UNIVERSITATEA DIN CRAIOVA UNIVERSITY OF ... - EURO4M

UNIVERSITAT EA DIN CRAIOVA UNIVERSITY OF CRAIOVA

Seria: 9 Biologie 9 Horticultură 9 Tehnologia prelucrării produselor agricole 9 Ingineria mediului

Vol . XVII ( LIII ) - 2012 MECHANISMS OF DROUGHT PERSISTENCE IN ROMANIA IN THE CLIMATE CHANGE PERSPECTIVE Bojariu R., 1 Velea L.F.1, Cicӽ R. D. 1, Dobrinescu A. E. 1, Bîrsan M. 1, Dumitrescu A. 1 ABSTRACT We used Palmer index of soil moisture anomalies computed on monthly basis for 113 stations which cover Romanian territory to analyze the interdecadal variability in the time interval 1961-2010. The EOF analysis of Palmer soil moisture index shows a dominant interdecadal signal covering the whole Romanian territory which seems to be related to large scale phenomena. A second signal could be associated to climate change and it shows a tendency for depletion of soil moisture in southern and eastern regions of Romania. Keywords: Palmer index; soil moisture depletion; NAO; climate change signal.

INTRODUCTION Drought has different definitions depending on the type of impact or socioeconomic activity which is affected. From the meteorological point of view, a drought period is defined by a significant deficit in the precipitation regime. Pedological drought refers to a significant deficit in the soil moisture and has a significant impact for agricultural activities. In the context of climate change, the frequency and intensity of droughts are changing and their social and economical impact increase. The main goal of the present study is to analyze interdecadal variability and change of droughts in Romania in order to further identify their predictive potential associated to this time scale. DATA AND METHODOLOGY The Palmer Drought Severity Index (PDSI) (Palmer, 1965) has been computed using the components of the local hydrological budget, taken into account the demand (e.g. evapotranspiration, runoff) and the supply of water resources (e.g. precipitation, water holding capacity of the soil) for a certain area (Palmer, 1965; Alley, 1984). Best results using PDSI are obtained for regions with relatively smooth topography, characterized by homogeneous physical and geographical conditions (Barbu and Popa, 2002). In order to calculate PDSI for a certain month (i), one have firstly to determine the index of soil moisture anomaly Zi for that month (i): 1

Administratia Nationala de Meteorologie, Sos. Bucuresti-Ploiesti 97, Bucuresti, Romania. [email protected]

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Zi

k * P  D * PE  E * PR  J * PRO  G * PL

PDSIi= PDSIi-1 + 1/3*Zi - 0,103* PDSIi-1 where: k is an empirical weighting factor, specific for each region; Į, ȕ, Ȗ, į are coefficients for evapotranspiration, soil water recharge, run-off and water loss from the soil computed as weights of real quantities from potential ones, for each variable; P, PE, PR, PRO, PL represents observed precipitation, potential evapotranspiration computed using Thornthwaite method (Thornthwaite, 1984), potential recharge, potential run-off and potential water loss from the soil. Here we used the method and software developed by Wells at al. (2004) to compute self calibrated PDSI values and related indices. The self calibrated PDSI numerically match the behavior of the index at any location by replacing empirical constants of Palmer (1965) with newly calculated constants based on local climate. In this study, we used only monthly values of Palmer index of soil moisture anomaly (Zi) which were computed for 113 stations covering the Romanian territory. Prior to computing PDSI, input data was homogenized and missing data was replaced, using software package M.A.S.H. (Szentimrey, 1998; Szentimrey, 2007; Szentimrey, 2011; Lakatos et al, 2011). The period of interest here is 1961-2010. Soil moisture measurements from agro-meteorological platforms relevant for maize and wheat indicated large values, statistically significant, of their correlation coefficient with the Palmer index Zi. Reference data used to test the representativeness of Zi index for the soil moisture anomalies in Romania covered the Southern part of Oltenia (Bojariu et al, 2012). The analysis of changes and variability of the monthly filtered values of the Palmer index for the soil moisture anomalies has been performed using the method of Empirical Ortogonal Functions (EOF) decomposition. Data used in the EOF analysis was first filtered using a 12-months running mean. RESULTS In order to focus the analysis on the interdecadal component of the variability, initial data was low pass-filtered using a 12-months running mean. The EOF analysis of the Palmer index for the monthly soil moisture anomalies computed for the 113 stations available in Romania indicates the existence of an interdecadal variability covering the entire Romanian territory, yet more pronounced in the southern regions (figure 1). The variance associated to this mode represents 50% of the total variance of the data field. The second EOF mode of the monthly soil moisture anomalies indicates a tendency towards soil moisture depletion in the southern and eastern parts of Romania. The depletion signal is stronger in the Baragan region (figure 2). At the same time, there is another signal associated with a tendency of increasing soil moisture in the north-western part of the country (figure 2). The variance associated with this mode represents 10% of the total variance of the data field.

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PC1 3 2 1 0 -1 -2

1961 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2008 2010

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Figure 1. First EOF (spatial pattern and associated time series) of the Palmer index for the monthly soil moisture anomalies computed for the 113 station in Romania (1961-2010). Initial data was filtered using a 12-months running mean. The variance associated to this mode is of 50% of the total variance of the data field. PC2 4 3 2 1 0 -1 -2

1961 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2008 2010

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Figure 2. Second EOF (spatial pattern and associated time series) of the Palmer index for the monthly soil moisture anomalies computed for 113 stations in Romania (1961-2010). Initial data was filtered using a 12-months running mean. The variance associated to this mode is of 10% of the total variance of the data field. CONCLUSIONS The spatial pattern of the first EOF mode (same sign over the entire Romanian territory) suggests the existence of a large-scale generating mechanism which acts on interdecadal time scale. This mechanism seems to be related to the natural variability of the climate system, the variation tendency being less clear (figure 1). The second EOF mode seems to be related to the climate change signal. It suggests for the Romanian territory, at annual time scales, a tendency for aridization especially in the southern part of the country, due to changes in the fields of precipitation, temperature and evapotranspiration during the warm season. Furthermore, the projections of the future climate obtained with global and regional climate models suggest the same pattern of the changes which is similar to the second EOF mode, for the hydrological regime in Romania (figure 3). Among the candidate phenomena responsible for the generation of the signal associated to the first EOF of soil moisture anomalies there are Atlantic Multidecadal Oscillation (AMO) and the North-Atlantic Oscillation (NAO). Relationships between these phenomena and the interdecadal variability of the soil moisture anomalies in Romania seem to be non-linear as correlation coefficients are not very high between their associated indices. These aspects related to the climate mechanisms responsible for the soil moisture variability will be clarified in a forthcoming study. 525

Figure 3. Changes of the multi-annual means of the evapotranspiration (in %) for 2011-2040 compared with the reference period 1961-1990. The means of an ensemble of 9 future projections using 9 regional climate models (FP6 ENSEMBLE) have been used.

ACKNOWLEDGEMENTS The study has been realized within the frame of FP7 EURO4M project under grant agreement no. 242093, with the financial support of the European Commission. REFERENCES Alley W.M. 1984. The Palmer Drought Severity Index: limitations and applications. Journal of Climate and Applied Meteorology 23:1100-1109. Barbu I., Popa I., 2002. Monitoring drought risk for Romanian forests, Rev. Bucovina Forestieră, Comentarii, IX, n° 1-2, p. 37-51 (in Romanian). Bojariu R., Velea L., Mateescu E., Cică R. D., Alexandru D., Dobrinescu A. E., Bîrsan M. úi Dumitrescu A., 2012. Mecanisme climatice locale implicate in aridizarea Campiei Olteniei, ConferinĠa NaĠionalӽ a StiinĠei Solului, Craiova, 28 August- 1 Septembrie, 2012. Lakatos M., Szentimrey T., Bihari Z., Szalai S, 2011. Homogenization of daily data series for extreme climate indices calculation, Proceedings of COST-ES0601 (HOME) Action Management Committee and Working Groups and Sixth Seminar for Homogenization and Quality Control in Climatological Databases, Budapest, 26-30 May 2008. WCDMP-No. 76, WMO/TD-NO. 1576, 2011, pp. 100-109. Palmer W.C. 1965. Meteorological drought. Research Paper No. 45. U.S. Weather Bureau. [NOAA Library and Information Services Division, Washington, D.C. 20852] Szentimrey T. 1999. Multiple Analysis of Series for Homogenization (MASH), Proceedings of the Second Seminar for Homogenization of Surface Climatological Data, Budapest, Hungary; WMO, WCDMP-No. 41: 27-46. Szentimrey T., 2007. Manual of homogenization software MASHv3.03, Hungarian Meteorological Service. Szentimrey T., 2011. Methodological questions of series comparison, Proceedings of COST-ES0601 (HOME) Action Management Committee and Working Groups and Sixth Seminar for Homogenization and Quality Control in Climatological Databases, Budapest, 26-30 May 2008, WCDMP-No. 76, WMO/TD-NO. 1576, 2011, pp. 1-7. Thornthwaite C. W. 1948. An approach toward a rational classification of climate. Geographical Review 38: 55-94. Wells S. Goddard and M. Hayes, 2004. A Self-Calibrating Palmer Drought Severity Index, J. Climate, 17, 2335-2351.

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