شبیه‌سازی خطر سیلاب با استفاده از مدل اتومات سلولی بر پایۀ GIS (مطالعۀ موردی: حوضۀ آبریز چرچر)

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 استادیار گروه جغرافیای طبیعی، دانشکدة علوم زمین، دانشگاه شهید بهشتی

2 استادیار گروه عمران، دانشکدة فنی‌- مهندسی مرند، دانشگاه تبریز

چکیده

اتومات سلولی ابزاری است برای مدل‌سازی و شبیه‌سازی فرایندهایی که در جهان واقعی رخ می‌دهد؛ این ابزار همچنین در زمینة مدیریت بحران نیز کاربرد دارد. در این تحقیق از اتومات سلولی بر پایة GIS برای شبیه‌سازی سیلاب در حوضة آبریز چرچر در شمال غرب ایران استفاده شده است. نتایج نشان داد بیشترین مساحت حوضة چرچر دارای کاربری مرتع و گروه‏ هیدرولوژیکی خاک D است و نفوذپذیریِ بسیار کمی دارد. ارتفاع رواناب در نیمة شرقی و جنوب شرقی حوضه، به دلیل قابلیت نفوذ کم و شیب زیاد، بالاست. همچنین، خطر سیلاب در مسیر رودخانه و اراضی اطراف آن، به‌ویژه در پایین‌دست جریان، زیاد است؛ به طوری که، علاوه بر کاربری اراضی، خاک، نفوذپذیری، و بارش، عامل شیب تأثیر بیشتری در تولید رواناب در حوضه گذارده است. سرانجام، مقایسة دبی محاسباتی با دبی مشاهداتی نشان داد مقادیر ضریب همبستگی دبی برای دو رویداد مورد بررسی به‌ترتیب برابر 82/0 و 70/0 است و درصد کم خطا نیز نشان‌دهندة کارایی بسیار مدل اتومات سلولی در پیش‏بینی دبی اوج سیلاب و زمان وقوع آن است. بنابراین، استفاده از اتومات سلولی در کنار GIS، علاوه بر سرعت‌بخشیدن به محاسبة رواناب، موجب افزایش نتایجِ دقیق نیز می‌شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Simulation of flood hazard using GIS-based cellular automata (Case study: Chirchir Catchment)

نویسندگان [English]

  • Somaiyeh Khaleghi 1
  • Leila Malekani 2
1 Assistant Professor, Department of Physical Geography, Earth Science Faculty, Shahid Beheshti University, Iran
2 Assistant Professor, Department of Civil Engineering, Faculty of Technical and Engineering of Marand, University of Tabriz, Iran
چکیده [English]

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 it also causes the loss of economy and health. Thus, estimation and prediction of flood hazard is very important spatially in the watersheds without measurement stations. There are many models in the water and environmental studies for investigation about the runoff and flood in the watersheds without measurement stations. One of the newest is cellular automata model that has been combined well with the GIS for simulation of 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 the areas of crisis management. Using GIS knowledge, it is possible to create cellular automata appropriately and authentically reflect the water flow on the Earth’s surface. Cellular automata tool (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 data such as Digital Elevation Model (DEM), landuse, 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 part 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 span. 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. 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 the 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 by GIS.
 
Results and Discussion
Results show that the most areas of the Chirchir catchment have pasture and type D of hydrological soil group. Therefore, it has very low permeability which means that a large amount of rainfall is converted into 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 of flood hydrograph, travel time was calculated using ratio between flow lengths to flow velocity. Flood hydrograph estimated for two events, June 17, 2009 and June 02, 2007. The computational runoff is very adapted to observational runoff. The correlation coefficient for the two events (0.82 and 0.70) indicates 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 are consistent 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 compared to the conventional methods by GIS, has higher accuracy and capable to estimate flood hydrograph. Therefore, the use of cellular automata with GIS, not only accelerates the calculation of runoff, but also increases the accuracy of the results.
 
Conclusion
Comparison of the results with the observation proved that the results are well accurate. Besides the advantages of this method in simplicity and implementation of the realistic rules, this method is good at gaining the runoff data at any point of the basin except the exit point. Good agreement between the model output and the empirical measurements revealed that a CA approach can provide realistic results for a complex natural process like flood.

کلیدواژه‌ها [English]

  • cellular automata
  • Chirchir catchment
  • Flood hazard
  • GIS
اعلمی، م.ت.؛ ملکانی، ل. و قربانی، م.ع. (1394). مدل‌سازی بارش- رواناب حوضة لیقوان‌چای با استفاده از مدل اتومات سلولی، پژوهش‌های ژئومورفولوژی کمی، 3(4): 60 ـ 73.

ثانی‌خانی، ه.؛ خراسانی، ع. و دین‌پژوه، ی. (1391). شبیه‌سازی رواناب و فرسایش خاک با استفاده از روش اتوماتای سلولی، مجلة پژوهش آب ایران، 6(11): 123 ـ 133.

ضیائیان فیروزآبادی، پ.؛ موسوی، الف.؛ شکیبا، ع.ر. و ناصری، ح.ر. (۱۳۸۲). شبیه‌سازی رخداد سیلاب با استفاده از داده‌های سنجش از دور و مدل سلول‌های خودکار (مطالعة موردی بخشی از حوضة رودخانة تالار قائم‌شهر)، نشریة علمی‌- پژوهشی انجمنجغرافیاییایران، 1: 129 ـ۱۳۰.

عباسی، م. (1393). برنامه‏نویسی شی‌ء‌‏گرا در ArcGIS با زبان برنامه‏نویسی Python، انتشارات نوآور.

فهیمی‌فر، الف.؛ بحری، م.ع. و بخشایش اقبالی، ن. (۱۳۸۵). تحلیل فرایند حرکت و لغزش زمین‌لغزه‌ها بر پایة مدل اتومات سلولی، بیستوپنجمین گردهمایی علوم زمینشناسی، سازمان زمین‌شناسی کشور.

Abbasi, M. (2014). Object-oriented programming in ArcGIS using Python language, First edition, Noavar Pablishing.

Abou El-Magd, I.; Hermas, E. and El Bastawesy, M. (2010). GIS-modeling of the spatial variability of flash flood hazard in Abu Dabbab catchment, Red Sea Region, Egypt. The Egyptian Journal of Remote Sensing and Space Sciences, 13: 81-88.

Aboudagga, N. (2005). Simulations by cellular automata of the flood in Littorallagoon areas. Retrieved from (http://www.isnoldenburg.de/projects/earsel-abstracts2005/ABS-Aboudagga -Nader.html) in 2005/8/15.

Alami, M.T; Malakan, L. and Ghorbani, M.A. (2015). Modeling of rainfall-runoff in Lighvan Chai catchment using cellular Automata, Quantitative Geomorphology Research, 4(4): 73-60.

Batty, M.; Xie, Y. and Sun, Z. (1999). Modeling urban dynamics through GIS-based cellular automata, Computers, Environment and Urban Systems, 23: 205-233.

Cai, X.; Li, Y.; Guo, X. and Wu, W. (2014). Mathematical model for flood routing based on cellular automaton, Water Science and Engineering, 7(2): 133-142.

Cirbus, J. and Podhoranyi, M. (2011). Cellular automata for earth surface flow simulation, GIS Ostrava, 23. - 26. 1. 2011, Ostrava, pp. 1-8.

Cirbus, J. and Podhoranyi, M. (2013). Cellular Automata for the Flow Simulations on the Earth Surface, Optimization Computation Process, Applied Mathematics & Information Sciences, 7(6): 2149-2158.

Dewan, A.M.; Islam, M.M.; Kumamoto, T. and Nishigaki, M. (2007). Evaluating flood hazard for land-use planning in Greater Dhaka of Bangladesh using remote sensing and GIS techniques, Water Resour Manage, 21: 1601-1612.

Dhawale, A.W. (2013). Runoff estimation for Darewadi Watershed using RS and GIS, International Journal of Recent Technology and Engineering, 1(6): 46-50.

Douvinet, J.; Delahaye, D. and Langlois, P.(2006). Application of cellular automata modeling to analyze the dynamics of hyper-concentrated stream ows on loamy plateaux (Paris Basin, North-west France), The 7th Hydro-Informatics Conference, Sep 2006, France. AISH, 4000p., 2006. , pp. 1-8.

Douvinet, J.; Delahaye, D. and Langlois, P. (2007). Use of cellular automata in physical geography, 15th European Colloquium of Theoretical and Quantitative Geography, Montreux, Switzerland.

Ebrahimian, M. and Abdul Malek, I. (2009). Application of natural resources conservation service curve number method for runoff estimation with GIS in the Kardeh Watershed, Iran, European Journal of Scientific Research, 34 (4): 575-590.

Elkhrachy, I. (2015). Flash flood hazard mapping using satellite images and GIS tools: a case study of Najran City, Kingdom of Saudi Arabia (KSA), The Egyptian Journal of Remote Sensing and Space Sciences, 18: 261-278.

Fahimifar, A .; Bahri, M.A. and Bakhshayesh Eghbali, N. (2007). Analysis of movement and slip landslide process based on cellular automata model, The twenty-fifth meeting of Geological Sciences, National Geological Organization.

Haq, M.; Akhtar, M.; Muhammad, S.; Paras, S. and Rahmatullah, J. (2012). Techniques of Remote Sensing and GIS for flood monitoring and damage assessment: A case study of Sindh province, Pakistan, The Egyptian Journal of Remote Sensing and Space Sciences, 15: 135-141.

Hawkins, R.H.; Hjelmfelt, A.T. and Zevenbergen, A.W. (1985). Runoff probability, storm depth and curve numbers, J. Irrig. Drain. Eng. ASCE, 111: 330-340.

Jenson, S.K. and Domingue, J.O. (1988). Extracting topographic structure from digital elevation data for geographic information system analysis, Photogrammetric engineering and remote sensing, 54(11): 1593-1600.

Kenny, F. and Matthews, B. (2005). A methodology for aligning raster flow direction data with photogrammetrically mapped hydrology, Computers & Geosciences, 31(6): 768-779.

Kopp, S. and Noman, N. (2008). ArcGIS Spatial Analyst - Hydrologic Modeling, ESRI User Conference Technical Workshop, http://www.scdhec.gov/gis/presentations/ESRI_Conference_08/tws/workshops/tw_37.pdf, visited 25 April 2011.

Kumar RAI, P. and Mohan, K. (2014). Remote Sensing data & GIS for flood risk zonation mapping in Varanasi District, India. Forum geografic, Studii și cercetări de geografie și protecția mediului, 13: 25-33.

Mishra, S.K. and Singh, V.P. (2003). Soil Conservation Service Curve Number (SCS-CN) Methodology, Dordrecht, Germany: Kluwer Academic Publishers, ISBN1-4020-1132-6.

Patil, J.P.; Sarangi, A.; Singh, O.P.; Singh, A.K. and Ahmad, T. (2008). Development of a GIS Interface for Estimation of Runoff from Watersheds, Water Resources Management, 22(9): 1221-1239.

Ponce, V.M. and Hawkins, R.H. (1996). Runoff curve number: Has it reached maturity?, Journal of Hydrologic Engineering, 1(1): 11-19.

Ramasubramaniam, K.; Pugazhendi, V.; Anitha, A. and Dawn, S.S. (2008). Estimation of surface runoff using geospatial technology Kombai Micro Watershed – a case study, International Journal on Applied Bioengineering, 2(1): 25-31.

Rinaldi, P.R.; Dalponte, D.D.; Vénere, M.J. and Clausse, A. (2012). Graph-based cellular automata for simulation of surface flows in large plains, Asian Journal of Applied Science, 5: 224-231.

Shao, Q.; Weatherley, D.; Huang, L. and Baumgartl, T. (2015). RunCA: A cellular automata model for simulating surface runoff at different scales, Journal of Hydrology, 529: 816-829.

Sanny Khani, H.; Khorasani, A. and Dinpajouh, Y. (2013).  Simulation of runoff and soil erosion using cellular automata, Journal OF Iran Water Research, 6(11): 133-123.

Schroeder, S.A.; Enz, J.W. and   Larsen, J.K. (1990). Antecedent moisture conditions for North Dakota runoff predictions North Dakota, Farm Research, 48(0097-5338): 8-11.

Thilagavathi, G.; Tamilenthi, S.; Ramu, C. and Baskaran, R. (2011). Application of GIS in flood hazard zonation studies in Papanasam Taluk, Thanjavur District, Tamilnadu. Advances in Applied Science Research, 2(3): 574-585.

Van, T.P.D.; Carling, Paul A.; Coulthard, Tom J. and Atkinson Peter M. (2007). Cellular automata approach for flood forecasting in a bifurcation river system, PUBLS. INST. GEOPHYS. POL. ACAD. SC., E-7 (401): 256.

Wu, H.; Yi, Y. and Chen, X. (2005). HydroCA: a watershed routing model based on GIS and cellular automata, Proceedings- Spie The International Society for Optical Engineering, 6199: 61990Q.

Xiao, B.; Wang, Q.H.; Fan, J.; Han, F.P. and Dai, Q.H. (2011). Application of the SCS-CN model to runoff estimation in a small watershed with high spatial heterogeneit, Pedosphere, 21(6): 738-749.

Zhan, X. and Huang, M.L. (2004). ArcCN-Runoff: an ArcGIS tool for generating curve number and runoff maps, Environmental Modeling & Software, 19: 875-879.

Zhao, G.J.; Gao, J.F;, Tian, P. and Tian, K. (2009). Comparison of two different methods for determining flow direction in catchment hydrological modeling, Water Science and Engineering, 2(4): 1-15.

Ziaeian Firouzabadi, P.; Mousavi, A.; Shakiba, A.R. and Naseri, H.R. (2004).  Simulation of flood event using remote sensing data and cellular automats model (Case study: part of the Talar river catchment in Ghaemshahr city), Journal of Iran Geographical Society, I: 129-130.