Abolghasem Dadrasi Sabzevar; Nader Jalali; Ebrahim Gohari
Abstract
Productivity of the floods in the country which suffering from a severe shortage of water resources, is inevitable and flood spreading in areas prone water is one of the simplest methods for efficiency of flood. Identification of suitable areas for flood spreading by traditional methods usually is very ...
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Productivity of the floods in the country which suffering from a severe shortage of water resources, is inevitable and flood spreading in areas prone water is one of the simplest methods for efficiency of flood. Identification of suitable areas for flood spreading by traditional methods usually is very expensive and takes up time. Using GIS and RS can reduce such costs and increase the speed and accuracy. The study area is part of the provinces of Khorasan Razavi and north Khorasan which included the cities of Sabzevar, Esferayen, Jagarm, Kashmar, Bardeskan and Khalilabad. In this study, seven layers, including units of the quaternary deposits, limitation of land use, slope, infiltration rate, runoff, storage coefficient and bed rock depth were selected and analyzed. Map of quaternary deposits was prepared from geological map at scale of 1:250000 and controlled by photo-interpretation. 43 sites were selected by field survey. A Land use restriction was prepared by field Investigation and visual interpretation of satellite images. The slope map was obtained by GIS method, using digital topographic map of the area with 100m interval and 30m Pixel size. Infiltration rate in each area was calculated by double ring test and geoelectric study was used for bed rock depth estimation. Also storage coefficient for each site was calculated by typical table related on soil texture using soil sampling to a depth of one meter. And finally volumes of overland flow were calculated for all sites by Jasten method. Booleain logic, index overlay and fuzzy logic were selected and tested in those areas to mapping suitable areas for flood spreading, using the weighting method. The results show that the fuzzy sum is the best and visual interpretation of RGB742 Integrating GIS, is more suitable to identify and prioritize areas prone to spreading.
