ارزیابی خطر سیلاب با استفاده از تلفیق مدل‌های اتومات سلولی و SCS در دامنه های شمالی البرز مرکزی (مطالعه موردی: حوضه آبخیز لاویج‌رود)
محورهای موضوعی : فصلنامه علمی برنامه ریزی منطقه ای
1 - دانشیار گروه جغرافیای دانشگاه آزاد اسلامی واحد نور.
کلید واژه: SCS, ارزیابی خطر سیلاب, اتومات سلولی, دامنه های شمالی البرز مرکزی, حوضه آبخیز لاویج‌رود,
چکیده مقاله :
رشد جمعیت، تجمع سرمایه ها و گسترش شهرنشینی در مکان های مستعد خطر، منجر به پدید آمدن جامعه شهری با آسیب پذیری بالا در برابر سوانح می گردد. توسعه روز افزون جوامع و پیچیده شدن روابط درونی و خارجی آنها، اهمیت پرداختن به مدیریت و برنامه ریزی در کاهش بلایا و اثرات آنها بر سکونتگاه های انسانی را بیش از پیش نمایان میکند. دامنه‌های شمالی البرز مرکزی بویژه حوضه آبخیز لاویج‌رود عمدتاً به دلیل وضعیت توپوگرافی و فیزیوگرافی، موقعیت اقلیمی، عدم رعایت مشخصات فنی در احداث راه و ابنیه فنی و تجاوز به حریم رودخانه، زمین-شناسی و دیگر عوامل مؤثر در ایجاد رواناب، دارای پتانسیل تولید سیل در برخی از مواقع سال می‌باشد. در این پژوهش کارایی مدل‌های اتومات سلولی و SCS به منزله روش‌های نوین و امکان تلفیق آنها با برنامه‌های GIS برای شبیه-سازی خطر سیلاب و هیدروگراف جریان برای لاویج‌رود مطالعه شد. از داده‌های کاربری اراضی، گروه‌های هیدرولوژیک خاک، DEM، داده‌های بارش و ضریب زبری استفاده شد که همه لایه‌ها در قالب رسترهای با اندازه سلول 30×30 تهیه شدند. بخش زیادی از حوضه دارای گروه هیدرولوژیکی C و D است که نفوذپذیری کم و خیلی کم دارند؛ بدین-معنی که حجم زیادی از بارش در این قسمت‌ها می‌تواند تبدیل به رواناب شود. نیمه شمالی به‌ویژه شمال غربی حوضه، به‌دلیل قابلیت نفوذ کم و نیز نزدیکی به خروجی حوضه، دارای ارتفاع و عمق رواناب بسیار زیادی است. همچنین، خطر سیلاب در مسیر رودخانه لاویج و در اراضی اطراف آن (دشت سیلابی رودخانه) به‌ویژه در پایین‌دست رودخانه بالاست. شبیه‌سازی سیلاب در حوضه لاویج‌رود نشان داد که علاوه بر کاربری اراضی، خاک، نفوذپذیری و شیب، پراکنش مکانی مقدار بارش عامل مهمی در تجمع رواناب به یک سمت از جریان پایین‌دست حوضه و تولید سیلاب است.
Population growth, accumulation of capital and the expansion of urbanization in places prone to danger, lead to the emergence of urban society with high vulnerability to accidents. The increasing development of societies and the complexity of their internal and external relations, the importance of management and planning in reducing disasters and their effects on human settlements is becoming increasingly apparent.
The northern slopes of the Central Alborz, especially the Lavijrood watershed, mainly due to topographic and physiographic conditions, climatic conditions, non-compliance with technical specifications in the construction of roads and technical buildings and encroachment on the river, geology and other factors affecting runoff, have The potential for flood production is at certain times of the year. In this research, we investigated the performance of the cellular automata (CA) and SCS model as a suitable estimation method, and examined the possibility of integrating the method with the ArcGIS application to simulate the flood hazards and the hydrograph flow for the Lavijrood. The runoff height and the flood hazard were obtained through the SCS method. The flood simulation using the SCS method requires the data of land use, hydrologic groups of soils, Digital Elevation Map (DEM), rainfall, and the roughness coefficient of the basin. The raster format of all these layers was prepared with cell sizes of 30×30 m. A large part of the Lavijrood watershed belongs to the hydrological groups C and D, which have a very low permeability. This means that a large volume of rainfalls converts into runoffs. Due to the low permeability and the vicinity to the watershed outlet, the northern half, especially in the northwest of the watershed, has a very high runoff depth and height. Also, the flood risk is high in Lavijrood River route and its surrounding area especially at the downstream. The runoff simulation in this watershed showed that land use, soil, permeability, slope, and the geographical distribution of rainfalls are the most important factors that control runoffs and their movement to downstream locations to produce floods
1.Abou El-Magd, I., Hermas, E., & 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, Vol. 13, pp. 81-88.
2.Aboudagga, N. (2005). Simulations by cellular automata of the floods in littoral lagoon areas, www.isn-oldenburg.de/projects/earselabstracts2005/ABS-Aboudagga-Nader.html.
3.Aelami, M.T., Malekani, L., & Ghorbani, M.A., (2015). Precipitation - Runoff modeling in Lighvan basin using cellular automata model. Quantitative Geomorphological Researches, 3(4): 60-73.
4.Cirbus, J., & Podhoranyi, M. (2013). Cellular Automata for the Flow Simulations on the Earth Surface, Optimization Computation Process. Applied Mathematics & Information Sciences, v. 7(6), p. 2149-2158.
5.Dewan, A.M., Islam, M.M., Kumamoto, T., & Nishigaki, M., (2007). Evaluating flood hazard for land-use planning in Greater Dhaka of Bangladesh using remote sensing and GIS techniques, Water Resources Manage, Vol. 21, pp. 1601-1612.
6.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, Vol. 18, pp. 261-278.
7.Fahimifar, A., Bahri, M.A., & Bakhshayesh Eghbali, N., (2006). Analysis of the Landslide moving and sliding process based on cellular automata model. The 25th Gathering of Geosciences, Geological Survey of Iran, Tehran.
11.Hjelmfelt, A.T., & Mockus, V., (2004). Estimation of Direct Runoff from Storm Rainfall, Part 630 Hydrology National Engineering Handbooks, Chapter 10, United States Department of Agriculture, Natural Resources Conservation Service, 235p.
12.Hosseinzadeh, M.M., Nosrati, K., & Imeni, S., (2018). Detection of curve number and estimation of runogg potential in Hesarak watershed, Journal of Applied Geoscience Research, Vol. 18(51), pp. 133-150.
13.Inci, T.Y., Akguül, S., Dengiz, O., & Aküzüm, T., (2006). Estimation of flood discharge for small watershed using SCS curves number and geographic Information System, River basin flood management journal, pp. 527-538.
14.June, S., Yuan, Y., & Jing, Z., (2001). The effect of landuse/cover change on surface Runoff in Shenzheh Region, china, Institute of Resouecess ScienceBeigining normal university, 100875, Beijing.
15.Khaleghi, S., & Malekani, L., (2016). Flood risk simulation using cellular automata model based on GIS (case study: CherCher watershed), Journal of Natural Geography Studies, Vol. 48(4), pp. 589-605.
16.Kopp, S., & 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
17.Kumar RAI, P., & 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, Vol. 13, pp. 25-33.
18.Lewis, D., Singer, M.J., & Kate, K.W., (2000), Applicability of SCS curve number method for a California Woodlands Watershed, Journal of Soil and Water Conservation, Second Quarter, pp. 48-55.
19.Mostafazadeh, R., Mirzai, Sh., & Nadiri, P., (2017). Determination of the curve number from rainfall and runoff events and its changes with rainfall components in a forest watershed, Journal of water and soil sciences (Agriculture and Natural Resources Sciences and Technologies), Vol. 14, pp. 15-28.
21.Motevalli. S. hosseinzadeh, M.M., & Esmaili. R. (2013). Relationship between Labbalbi discharge and geomorphic units in mountain rivers: A case study of Lavij river in the northern slope of central Alborz, Journal of Researches in Earth Science, vol 14, pp.17-33
22.Rinaldi, P.R., Dalponte, D.D., Vénere, M.J., & Clausse, A. (2012). Graph-based cellular automata for simulation of surface flows in large plains. Asian Journal of Applied Science, v. 5, p. 224-231.
23.Sanikhani, H., Khorasani, A., & Dinpajhooh, Y., (2012). Runoff and soil erosion model using cellular automata model. Iranian Water Research Journal, 6(11): 123-133.
24.Saravanan, S., & Manjula, R., (2015). Geomorphology based semi-distributed approach for modeling rainfall-runoff modeling using GIS, Aquatic Processes, Vol. 4, pp. 908-916.
25.Satheeshkumar, S., Venkateswaran, S., & Kannan, R., (2017). Rainfall–runoff estimation using SCS–CN and GIS approach in the Pappiredipatti watershed of the Vaniyar sub basin, South India, Modeling Earth Systems and Environment, Vol. 3(24), pp. 1- 8.
26.Schroeder, S.A., & Larsen, J.K., (1990). Antecedent moisture conditions for North Dakota runoff predictions North Dakota, Farm Research, Vol. 48(0097, 5338), pp. 8-11.
27.Soulis, K.X., & Valiantzas, J.D., (2012). SCS-CN parameter determination using rainfall-runoff data in heterogeneous watershed-the two-CN system approach, Hydrology Earth System Science, Vol. 16, pp. 1001-1015.
28.Van, T.P.D., Carling, Paul, A., Coulthard, Tom, J., & Atkinson Peter, M. (2007). Cellular automata approach for flood forecasting in a bifurcation river system, PUBLS. INST. GEOPHYS. POL. ACAD. SC., v. E-7 (401), 256 pages.
29.Vinithra, R., & Yeshodha, L., (2016). Rainfall–runoff modelling using SCS–CN method: a case study of Krishnagiri District, Tamilnadu, International Journal of Science Research, Vol. 5(3), pp. 2319–7064.
30.Xiao, B., Wang, Q.H., Fan, J., Han, F.P., & Dai, Q.H., (2011). Application of the SCSCN model to runoff estimation in a small watershed with high spatial heterogeneity, Pedosphere, Vol. 21(6), pp. 738-749.
31.Zhan, X., & Huang, M.L. (2004). ArcCN-Runoff: an ArcGIS tool for generating curve number and runoff maps, Environmental Modeling & Software, v. 19, p. 875-879.
_||_
