H. Khedmati; M. Manshouri; M. Heydarizadeh; H. Sedghi
Abstract
Abstract
South-east basins of Iran which is including Sistan and Baluchistan, Kerman, Yazd and Hormozgan provinces has an extensive desert called " Loot " and this region is one of the hottest and driest parts of Iran. Few numbers and the lack of uniformity in scattering of hydrometric stations are ...
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Abstract
South-east basins of Iran which is including Sistan and Baluchistan, Kerman, Yazd and Hormozgan provinces has an extensive desert called " Loot " and this region is one of the hottest and driest parts of Iran. Few numbers and the lack of uniformity in scattering of hydrometric stations are main characteristics of this region.
Another problem in this region is that data are short-term, so it leads to have unguaged sites.Data generation helped us to have at most 43 hydrometric stations with 20 years data plus 10 stations with 30 years data.On the other hand, in hydrology for fitting statistical distribution on rainfall and runoff, at least we need to have 30 years data and even more. Thus for analyzing different methods of flood estimation and presenting logical relationships in sub-basins, first of all we gathered meteorology, hydrology and ecological features and also morphometric characteristics were measured.Homogeneity test was done for data and they have been completed in a 20 years data.The group of homogeneous sub-basins has been determined using some methods like: Index Flood, cluster analysis and, multi-variable regression while some physiographic properties and ordinary and linear moments were used.Common statistical distributions have been tested and dominant statistical distribution of region was finally determined Log Pearson type III and based on that peak discharge with different return periods have been estimated to present mathematical models.Then, generated models have been tested using three other sub-basins which hadn't participated in presenting mathematical models.At last, the most appropriate mathematical relationships for flood discharge estimation in different return periods have been achieved in unguaged sites of south-east basins of Iran.
Keywords: Regional flood analysis, Unguaged sites, Flood index, Mathematical model, Cluster analysis, Multi-variable regression
F. Khamchin Moghadam; H. Sedghi; F. Kaveh; M. Manshouri
Abstract
Abstract
Most heavy storms result in destructive floods. One of the basic elements in analyzing floods in watersheds without data is hourly storms. The Determination of the storm of the watershed needs regional analysis of storms and transferring them to the gravity center of the watershed. Maximum ...
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Abstract
Most heavy storms result in destructive floods. One of the basic elements in analyzing floods in watersheds without data is hourly storms. The Determination of the storm of the watershed needs regional analysis of storms and transferring them to the gravity center of the watershed. Maximum daily precipitation ( ), is the most accessible storm in any region, which can be converted to hourly precipitation. The analysis of the point and regional is one of climate studies requirement. Regionalization of , can be an influential step toward analyzing storms and floods. In order to accomplish such a task, two approaches are possible, one is using the old methods of geographical regionalization and the other one is using the new methods like "Cluster Analysis" and "L-Moments Homogenous Tests". In this paper second approach was employed. All existing rain-gauge stations (N=396) were considered and their available data were collected in this study. Basic tests were applied and 266 stations were removed due to the lack of the required conditions and only 130 stations were used in analysis. "Principal Components" method was used to omit the uninfluential variables (only 6 variables out of 21 were proved as basic and important). "Hierarchical Clustering" was used in the process of regionalization of the stations indicated of seven different regions. These regions were distributed in different locations throughout the country and the regionalization map is presented. The "L-Moments Homogenous Tests" were also employed for further indication. According to the final results, the regionalization of of Iran's rain-gauge stations can be defined as 7 homogenous regions.
Keywords: Regionalization, Maximum daily precipitation, Principal Components, Cluster Analysis, L-Moment
