ASSESSMENT OF METEOROLOGICAL DROUGHT STATISTICS AND PATTERNS IN CENTRAL GREECE

Proceedings of the 13 th International Conference on Environmental Science and Technology Athens, Greece, 5-7 September 2013 ASSESSMENT OF METEOROLOGICAL DROUGHT STATISTICS AND PATTERNS IN CENTRAL GREECE C.A. KARAVITIS 1, S.G. ALEXANDRIS 1, V.P. FASSOULI 1, D.V. STAMATAKOS 1, C.G. VASILAKOU 1, D.E. TSESMELIS 1 AND N.A. SKONDRAS 1 1 Department of Natural Resources Development & Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos St., 11855 Athens, Greece. e-mail: ckaravitis@aua.gr ABSTRACT Drought is a multifaceted physical event that may seriously affect human activities, sometimes more than any other natural hazard. It is a frequent phenomenon, occurring in many locales worldwide regardless of their natural humidity/aridity status, while attracting public, stakeholder and scientific attention as it causes a plethora of social, economic and environmental impacts. Overall, the impacts magnitude is affected by the density of human activities, needs, demands, the level of socioeconomic development and their environmental interconnectivity. However in many areas, precipitation seems to be the major factor that determines drought severity and more specific the meteorological drought characteristics. In the present effort, precipitation and drought patterns and trends are examined over the region of Central Greece. The region is of great importance, since it contains the water bodies that supply the Attica prefecture, including Metropolitan Athens, whereas almost fifty percent of the total Greek population is concentrated. The methodology followed uses precipitation data, from twenty-four meteorological stations located in the region. For the drought assessment the Standard Precipitation Index is calculated for a variety of temporal steps (i.e. SPI 6, 12, and 24). The statistical analysis of the examined parameters, apart from the basic attributes, has also applied the Auto Regressive Model ARIMA (Auto Regressive /Integrated/Moving Average).Then, forecasting efforts based on those patterns are also derived and presented. It is believed that such a methodology may provide useful information on the area s vulnerability to drought and thus portraying the system s susceptibility to change, damages and losses. In this context, vulnerability may be included in the decision making arsenal in order to also widen existing perceptions of the area s inherent weaknesses and limited resilience to both manmade and natural hazards, serving at the same time as an early warning mechanism. Keywords: Statistical Analysis, Meteorological Drought, Forecasting, Drought Vulnerability, Central Greece. 1. INTRODUCTION Drought is a multifaceted physical event that may seriously affect human activities, sometimes more than any other natural hazard (Hagman, 1984; Bruce, 1994). It is a frequent phenomenon, occurring in many locales worldwide regardless of their natural humidity/aridity status, while attracting public, stakeholder and scientific attention as it causes a plethora of social, economic and environmental impacts (Yevievich et al., 1983; Rossi et al., 1992; Karavitis, 1999b; Wilhite et al., 2000; Cancelliere et al., 2005). Overall, the impacts magnitude is affected by the density of human activities, needs, demands, the level of socioeconomic development and their environmental interconnectivity. Drought literature is rich and provides a series of case studies all over the world. All in all, drought is a dynamic phenomenon seemingly difficult to confront (DMCSEE, 2012).

Nascent sources of difficulties in applying appropriate management responses may be derived from the following causes of confusion: elusive drought definitions; diversified and devastating drought impacts; and absence of systematic response mechanisms. Such causes are further exemplified in the following (Karavitis, 1992; 1999a). Nevertheless, there is a tendency that drought may be defined as a precipitation deficit over an extended period of time (NDPC, 2000; Cancelliere et al., 2005; Wilhite et al, 2006; Eriyagama et al, 2009). Based on that, precipitation seems to be the major factor that determines drought severity and more specific the meteorological drought characteristics. There are many efforts for planning and management actions for droughts, nevertheless, deficiencies still pertain in such attempts. Such efforts are becoming even more difficult given the latest climatic anomalies and instabilities (Milly at al., 2008). Nevertheless, the major challenge for any drought related research may be the development of comprehensive and effective drought management and decision making schemes. In such quests, forecasting may provide some help (Miles and Keenan, 2002). Within that context, in the present effort, precipitation and drought patterns and trends are statistically examined over the region of Central Greece. The region is of great importance, since it contains the water bodies that supply the Attica prefecture, including Metropolitan Athens, whereas almost fifty percent of the total Greek population is concentrated. For the conveyed statistical analysis of the examined parameters, apart from the basic attributes, the Auto Regressive Model ARIMA (Auto Regressive /Integrated/Moving Average) has also been applied for the forecasting section to be developed. There has to be mentioned that the methodology employed in this study was adopted from Mishra and Desai (2005), Durdu (2010) and Myronidis et al, (2012). It is believed that such a methodology may provide useful information on the area s vulnerability to drought and thus portraying the system s susceptibility to change, damages and losses. 2. METHODOLOGY 2.1. Area of Interest Central Greece (Fig.1) covers an area of 23,818.3 Km 2 while being the most populous geographical region of Greece with a population that exceeds 4,500,000 inhabitants. Within that region, Attica displays the greatest population density (987.9 inhabitants/km 2 ) in the country. Figure 1. The geomorphology of the area of interest

The population of Attica is supplied with water by the artificial reservoirs of Marathon, Mornos and Evinos as well as from the natural lake of Yliki (EYDAP, 2004). Three of these water bodies are enclosed within the area of interest with Mornos reservoir and Yliki Lake (regional unit of Phocis and Boeotia respectively) being the most important by discharging an average volume of 530 x 10 6 m 3 /year. That volume may be decreased during periods of drought leaving almost half the country s population vulnerable to pertinent impacts. In general, the area s climate is temperate along its coastlines and dry in the interior while its average annual precipitation (Fig.2 and Table 1) which is the most important climatic factor within the drought conceptualization reaches 628.37 mm (1981-2010). According to that figure the rainfall reaches greater heights at the central and north-western part of the examined area. Table 1. The basic statistics of precipitation in the area of interest Station X Y Precipitation Average (1981-2010) Standard deviation Minimum Maximum Stnd. skewness Stnd. kurtosis Agia Triada 405136 4244800 986.56 220.27 534.3 1382.7-1.10-0.30 Amfissa 358886 4265827 681.94 161.66 328.4 1025.7 0.34 0.42 Gravia 363497 4280548 822.31 143.23 431.3 1048.9-1.87 0.80 Davlia 389166 4248703 770.83 153.20 463.5 1062.1-0.63-0.89 Distomo 383406 4253888 605.78 121.66 329.4 821.7-0.78-0.45 Eptalofos 367725 4273077 972.64 221.66 612.1 1420.1 0.41-1.22 Zilefto 349557 4310404 394.19 166.89 96.9 835.7 0.93 0.22 Thisvi 409381 4233654 458.00 149.83 185.6 874.9 0.93 0.90 Itea 363056 4254654 377.45 119.48 187.7 660.9 0.80-0.53 Kalithea 451708 4238840 468.85 157.18 0 753.6-1.35 1.61 Kaloskopi 354830 4282551 879.01 235.07 568.0 1740.1 4.01 5.87 K.Tirothea 388071 4274616 655.60 129.32 388.8 991.9 0.31 0.84 Lamia 361050 4306493 538.38 115.08 328.0 870.5 1.12 1.27 Livadia 400880 4254100 737.21 156.38 414.1 1037.8 0.51-0.43 Lilea 369237 4276752 848.55 335.74 0 1272.4-3.59 2.44 Pavlos 421355 4264972 479.07 160.67 82.5 741.5-0.86-0.23 Trilofo 345367 4317887 598.79 126.42 373.0 826.5 0.72-0.85 Tymfristos 319174 4309189 1022.99 382.17 141.0 1713.3-1.55 0.09 Elliniko 475537 4194336 363.63 96.39 155.4 546.6-0.82 0.06 Asteroskopio 475089 4202597 397.06 138.72 150.6 896.0 3.30 5.36 Elefsina 440916 4212390 344.17 91.06 117.4 468.3-1.48-0.16 Tatoi 480783 4218209 435.09 153.46 169.6 812.7-0.03-0.15 Filladelphia 477479 4210345 452.17 177.52 180.5 1015.5 2.95 3.35 Ypati 346524 4303061 790.74 293.18 327.6 1431.0 0.98-0.57 Figure 2. The average precipitation 1981-2010

2.2. Adopted Methodology The present effort focuses on the drought event occurred in 2007 and the adopted methodology is divided into three distinct steps: 1. Based on the available raw data obtained from twenty-four meteorological stations and for a calibration period of thirty years (1981 2010), the SPI (3, 6, 9, 12 and 24) values were calculated on a monthly scale. 2. The SPI values from January, 1981 to December, 2006 were used as input data in the ARIMA (Auto Regressive /Integrated/Moving Average) model for a forecasting period of three months (January March, 2007) to be developed. 3. The final step is composed of the comparative analysis between the actual and the anticipated values of the SPI for those three months. The results are presented both in map (using GIS environment) and graphical formats 3. RESULTS According to the calculated SPI values, the fact that the area of interest is drought prone with several drought events in the examined period of the last thirty years can be easily conducted. Figure 3 indicatively highlights the constant switching between below and above average precipitation patterns for the meteorological station of Itea (regional unit of Phocis) which is close enough to describe the various climatic conditions that occur in the area of Mornos reservoir. Based on that figure, the reservoir of Mornos had been affected highly during the 2007 drought event. Figure 3. The SPI values as calculated for the meteorological station of Itea For the next step of the whole process, the ARIMA model had to be used for the projected values of SPI to be produced. In that process, the SPI values from January, 1981 to December, 2006 were used as input data. Figures 4 7 indicatively illustrate the projected values of SPI 6 and 12 for January and March of 2007. According to these figures, the projected SPI values portray two distinct areas with great differences in drought severity. More specific, the projected drought severity is higher in the north-western part of the examined area, compared to that presented in the southeastern part. Thus, based on that projection, and without any other data, the northwestern part of the region, where the water bodies that supply Attica are located, can be perceived as vulnerable to change that may cause considerable impacts to the supplied population and activities.

Figure 4. SPI 6 visualization as calculated by ARIMA model for January 2007 Figure 5. SPI 12 visualization as calculated by ARIMA model for January 2007 Figure 6. SPI 6 visualization as calculated by ARIMA model for March 2007

Figure 7. SPI 12 visualization as calculated by ARIMA model for March 2007 Continuing, based on the previously calculated SPI values, the ones referring to the three examined months of 2007 (January March) were visualized in a GIS environment (Kriging). Figures 8 11 indicatively illustrate the occurred drought conditions of January and March of 2007 in the area of interest through the visualization of the SPI 6 and 12 values. Based on these illustrations, the severity pattern remains with the north-western part of the examined area still presenting higher severity than the south-eastern part in almost all the cases. The main difference compared to the projected values is that the actual/ observed values decline almost gradually and in a smoother way. Furthermore, the produced results confirmed that the water bodies of interest are vulnerable to limited precipitation. Therefore the results may be considered as satisfactory without underestimating the need for further research on that topic. Figure 4. SPI 6 visualization for January 2007

Figure 5. SPI 12 visualization for January 2007 Figure 6. SPI 6 visualization for March 2007 Figure 7. SPI 12 visualization for March 2007

Figure 12 presents the divergence of the projected values from the actual ones for the meteorological stations of Livadia (near to Yliki Lake) and Tatoi (within the regional unit of Attica) for that period. Figure 12. The divergence of the projected SPI values compared to the observed ones Nevertheless, there has to be stated that the results produced by ARIMA model can be trusted up to a certain degree since forecasting in complex and random variables can be elusive. In the present case, the results cannot be trusted for a period that exceeds two months. That issue can be regulated by obtaining longer time series of precipitation data that could cover greater diversity of precipitation and drought events. 4. CONCLUSIONS According to the findings, Central Greece is a drought prone area while its water bodies and especially those that supply Attica are susceptible to limited precipitation. That fact increases the vulnerability of the supplied population to drought leaving them unprotected towards future events. In order to deal with drought events and impacts, concrete drought management plans and tools need to be developed. The present effort, as part of other pertinent research, confirms the fact that drought events can be anticipated with some certainty. That may be of great value since the forecasting processes can be used for early warning mechanisms to be developed as part of integrated drought monitoring and management. Such mechanisms may be able to minimize the impacts of the various drought events while increasing the area s absorbing capacity. REFERENCES 1. Athens Water Supply and Sewerage Company S.A. (EYDAP S.A), (2004) Brochure: Highlights. Division of Corporate Analysis and Planning. 2. Bruce, J.P. (1994). A perspective on reducing losses from natural hazards. Bull Am Meteorol Soc 75:1237 1240. 3. Cancelliere, A., di Mauro,G., Bonaccorso, B., Rossi, G. (2005). Stochastic forecasting of Standardized Precipitation Index. September 11-16, 2005, Seoul, Korea.

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