Mojtaba Nassaji zavareh; Ali Khorshiddoust; Ali Rasouli; Ali Salajegheh
Abstract
Temperature and precipitation are among important atmospheric parameters for watershed planning. Assessment of temperature and precipitation trends is very important for future watershed planning. In this paper, trends of atmospheric parameters such as seasonal and annual temperature and precipitation ...
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Temperature and precipitation are among important atmospheric parameters for watershed planning. Assessment of temperature and precipitation trends is very important for future watershed planning. In this paper, trends of atmospheric parameters such as seasonal and annual temperature and precipitation were examined for the synoptic stations of Bandar Anzali, Rasht, Ramsar, Babolsar and Gorgan. In order to detect temperature and precipitation trends, homogeneous time series are needed. Expert judgment, metadata and standard normal homogeneity test (SNHT) were used to assess homogeneity of seasonal and annual time series. Some seasonal and annual time series were heterogeneous which were adjusted to homogeneous time series. The results show positive trends of annual and seasonal maximum and minimum temperature, and negative trends of annual and seasonal maximum and minimum precipitation. Also the trend of minimum temperature is higher than the trend of maximum temperature. Mean trends of annual minimum and maximum temperature and annual precipitation are 0.39 ◦c/decade, 0.05◦c/decade and -31/8mm/decade, respectively. The highest average trend of seasonal maximum and minimum temperature is related to the summer season, whereas the highest of average trend of seasonal precipitation is related to the winter season.
The lowest of average seasonal trend of minimum and maximum temperatures are related to the winter and spring seasons, respectively. Mean of seasonal precipitation trends of spring, summer and autumn are almost similar each other.
Mojtaba Nassaji zavareh; Bagher Ghermezcheshmeh; Fatemeh Rahimzadeh
Abstract
Daily constant discharges are needed estimating daily discharge in the hydrological model. The different number of statistical years, statistical deficiencies, and measurement error leads to the formation of time series with an uncommon time base. Hence the reconstruction of daily discharge data is of ...
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Daily constant discharges are needed estimating daily discharge in the hydrological model. The different number of statistical years, statistical deficiencies, and measurement error leads to the formation of time series with an uncommon time base. Hence the reconstruction of daily discharge data is of paramount importance. In this research, daily discharge was reconstructed in two stages in one of the upstream of Karoun River. In both stages of research, daily discharge data from two upstream stations were used to reconstruct daily discharge of the downstream station using artificial neural networks, neuro-fuzzy and two variables regression methods. In the second stage, the magnitudes of discharge, based on dry, normal and wet years was used to reconstruct the daily discharge. The results showed higher accuracy in the artificial neural network and neuro-fuzzy methods compared to two variable regression methods in the reconstruction of daily discharge. Multi-layer perceptron model has better potential among all different method of artificial neural network and neuro-fuzzy models. Classification of discharge into dry, normal, and wet years decreases error in the reconstruction of daily discharge. Based on the mean relative error (MRE), error in reconstruction of daily discharge is the least in normal, wet, and dry years, respectively
