Showing 3 results for Radmanesh
H. Rezaei-Sadr, A. M. Akhoond-Ali, F. Radmanesh, G. A. Parham,
Volume 17, Issue 66 (winter 2014)
Abstract
In this study, the influence of spatial heterogeneity of rainfall on flood hydrograph prediction in three mountainous catchments in south west of Iran was studied. Two interpolation techniques including Thiessen polygons method and Inverse Distance Weighting method were applied to compare the rainfall patterns of surrounding rain-gages in hydrograph simulation with rainfall patterns of nearest rain-gage from the catchment outlet. It was found that the best simulated hydrograph is obtained from rainfall pattern of the nearest rain gage. Moreover, the results did not show any relationship between spatial variation of rainfall and outlet hydrograph. Formation of different local rainfall patterns due to non-stationary rainfall field provoked by irregular topography and their effect on interpolation procedure caused important biases in interpolated rainfall hyetographs obtained by Thiessen and IDW methods. It seems that the observed biases in the response of the catchments are the result of inaccurate representation of spatially averaged rainfall rather than its spatial variability. Hence, in mountainous catchments with irregular topography, the lack of sufficient records caused by poor rain gage arrangement can be highlighted as the dominant source of uncertainty in modeling the spatial variations of rainfall.
M. Saeidipour, F. Radmanesh, S. Eslamian, M. R. Sharifi,
Volume 23, Issue 2 (Summer 2019)
Abstract
The current study was conducted to compute SPI and SPET drought indices due to their multi-scale concept and their ability to analyze different time-scales for selected meteorological stations in Karoon Basin. Regionalization of SPI and SPEI Drought indices based on clustering analysis was another aim of this study for hydrological homogenizing. Accordingly, to run test through data and determine similar statistical periods, 18 stations were selected. SPI and SPEI values were plotted in the sequence periods graphs and their relationships were analyzed using the correlation coefficient. The results were compared by Pearson correlation coefficient at the significance level of 0.01. The results showed that correlation coefficients (0.5-0.95) were positive and meaningful for all stations and the correlation coefficient between the two indices were increased by enhancing the time-scales. Also, time-scales enhancement decreased the frequency of dry and wet periods and increased their duration. Through regionalization of basin stations based on clustering analysis, the stations were classified into 7 classes. The results of SPEI regionalization showed that the frequency percentage of the normal class was more than those of dry and wet classes.
J. Jalili, F. Radmanesh, A. A. Naseri, M. A. Akhond Ali, H. A. Zarei,
Volume 24, Issue 3 (Fall 2020)
Abstract
Agricultural water management studies require accurate information on actual evapotranspiration. This information must have sufficient spatial detail to allow analysis on the farm or basin level. The methods used to estimate evapotranspiration are grouped into two main groups, which include direct methods and indirect or computational methods. Basics of the indirect methods are based on the relationship between meteorological parameters, which impedes the use of these data with a lack or impairment. On the other hand, this information is a point specific to meteorological stations, and their regional estimates are another problem of uncertainty of their own. To this end, the use of remote sensing technology can be a suitable approach to address these constraints. Real evapotranspiration can be estimated by satellite imagery that has short and long wavelengths and is estimated using surface energy equations. Examples of such algorithms include SEBAL, METRIC, SEBS. Among the above mentioned algorithms, SEBAL and SEBS have been used. Among the factors of superiority of the SEBAL and SEBS algorithms, in comparison with other remote sensing algorithms, is a satellite imagery analysis algorithm based on physical principles and uses satellite simulation and requires minimum meteorological information from ground measurements or air models.
