CHANGES IN TOTAL PRECIPITATION, RAINY DAYS AND EXTREME EVENTS IN NORTHEASTERN ITALY

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 21: (2001) DOI: /joc.660 CHANGES IN TOTAL PRECIPITATION, RAINY DAYS AND EXTREME EVENTS IN NORTHEASTERN ITALY MICHELE BRUNETTI a, MAURIZIO MAUGERI b and TERESA NANNI a, * a Istituto ISAO CNR, Bologna, Italy b Istituto di Fisica Generale Applicata, Milan, Italy Recei ed 14 August 2000 Re ised 26 January 2001 Accepted 26 January 2001 ABSTRACT A new daily precipitation dataset, for the period , comprising seven stations located in the northeastern Italy is presented. Seasonal and annual precipitation and number of rainy days have been analysed and a study aimed at investigating precipitation intensity and extreme events has been performed. Precipitation intensity has been analysed through studying both the mean precipitation amount per wet day and dividing precipitation into heavy and non-heavy classes. In addition, the return period of the extreme events has been calculated for 30-year running windows and its variations have been examined. The results show a negative trend in the number of wet days associated with an increase in the contribution of heavy rainfall events to total precipitation. This is in agreement with a reduction in return period for extreme events since Copyright 2001 Royal Meteorological Society. KEY WORDS: daily precipitation; extreme events; Italy; precipitation intensity; return period; trend analysis 1. INTRODUCTION Studies of yearly and seasonal precipitation on global and local scales reveal trends over many regions of the world (Houghton et al., 1996). The trend is globally positive (even if small) throughout the 20th century, although large areas are characterized by negative trends (Houghton et al., 1996). For Europe, the precipitation trend is positive in the north (Førland et al., 1996; Schönwiese and Rapp, 1997) and negative in the south (Schönwiese and Rapp, 1997). For Italy, recent studies confirm a negative precipitation trend (Buffoni et al., 1998, 1999; Piervitali et al., 1998), steeper in the central and southern areas. It seems to be related to an increase in the frequency and persistence of sub-tropical anticyclones over the Mediterranean regions (Piervitali et al., 1997; Brunetti et al., 2000a,b). Variations in total precipitation can be caused by a change in the frequency of precipitation events, or in the intensity of precipitation per event, or a combination of both. In order to improve the understanding of precipitation behaviour as an indicator of climate changes in the last century, daily precipitation series must be analysed. In particular, the studies aimed at analysing the variations in heavy and extreme precipitation are particularly interesting as these events cause considerable damage and loss of life worldwide each year. However, there are very few studies about this topic, probably because of the lack of high-quality daily data. Some studies were performed for the USA (Karl et al., 1995; Karl and Knight, 1998; Trenberth, 1998; Kunkel et al., 1999), Japan (Iwashima and Yamamoto, 1993), eastern and northeastern Australia (Suppiah and Hennessy, 1998; Hennessy et al., 1999; Plummer et al., 1999), South Africa (Mason et al., * Correspondence to: Istituto ISAO CNR, Via P. Gobetti, 101, I Bologna, Italy; t.nanni@isao.bo.cnr.it Copyright 2001 Royal Meteorological Society

2 862 M. BRUNETTI ET AL. 1999), the UK (Osborn et al., 2000) and Italy (Brunetti et al., 2000c, 2001). Karl et al. (1995) and Karl and Knight (1998) observed a significant positive trend in the frequency of extreme rainfalls (greater than 50 mm per day) over the last few decades in the USA. For Australia, Suppiah and Hennessy (1996, 1998) showed a significant increase in the 90th and 95th percentiles, while Hennessy et al. (1999) and Plummer et al. (1999) showed increases in the 99th percentile. Iwashima and Yamamoto (1993) found that, in Japan, more stations recorded their highest precipitation events in recent decades. Groisman et al. (1999) recently performed a study on heavy precipitation over a wide area comprising Canada and Norway (for the period ), the USA and Australia (spanning the period 1910 today), the former Soviet Union ( ), Mexico, China, Alaska and Poland (whose data are available for the post-world War II period). They found an increase both in summer rainy days and in heavy precipitation frequency over the past century for the USA, Norway and Australia, but they found no significant trend for any other country where the series are shorter and/or have many missing data. In most of the analysed areas, the positive trend observed in rain intensity is generally associated with an increase in total precipitation. Groisman et al. (1999) studied the relationship between the increase in total precipitation and the frequency of heavy rain events. They found that the shape parameter of the precipitation gamma distributions remains rather stable, independent of total precipitation, while the scale parameter is most variable. Using these results as a model for the future, as total precipitation increases, a disproportionate increase in heavy precipitation is expected (Groisman et al., 1999). However, the relationship between precipitation intensity and total precipitation is not universal: in some places, like Siberia in the summer season in the period , an increase in heavy precipitation was observed together with a tendency toward a decrease in total precipitation (Groisman et al., 1999). Another area that has this behaviour is Italy. Results for this area are presented in Brunetti et al. (2000c, 2001). Brunetti et al. (2000c) analysed the secular daily precipitation series of five stations located in northern Italy. They observed a decrease in the number of rainy days in the period , which was more significant than the decrease of total precipitation. As a consequence, the rainfall per rainy day in the last 150 years has increased and there is an increase in the proportion of daily precipitation falling in higher class intervals (daily precipitation greater than 25 mm and greater than 50 mm) and a decrease in the proportion falling in the lower ones (daily precipitation less than 12.5 mm). This behaviour was found to differentiate between the western and eastern parts of the examined region. These results were confirmed by Brunetti et al. (2001) who analysed 67 sites of daily precipitation records over the period for the whole Italian territory. They found that in northern Italy the increase in the mean precipitation intensity is mainly due to a strong positive trend in the contribution of the heavy precipitation events. The analysis of sub-regions (northwest, northern northeast and southern northeast) showed that the whole of northern Italy contributes to the strong increase in the higher precipitation categories with the strongest contribution given by northwest and the Alpine region of the northeast (northern northeast). It is evident both from Brunetti et al. (2000c) and Brunetti et al. (2001) that the biggest contribution to the heavy precipitation trend is due to the recent period. However, in Brunetti et al. (2000c), studying longer daily precipitation series (starting more than a century ago), a strong increase in precipitation intensity was also found in the period. This is also a key period for temperature (Maugeri and Nanni, 1998; Brunetti et al., 2000b): from a progressive trend analysis of temperature and daily temperature range series, besides the increase in the last 20 years, they observed an increase in the period between 1920 and Within this context, a program was set up to create a dataset of daily precipitation series for Italy, starting at least in the 1920s. Together with the five series studied in Brunetti et al. (2000c), seven series have been set up for the Alpine region of northeastern Italy. The availability of long daily precipitation series (from 1920 to 1998) allows us also to study the evolution of the return period of extreme events. This is very important for northern Italy where heavy rainfall events are rather frequent and many disastrous floods have been reported in the last century (Giuliacci, 1988; Maugeri et al., 1999).

3 CHANGES IN TOTAL PRECIPITATION IN NORTHEASTERN ITALY DATA Meteorological observations in Italy have been recorded since 1654 by the Accademia del Cimento, but they only became a regular practice in the 18th century, when observatories were founded in Bologna (1716), Padua (1725), Turin (1756), Milan (1763), Rome (1782) and Palermo (1791) and then, in the 19th century, at a number of other cities. At the present these data are still almost entirely available, even if a great part of them has not yet been digitized for computer. Both in the 1970s (Anzaldi et al., 1980) and in the 1990s the Italian National Research Council (CNR) carried out two projects of reconstruction and digitization of Italian meteorological series. An updated, revised, and homogenized version of the 1970s CNR database was set up and analysed (Buffoni et al., 1998, 1999). The new database contains 32 secular monthly precipitation series. Here a daily precipitation dataset for Italy is being created. Five daily series were already set up (Milan, Genoa, Bologna, Mantova and Ferrara) and they were analysed to look for a trend in precipitation intensity. In this paper, the results obtained from the analysis of daily series recovered from the Alpine eastern part of northern Italy, an area covering about km 2, will be described. The new database includes Belluno, Bolzano, Bressanone, Monte Maria, Riva, Rovereto and Trento. The location of the stations is shown in Figure 1. All the series cover the period METHODS After digitizing the data, each daily value was checked with the same procedure adopted for the series analysed in Brunetti et al. (2001). This procedure consists of: (i) carrying out cross checks among the stations; (ii) considering as possible errors the data which may be very different from the rainfall records of all other stations; (iii) checking these data by means of comparison with precipitation data of nearby stations. Precipitation data are available with rather high resolution (see e.g. Servizio Idrografico, ), the only problem is that generally they have not been digitized. After these checks, monthly precipitation series were calculated for each station and then the monthly dataset was tested for homogeneity by means of the Craddock homogeneity test (Craddock, 1979). In order to minimize errors, average series for northeastern Italy were used as references. They were based also on the data described in Buffoni et al. (1999). For each series, the reference was calculated excluding Figure 1. Location of the seven stations

4 864 M. BRUNETTI ET AL. the series to be tested and weighting each series by the square of its correlation coefficient with the series to be tested (Alexandersson, 1986). The tests showed a good level of homogeneity for all the series. After the data check, the records were completed using a procedure described in Karl et al. (1995) and Karl and Knight (1998) to estimate missing data. It is widely recognized that daily precipitation statistics are realistically represented by the gamma distribution (x,, ) (Guttman et al., 1993; Wilks, 1995) whose shape is: (x,, )= x 1 e x/ ( ) when x 0 and (x,, )=0 when x 0. The parameter defines the shape of the distribution, while the parameter characterizes the scale of intensity of the daily precipitation (the higher is, the higher the intensity is). The mean and the variance 2 of this distribution are defined by these two parameters as follows: = 2 = 2 The computation of these parameters with the sample data yields a set of two equations in two unknowns: ˆ = 2 2 = 2 But this kind of estimation is only suitable for large values of the shape parameter ( 10). A much better approach is to use the method of maximum likelihood employing the sample statistic A=ln x ln x and the estimations for the shape and scale parameters are A ˆ = 4A = x ˆ The distribution function of precipitation totals P(X x) is expressed as: x P(X x)= (x,, ) dx 0 To complete our series, a gamma function was fitted to each station s daily data for each month of the year taking into account only rainy days for the period , obtaining 12 gamma functions for each station. A rainfall event was generated on any missing data (using a random number generator following the gamma distribution to determine the amount that falls for that day) only if the day was classified as a rainy one by generating a random number, such that the probability of precipitation was set to be equal to the empirical value on that day. Globally, the proportion of data reconstructed in this way was less than 4%. After filling missing data, seasonal and yearly anomalies were calculated, relative to mean, for each station for precipitation amount, number of rainy days and precipitation intensity, i.e. average rain amount per rainy day. Yearly values are defined as being the period from 1 December to 30 November; each year is dated by its respective January. Winter values refer to December January February, spring to March April May, summer to June July August and autumn to September October November.

5 CHANGES IN TOTAL PRECIPITATION IN NORTHEASTERN ITALY 865 The three statistics were then averaged over all the stations to obtain yearly and seasonal mean anomalies series for total precipitation (TP), number of wet days (WD) and precipitation intensity (PI). TP, WD and PI are insufficient, however, to describe the nature of precipitation changes. It is important to know whether the change in precipitation frequency is due to a change in the number of days with heavy precipitation or with light precipitation. Information about these kinds of events was obtained by analysis of specific class intervals defined by the daily precipitation quantiles. A gamma function was again fitted to each station s daily data for each month of the year (following the same criteria described before) and the thresholds of the selected quantiles were calculated. The proportions of total yearly and seasonal precipitation falling in the specific class intervals were calculated for each station. Then their anomalies were averaged over the whole area. Taken into account, as suggested by Nicholls and Murray (1999), were the class intervals defined by the precipitation falling on days with rainfall above the 90th, 95th and 99th percentiles (class intervals C2, C3 and C4) to study intense events, and the precipitation falling on days with rainfall below the 90th percentile was analysed (class interval C1) as the not heavy precipitation category. In other words, three classes were analysed within decile 10 and one class that combines deciles 1 9. The mean contribution of the four categories to total yearly and seasonal precipitation is indicated in Table I. As for TP, WD and PI, the seasonal and yearly anomalies for class interval series were also calculated for each station and were then averaged over all the stations to obtain the mean anomalies series. All the series (TP, WD, PI, C1,..., C4) were subjected to the non-parametric Mann Kendall test to detect any trend. Use and computation of this test have been well described by Sneyers (1990). The slopes of the trends were calculated by least square linear fitting. In the Mann Kendall test, for each element x i (i=1,...,n) of the series, the number n i of lower elements x j (x j x i ) preceding it ( j i ) is calculated and the test statistic t is given by t= i n i. In the absence of any trend (null hypothesis), t is asymptotically normal, independently from the distribution function of the data and u(t)=(t t )/ var(t) has a standard normal distribution, with t and var(t) given by: t = n (n 1) 4 var(t)= n (n 1) (2n+5) 72 The null hypothesis can, therefore, be rejected for high values of u(t) being the probability 1 of rejecting the null hypothesis when it is derived by a standard normal distribution table: 1 =P( u u(t) ) The sequential form of the Mann Kendall test, consisting of the application of the test to all the series starting with the first term and ending with the ith and to those starting with the ith one and ending with the last, was also used for a progressive analysis of the series. In the absence of any trend, the graphical Table I. Mean percentage contributions of the four class intervals to total yearly and seasonal precipitation Year Winter Spring Summer Autumn (%) (%) (%) (%) (%) C C C C

6 866 M. BRUNETTI ET AL. representation of the direct (u i ) and the backward (u i ) series obtained with this method gives curves which overlap several times, whereas in the case of a significant trend the intersection of the curves enables to detect approximately its time of occurrence (Sneyers, 1990). Finally, a statistical analysis of extreme events was performed to study their return period (RP). Extremes were defined as the events in the top 0.1% of the distribution of rainy days (the 99.9th percentile). The sixtiles method was adopted to calculate the Jenkinson solution (Jenkinson, 1955, 1969) of the Fréchet functional equation (Fréchet, 1958) which the distribution of the extreme values fulfils, then the RP was calculated on the basis of the theoretical asymptotic distribution. This procedure was applied to 30-year running windows in order to identify variations in the extreme events RP along the series. 4. RESULTS 4.1. Total precipitation TP has no significant trend in the period, even if the general tendency is towards a slow decrease (stronger in spring than in the other seasons, as is evident from Figure 2(a)) in agreement with northern Italy total precipitation (Buffoni et al., 1999; Brunetti et al., 2000c, 2001) Wet days Figure 2(b) shows the percentage variations of WD for the period for both year and seasons. On a yearly basis, WD has a negative trend (significance level (SL) greater than 95%) of 8.3 days/100 years, corresponding to a reduction of 7.2% of the mean yearly WD. The strongest contributions to yearly trend come from spring ( 4.7 days/100 years, SL 90%) and autumn ( 4.2 days/100 years, SL 90%), corresponding to 14.9% and 15.2% of the mean spring and autumn (respectively) rainy days. The seasonal behaviour of WD is quite different from that observed for the last 50 years in Brunetti et al. (2001) due to the contribution of the first 30 years as is evident in Figure 2(b), where regression lines are indicated both for the period and for the last 50 years. This indicates that, particularly on a seasonal basis, is a key period in understanding changes in precipitation events Precipitation intensity and class inter als The weak and insignificant negative trends in TP and the stronger and more significant negative trends in WD indicate an increase in PI. The application of the least square linear fitting to the PI series gives evidence of an increase in the precipitation amount per wet day (see Figure 2(c)) with an increase of about 5% of the mean yearly intensity for the period. The strongest contribution comes from winter and autumn, with an increase of about 20% and 10% of the mean winter and autumn intensity, respectively. However, the Mann Kendall test indicates that there is no significant trend in the year or seasons. The tendency in Italy toward an increase in the mean of precipitation per wet day can be statistically studied in more detail using the class-interval contributions (C1,..., C4). This allows a better evaluation of changes in the shape of the frequency distribution. Figure 3 shows the percentage variations compared to the mean values of the class interval contributions to yearly and seasonal total precipitation. As is evident from Table II, there is a clear increase in the contribution from the high class intervals (C2, C3 and C4) and a decrease in that from the lower one (C1). Yearly trends are all significant (SL 95% for C4 and SL 90% for C1, C2 and C3). The positive trends are greater for higher thresholds and range from 13%/100 years for C2 to 66%/100 years for C4. On a seasonal basis, only C4 has significant trends in winter and autumn (SL 90%).

7 CHANGES IN TOTAL PRECIPITATION IN NORTHEASTERN ITALY 867 Figure 2. Anomalies of yearly and seasonal (a) TP, (b) WD and (c) PI series. Thin straight lines indicate the linear fitting of the series for the period , thick straight lines indicate the linear fitting of the series for the period

8 868 M. BRUNETTI ET AL. Figure 3. Anomalies of yearly and seasonal mean relative contributions of precipitation falling into the four class intervals. The straight lines indicate the linear fitting of the series

9 CHANGES IN TOTAL PRECIPITATION IN NORTHEASTERN ITALY 869 Table II. Results of the application of the Mann Kendall test and of least square linear fitting to the four class intervals Year Winter Spring Summer Autumn C C C C In order to allow comparison between the different data, the results are expressed as ratios (in percentages) between the linear regression coefficient and the mean values in the period. Bold numbers, significance level greater than 95%; non-bold numbers, significance level greater than 90%; when the significance level is lower than 90% only the sign of the slope is given Extreme e ents Figure 4 shows the results of the calculation of the 30-year running RP for the rainfall events falling above the 99.9th percentile (yearly mean averaged over all sites=70 mm/day); each value is dated with the middle year of the 30-year window. There is a strong decrease in RP, indicating that extreme events were becoming more and more frequent during the last century. This confirms the results obtained for the highest class intervals. The curve in Figure 4 decreases slowly until , then it drops sharply and remains almost constant after about This is in agreement with the results of the progressive Mann Kendall analysis performed on the C4 category (Figure 5): here the crossing point between the direct and the backward curves indicates that the beginning of the trend is located around A similar analysis of extreme events RP was performed by Russo et al. (2000) for the Genoa daily precipitation series and they found a strong reduction, particularly in the last years. 5. CONCLUSIONS The analysis of daily precipitation series from seven eastern Italy Alpine region stations gives the following results: (i) the average annual number of wet days has a significant negative trend. On a seasonal basis the trend is more evident in spring and autumn; (ii) the negative trend in WD and the weak reduction in TP indicate an increase in PI which is not significant; Figure 4. Thirty-year running RP of the extreme events. Each RP value is dated with the middle year of the 30-year box. The dashed straight lines indicate the linear fitting of the series

10 870 M. BRUNETTI ET AL. Figure 5. Progressive application of the Mann Kendall test to the C4 series u i : continuous line; u i : dashed line. Straight lines indicate confidence levels of 95% (iii) the analysis of class intervals series shows that the increase in PI is due to a strong increase in extreme rainfall events (decile 10) and a decrease in non extreme events (deciles 1 9); (iv) the return period of extreme events (defined as the events falling above the 99.9th percentile) has become shorter during the period. These results can be compared with those obtained by Brunetti et al. (2001) for the same area, but using shorter series and adopting another class interval classification. Both longer and shorter series lead to the same results indicating an interesting change in the distribution of the rainy days amounts that seems to be mainly located in the 1960s. So, at least for northeastern Italy, the observed increase in precipitation intensity is due to the last few decades, whereas the period does not show any significant trend. The availability of longer time series allowed analysis of trends in the RP of extreme events. The decrease in RP may be relevant to design standards for infrastructure like drains, bridges, roads and dams. This is very important for areas like northern Italy that are frequently affected by heavy rain and flooding. ACKNOWLEDGEMENTS The authors would like to thank the Servizi Idrografici for the cooperation and the referees for helpful suggestions. REFERENCES Alexandersson H A homogeneity test applied precipitation data. Journal of Climatology 6: Anzaldi C, Mirri L, Trevisan V Archivio Storico delle osservazioni meteorologiche. National Research Council (CNR) Report AQ/5/27, Rome. Brunetti M, Maugeri M, Nanni T. 2000a. Variations of temperature and precipitation in Italy from 1866 to Theory of Applied Climatology 65: Brunetti M, Maugeri M, Nanni T. 2000b. Trends of minimum and maximum daily temperatures in Italy from 1865 to Theory of Applied Climatology 66: Brunetti M, Buffoni L, Maugeri M, Nanni T. 2000c. Precipitation intensity trends in Northern Italy. International Journal of Climatology 20: Brunetti M, Colacino M, Maugeri M, Nanni T Trends in the daily intensity of precipitation in Italy from 1951 to International Journal of Climatology 21: Buffoni L, Maugeri M, Nanni T Analysis of Italian monthly precipitation series. In Proceedings of the 2nd International Conference on Climate and Water, Espoo, Finland, Lemmela R, Helenius N (eds); Buffoni L, Maugeri M, Nanni T Precipitation in Italy from 1833 to Theory of Applied Climatology 63: Craddock JM Methods for comparing annual rainfall records for climatic purposes. Weather 34: Førland EJ, van Engelen A, Ashcroft J, Dahlström B, Demaree G, Frich P, Hanssen-Bauer I, Heino R, Jonsson T, Mietus M, Müller-Westermeier G, Pålsdottir T, Tuomenvirta H, Vedin H Change in normal precipitation in the North Atlantic region (2nd edn). DNMI Report 7/96 Klima.

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