Climatic characteristics of middle-southern Apulia (southern Italy)

Transcription

1 Climatic characteristics of middle-southern Apulia (southern Italy) ANTONELLA MARSICO, MASSIMO CALDARA, DOMENICO CAPOLONGO and LUIGI PENNETTA Dipartimento di Geologia e Geofisica, Università di Bari, Italia; a.marsico@geo.uniba.it (Received 11 th June 2007; Revised 20 th November 2007; Accepted 23 rd November 2007) Abstract: The purpose of this work is to make a climatic map in order to show several variables concerning climate type in a Mediterranean region. The study is focused on the middle and southern part of Apulia region, southern Italy, an area of approximately 12,170 km 2 between and N and and E. The map uses mean annual temperature as a background for the number of dry and cold months, and is supplemented with secondary maps which highlight the characteristics of a dry climate. Rainfall and temperature records of 46 thermo-pluviometric stations, over a 30 year period, were interpolated by kriging: the main map takes into account the Rapetti and Vittorini pattern, while for the other graphics the Thornthwaite and Mather water balance model was followed. Both these methods focus on factors which determine drought in a land of low rainfall such as Apulia region. ISSN

2 1. Introduction Apulia is the emerged part of a plate stretched between the Ionian Sea and the Adriatic Sea, constituting the foreland of Apenninic orogens. Two natural regions can be identified in the study area: the Murge plateau, with a maximum altitude of 650 m a.s.l., and the Salento Peninsula, with an average altitude of 100 m a.s.l.. A thick Giurassic-Cretaceous carbonate sequence widely outcrops on the Murge plateau and on the Serre Salentine. The carbonate rocks are overlain by thin transgressive calcareous and marly-calcareous deposits of Tertiary and Lower Pleistocene age. During the Middle and Upper Pleistocene, terrigenous and carbonate marine terraced deposits formed during several cycles produced by the superimposition of glacioeustatic sea level changes to the general uplift of the region (Ciaranfi et al., 1992). Finally, the Apulia landscape is characterized by marine and complex karst landforms. The Apulian region climatologically belongs to the south-eastern Mediterranean area. The climate is designated as Cs in the Koppen classification (Pinna, 1977) to indicate a sub-tropical climate with dry summers (Zito et al., 1989). In particular, the Cs climate is characterised by winter rainfall and summer drought: the maximum rainfall falls in autumn with another peak in March, while July and August are the driest months (Zito and Cacciapaglia, 1993). In this work climatic features are measured using 30 years of meteorological data. For the period, monthly mean temperature and total monthly rainfall time series were interpolated. The map is constructed using the mean annual temperature as the background for the distribution of climatic constraining factors (Rapetti and Vittorini, 1994b;a): the number of dry and cold months. In the bordering maps and graphs, the assessment of potential evapotranspiration (PET), actual evapotranspiration (AET), water surplus and water deficit, according to the Thornthwaite and Mather (1955; 1957) water balance model were calculated. Finally, diagrams of PET, AET and rainfall were plotted for four sample stations in order to usefully summarize the annual water balance. These diagrams allow the map user to determine periods of soil water deficit, soil water recharge, soil water utilization and soil water surplus. 343

3 2. Methods The meteorological dataset is made from records of 63 pluviometric stations and of 47 thermometric stations collected from the Hydrographical Service of Apulia Region. According to WMO (World Meteorological Organization) recommendations, a data period of 30 years is used; in this case the period is Daily records were recorded at stations widespread in the study area, with monthly means correlated and homogenizated. Each single series was compared to the others within the same climatic area by making a significant number of differences. These differences are then tested for discontinuities and all breaks were estimated. Automatic data processing was performed in a GIS using a kriging interpolator in order to analyse the spatial distribution of climatic records (Biau et al., 1999; Hartkamp et al., 1999). Kriging uses the results of variogram analysis to compute the weights for observed data. Variogram modelling focuses on the evaluation of how spatial variability develops over specific distances and in different directions. The effectiveness of the method was evaluated by comparing true and estimated values produced by a cross-validation procedure, in order to estimate the mean error and variance of errors. This interpolation technique was considered suitable for the study area which is not a morphologically complex territory and its climatic parameters, temperature in particular, change according to elevation. Based on the spatial distribution of primary data, the longitude and latitude grid resolution is 100 metres for all interpolated maps. The spatial distribution of mean annual temperature constitutes the background for the main map: the climatic constraining factors are superimposed according to the Carte Climatique Détailléé de la France (Centre C.N.R.S. - Section de Geographie, 1970) and the Carta Climatica della Toscana (Rapetti and Vittorini, 1994b;a). The climatic constraining factors are the number of dry and cold months. According to Bagnouls and Gaussen (1957) a month is considered dry when the average total rainfall (P), in mm, is less than twice the mean temperature (T), in C (P 2T); a dry period is then defined as the number of dry months. A cold month is one that has a mean temperature less than 7 C, representing conditions under which vegetation is largely dormant. Dry and cold months are represented using an oblique bands plot: these are red and oriented in a NW-SE direction for the number of dry months, and blue bands oriented in a NE-SW direction for 344

4 the number of cold months. The thickness of bands represents the number of months of the constraint factor. In this way a grid is made which is overlaid on the thermal background in order to understand the variability of constraining factors in the study area. The meteorological dataset of all stations was also used to estimate evapotranspiration and other water balance variables as represented in the bordering graphs and maps. The potential evapotranspiration, PET, is a measure of atmospheric demand for water vapour from evaporation and transpiration. PET is the amount of moisture which, if available, would be removed from a given land area by evapotranspiration expressed in terms of depth (mm) of water. The Thornthwaite formula for estimating PET requires only two variables, mean monthly temperature values, and the average monthly number of daylight hours. The actual evapotranspiration, AET, is the amount of water that is actually removed from a surface due to the processes of evaporation and transpiration (Pidwirny, 2006). Water deficit is the amount by which the available moisture fails to meet the demand for water. Water surplus is the excess remaining after the evaporation needs of the soil have been met (i.e., when actual evapotranspiration equals potential evapotranspiration) and soil storage has been returned to the water holding capacity level. Water deficit and surplus are calculated from the potential and actual evapotranspiration values. To automate the process, a program was used for calculating the annual climatic water balance, according to the modified Thornthwaite method (Willmott et al., 1985). The potential evapotranspiration was computed using methods by Forsythe et al. (1995) to calculate day length from the latitude. The program allows the field capacity to be chosen according to the climatic features of the study area. Once the field capacity is chosen, a declining availability function must be set. This describes the proportion of evaporative demand on soil moisture that is met for a given soil wetness (percent field capacity); generally, as soils dry, it is harder to remove the remaining water (Gavin, 2007). The output file lists monthly PET, AET and surplus. Deficit was computed by subtracting the potential evapotranspiration from the actual evapotranspiration for the considered period. The output monthly PET, AET and total monthly rainfall for four sample stations (Barletta, Gioia del Colle, Otranto and Taranto), representing different portions of the study area, were graphed in order to summarize the annual water balance. The monthly average soil water balance graphs allow 345

5 periods of soil water deficit, soil water recharge, soil water utilization and soil water surplus to be determined. The soil water deficit occurs when AET<PET; typically from May to October at these sites. The soil water recharge is plotted from P>AET until accumulated (AET-P) and is replenished. Recharge typically occurs from November through January in Barletta and Taranto, where rainfall is lower, and from October to December in Gioia del Colle and particularly Otranto where autumn rainfall is quite abundant. The soil water surplus period starts after recharge is complete and so differs between the sites, but it finished in May when the water deficit period begins. The soil water utilization occurs when P<AET (Mehta, 2006): this period is from April to July in Barletta and Taranto while it extends until the begining of August in Gioia del Colle and Otranto. 3. Conclusions The distribution of both the thermal background, and climatic constraining factors, in the main map reflects the assessment of water balance. Differences in the climate in different parts of the region are also demonstrated by the four sample diagrams. Therefore, the map synthetically shows the climate of the middle-southern Apulia. Although all the Apulia region belongs to the south-eastern Mediterranean area that is a sub-tropical climate with dry summers, the maps and the diagrams show that it can be divided into homogeneous climatic areas according to the physical-geographical features. In fact, its geographical position, the strong influence of both Adriatic and Ionian sea and the occurrence of low relief causes a changes in average temperature and rainfall distribution over the whole study area. Acknowledgements We wish to thank G. Amoruso and the employees of the Hydrographical Service of Apulia Region for temperature and rainfall dataset. 346

6 4. Software In order to collect daily data at each station, calculate mean monthly temperature and total monthly rainfall for the reference period and to produce the annual water balance diagrams, Microsoft Excel software was used. The annual climatic water balance was calculated using AET Calculator 1.0 (March 2007 revision). This program was written by Daniel Gavin. The map was produced using ESRI ArcMap 9, including the kriging interpolator for the main maps and the distribution of PET, AET, water deficit and water surplus. The climatic constraining factors plot was designed using Adobe Photoshop CS and also used to complete diagrams and to make the monthly average soil water balance legend. References BAGNOULS, F. and GAUSSEN, H. (1957) Les climats biologiques et leur classification, Ann. de geogr., 355, BIAU, G., ZORITA, E., VON STORCH, H. and WACKERNAGEL, H. (1999) Estimation of Precipitation by Kriging in the EOF Space of the Sea Level Pressure Field, Journal of Climate, 12, CENTRE C.N.R.S. - SECTION DE GEOGRAPHIE (1970) Carte Climatique Détailléé de la France, Editions Ophrys, Paris à Gap. CIARANFI, N., PIERI, P. and RICCHETTI, G. (1992) Note alla carta geologica delle Murge e del Salento (Puglia centro-meridionale), Mem. Soc. Geol. It., 106, FORSYTHE, W. C., RYKIEL, E. J., STAHL, R. S., WU, H. and SCHOOLFIELD, R. M. (1995) A model comparison for daylength as a function of latitude and day of year, Ecological Modelling, 80, GAVIN, D. (2007) AET Calculator 1.0 [online], Available from: [Accessed: February-March 2007]. HARTKAMP, A. D., DE BEURS, K., STEIN, A. and WHITE, J. W. (1999) Interpolation Techniques for Climate Variables, NRG-GIS Series Mexico, D.F.: CIMMYT. 347

7 MEHTA, V. K. (2006) A simple water balance model [online]. SArghyam/Cornell University, Available from: onlinetext.doc [Accessed: February 2007]. PIDWIRNY, M. (2006) Physical Geography, Glossary of terms [online], Available from: [Accessed: April 2007]. PINNA, M. (1977) Climatologia, UTET, Torino. RAPETTI, F. and VITTORINI, S. (1994a) Carta climatica della Toscana centro-meridionale e insulare, C.N.R. Centro di Studio per la geologia strutturale e dinamica dell Appennino. Pisa, 4 pp., 1 carta f.t. RAPETTI, F. and VITTORINI, S. (1994b) Carta climatica della Toscana centro-settentrionale, C.N.R. Centro di Studio per la geologia strutturale e dinamica dell Appennino, Pisa, 4 pp., 1 carta f.t. THORNTHWAITE, C. W. and MATHER, J. R. (1955) The Water Balance, Publications in Climatology, 8, 1, Drexel Institute of Technology, Centerton, NJ. THORNTHWAITE, C. W. and MATHER, J. R. (1957) Instruction and tables for computing potential evapotranspiration and the water balance, Publications in Climatology, 10, 3, Drexel Institute of Technology, Centerton, NJ. WILLMOTT, C. J., ROWE, C. M. and MINTZ, Y. (1985) Climatology of the terrestrial seasonal water cycle, Journal of Climatology, 5, ZITO, G. and CACCIAPAGLIA, G. (1993) Precipitazioni in Puglia: mappe stagionali, In Proceedings of 5 th Workshop Progetto Strategico Clima, Ambiente e Territorio nel Mezzogiorno, Amalfi, Aprile 1993, pp ZITO, G., RUGGIERO, L. and ZUANNI, F. (1989) Aspetti metereologici e climatici della Puglia, In Proceedings of 1 st Workshop Progetto Strategico Clima, Ambiente e Territorio nel Mezzogiorno, Taormina, pp