Abstract
Droughts may be classified as meteorological, hydrological or agricultural. When meteorological drought appears in a region, agricultural and hydrological droughts follow. In this study, the standardized precipitation index (SPI) was applied for meteorological drought analysis at five stations located around the Lakes District, Turkey. Analyses were performed on 3-, 6-, 9- and 12-month-long data sets. The SPI drought classifications were modeled by artificial neural networks (ANN), which has the advantage that, in contrast to most of the time series modeling techniques, it does not require the model structure to be known a priori. Comparison of the observed values and the modeling results shows a better agreement with SPI-12 and ANN models. While the mean square error (MSE) values varied between 0.061 and 0.153 for training stage, they varied between 0.09 and 0.147 for testing stage of SPI-12 values. Also, the highest R2 values obtained as 0.930 for training stage and 0.923 for testing stage of Sütçüler station between SPI-12 and ANN models. Key words: Drought, standardized precipitation index (SPI), artificial neural networks.
Highlights
Drought analysis is important in water resources management, planning and for long term economic and social planning
Time series of standardized precipitation index (SPI) are constituted for different periods based on monthly mean precipitation values for determination of drought categories for wet and drought periods
The comparison shows that there is a better agreement between the results of the artificial neural networks (ANN) models and SPI values for only 12-month periods as the highest R2 and the lowest mean square error (MSE) values are obtained for 12 month periods for each station
Summary
Drought analysis is important in water resources management, planning and for long term economic and social planning. Considering that the population of Turkey is 70 million, about 1570 m3 of water is available per capita annually, which indicates that in future Turkey may encounter serious problems especially in drought periods (Keskin et al, 2009). Türkeş (1999) examined regional and historical changes of precipitation and drought index series as climatic factors for 1930 to 1993. He indicated that extreme drought is seen in Southeast and Middle Anatolia regions due to climatic effects while in the Mediterranean and Aegean regions due to human effects. He indicated that extreme drought is seen in Southeast and Middle Anatolia regions due to climatic effects while in the Mediterranean and Aegean regions due to human effects. Pongracz et al (1999) used as input El Niño
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