Abstract
Drought is the least understood of all weather phenomena, since it differs from other natural hazards in several ways. The hazardous of drought can be better understood by analyzing drought indices. The Standardized Precipitation Index (SPI) has been one of the most widely used indices for drought studies, as it can provide satisfactorily results for the appearance, variability and intensity of drought. Moreover, SPI has been developed in order to quantify and record drought episodes on multiple time scales (3 months, 6 months, 1 year, 2 years). In the present study a statistical downscaling technique based on artificial neural network was employed for the estimation of SPI over Greece. The aim of the study is to evaluate the simulated SPI index against the observational one. The performance of the statistical downscaling model is assessed utilizing biases, standard deviation and correlation coefficient between observed and simulated indices. An overestimation of the simulated mean SPI indices accompanied by a decrease in standard deviation is evident for all stations and all time periods. The reproduction of SPI3 and SPI6 for winter, spring seasons display rather promising results.
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Anagnostopoulou, C., Tolika, K., Maheras, P. (2013). Drought Index Over Greece as Simulated by a Statistical Downscaling Model. In: Helmis, C., Nastos, P. (eds) Advances in Meteorology, Climatology and Atmospheric Physics. Springer Atmospheric Sciences. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29172-2_54
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DOI: https://doi.org/10.1007/978-3-642-29172-2_54
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