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Spatiotemporal analysis of droughts in Hirfanli Dam basin, Turkey by the Standardised Precipitation Evapotranspiration Index (SPEI)

Zeybekoglu Utku

Acta Geophysica

2022

Vol. 70, no 1

361--371

Drought is generally defined as a disaster affecting vital activities negatively because of water scarcity as a result of precipitation falling below the recorded normal levels. In the present study, the Standardized Precipitation Evapotranspiration Index was applied for the first time in Hirfanli Dam basin, which has the characteristics of a semiarid climate in Turkey. The annual drought events in the basin between 1968 and 2017 were investigated by using the precipitation and temperature data obtained from Gemerek, Kayseri, Kirsehir, Nevsehir, Sivas, and Zara meteorology observation stations located in Hirfanli Dam basin. The dry and wet years were determined in the basin, and evaluations were made in this respect. The years when the most severe droughts happened in the basin were determined, and drought maps, which showed the spatial distribution of drought, were prepared. In the light of the analyses and maps made, it was found that the most severe drought happened in 2001 in Hirfanlı Dam basin.

The Investigation of the Applicability of Data-Driven Techniques in Hydrological Modeling: The Case of Seyhan Basin

Turhan Evren, Keleş Mümine Kaya, Tantekin Atakan, Keleş Abdullah Emre

Rocznik Ochrona Środowiska

2019

Tom 21, cz. 1

29--51

Proper water resources planning and management is based on reliable hydrological data. Missing rainfall and runoff observation data, in particular, can cause serious risks in the planning of hydraulics structures. Hydrological modeling process is quitely complex. Therefore, using alternative estimation techniques to forecast missing data is reasonable. In this study, two data-driven techniques such as Artificial Neural Networks (ANN) and Data Mining were investigated in terms of availability in hydrology works. Feed Forward Back Propagation (FFBPNN) and Generalized Regression Neural Networks (GRNN) methods were performed on rainfall-runoff modeling for ANN. Besides, Hydrological drought analysis were examined using data mining technique. The Seyhan Basin was preferred to carry out these techniques. It is thought that the application of different techniques in the same basin could make a great contribute to the present work. Consequently, it is seen that FFBPNN is the best model for ANN in terms of giving the highest R2 and lowest MSE values. Multilayer Perceptron (MLP) algorithm was used to predict the drought type according to limit values. This system has been applied to show the relationship between hydrological data and measure the prediction accuracy of the drought analysis. According to the obtained data mining results, MLP algorithm gives the best accuracy results as flow observation stations using SRI-3 month data.