تعیین محدوده خطر سیلاب با استفاده از تحلیل مکانی هیدرولوژیک در GIS و تفسیر تصاویر ماهواره ای: مطالعه موردی حوضه آبریز اهرچای
محورهای موضوعی :
آلودگی خاک
مهدی قره خانی
1
,
حسین آقامحمدی
2
,
محمد حسن وحیدنیا
3
1 - کارشناس ارشد سنجش از دور و GIS، دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی واحد علوم و تحقیقات، تهران، ایران
2 - استادیار گروه سنجش از دور و GIS، دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی واحد علوم و تحقیقات، تهران، ایران
3 - استادیار گروه سنجش از دور و GIS، دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، تهران، ایران
تاریخ دریافت : 1401/05/19
تاریخ پذیرش : 1401/07/10
تاریخ انتشار : 1401/04/01
کلید واژه:
حوضه اهرچای,
نقشه خطر سیلاب,
منطق فازی,
روش SCS,
GIS,
چکیده مقاله :
زمینه و هدف: شناخت محدوده های سیلاب و تحلیل مکانی آن گامی موثر در پیشگیری از خسارات مالی و جانی این بلای طبیعی بوده و در نتیجه موجب پایداری محیط زیست می شود. در پژوهش حاضر به ارزیابی خطر سیلاب در سطح حوضه آبریز اهرچای واقع در استان آذربایجان شرقی پرداخته می شود.روش بررسی: در این راستا از سیستم اطلاعات جغرافیایی و تحلیل های مکانی استفاده می شود و به منظور ایجاد انعطاف پذیری در تلفیق اطلاعات موجود، منطق فازی برای پهنه بندی خطر سیلاب بکار می رود. همچنین از روش SCS برای برآورد ارتفاع رواناب و دبی پیک بهره گرفته شد.یافته ها: پهنه بندی خطر سیلاب با کاربست منطق فازی نشان می دهد که در حدود 10 درصد سطح حوضه آبریز اهرچای در کلاس های با خطر زیاد و بسیار زیاد قرار گرفته اند. بر این اساس دشت های سیلابی و سطوح هموار پایکوه ها به دلایلی از جمله ارتفاع نسبی پایین، شیب اندک، نزدیکی به آبراهه های اصلی، تلاقی انشعابات و تراکم زهکشی بالا، مقادیر زیاد شاخص عمق دره و مقادیر پایین شاخص تحدب سطح زمین از خطرپذیری و آسیب پذیری بیشتری نسبت به سیلاب ها برخوردارند. محاسبات بیانگر تباین فضایی زیاد ارتفاع رواناب و دبی پیک در سطح زیرحوضه های اهرچای می باشد.بحث و نتیجه گیری: به کمک روش پیشنهادی عوامل تعیین کننده در وقوع سیلاب و همچنین نواحی پتانسیل رواناب مشخص گردید. در حالت کلی زیرحوضه های جنوبی حوضه آبریز اهرچای از پتانسیل تولید رواناب و دبی های پیک پایینی برخوردارند. برعکس، بخش قابل توجهی از زیرحوضه های شمالی حوضه مطالعاتی از پتانسیل تولید رواناب و دبی پیک بالایی برخوردارند. نیمه شمالی حوضه اهرچای منطبق بر دامنه های جنوبی رشته کوه قره داغ می باشد که ساختار زمین شناسی عمدتا متشکل از سنگ های آذرین با نفوذپذیری پایین است. بدین ترتیب زیرحوضه های نیمه شمالی حوضه به دلیل دریافت بارش بیشتر و نفوذپذیری اندک خاک از پتانسیل تولید رواناب بالایی برخوردارند.
چکیده انگلیسی:
Background and Purpose: Recognizing the flood hazard zones and its spatial analysis is an effective step in preventing financial and human losses from this natural disaster, and as a result, it contributes to the sustainability of the environment. In the current research, the flood risk assessment in the Aharchai catchment area located in East Azarbaijan province is presented.Methods: In this regard, geographic information system and spatial analysis are used, and in order to create flexibility in the integration of existing information, fuzzy logic is used for flood risk zoning. SCS method was also used to estimate runoff height and peak discharge.Findings: Flood risk zoning using fuzzy logic shows that about 10% of Ahrchai catchment area is in high and very high-risk classes. Based on this, the floodplains and flat surfaces of the foothills for reasons such as low relative height, low slope, proximity to the main waterways, intersection of branches and high drainage density, high values of valley depth and low values of land surface convexity are more vulnerable to floods. The findings show the high spatial variation of runoff height and peak discharge at the level of Ahrchai sub-basins.Discussion and Conclusion: With the help of the proposed method, the determining factors in the occurrence of floods and also the potential areas of runoff were determined. In general, the southern sub-basins of Ahrchai catchment have the potential to produce runoff and low peak flows. On the contrary, a significant part of the northern sub-basins of the study basin has high runoff and peak flow production potential. The northern half of the Ahrchai basin corresponds to the southern slopes of the Qara Dagh mountain range, whose geological structure is mainly composed of igneous rocks with low permeability. In this way, the sub-basins in the northern half of the basin have a high potential for producing runoff due to receiving more rainfall and low permeability of the soil.
منابع و مأخذ:
References
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Behrooz, M., Alimohammadi, S., Atari, J. Sensitivity Analysis of Hydrologic,Hydraulic and Economic Uncertainties in Design of Flood Control Systems. Iran-Water Resources Research, 2014; 10(2): 69-81.
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Lee S. Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environmental Geology. 2007 Apr;52(4):615-23.
Qin CZ, Zhu AX, Pei T, Li BL, Scholten T, Behrens T, Zhou CH. An approach to computing topographic wetness index based on maximum downslope gradient. Precision agriculture. 2011 Feb;12(1):32-43.
Kumar S, Gupta S. Geospatial approach in mapping soil erodibility using CartoDEM–A case study in hilly watershed of Lower Himalayan Range. Journal of Earth System Science. 2016 Oct;125(7):1463-72.
Liu JG, Mason PJ. Image processing and GIS for remote sensing: techniques and applications. John Wiley & Sons; 2016 Mar 21.
Alizadeh A, 1386. Principles of Applied Hydrology, Astan Qods Razavi Publications. (In Persian)
Iwahashi J, Pike RJ. Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology. 2007 May 1;86(3-4):409-40.
Reisenbüchler M, Bui MD, Skublics D, Rutschmann P. An integrated approach for investigating the correlation between floods and river morphology: A case study of the Saalach River, Germany. Science of the Total Environment. 2019 Jan 10;647:814-26.
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Li MS, Li ZC, Wang DL, Yang X, Zhong X, Li Z, Li Y. Impact of natural disasters change on grain yield in China in the past 50 years. Journal of Natural Disasters. 2005;14(2):55-60.
Toya H, Skidmore M. Economic development and the impacts of natural disasters. Economics letters. 2007 Jan 1;94(1):20-5.
Chen Y, Liu R, Barrett D, Gao L, Zhou M, Renzullo L, Emelyanova I. A spatial assessment framework for evaluating flood risk under extreme climates. Science of the Total Environment. 2015 Dec 15;538:512-23.
Alderman K, Turner LR, Tong S. Floods and human health: a systematic review. Environment international. 2012 Oct 15;47:37-47.
Behrooz, M., Alimohammadi, S., Atari, J. Sensitivity Analysis of Hydrologic,Hydraulic and Economic Uncertainties in Design of Flood Control Systems. Iran-Water Resources Research, 2014; 10(2): 69-81.
Poursamsam H, Akbari E, Hemmadi K, Akhond Ali AM. Location of Dez River flood risk maps with passive defense approach (Study area: Dezful city). Passive Defense Quarterly. 2022 Jun 21.
Alcántara I, Goudie AS, editors. Geomorphological hazards and disaster prevention. Cambridge University Press; 2010 Mar 4.
Proverbs DG, Soetanto R. Flood damaged property: a guide to repair. John Wiley & Sons; 2008 Apr 15.
Alkema D. RS and GIS applications in flood forecasting. InProceedings of the national workshop on flood disaster management: space inputs 2004 Jun (pp. 3-4).
Murphy A, Colleton N, Downs R, Goodchild M, Hanson S, Lawson V, Macdonald G, Magilligan F, Moseley W, Polsky C, Seto K, Wright D. Understanding the Changing Planet: Strategic Directions for the Geographical Sciences. National Research Council. The National Academies Press. Washington, D.C. 2010.
Al-Tahir A, Baban SM, Ramlal B. Utilizing emerging geo-imaging technologies for the management of tropical coastal environments. West Indian Journal of Engineering. 2006 Jul 1;29(1).
Altan O, Kemper G. Spatial information for disaster management using examples from Istanbul. InGeographic Information and Cartography for Risk and Crisis Management 2010 (pp. 23-37). Springer, Berlin, Heidelberg.
Ehlers M. Remote sensing for GIS applications: New sensors and analysis methods. InRemote Sensing for Environmental Monitoring, GIS Applications, and Geology III 2004 Feb 13 (Vol. 5239, pp. 1-13). SPIE.
Roustaei S, Mousavi R, Alizadeh G. Watershed Flood Zoning Map Preparation Using CN and GIS/RS Methods: A Case Study on Nekarood. Quantitative Geomorphological Research, 2018; 6(1): 108-118.
Mahmoudzadeh H, Bakoi M. Flood zoning using fuzzy analysis (case study: Sari city). Journal of Natural Environmental Hazards. 2019 Feb 20;7(18):51-68.
Madadi A, Piroozi E, Aghayary L. Flood Hazard Zonation by Combining SCS-CN and WLC Methods (Case study: Khiyave Chay Meshkinshahr Basin). Hydrogeomorphology. 2019 Mar 16;5(17):85-102.
Esfandiary Darabad, F., rahimi, M., Pourmortaza, G. Flood zonation of Agerloo Cay Basin using the L-THIA method and fuzzy logic. Quantitative Geomorphological Research, 2019; 8(2): 155-171.
Fathalizadeh B, Abedini M, Rajabi M. Investigating the Causes and Hazards of flood in Zunuzchay Watershed Using HEC-HMS Hydrological Model and Fuzzy Logic. Quantitative Geomorphological Research. 2020 Jun 21;9(1):134-55.
Fernández DS, Lutz MA. Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Engineering Geology. 2010 Feb 26;111(1-4):90-8.
Thilagavathi G, Tamilenthi S, Ramu C, Baskaran R. Application of GIS in flood hazard zonation studies in Papanasam Taluk, Thanjavur District, Tamilnadu. Advances in Applied Science Research. 2011;2(3):574-85.
Nandalal HK, Ratnayake UR. Flood risk analysis using fuzzy models. Journal of Flood risk management. 2011 Jun;4(2):128-39.
Rashetnia S. Flood vulnerability assessment by applying a fuzzy logic method: a case study from Melbourne(Doctoral dissertation, Victoria University), 2016.
Shivaprasad Sharma SV, Roy PS, Chakravarthi V, Srinivasa Rao G. Flood risk assessment using multi-criteria analysis: a case study from Kopili River Basin, Assam, India. Geomatics, Natural Hazards and Risk. 2018;9(1):79-93.
Donyari S, Vahidnia MH, Baikpour S. Investigation of urban flooding in Ahvaz using the combination of spatial and hydrological analysis in GIS and HEC-RAS plugin. Iranian journal of Ecohydrology. 2022 Feb 20;8(4):989-1006.
Rezaee Z, Vahidnia MH. Offering flood prevention solutions using remote sensing and approaches integrating fuzzy logic and agent-based modeling. Scientific-Research Quarterly of Geographical Data (SEPEHR). 2022 May 22;31(121):111-25.
Dattawadkar DJ, Vani SB. A Review on Fuzzy Based Flood Warning Expert System using IoT and LoRa Technology. International Research Journal of Engineering and Technology (IRJET). 2021 08;03:1601-1603.
Lee S. Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environmental Geology. 2007 Apr;52(4):615-23.
Qin CZ, Zhu AX, Pei T, Li BL, Scholten T, Behrens T, Zhou CH. An approach to computing topographic wetness index based on maximum downslope gradient. Precision agriculture. 2011 Feb;12(1):32-43.
Kumar S, Gupta S. Geospatial approach in mapping soil erodibility using CartoDEM–A case study in hilly watershed of Lower Himalayan Range. Journal of Earth System Science. 2016 Oct;125(7):1463-72.
Liu JG, Mason PJ. Image processing and GIS for remote sensing: techniques and applications. John Wiley & Sons; 2016 Mar 21.
Alizadeh A, 1386. Principles of Applied Hydrology, Astan Qods Razavi Publications. (In Persian)
Iwahashi J, Pike RJ. Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology. 2007 May 1;86(3-4):409-40.
Reisenbüchler M, Bui MD, Skublics D, Rutschmann P. An integrated approach for investigating the correlation between floods and river morphology: A case study of the Saalach River, Germany. Science of the Total Environment. 2019 Jan 10;647:814-26.
