مدل ارتفاع رقومی

Storm-inundation models based on hydrology and hydrodynamics require a large amount of input data (detailed terrain, sewer system and land use data). In this paper, in order to determine inundation conditions quickly with only a few usually available input data is proposed an urban storm-inundation simulation method (USISM) based on Geographic Information System (GIS). The USISM is a simplified method of distributed hydrological model based on DEM, in this method depressions in terrain are regarded as the basic inundated area. The amount of water that can be stored in a depression indicates the final inundation distribution. The runoff and maximum storage volume for each depression and the flow direction between these depressions are all considered in the final inundation simulation. The SCS method is used to calculate storm runoff and a water balance equation is used to calculate the water storage in each depression. The result shows that in all four-storm event, the average relative depth errors of depth in all inundation sites are less than 20%, while the average relative errors of area and volume are more than 60% Therefore, the USISM method has a higher ability to simulate the final depth of inundation than the surface and volume of inundation. The result reveals that the USISM method could find the inundation locations in the Damghan Urban Watershed and calculate inundation depth and area quickly and therefore display a significant role in the management of the urban crisis.

This study was conducted to evaluate using terrain attributes derived from digital elevation model (DEM) as ancillary data to predict soil organic carbon (SOC) by implementing different statistical and geostatistical techniques. A linear regression model (LR)، Artificial Neural Network model (ANN)، ordinary kriging (OK)، ordinary co-kriging (OCK)، regression kriging (RK) and kriging with an external drift (KED) were performed to predict spatial distribution of SOC in an area of 2400 km2 in mashhad، iran. The SOC was measured for 200 soil samples of the study area and their corresponding Terrain attributes value was extracted from derived from 10-m resolution DEM. correlation between measured SOC and individual terrain attributes was determined، the number of 160 data were used for model development and 40 as validation data set. Resulting maps of different interpolation methods were compared to evaluate map quality using MAE and R2 criteria calculated from plotting measured versus estimated data. The results showed that there is a significant but not strong correlation between SOC and terrain attributes. The comparison of estimation techniques showed that the KED technique with wetness index as ancillary data has the best performance (MAE=0. 18 %، R2=0. 67) of all، but no significant difference with RK. There were modest differences between maps created with geostaistical technique but sensible difference with LR and ANN ones. The results of this study propose that although there is a significant correlation between SOC and terrain attributes therefore It can be use for enhancing the quality of map، but it is not able to express the spatial variability of SOC as it is necessary for detailed soil map. Because there is other factors controlling SOC spatial distribution