Document Type : Research Paper
Authors
Abstract
Zabol region is one of the places that is exposed to severe wind erosion, which is located in southeast Iran Due to the fact that, some stations like Zabol suffers from missing data (having five observations instead of eight), thus, the effects of missing data on prediction of wind speed and direction also needed to be considered. This study aim to determine the minimum years of data required in predicting hourly wind speed and direction, and predicts hourly wind speed and direction, also to verify suitability of Weibull distribution in predicting hourly wind speed and direction and finally to analyze the erosive winds in Zabol region. In this study, the regression coefficients (r) of probability of wind occurrence in various speeds for 16 cardinal directions in two different periods (1986- 1990 and 1986-1995) were separately calculated and compared. To predict hourly wind speed and direction by Weibull distribution, at first its scale and shape parameters (c and k) were determined using the least square method. Then, wind direction distribution, the ratio of maximum to minimum of wind speed, and the hours with maximum wind speed during any month were determined. Using these parameters along with generation of random numbers, the hourly wind speed and direction were simulated. The results indicated five years instead of ten years of data can be used to predict wind speed and direction with a confidence level at 99%. Weibull distribution provided best fit during the months that both the probability of calm periods or standard deviation of probability of wind occurrence in different directions were low. The maximum and minimum wind speed occurred at about 6:00 AM and 6:00 PM, respectively. The probability of occurrence of erosive winds (V≥8m/s) were maximum in June, July, August, and September. The analyses of wind data indicated that the most erosive winds were from North-Northwest, Northwest, and North. The wind speed and direction were predicted by Weibull distribution in the region with 99% accuracy. The results obtained from this research can help researchers and soil conservationists to predict and control wind erosion.
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