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AI InsightThe results showed that the predicted data represent the actual data very well for each hydrologically homogeneous region.
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Özet
This paper presents a methodology for the modeling of the agricultural drought duration in the Tokat region. For this purpose, the study area was divided into 4 hydrologically homogeneous regions. A rain gauge with the longest observation period was selected for each region. Days with rainfall equal to or less than a threshold of 6.4 mm and days with no rainfall were assumed to be drought periods in the 4 hydrologically homogeneous regions. The monthly data sequences were constituted by counting days with rainfall equal to or less than the threshold value of 6.4 mm and days with no rainfall. Linear stochastic models known as ARIMA were used to simulate drought periods in the hydrologically homogeneous regions. For 5 years, the predicted data using the best models were compared to the observed data. The results showed that the predicted data represent the actual data very well for each hydrologically homogeneous region.
Abstract
This paper presents a methodology for the modeling of the agricultural drought duration in the Tokat region. For this purpose, the study area was divided into 4 hydrologically homogeneous regions. A rain gauge with the longest observation period was selected for each region. Days with rainfall equal to or less than a threshold of 6.4 mm and days with no rainfall were assumed to be drought periods in the 4 hydrologically homogeneous regions. The monthly data sequences were constituted by counting days with rainfall equal to or less than the threshold value of 6.4 mm and days with no rainfall. Linear stochastic models known as ARIMA were used to simulate drought periods in the hydrologically homogeneous regions. For 5 years, the predicted data using the best models were compared to the observed data. The results showed that the predicted data represent the actual data very well for each hydrologically homogeneous region.