چكيده لاتين :
Introduction: Flood is an inevitable natural phenomenon occurring from time to time in all rivers and natural drainage systems, which not only damages the lives, natural resources and environment, but also causes the loss of economy and health. So estimation and prediction flood hazard is very important spatially in the watersheds without measurement station. There are many models in the water and environmental studies which have been used for runoff and flood in the watersheds without measurement station. One of the newest is cellular automata that has been combined well with the GIS for simulation runoff and flood hazard. Cellular automata as a tool for modeling and simulation of processes taking place in the real world are now increasingly used, as evidenced by their use not only as a tool for creating simulations, but also by their use in areas of crisis management. Using GIS knowledge, it is possible to create cellular automata, which can appropriately and authentically reflect the water flow on the Earth’s surface. Cellular automata (CA) is a mathematical model that can be used for computation and simulation of the systems. In this method, the basin is defined with a network of the rectangular cells, and the interactions between the cells together with the geographic rules that govern the area result in the runoff modeling. This model relies on the GIS and satellite images. Cellular automata model uses various information such as digital elevation model (DEM), land use, hydrologic soil groups, rainfall, slope and etc for runoff estimation. In the present study, the runoff of the Chirchir catchment in East Azarbaijan province, Iran, has been modeled by means of the GIS-based cellular Automata.
Materials and methods: In this study, GIS-based cellular automata were used to simulate flood in the Chirchir catchment in the northwest of Iran. CA models use several primary components including the cells arranged in a regular mosaic pattern (raster, grid), transition rules determining the changes in cell properties, neighborhood of the cell, and boundary conditions. These components affect the status of each individual cell in a network in a given time step. In this research, Chirchir catchment in East Azerbaijan province is modeled using cellular automata. First, SCS formula is used to predict the runoff in each cell. Map of hydrological soil groups of Chirchir catchment is determined by means of soil texture map, then land use and SHG maps are prepared for calculating the runoff curve number (CN) map for the normal conditions. Since the soil has dry moisture condition and the slope is greater than 5%, adjusted CN is calculated for dry antecedent moisture condition and catchment slope using the common relationship (Ponce and Hawkins, 1996; Hawkins et al., 2009). After reading the rainfall and the CN map for dry antecedent moisture condition, the runoff was calculated using the SCS equation. Then Kinematic wave model is used for flow depth in the cells and runoff production within each cell is simulated by determining the cell state (water surface elevation) that included both the cell altitude and the water depth. The distribution of water flow among cells was determined by applying CA transition rules based on conservation of energy and continuity equations. D8 algorithm is used to simulate flow direction during the calculation of the surface convergence. The procedures for channel network delineation are based on the D8 model for flow over a terrain surface represented by a grid DEM. In this model, a single flow direction in the direction of steepest slope towards one of the eight (cardinal and diagonal) grid cells neighboring is used to represent the flow field. Also for calculating flood hydrograph, travel time is calculated using flow length and flow velocity. So roughness coefficient and flow depth is used for flow velocity and then travel time map is obtained. Finally, Python programming language is used to estimating flow hydrograph due to simplicity, powerful and object-oriented programming language and supported with GIS.
Results and discussion: Results show that the most area of the Chirchir catchment has pasture and type D soil hydrological group, so that it has very low permeability which means that a large amount of rainfall convert to runoff. Runoff depth is high in east and southeast of the Chirchir catchment due to physical characteristics and rainfall of the catchment but among these parameters, the slope was the most important parameter in the runoff generation. Also, map of the flood hazard shows that downstream river has high potential in flood hazard due to receiving water from upstream. Then, for simulation flood hydrograph, travel time was calculated using ratio between flow lengths to flow velocity. Flood hydrograph estimated for two events, 27/3/1388 and 12/3/1386 so that the computational runoff is very adapt to observational runoff. The correlation coefficient for two events (0.82 and 0.70) indicate the good accuracy of the model. Low error rates also indicated that the cellular automata model has the high efficiency to predict the flood peak and the time of its occurrence in the Chirchir catchment. The results of this study matches with the results of researchers such as Aboudagga (2005), Rinaldi et al. (2012) and Cirbus and Podhoranyi (2013). They stated that the use of cellular automata model with compared to the conventional methods that use GIS environment, has higher accuracy and capable to estimate flood hydrograph. Therefore, the use of cellular automata with GIS, not only speed up the calculation of runoff, but also increases the accuracy of the results.
