Estimated changes of drought tendency in the Carpathian Basin 365 Hungarian Geographical Bulletin 63 (4) (2014) 365–378. DOI: 10.15201/hungeobull.63.4.1 Estimated changes of drought tendency in the Carpathian Basin Judit SÁBITZ, Rita PONGRÁCZ and Judit BARTHOLY1 Abstract Drought conditions are oft en characterized by various drought indices. In this paper diff er- ent types of indices (i.e. standardized precipitation anomaly index, Thornthwaite’s aridity index, and Ped’s drought index) are used to estimate the future changes in drought condi- tions in the Carpathian Basin. For this purpose 25 km horizontal resolution gridded outputs of several regional climate models are used from the project ENSEMBLES covering the period 1951–2100 and taking into account the A1B emissions scenario. The results suggest remarkable drying in the region, especially, in summer, which emphasize the importance of developing appropriate adaptation strategies addressing this issue. Keywords: drought index, ENSEMBLES, regional climate model simulation, climate change Introduction Climatic conditions evidently aff ect the biosphere as well as the human so- cieties. Anthropogenic activity infl uences the biosphere through land use change and agriculture (e.g. cultivating selected crops and thus decreasing biodiversity) for several centuries. Moreover, the 250 year long industrial ac- tivities (especially, fossil fuel combustion) resulted in increasing atmospheric concentration of greenhouse gases. As a consequence, global and regional warming has been detected (IPCC, 2013), which intensifi ed drought condi- tions in many regions including Central and Southern Europe (IPCC, 2012). Hungary is certainly aff ected by this potential risk since it is located in the continental Central European zone. In the recent years, the entire continent was hit by a severe drought event in 2003, which included Hungary as well, as the whole Carpathian Basin (Tallaksen, L.M. et al. 2011). On the basis of 1 Department of Meteorology, Eötvös Loránd University, Budapest 1117 Hungary. E-mails: sabitzj@nimbus.elte.hu, prita@nimbus.elte.hu, bartholy@caesar.elte.hu 366 the measurement recorded by the Hungarian Meteorological Service the an- nual precipitation of the country was only 75 percent of the climatic mean (for the period 1971–2000). In 2011 the annual precipitation in Hungary was even smaller, only 72 percent of the normal value. Parts of the country were aff ected by drought events in 2002, 2007 and 2012. Due to the recent increasing frequency of unusual years, it is essential to assess the possible future conditions in the country using regional climate model (RCM) simulations. These tools are widely used to estimate the primary climatic conditions, i.e. temperature and precipitation. Projected changes are summarized for Hungary in Pongrácz, R. et al. (2011), according to which re- gional warming is very likely to continue and increase in this century. Projected precipitation changes are varying throughout the year. RCMs clearly estimate summer drying for this century, however, in the other seasons diff erent RCMs estimate diff erent tendencies both in intensity and sign. Drought conditions are not determined only by precipitation conditions but also by temperature changes, this bivariate dependence can be assessed by drought indices. In this paper fi rst, the diff erent types of drought indices are summarized and the three indices used in this paper are presented in details. Then, data outputs of RCM simulations available from project ENSEMBLES are described, followed by the discussion of the results. Finally, the conclusions are drawn. Drought indices Several aspects of the climatic system are directly or indirectly aff ected by pre- cipitation defi ciency, i.e. drought events. Therefore diff erent scientifi c commu- nities use diff erent approaches to defi ne drought itself and measures to charac- terize it. For instance, atmospheric science defi nes meteorological drought as a long period of time with considerably less precipitation amount than climatic mean. Other aspects may highlight the agricultural consequences, and defi ne agricultural drought when the soil moisture is inadequate, and yields are considerably less than average because of the water shortage. Furthermore, hydrological drought refers to a period of below normal stream-fl ow, thus, focusing on the hydrological impacts of the lack of precipitation, such as re- duced groundwater levels. Finally, economic drought considers the monetary value of drought-related damages, which can happen when the water shortage has an eff ect on human activity and on economy. One of the most oft en used measures of drought includes the defi ni- tion of several drought indices, which are able to quantify the temporal and spatial range of dry periods. The can be classifi ed into diff erent categories. Typically, there are four main groups of indices, which are widely used: the precipitation, the water balance, the recursive and the soil moisture indices 367 (Faragó, T. et al. 1988). Table 1 summarizes the traditional classifi cation of drought indices, whereas Table 2 lists most of the well-known drought indices according to Dunkel, Z. (2009). Precipitation indices are suitable for the separation of wet and dry periods, as well as for the determination of variability. They are simple and do not require large datasets. Water balance indices are more complex. In ad- dition to precipitation they also take into account temperature, which is used as the main factor of evaporation from the output side of the water balance. Recursive indices consider cumulative eff ects of precipitation shortage since they use data from the preceding period and hence characterize longer time periods. Soil moisture indices are able to estimate crop loss and agricultural water shortage. The main advantage of the indices based on remotely sensed information is the good spatial coverage for large areas. In order to keep a reasonable length of this paper, standardized precip- itation anomaly index (SAI), Ped’ s drought index (PDI) and Thornthwaite‘s aridity index (TAI) are used to estimate the projected trends of dry climatic con- ditions by the end of the 21st century in the Carpathian Basin (Thornthwaite, C.W. 1948; Ped, D.A. 1975). One of the most simple indices is SAI (Katz, R.W. and Glantz, M.H. 1986). The main advantage of this dimensionless index that it can be calculated only from precipitation time series. In addition, SAI is a standardized measure for seasonal diff erences and for precipitation in diff erent climatic areas. Based on the defi nition the negative/positive trend of SAI implies drier/wett er climatic conditions. The drought classifi cation using SAI values is shown in Table 3. TAI is widely used in agrometeorological studies (Thornthwaite, C.W. 1948). For calculating TAI temperature time series are also used in ad- Table 1. Classifi cation of drought indices Index types Examples Precipitation indices Relative anomaly index Standardized precipitation anomaly index Relative precipitation anomaly index Precipitation anomaly index Water balance indices Lang’s rainfall index De Martonne aridity index Thornthwaite index Recursive indices Foley’s anomaly index Palmer’s drought index Soil moisture indices Ped’s drought index Relative soil moisture index Remotely sensed indices Vegetation index Normalized diff erence vegetation index 368 Ta bl e2 .D efi ni tio n of dr ou gh ti nd ic es N r. In de x Te m po ra l re so lu tio n D ef in iti on U se d da ta 1. Pr ec ip ita tio n in de x [m m ] w ee k, m on th , se as on P m P pr ec ip ita tio n su m (P ) m ea n pr ec ip ita tio n (m (P )) 2. St an da rd iz ed p re ci pi ta tio n an om al y in de x (S A I) [% ] w ee k, m on th P d P m P pr ec ip ita tio n su m (P ) m ea n pr ec ip ita tio n (m (P )) st an da rd d ev ia tio n of pr ec ip ita tio n (d (P )) 3. Re la tiv e pr ec ip ita tio n am ou nt w ee k, m on th P m P pr ec ip ita tio n su m (P ) m ea n pr ec ip ita tio n (m (P )) 4. Re la tiv e pr ec ip ita tio n an om al y in de x w ee k, m on th P m P m P pr ec ip ita tio n su m (P ) m ea n pr ec ip ita tio n (m (P )) 5. D e M ar to nn e in de x [m m /° C ] m on th 10 12 T P pr ec ip ita tio n su m (P ) te m pe ra tu re (T ) 6. Th or nt hw ai te in de x m on th 910 12 .2 65 1 T P . pr ec ip ita tio n su m (P ) te m pe ra tu re (T ) 7. La ng ra in fa ll in de x [m m /° C ] an y gi ve n tim e pe ri od TP pr ec ip ita tio n su m (P ) te m pe ra tu re (T ) 369 Ta bl e 2. C on tin ue d N r. In de x Te m po ra l re so lu tio n D ef in iti on U se d da ta 8. Se ly an in ov ’s h yd ro -t he rm al co ef fic ie nt [m m /° C ] da y 10 T TP pr ec ip ita tio n su m (P ) te m pe ra tu re (T ) 9. A ri di ty in de x – PEP , LRP n pr ec ip ita tio n su m (P ) ev ap ot ra ns pi ra tio n (P E) ra di at io n ba la nc e (R n) la te nt h ea t ( L) 10 . Bo w en r at io da y LEH se ns ib le (H ) a nd la te nt (L E) h ea t f lu x 11 . Pe d’ s dr ou gh t i nd ex , 1 st ap pr ox im at io n – P d P T d T pr ec ip ita tio n su m (P ) te m pe ra tu re (T ) st an da rd d ev ia tio n of te m pe ra tu re (d (T )) a nd p re ci pi ta tio n (d (P )) 12 . Pe d’ s dr ou gh t i nd ex , 2 nd ap pr ox im at io n – W d W P d P T d T pr ec ip ita tio n su m (P ) te m pe ra tu re (T ) so il m oi st ur e (W ) 13 . R el at iv e so il m oi st ur e co nt en t – AW C W ac tu al (W ) a nd a va ila bl e (A W C ) s oi l m oi st ur e 14 . Fo le y’ s an om al y in de x (F A I) [m m ] m on th 1 1 P FA I k k k P FA I FA I 1 pr ec ip ita tio n su m (P ) 370 Ta bl e2 .C on tin ue d N r. In de x Te m po ra l re so lu tio n D ef in iti on U se d da ta 15 . Pa lm er d ro ug ht se ve ri ty in de x m on th 0 0 PD SI 1 1 10 3 0 3 k k k k PD SI . Z PD SI PD SI m oi st ur e an om al y in de x (Z ) 16 . Bh al m e- M oo le y dr ou gh t i nd ex m on th 2 1 1 cSA I i c i k k k n i ki n BM D I 1 1 SA I i nd ex re gi on s pe ci fic v al ue fo r t he c oe ff ic ie nt s (c 1 an d c2 ) 17 . V eg et at io n in de x da y VI S N IR re fle ct ed ra di at io n in th e ne ar in fr ar ed el ec tr om ag ne tic w av el en gt h (N IR ) a nd in th e vi si bl e el ec tr om ag ne tic w av el en gt h (V IS ) 18 . N or m al iz ed di ff er en ce ve ge ta tio n in de x (N D V I) da y VI S N IR VI S N IR re fle ct ed ra di at io n in th e ne ar in fr ar ed el ec tr om ag ne tic w av el en gt h (N IR ) a nd in th e vi si bl e el ec tr om ag ne tic w av el en gt h (V IS ) 19 . C ro p w at er s tr es s in de x (C W SI ) da y PE ET PE po te nt ia l ( PE ) a nd a ct ua l ( ET ) ev ap ot ra ns pi ra tio n 20 . St re ss d eg re e da y (S D D ) m on th k A C T T SD D re m ot el y se ns ed s ur fa ce a nd s ta nd ar d ai r te m pe ra tu re (T C an d TA , r es pe ct iv el y) 371 dition to precipitation (Kemp, D. 1990). Decreasing/increasing trend of TAI means drier/wett er climatic conditions. Table 4 shows the diff erent drought cat- egories according to TAI. For complex studies it can be useful to compare standardized values of temperature and precipitation in or- der to obtain a more accurate result. PDI (Bagrov, N.A. 1983; Ped, D.A. 1975) is a soil moisture index, which trends are op- posite to SAI or TAI, namely, decreasing/ increasing trend indicates wett er/drier conditions. PDI values close to zero (be- tween –1 and +1) implies neutral states. Drought classifi cation using PDI values is shown in Table 5. Data To assess uncertainty due to natural and anthropogenic forcing factors, future cli- matic conditions are estimated with an ensemble of climate models. For Europe the fi ve-year-long project ENSEMBLES studied the projected climate changes (van der Linden, P. and Mitchell, J.F.B. 2009). The regional climate models (RCMs) run at 25 km spatial resolution for 1951–2100 used the SRES A1B emissions scenario, which estimates the atmospheric carbon-dioxide level to 532 ppm and 717 ppm by 2050 and 2100, respectively (Nakicenovic, N. and Swart, R. 2000). The necessary initial and lateral boundary conditions were provided by outputs of global climate models (GCMs). Here we use 9 RCM experiments driven by ECHAM5 (Roeckner, E. et al. 2006) and HadCM3Q (Gordon, C. et al. 2000; Rowell, D.P. 2005) GCMs. These global models were run at the Max Planck Institute in Hamburg Germany, and the Hadley Centre of the UK MetOffi ce, respectively. For the analysis of drought conditions in the Carpathian Basin gridded monthly mean temperature values and monthly precipitation amounts of the RCM outputs (Table 6) were used for the end of the 21st century (2071–2100). As a reference period 1961–1990 was selected. Table 3. Drought categories defi ned on the basis of SAI values SAI values Category > 2.0 1.5 to 2.0 1.0 to 1.5 –1.0 to +1.0 –1.0 to –1.5 –1.5 to –2.0 < –2.0 extremely wet severely wet moderately wet near normal moderately dry severely dry extremely dry Table 4. Drought categories defi ned on the basis of TAI values TAI values Category > 6.4 3.2 to 6.4 1.6 to 3.2 < 1.6 wet semi-arid arid extremely dry Table 5. Drought categories defi ned on the basis of PDI values PDI values Category < –3 –3 to –2 –2 to –1 1 to 2 2 to 3 > 3 extremely wet severely wet moderately wet moderately dry severely dry extremely dry 372 Results In order to investigate the future change of the Hungarian drought condi- tions seasonal mean drought index values have been calculated for the last three decades of the 21st century using the gridded outputs of each RCM, and compared to the reference period. For the spatial analysis the diff erences are mapped in Figures 1, 2 and 3 using SAI, TAI and PDI, respectively. The four columns represent the diff erent seasons. The maps in the upper four rows show the results from the RCM simula- tions driven by HadCM3Q GCM, whereas the lower fi ve rows contain the results from the ECHAM5-driven RCM simulations. Yellow and red colors of the scale indicate drier conditions, while green and blue colors suggest wett er climate. In case of SAI (standardized precipitation index) and TAI (Thornthwaite‘s aridity index) decreasing trends imply drying. Opposite to these indices, increasing PDI (Ped’s drought index) values indicate drier conditions. From the maps the drying tendency in summer is clearly seen in us- ing any of the three indices. The other three seasons are also dominated by drying tendencies, however, winter is likely to become wett er according to SAI (Figure 1), which can be explained by the defi nition of this index, namely, it is based only on precipitation amount, whereas TAI and PDI also consider temperature. The average seasonal projected changes are summarized in Tables 7, 8 and 9 for Hungary using the grid-cells located within the country. Besides all the individual RCM results, the averages and the standard deviations of the 9-member-ensemble are calculated. The larger projected changes are indicated by italic characters. Since the scales of the three indices are diff erent therefore diff erent thresholds are used: in case of SAI, TAI and PDI large changes are defi ned as exceeding 0.3, 2.0 and 0.4 in absolute value, respectively. Again note that the signs of the PDI changes are opposite to those of SAI or TAI changes. Table 6. Used regional climate model simulations, their running institutes, and the driving global climate models RCM Institute, country Driving GCM HadRM3Q CLM RCA3 RCA METO-HC, United Kingdom ETHZ, Switzerland C4IR, Ireland SMHI, Sweden HadCM3Q RegCM RACMO2 REMO HIRHAM ICTP, Italy KNMI, Netherlands MPI, Germany DMI, Denmark ECHAM5 373 Fig. 1. Projected seasonal changes of SAI by 2071–2100 relative to the 1961–1990 reference period using 9 diff erent RCM simulations 374 Fig. 2. Projected seasonal changes of TAI by 2071–2100 relative to the 1961–1990 reference period using 9 diff erent RCM simulations 375 Fig. 3. Projected seasonal changes of PDI by 2071–2100 relative to the 1961–1990 reference period using 9 diff erent RCM simulations 376 Table 7. The average projected seasonal changes by 2071–2100 relative to the 1961–1990 reference period for Hungary in case of SAI using 9 diff erent RCM simulations* RCM DJF MAM JJA SON Driving GCM HadRM3Q CLM RCA3 RCA 0.21 0.18 0.37 0.19 -0.10 -0.18 0.05 -0.09 -0.37 -0.34 -0.08 -0.50 0.05 -0.16 -0.19 0.39 HadCM3Q RCA RegCM RACMO2 REMO HIRHAM 0.19 0.22 0.29 0.18 0.29 -0.05 0.09 -0.12 0.02 0.10 -0.15 -0.07 -0.19 -0.48 0.09 -0.04 -0.18 0.00 -0.15 -0.03 ECHAM5 Ensemble-average Standard deviation 0.24 0.07 -0.03 0.10 -0.23 0.20 -0.03 0.18 * Changes exceeding 0.3 in absolute value are indicated by italics. Table 8. The average projected seasonal changes by 2071–2100 relative to the 1961–1990 reference period for Hungary in case of TAI using 9 diff erent RCM simulations* RCM DJF MAM JJA SON Driving GCM HadRM3Q CLM RCA3 RCA -1.92 -1.88 -1.64 1.71 -1.91 -2.20 -1.66 -1.72 -6.17 -11.80 -2.18 -6.79 -1.80 -1.76 -1.11 -1.92 HadCM3Q RCA RegCM RACMO2 REMO HIRHAM -0.57 -0.87 -0.17 -0.93 -0.58 -1.02 -1.16 -1.79 -0.71 -1.19 -1.89 -2.14 -1.95 -2.01 0.08 -1.73 -1.72 -1.76 -1.87 -0.40 ECHAM5 Ensemble-average Standard deviation -0.76 1.11 -1.48 0.48 -3.87 3.69 -1.56 0.50 * Changes exceeding 2.0 in absolute value are indicated by italics. Table 9. The average projected seasonal changes by 2071–2100 relative to the 1961–1990 reference period for Hungary in case of PDI using 9 diff erent RCM simulations* RCM DJF MAM JJA SON Driving GCM HadRM3Q CLM RCA3 RCA 0.16 0.10 -0.04 0.14 0.31 0.32 0.28 0.16 0.74 0.63 0.44 0.72 0.30 0.28 0.36 -0.11 HadCM3Q RCA RegCM RACMO2 REMO HIRHAM -0.13 -0.01 -0.14 0.11 -0.19 0.14 0.18 0.23 0.17 0.12 0.38 0.39 0.48 0.71 0.01 0.19 0.25 0.19 0.28 0.23 ECHAM5 Ensemble-average Standard deviation 0.00 0.13 0.21 0.08 0.50 0.23 0.22 0.13 * Changes exceeding 4.0 in absolute value are indicated by italics. 377 The values suggest that the largest drying in Hungary is projected for summer. Compared to the summer changes less intense drying tendencies are likely to occur in spring and autumn. Winters may result more precipitation in the future (Table 7. – SAI), however, due to the warming trend TAI and PDI suggest overall drier winters in the late 21st century compared to the reference period (Tables 8 and 9, respectively). This can be explained by the increasing evaporation in the warmer environment. Conclusions Precipitation and temperature gridded monthly outputs of 9 RCM simulations (available from the ENSEMBLES project) were used to calculate diff erent type of drought indices (SAI, TAI, PDI) for the Carpathian Basin considering the A1B emissions scenario. Based on the analysis presented in this paper the fol- lowing conclusions can be drawn: (i) Summers of the late 21st century are clearly projected to be substan- tially drier than the 1961–1990 reference period. (ii) Winter precipitation tends to increase in the future. However, be- cause of the regional warming and the consequent increase of evaporation climatic conditions are projected to become drier in winter, too. (iii) Springs and autumns tend to become also slightly drier by 2071– 2100 relative to the 1961–1990 reference period. The overall drying tendency in the region highlights the necessity to develop the appropriate strategies to adapt to the regional climate change. This is especially important for end-users and decision-makers related to agriculture, food and drinking water security. Acknowledgements: Research leading to this paper has been supported by the follow- ing sources: the Hungarian National Science Research Foundation under grant K-78125, K-83909 and K-109109, the European Union and the European Social Fund joint supports (FuturICT.hu TÁMOP-4.2.2.C-11/1/KONV-2012-0013, GOP-1.1.1.-11-2012-0164, and KMR- 12-1-2012-0206). The AGRÁRKLÍMA2 project (VKSZ-12-1-2013-0001) and the EEA Grant HU04 Adaptation to Climate Change (EEA-C13-10). The ENSEMBLES data used in this work was funded by the EU FP6 Integrated Project ENSEMBLES (Contract number 505539) whose support is gratefully acknowledged. 378 REFERENCES Bagrov, N.A. 1983. On the meteorological index of yields. Meteorologiya i Gidrologiya 11. 92−99. Dunkel, Z. 2009. Brief surveying and discussing of drought indices used in agricultural meteorology. Időjárás 113. 23−37. Faragó, T., Kozma, E. and Nemes, Cs. 1988. Quantifying droughts. In Identifying and cop- ing with extreme meteorological events. Eds. Antal, E. and Glantz, M., Budapest, Hungarian Meteorological Service, 62−111. Gordon, C., Cooper, C., Senior, C.A., Banks, H., Gregory, J.M., Johns, T.C., Mitchell, J.F.B. and Wood, R.A. 2000. The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without fl ux adjust- ments. 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Met Offi ce Hadley Centre, UK, 160 p. << /ASCII85EncodePages false /AllowTransparency false /AutoPositionEPSFiles true /AutoRotatePages /None /Binding /Left /CalGrayProfile (Dot Gain 20%) /CalRGBProfile (sRGB IEC61966-2.1) /CalCMYKProfile (U.S. Web Coated \050SWOP\051 v2) /sRGBProfile (sRGB IEC61966-2.1) /CannotEmbedFontPolicy /Error /CompatibilityLevel 1.3 /CompressObjects /Tags /CompressPages true /ConvertImagesToIndexed true /PassThroughJPEGImages true /CreateJobTicket false /DefaultRenderingIntent /Default /DetectBlends true /DetectCurves 0.0000 /ColorConversionStrategy /LeaveColorUnchanged /DoThumbnails false /EmbedAllFonts true /EmbedOpenType false /ParseICCProfilesInComments true /EmbedJobOptions true /DSCReportingLevel 0 /EmitDSCWarnings false /EndPage -1 /ImageMemory 1048576 /LockDistillerParams false /MaxSubsetPct 100 /Optimize false /OPM 1 /ParseDSCComments true /ParseDSCCommentsForDocInfo true /PreserveCopyPage true /PreserveDICMYKValues true /PreserveEPSInfo true /PreserveFlatness true /PreserveHalftoneInfo false /PreserveOPIComments true /PreserveOverprintSettings true /StartPage 1 /SubsetFonts true /TransferFunctionInfo /Apply /UCRandBGInfo /Preserve /UsePrologue false /ColorSettingsFile () /AlwaysEmbed [ true ] /NeverEmbed [ true ] /AntiAliasColorImages false /CropColorImages true /ColorImageMinResolution 300 /ColorImageMinResolutionPolicy /OK /DownsampleColorImages true /ColorImageDownsampleType /Bicubic /ColorImageResolution 300 /ColorImageDepth -1 /ColorImageMinDownsampleDepth 1 /ColorImageDownsampleThreshold 1.50000 /EncodeColorImages true /ColorImageFilter /DCTEncode /AutoFilterColorImages true /ColorImageAutoFilterStrategy /JPEG /ColorACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /ColorImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000ColorACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000ColorImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 300 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages true /GrayImageDownsampleType /Bicubic /GrayImageResolution 300 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages true /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /GrayImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000GrayACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000GrayImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 1200 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages true /MonoImageDownsampleType /Bicubic /MonoImageResolution 1200 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages true /MonoImageFilter /CCITTFaxEncode /MonoImageDict << /K -1 >> /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile (None) /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False /CreateJDFFile false /Description << /ARA /BGR /CHS /CHT /CZE /DAN /DEU /ESP /ETI /FRA /GRE /HEB /HRV (Za stvaranje Adobe PDF dokumenata najpogodnijih za visokokvalitetni ispis prije tiskanja koristite ove postavke. Stvoreni PDF dokumenti mogu se otvoriti Acrobat i Adobe Reader 5.0 i kasnijim verzijama.) /ITA /JPN /KOR /LTH /LVI /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.) /NOR /POL /PTB /RUM /RUS /SKY /SLV /SUO /SVE /TUR /UKR /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.) /HUN >> /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ << /AsReaderSpreads false /CropImagesToFrames true /ErrorControl /WarnAndContinue /FlattenerIgnoreSpreadOverrides false /IncludeGuidesGrids false /IncludeNonPrinting false /IncludeSlug false /Namespace [ (Adobe) (InDesign) (4.0) ] /OmitPlacedBitmaps false /OmitPlacedEPS false /OmitPlacedPDF false /SimulateOverprint /Legacy >> << /AddBleedMarks false /AddColorBars false /AddCropMarks false /AddPageInfo false /AddRegMarks false /ConvertColors /ConvertToCMYK /DestinationProfileName () /DestinationProfileSelector /DocumentCMYK /Downsample16BitImages true /FlattenerPreset << /PresetSelector /MediumResolution >> /FormElements false /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles false /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /DocumentCMYK /PreserveEditing true /UntaggedCMYKHandling /LeaveUntagged /UntaggedRGBHandling /UseDocumentProfile /UseDocumentBleed false >> ] >> setdistillerparams << /HWResolution [2400 2400] /PageSize [612.000 792.000] >> setpagedevice