SOUTHERN OSCILLATION EFFECT ON BALKAN PRECIPITATION Dr. Ali ihsan MARTI M. Sc. Volkan YILMAZ

1. SOUTHERN OSCILLATION EFFECT ON BALKAN PRECIPITATION Dr. Ali ihsan MARTI M. Sc. Volkan YILMAZ Dr. Meral BUYUKYILDIZ Selcuk University, Department of Civil Engineering Konya - TURKEY

2. Forecasting and Analysis of Meteorological Variables * Precipitation - significant meteorological variable (directly affects life on the world) - Short duration excessive rainfalls ----- Floods - Long duration insufficient rainfalls ----- Droughts * All factors affecting ppt. formation - not determined yet - Estimation of precipitation data is a difficult process. Water Resources Planning & Operating Processes 1 / 11

3. Artificial neural network (ANN) methods • * widespread usein estimating hydrometeorological parameters. • * successfully applied for the behavior of variables whose interrelations are not exactly known. • * easy practicability and not requiring more data. 2 / 11

4. El Nino Southern Oscillation (ENSO) • sporadic, intensive and extensive climatic ocean-atmosphere phenomenon affecting climatic characteristics of many places • occurs due to the changes in the usual atmospheric pressure patterns and in the sea surface temperature of the tropical Pacific • ENSO has two opposite extreme phases - El Niño (warm phase) • La Niña (cold phase) 3 / 11

5. The objective of this study is to determine whether the occurrence of El Niño(warm phase of ENSO) affects the precipitation behavior of a part of Balkans or not. DATA & METHODOLOGY • 16 ppt. gauging stations in • Marmara Region of • Turkey • Seven of them in Balkans • The rest nine in Anatolia. 4 / 11

6. * Ppt. data sets span from 1937 to 2004 for all stations * The data sets are homogeneous and have no missing record * Warm phase ENSO events (El Niño) took place in years 1939, 1941, 1951, 1953, 1957, 1963, 1965, 1969, 1972, 1976, 1982, 1987, 1991, 1993, 1997, 2002 and 2004 (Kiladis and Diaz, 1989) * At first, the non-occurrence of El Niño events in the historical records was considered. The original monthly precipitation values corresponding to El Niñoyears were removed and replaced by monthlyprecipitation values generated by Feed Forward BackPropagation Artificial Neural Network model (FFBPANN) *After completing the generation of synthetic data, the comparison of synthetic data with the original datashould be done. 5 / 11

7. Comparison Scenarios • Scenario A • Two different time series of 68 years length for each ppt. station • original annual precipitation records • 2) synthetic annual precipitation records • Scenario B • Two different ppt. time series for El Niñoyears • historical ppt. values for 17 El Niño event years • 2) synthetic ppt. values for 17 El Niño event years • * Comparison in terms of some statistical characteristics 6 / 11

8. - Variances:F-test - Means:t-test - Populations: Mann-Whitney U test - Autocorrelations:The original and synthetic precipitation series were examined to see whether ENSO events influence the autocorrelation structure of the observed series or not. 7 / 11

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10. CONCLUSIONS • Scenario B presented more significant variance differences than that of Scenario A. It is worthwhile to note that these identified scenarios were distinctive in reflecting the ENSO modulation on precipitation variance among all stations in the entire study. • Since the mean characteristics of the stations did not vary for most of the stations except three of them, it can be said that El Nino does not affect the mean precipitation amount observed in this region. • The non-occurrence of the population differences in Scenario A for all stations can be explained by the masked El Nino effects among the 68-year time series. • It can be said that ENSO events caused modification in the persistency characteristic of Turkish precipitation patterns. 9 / 11

11. Since the results of this study’s data generation using FFBPANN model and the results of data generation using Radial Based Artificial Neural Network (RBANN) in Martı A.İ (2007) presented similar results, both ANN methods can be used for rainfall data generation where other forecasting models do not work. • According to the aforementioned analyses, it is understood that El Nino Southern Oscillation events have influences on the precipitation values of the Balkans especially by changing the persistency and the variance characteristics of the data sets. • The supplementary gauging stations became useful for the final evaluations of ENSO effects on the precipitation data of the Marmara Region. 10 / 11

12. This conclusion proved the influence of large-scale atmospheric oscillation patterns such as Southern Oscillation, North Atlantic Oscillation etc. on planning and managing water resources. • However, since the half of the Marmara Region exists in the Balkans, more evident influences of ENSO events can be observed and determined when the analysis of this study is applied to the precipitation time series of the all Balkan countries (Macedonia, Greece, Bulgaria, etc.). • Additionally, the detection of ENSO effects in terms of streamflow, evaporation, temperature parameters using the same analysis procedure of this study will also make the possible ENSO influences more evident in the Balkans. • THANK YOU FOR YOUR PATIENCE… 11 / 11