 1999]. However, we did notfind in the literature any attempt at homogenizing dailyseries of precipitation.[17] One of the biggest problems concerning daily precip-

itation data is that the series could be affected by two kinds ofinhomogeneities: (1) in the precipitation amount and (2) in thenumber of rainy days. The latter can obviously generate aninhomogeneity also in the precipitation amount series. In fact,a period with some nonindicated missing data could beinterpreted as a period with an underestimation of totalprecipitation and it could be badly corrected by increasingeach single rainy day and originating some erroneous extremeevents. So, we decided to check separately both total precip-itation and the number of rainy days.[18] Concerning the inhomogeneities due to the number of

rainy days, their signal often disappeared from the homoge-neity test curve if a different threshold to define a day as rainywas used (1mm rather than 0.1mm). This is due to instrumentresolution: sometimes improvement in precipitation gaugesleads to a higher number of rainy days with low precipitationamount [Nicholls andMurray, 1999]. Therefore the choice ofa threshold to define the rainy days is not a negligible problemin this kind of analysis. Also the precipitation intensity,defined as precipitation amount per rainy day, is influencedby this choice. In the following statistical analysis all theprecipitation events lower than 1 mm were neglected andconsidered as 0 mm events. Some other inhomogeneities inthe number of rainy days were adjusted by the precipitationhomogenization procedure, because, by correcting total pre-cipitation, some days with below threshold amount wereincreased above threshold and vice versa, and the break signalin the test curve of the number of rainy days disappeared.[19] We considered checks of both rainy days, and total

precipitation, and we decided to correct only inhomogene-ities in precipitation amounts if they corresponded toperiods without problems in rainy days or with problemsthat could be solved by total precipitation homogenization.The remaining inhomogeneities in the rainy days wereeliminated by invalidating the erroneous periods.[20] Six of the 45 series displayed so many homogeneity

problems that they were classified ‘‘not homogenisable’’. Atthe end, a set of 39 single series constituted the finalhomogenized database.[21] It was possible to perform a comparison of the iden-

tified inhomogeneities with the history of the stations and ahigh percentage of the breaks were explained through meta-data. A total of 36 breaks were homogenized: for 15 of themthere was a relocation or some changes in the instruments, 4correspond to the World War periods and 5 to a re-start of thedata collection after an interruption. So, a total of 24 breaksout of 36 were supported by information about the history ofthe stations.

4. Method for Checking and Adjusting Series ona Daily Basis

[22] Homogeneity testing and adjusting was performed inregional subgroups of 10 series using a version of the

Figure 1. Map of the stations (filled circles indicatestations that passed the homogenization procedure; opencircles indicate stations rejected from the homogenizationprocedure). The five regions obtained from the PCAanalysis (Figure 2) are indicated too.

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HOCLIS procedure [Auer et al., 1999] modified and adap-ted to daily data. HOCLIS rejects the a priori existence ofhomogeneous reference series. It consists of testing eachseries against other series, by means of a multiple applica-tion of the Craddock test [Craddock, 1979], in subgroups of10 series. The break signals of one series against all othersare then collected in a decision matrix and the breaks areassigned to the single series according to probability. Thissystem also avoids trend imports and an inadmissibleadjustment of all series to one or a few ‘‘homogeneousreference series.’’[23] The adjusting coefficients were calculated on a

monthly basis, then they were fitted with a trigonometricfunction in order to obtain daily corrections without dis-continuities from one month to another. These correctionswere used to homogenize the daily records. Moreover,while in the HOCLIS procedure only one neighborhoodseries was used as reference to adjust one single break, herewe considered more reference series and we calculated thecorrecting coefficients against each of them, and the averageamong the most coherent subgroup of coefficients (with thesame yearly cycle) was used to homogenize the break.[24] At the end, since the homogenization of daily data is

a delicate topic, once all the series were homogenized, thehomogeneity test was applied also to the precipitationintensity series (precipitation amount per rainy day) toverify if the procedure had introduced any errors, whichwas not the case. Moreover, a comparison between theoriginal and the homogenized data set was performed inorder to evaluate the degree of bias of the original data - arethe data systematically biased or are all adjustments ran-dom? This issue was investigated by analyzing the adjust-ment series. As these series contain the factors that wereused to increase or decrease original records in order toproduce homogeneous data, this analysis will reveal anysystematic errors in the original records. The results (notshown) highlighted that homogenization did not introduceany systematic change.[25] After the homogenization, to prevent missing data

from introducing any bias we used a procedure described inKarl et al. [1995] and widely used in many works [Karl andKnight, 1998; Brunetti et al., 2000, 2001a, 2001b] toestimate them. Basically, a gamma function is fit to eachstation’s daily data for each month of the year. To determineif precipitation occurs on any missing day, a randomnumber generator is used such that the probability ofprecipitation is set equal to the empirical one on that day.If precipitation occurs, then the gamma distribution is usedto determine the amount that falls for that day, again using arandom generator.

5. Regionalization

[26] The first step in analyzing the data was to cluster ourdata set into homogeneous precipitation areas by means ofPrincipal Component Analysis (PCA) applied to monthlytotal precipitation series. PCA allows the identification of asmall number of variables known as principal components(PCs), which are linear functions of the original data, thatmaximize the amount of their explained variance. Thetechnique can be applied both to correlation and covariancematrixes. We used the correlation matrix R (in order to

avoid a domination of the series with stronger variance)based on the monthly anomalies (in order to avoid thedominance of the annual cycle). R is defined by ZZt, Zbeing a matrix containing the standardised precipitationanomalies series (for a review on PCA see Jolliffe [1990,1993]).[27] The eigenvalues of R reveal that only 5 PCs account

for more variance than the original variables (having eigen-values greater than 1) and that these 5 PCs account globallyfor 65% of the variance of the standardised data. The weightof each station in each PC is given by the eigenvectorcomponents (the factor loadings) corresponding to theeigenvalues. The components of each eigenvector (thefactor loadings) represent the correlation between the stationseries and the corresponding PC.[28] The identified PCs (and consequently the corre-

sponding loadings) are however, not unique. By rotatingthe PCs it is possible to obtain other sets of 5 PCs thataccount for the same fraction of variance of the data. Thebenefit of rotation is that it can be carried out to allow amore simple physical interpretation of the loadings. In ouranalysis we used VARIMAX rotation [see, e.g., Richman,1986].[29] The resulting loadings are 5 vectors of the 39 compo-

nents. We represent the loadings on geographic maps,drawing contours through the points with the same loadings(Figure 2).[30] The loadings patterns allow the following regions to

be identified: the north-western Italy (NW) with eightstations; the northern part of northeastern Italy (NEN) withnine stations; the southern part of northeastern Italy (NES)with seven stations; central Italy (CE), comprising alsoSardinia, with eight stations; and the southern Italy (SO),comprising also Sicily, with seven stations.[31] As indicated in Table 1, the longest series starts in

1787, but most of them have data only since 1880. More-over, there are also some stations that start in only in 1920s.Sometimes the starting year is linked to the basin to whichthe station belongs, so we have different series lengths forthe different regions: NW, NES and CE have data startingfrom 1880, but for NEN and SO most of the series have dataonly since 1920 and 1900 respectively.

6. Methods

[32] After setting up the final data-set in terms of homog-enisation and gap filling, we calculated, for each station,some simple statistics of the precipitation series: the totalseasonal precipitation (TP), the number of wet days perseason (WD) and the mean amount of precipitation per wetday (precipitation intensity, hereinafter PI). Then we calcu-lated the proportion of daily rainfall amounts, belonging tosix precipitation class-intervals for each season and eachyear (C1, . . ., C6), compared with the corresponding totalprecipitation, and the number of events falling into these sixclasses (fC1, . . ., fC6).[33] These categories were defined on the basis of

some percentiles of the precipitation distribution of eachseries as follows: C1, precipitation lower than the 50thpercentile; C2, precipitation between the 50th and the 75thpercentiles; C3, precipitation between the 75th and the90th percentiles; C4, precipitation between the 90th and

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the 95th percentiles; C5, precipitation between the 95th andthe 99th percentiles; C6, precipitation greater than the 99thpercentile. As suggested by Nicholls and Murray [1999] wechose percentiles, rather than some fixed thresholds to beused for all the stations (as in Karl et al. [1995]), to classifyprecipitation because of the strong differences betweennorthern and southern Italy precipitation regimes: a precip-itation event of 50 mm could be a normal event in somestations of northern Italy but an extreme event in southernItaly. Moreover, considering a single station, there are manydifferences also among the different seasons, and using thesame threshold for each day of the year could lead to anoverestimation of the number of extreme events in someseasons and an underestimation in others. Then, we decidedto fit a precipitation distribution to each station and each dayof the year, from which we estimated the thresholds (i.e., themillimeters corresponding to the different percentiles abovedefined to classify precipitation into the six classes). Tocalculate each daily gamma distribution we considered thedata within a 31-day window around that particular day for

every year of the series. At the end, for each station, wecalculated 366 (by also considering the 29th of Febrary)threshold values for each precipitation category. Aftercalculating TP, WD, PI, C1, . . ., C6, fC1, . . ., fC6 seasonaland yearly series for each station, they were converted intoanomalies series and the regional average series werecalculated by simply averaging the yearly and seasonalTP, WD, PI, C1, . . ., C6 anomalies series over all thestations of the area.[34] The regional series were analyzed for trend with the

Mann-Kendall nonparametric test. Use and computation ofthis test has been well described by Sneyers [1990]. Theslopes of the trends were calculated by least squares linearfitting.[35] In the Mann-Kendall test, for each element xi (i =

1 ,.., n) of the series, the number ni of lower elements xj(xj < xi) preceding it ( j < i) is calculated [Sneyers, 1990] andthe test statistic t is given by t = Si ni.[36] In the absence of any trend (null hypothesis), t is

asymptotically normal, independently of the distribution

Figure 2. Factor loading patterns of the first five rotated Principal Components obtained from the PCA.

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function of the data and u(t) = (t � hti)/ffiffiffiffiffiffiffiffiffiffiffiffivar tð Þ

phas

standard normal distribution, with hti and var(t) given by:

th i ¼ n � n� 1ð Þ4

var tð Þ ¼ n � n� 1ð Þ � 2nþ 5ð Þ72

The null hypothesis can therefore be rejected for high valuesof ju(t)j being the probability a1 of rejecting the nullhypothesis when it is true derived by a standard normaldistribution table:

a1 ¼ P uj j > u tð Þj jð Þ

7. Results

[37] Figure 3 shows the yearly filtered (with a 31-yearrunning window and 5-year s gaussian filter) series for TP,WDs and PI.

7.1. Total Precipitation

[38] TP shows no trend in northern regions (NW, NENand NES) and a significantly negative trend in southernregions (CE and SO) with a decrease of about �10% percentury in total yearly precipitation (Table 2). CE trend ismainly due to the spring season, that displays a �23%decrease per century, corresponding to �46 mm/100 ywhich is greater than half of the yearly trend amount(�82 mm/100 y). SO yearly trend (�9%/100 y, corre-sponding to �56 mm/100 y) is mainly due to the winterseason, with a decrease of �17%/100 y (corresponding to�35 mm/100 y).

7.2. Number of Rainy Days

[39] WDs show similar behavior toward a decreasingtrend in all regions, with the greatest decreases from1930s to 1940s and from 1960s to 1980s.[40] On a yearly basis there is a highly significant (greater

than 90% in NEN and greater than 99% in all the otherregions) negative trend in the number of WDs in all theregions (Table 2), ranging from �7% per century in NEN to�15% per century in CE (corresponding respectively to �6and �14 days per century). On a seasonal basis, the trend isalways negative and the temporal variability is similar in allregions (not shown). The strongest contribution to yearlytrend comes from Spring (s.l. > 95% in NW and s.l. > 99%in NES and CE, with trend ranging from �14%/100 y inNES to �22%/100 y in CE, corresponding respectively to�3.2 and to �5.5 days) and Autumn (s.l. > 90% in NW andNES and s.l. > 99% in CE, with trend ranging from �12%/100 y in NES to �17%/100 y in CE, corresponding to �2.5and �4 days respectively) in all regions but SO, where thegreatest part of the trend is due to Winter.

7.3. Precipitation Intensity

[41] As a consequence of the decreasing trend in WDs allover Italy and of the nonuniform behavior of TP, PI shows atrend, with different coefficients and significance level,which is positive almost everywhere and in all seasons(apart from Summer in NEN, Spring in CE and Winter inSO).

[42] On a yearly basis, significant (s.l. greater than 99%)positive trends (Table 2) were registered in NW and NES,because of a strong decrease in the number of WDs. Thistrend is mainly due to summer and autumn (with a littlecontribution coming from winter, but only for NES). In CEthere are significant positive trends only in autumn(s.l. greater than 99%) and winter (s.l. greater than 95%),but not in the year. In SO no significant values were found.This is because in southern Italy (CE and SO), both WDsand TP show a significant decrease.

7.4. Progressive Trend Analysis

[43] Trend values and significance vary with the length ofthe analyzed period. No trends in NEN and lower signifi-cance of the SO trends could be due to the later starting yearof series (1921 and 1901 respectively). Therefore to realizea more comparable analysis, the trends were calculated alsoin a progressive way. This provided us also with a moredetailed view of the time behavior of trends.[44] The previously described trend analysis was then

applied to the series starting from the i-th year, with irunning from the beginning of each series to 1962 (wechose 1962 in order to have series not shorter than 40 yearsto calculate the trends), and ending with the last one. The

Figure 3. Yearly TP, WDs and PI series for the fiveregions. The data were smoothed with a 31-year widthwindow and 5-year s gaussian filter.

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results are shown in Figure 4 (only for the year) for TP,WDs and PI.[45] Yearly TP have a significant negative trend in CE

and SO only if the series starts before the 1910s. On aseasonal basis (not shown) the long period trend is alwaysnot significant, apart from the spring in CE, where it issignificantly negative if the series starts before 1920s.Winter (not shown) is particularly interesting: in all regionsbut SO the trend becomes more and more negative the laterthe starting year is, even if it reaches significant values onlyin CE.

[46] Yearly WDs have a constant negative trend in allregions that is nearly always significant considering thesubseries beginning before 1930s. Subseries starting after1940 have a temporary attenuation of the trend strength,followed by a faster decreasing rate, reaching their lowestamount around 1960, with significant values in NES, CEand SO (the strongest trends are in NES and CE with aregression coefficient of about �45%/century for the period1960–2002). This behavior seems to be due to the strongminimum in the number of rainy days located around 1940s(see Figure 3). Also for WDs, winter (not shown) is

Table 2. Trends of TP, WDs, and PIa

W Sp S A Y

NWTP

mm � � + + �% � � + + �

WDRainy days � �(3.4 ± 1.3) � �(2.5 ± 1.4) �(7.5 ± 2.7)% � �(18 ± 7) � �(13 ± 7) �(9 ± 3)

PImm/rainy day + + +(0.8 ± 0.4) +(2.2 ± 0.8) +(1.1 ± 0.3)% + + +(7 ± 4) +(17 ± 6) +(10 ± 3)

NENTP

mm + � + + �% + � + + �

WDRainy days � � + � �(6.3 ± 4.9)% � � + � �(7 ± 5)

PImm/rainy day + + � + +% + + � + +

NESTP

mm + � + + +% + � + + +

WDRainy days � �(3.2 ± 1.1) � �(2.5 ± 1.2) �(7.4 ± 2.4)% � �(14 ± 5) � �(12 ± 5) �(9 ± 3)

PImm/rainy day +(0.8 ± 0.4) + +(1.7 ± 0.4) +(1.3 ± 0.4) +(1.0 ± 0.2)% +(10 ± 5) + +(17 ± 4) +(13 ± 4) +(11 ± 2)

CETP

mm � �(46 ± 12) � � �(82 ± 31)% � �(23 ± 6) � � �(10 ± 4)

WDRainy days � �(5.5 ± 1.3) �(1.5 ± 0.9) �(4.0 ± 1.4) �(13.6 ± 2.6)% � �(22 ± 5) �(11 ± 7) �(17 ± 6) �(15 ± 3)

PImm/rainy day +(0.5 ± 0.3) � + +(1.1 ± 0.4)% +(6 ± 4) � + +(9 ± 3)

SOTP

mm �(35 ± 19) � � � �(56 ± 32)% �(17 ± 9) � � � �(9 ± 5)

WDRainy days �(4.0 ± 1.7) � � � �(7.5 ± 2.5)

% �(16 ± 7) � � � �(11 ± 4)PI

mm/rainy day � + + + +% � + + + +aThe values are expressed in trend amount per century. When the trend has a significance level lower than 90% only the sign is reported, otherwise it is

expressed with the regression coefficient. The trends expressed as percentage variation per century (relative to the yearly and seasonal means of the 1961–1990 standard period) are indicated too. (Boldface values indicate significance greater than 99%; Italic values indicate significance greater than 95%).

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particularly interesting: it displays the same characteristicsobserved for TP.[47] PI yearly trend is always positive with higher values

the more recent the starting year of the subseries is. It hassignificant values for nearly all the subseries inNES, for thosestarting before 1930s inNWandonly for those starting around1920 in CE. This behavior is similar in all seasons with anincreasing noise in the last years due to the decrease in theportion of series where the trend is calculated.

7.5. Analysis of Precipitation Categories

[48] The tendency in Italy toward an increase in the meanamount of precipitation per wet day can be statisticallystudied in more detail using the class-interval contributions(C1, . . ., C6). This allows a better evaluation than PI of theevolution of the shapes of the wet day amount distributions.[49] The series of the relative contribution of the six class

categories to total yearly precipitation are shown in Figure 5for the five regions. Figure 6 shows the results of theapplication of trend analysis to seasonal and yearly regional

C1, . . ., C6 series. There is a general tendency in northernregions (NW,NEN andNES) toward a decrease in the relativecontribution of the lower classes (C1, C2 and C3) and anincrease in that of higher classes (C4, C5 and C6). This isparticularly evident in NWand NES on a yearly basis, wherenearly all category trends reach significant values (negativefor the lower classes and positive for the higher ones), mainlydue to summer and autumn forNWand to all seasons forNES.CE has a similar well-defined behavior, even if the signifi-cance is lower (here the strongest contribution comes fromAutumn). SO has a nondefined tendency in the precipitationdistribution and rarely the trends are significant. NENhas fewsignificant trends (only a positive trend inC6, for the year, anda negative trend for C2 in winter), probably because the seriesis shorter.[50] To understand if the above described behavior (the

increase in the relative contribution of higher events to totalprecipitation amount and the decrease of the lower ones) islinked to a tendency of Italian precipitation to have morefrequent extreme events, we also analyzed the number ofevents falling into each class category (fC1, . . ., fC6). Figure 7shows yearly series of fC1, . . ., fC6 for each region.[51] The results of trend analysis are shown in Figure 8

following the same representation procedure of Figure 6 forC1, . . ., C6. The signal is very clear: there is a strong andhighly significant negative trend in the number of eventsfalling into the lower classes. This decrease is evident in allregions, and also in many seasons. Positive trends in thenumber of events falling into the higher classes is observedtoo, but only in northern regions (as for NEN and NES in theyear), while in CE and SO all categories display negativetrends, even if the higher ones do not reach significant values.

7.6. Progressive Analysis of Precipitation Categories

[52] A progressive trend analysis was applied to classcategories too (Figures 9 and 10). The clearest signal in theC1, . . ., C6 and fC1, . . ., fC6 trends are in the northernregions, where the highest class shows positive trends, oftensignificant, for all the subseries. This tendency is evidentalso in CE and SO, but only in the subseries starting after1900 in CE and after 1930 in SO, even if no significantvalues are reached. Also in the lowest class, the spatialpattern is similar: there are negative, often significant,trends in all NW and NES subseries, while in CE and SOthey are negative, but not significant, only for subseriesstarting after 1900 and 1930 respectively.

7.7. Remarks

[53] On average the last decade presents the lowestnumber of rainy days per year and the highest amount ofprecipitation per rainy day (Figure 3). This tendency towarda decrease in precipitation events and an increase in theirintensity persists for more than one century. Figure 4b (4c)shows that the WDs (PI) trend is always negative (positive)whatever the starting year of the series is.[54] Aminimum in the number ofWDs similar to that of the

1990s happened also in the 1940s, but it wasmainly related toa minimum in total precipitation (in that period the minimumprecipitation amount of the series was reached) then noparticular increase in the intensity was observed.[55] By considering the time series of the class catego-

ries (Figures 5 and 7) it is worth noticing that there are

Figure 4. Progressive trend analysis for (a) TP, (b) WDs,and (c) PI. Each point of a curve represents the regressioncoefficient (expressed as variation per century) calculatedfor the series beginning from the year in which it is locatedand ending with the last one (2002). Thick portions of thecurves indicate trends with s.l. > 95%.

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high values between the end of the 19th and thebeginning of the 20th Century, both in the relativecontribution to total precipitation and in the frequencyof the C4, C5 and C6 class categories. Their values arecomparable with the recent ones for C4 and C5 but notfor C6, for which the recent period shows the highestvalues of the series.[56] A comparison with similar studies, performed by

other researchers for other regions of the globe, isdifficult to make because of the lack of long dailyprecipitation series. Only for the United States, daily datastarting from the end of the 19th century have beenstudied [Kunkel et al., 2003]. Even if the analysisperformed is different from ours, some similar resultscan be observed. They studied event durations of 1, 5, 10and 30 days, and return period of 1, 5, and 20 years. Forall combinations of duration and return period, heavyprecipitation frequencies were relatively high during thelate 19th/early 20th Centuries, decreasing to a minimumin the 1920s and 30s, followed by a general increase intothe 1990s. For 1-day duration events, frequencies at thebeginning of the 20th Century are comparable in magni-tude to frequencies in recent decades, and for 5 and10-day duration events, frequencies during 1895–1905are only slightly smaller than those of the late 20thCentury, while for the 30-day return period events fre-

quencies during the late 19th/early 20th Centuries aresmaller than in the recent period.

8. Conclusions

[57] An analysis of 45 daily precipitation records over120 years for Italy was undertaken to identify any changesin the characteristics of precipitation distribution that mayhave occurred. The series were homogenized on a dailybasis, completed by means of statistical methods andgrouped into five regions by a Principal Component Anal-ysis. The research concerned seasonal and yearly totalprecipitation, number of wet days, precipitation intensityand proportion and frequency of daily precipitation fallinginto six precipitation class-intervals defined on the basis ofsome percentiles of the precipitation distribution.[58] The principal results are as follows:[59] 1. The number of wet days in the year has a clear and

highly significant negative trend all over Italy, ranging from�7% per century to �15% per century. The strongestcontributions to yearly trend come from spring and autumn.[60] 2. Besides the reduction in the number of wet days,

there is a tendency toward an increase in precipitationintensity. This increase is globally less marked than thedecrease in the number of wet days and reaches significantvalues in the year only in northern regions.

Figure 5. Yearly series of the relative contribution of the six class-categories to TP for the five regions.The data were smoothed with a 31-year width window and 5-year s gaussian filter.

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NW NEN NES CE SO

W

Sp

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Figure 6. Trends (expressed as percentage variation per century relative to the mean value of the 1961–1990 standard period) of the relative contribution to TP of the six precipitation class categories. (Black =s.l. > 99%; grey = s.l. > 95%; light grey = s.l. > 90%).

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Figure 7. Yearly series of the number of events falling into the six class-categories for the five regions.The data were smoothed with a 31-year width window and 5-year s gaussian filter.

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NW NEN NES CE SO

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Figure 8. Like Figure 6, but for the number of events belonging to each class category.

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Figure 9. Like Figure 4, but for the relative contribution of each class category to TP.

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[61] 3. The increase in precipitation intensity is due to adecrease in the contribution of low precipitation categoriesto total precipitation and to an increase in that of highercategories corresponding to heavy precipitation events. Thisis more evident in northern regions (NW, NEN and NES)with highly significant trends and in CE even if thesignificance is low, while no clear signal is present in SO.[62] 4. Considering the frequency of events falling into

each category, rather than their relative contribution to totalprecipitation, a more uniform behavior all over Italy wasobserved: there is a highly significant decrease in thenumber of events falling into lower categories in all regions.[63] 5. In northern regions there is also evidence of a

significant increase in the number of events falling into thehighest class-interval (comprising events above the 99thpercentile).[64] Italy, due to its orography, is particularly exposed to

the negative effects of high intensity precipitation events. Itis one of the European countries with the greatest humanloss due to flood events. At the same time droughts are afrequent problem for agriculture, in particular during sum-mer, when the lack of water resources often compromise thecrop in large regions, especially in Southern Italy. Theresults of this paper highlight both these two aspects,indicating the last decade as the one with the lowest numberof rainy days per year and the highest amount of precipi-tation per rainy day.

[65] However, the tendency toward a decrease in thenumber of rainy days, and an increase in intensity, that wasalready observed in a previous study for the last 50 years[Brunetti et al., 2001a], is not a peculiarity of recent decades,but it has persisted since the end of the 19th century. From thisstudy, it is evident that most of this negative trend is explainedby the marked decrease in the frequency of low intensityprecipitation events (below the 75th percentile).

[66] Acknowledgments. Wewould like to thank theUfficioCentrale diEcologia Agraria (UCEA) and the Servizi Idrografici for their cooperation ingiving access to databases. Special thanks go to the Consorzio Interuniversi-tario per il Calcolo Automatico (CINECA) for computing support. Theresearch was partially developed in the framework of the FIRB and theCLIMAGRI projects.

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�����������������������M. Brunetti and T. Nanni, Consiglio Nazionale delle Ricerche, Istituto di

Scienze dell’Atmosfera e del Clima, via Gobetti, 101–I-40129 Bologna,Italy. (m.brunetti@isac.cnr.it)M. Maugeri and F. Monti, Istituto di Fisica Generale Applicata, via Brera,

28– I-20121 Milan, Italy.

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