Combination of Multi-criteria Decision-making Models and Regional Flood Analysis Technique to Prioritize Sub-watersheds for Flood Control (Case study: Dehbar Watershed of Khorasan)

جغرافیا و مخاطرات محیطی

Article 2, Volume 8, Issue 2 - Serial Number 30, October 2018, Pages 27-45   

Document Type: Research Article

DOI: 10.22067/geo.v0i0.76607

Authors

Ali Reza Nafarzadegan^{* 1}; Ali Akbar Mohammadifar¹; Hassan Vagharfard¹; Masome Foruzanfard²

¹University of Hormozgan

², Saravan Higher Education Center

Abstract

1 Introduction
The flood is one of the most important natural disasters, which is causing significant damage to affected areas. In flood management process, the factors which effectively responsible in flood formation are identified, and then areas with high potential for flood occurrence are identified. Because of the vast extent of catchment areas and the limited economic and administrative resources, the implementation of flood control projects in all flood-producing areas is not feasible. Therefore, prioritizing sub-watersheds is one of the chief measures for sustainable management of watersheds, with the purpose of controlling the flood. Multi-criteria decision-making (MCDM) methods such as the analytic hierarchy process (AHP) method and the analytic network process (ANP) are the techniques for identifying the areas with high flood-producing potential.
Since soil properties, infiltration rate, and quantitative geomorphological characteristics determine the amount of excess rainfall and runoff production, thus, the simultaneous application of morphometric analysis method and decision making models is very useful in the areas with data scarcity. In morphometric analysis, the physiographic and morphological characteristics of the watershed are analyzed based on the digital elevation model and finally the sub-watersheds are prioritized. Dividing large areas into multiple sub-watersheds and prioritizing these sub-watersheds reduce the time and cost of running watershed operations as well as making watershed projects more efficient.
The purpose of this study was to determine the sub-watersheds with critical conditions in terms of flooding risk in Dehbar watershed, Khorasan-e Razavi Province, Iran to reduce the costs of carrying out the watershed management projects focusing on flood control. It is worth noting that due to the lack of required data, morphometric and hydrologic analysis methods were used. In order to prioritize the sub-watersheds of Dehbar watershed, multi-criteria decision-making methods including AHP, VIKOR, and Permutation were employed. Afterwards, the results of these models were compared and verified by regional flood analysis method.
2 Materials and Methods
The study area was Dehbar watershed located in Torqabeh and Shandiz County, Khorasan-e Razavi Province, Iran. The area of the Dehbar watershed was estimated to be 115.73 km2. In order to better identify and evaluate runoff production capabilities, the watershed is divided into smaller hydrological units that have been separately investigated. This classification was made based on the location of the water resources, the location of the villages, the hydrographic network, the topographic contour lines, the satellite imagery, the field visit, and the integrative view in the GIS system, so that through the use of the ArcHydro extension in ArcMap, the Dehbar watershed was divided into 6 hydrologic and 4 non-hydrologic sub-watersheds.
The Dehbar watershed was divided into 10 sub-watersheds. In the current study, 13 evaluation criteria including area, compactness coefficient, drainage density, circularity factor, form factor, curve number, bifurcation ratio, main channel length, average slope, average height, time of concentration, rainfall and runoff coefficient were selected, and the amount of each for each sub-watershed was calculated. The weight of parameters was derived by the AHP technique. After determining the weights of the evaluation criteria and the preparation of decision matrix, VIKOR and Permutation models were employed for prioritization. After prioritizing, the regional flood analysis method (based on existing stations in the watershed) was used to compute maximum flood discharge in different return periods in order to evaluate and validate the considered models.
To this end, a homogeneous area with hydrometric stations was first identified based on geographical and climatic conditions within the region of the study area. The outliers were then eliminated and the frequency analysis was performed for each station individually and the best-fit statistical distribution was identified and selected. Finally, for regional flood analysis, a regression relation was acquired between peak discharges and contributing area in adjacent catchments; thus, it is possible to estimate peak discharge in the study area.
Finally, the outcomes of three employed multi- criteria decision making methods were combined using the average rating method.
3 Results and Discussion
Pairwise comparisons between considered criteria were performed based on AHP method and the relative weight of each criterion was obtained. Runoff coefficient with the relative weight of 0.221 had the highest importance among the considered criteria. Subsequently, rainfall criterion, time of concentration criterion, and curve number criterion were in the following ranks with the relative weights of 0.148, 0.116 and 0.109, respectively. The criteria of average elevation and form factor also had the lowest relative weights. Meanwhile, the inconsistency rate in AHP method was 0.04, indicating that the decision making process is consistent.
After determining the relative weights of each criterion for each sub-watershed, the VIKOR model was applied. According to this method, sub-watershed No.1 ranked first with Q index of 0.9715, sub-watershed No.3 ranked second with Q index of 0.8739, and sub-watershed No. 2 ranked third with Q index of 0.6030. Therefore, these sub-watersheds should gain high priority in watershed and flood control operations. Meanwhile, sub-watershed No. 7 with Q index of 0.0312, sub-watershed No. 10 with Q index of 0.0950 and sub-watershed No. 9 with Q index of 0.3132 were in tenth, ninth and eighth priority, respectively. Thus, these sub-watersheds had the lowest priority for implementing watershed management activities.
In the next step, the Permutation model was employed. According to the results of this model, sub-watersheds No. 1, No. 3 and No. 2 were ranked first to third, respectively. This is due to the high amount of rainfall, runoff coefficient and curve number in these sub-watersheds. Meanwhile, sub-watersheds 5, 8 and 10 were in the lowest ranks.
In addition, the results of regional flood analysis showed that sub-watersheds 1, 3, 8 and 2 had higher flood peak discharge, respectively. Meanwhile, sub-watersheds 5, 10, 4 and 9 had lower flood peak discharge and were in the last priority in terms of potential for flood generation.
In the final step, in order to provide a proper ranking for sub-watersheds, we used the average rating method to combine the obtained priorities by three different applied techniques. The outcomes showed that sub-watersheds No. 1, No. 3, and No. 2 were in the first rank, and therefore, in terms of the need for watershed management measures were in the top priority.
4 Conclusion
The results of this study showed that the derived priority of the sub-watersheds using morphometric and hydrological parameters as evaluation criteria to identify flood-producing areas is a suitable and appropriate method. Therefore, it is recommended that, in order to reduce costs and gain optimal outcomes, watershed management projects focusing on flood control should be implemented in the identified sub-watersheds which are in top priority in terms of generating flood discharge. The outcomes also showed that one or two factors alone could not determine the priority of flood-producing capability of sub-watersheds and a sub-watershed with a larger area does not necessarily have the higher potential for generating flood, but the interaction of different factors ultimately determines the priority of the sub-watershed in terms of flood-producing potential.
 

Keywords

Prioritization of sub-watershed; Vikor model; Permutation model; regional flood analysis; Dehbar watershed

References

اصغرپور، محمد جواد؛ 1380. تصمیم‌گیری چند معیاره. انتشارات دانشگاه تهران.

آمانی، محمد؛ نجفی‌نژاد، علی؛ 1393. اولویت بندی زیرحوزه ها با استفاده از آنالیز مورفومتری، فنون سنجش از دور و GIS، حوزه آبخیز لهندر، استان گلستان. پژوهشنامه مدیریت حوزه آبخیز. شماره 9. 1-15.

ایزانلو، حسن؛ 1385. اولویت‌‌بندی زمانی و مکانی سیل خیزی زیر حوزه‌های آبخیز کوشک آباد خراسان رضوی با استفاده از مدل ‏‎HEC-HMS‎‏. پایان‌نامه کارشناسی ارشد مهندسی آبخیزداری. استاد راهنما: حمیدرضا مرادی. دانشگاه تربیت مدرس.

بدری، بهرام؛ زارع بیدکی، رفعت؛ هنربخش، افشین؛ آتشخوار، فاطمه؛ 1395. اولویت‌بندی زیرحوضه‌های آبخیز بهشت آباد ازنظر پتانسیل سیل‌خیزی. پژوهش‌های جغرافیای طبیعی. شماره 48(1). 143- 158.

تاجیکی، مریم؛ 1386. ارزیابی اثر اقدامات آبخیزداری بر روی سیلخیزی و رسوبدهی (مطالعه موردی: حوزه آبخیز رامیان. پایان‌نامه کارشناسی ارشد. استاد راهنما: علی نجفی‌نژاد. دانشگاه علوم کشاورزی و منابع طبیعی گرگان.

ثقفیان، بهرام؛ فرازجو، حسن؛ 1386. تعیین مناطق مولد سیل و اولویت بندی سیل‌خیزی واحدهای هیدرولوژیک حوزه سد گلستان. علوم و مهندسی آبخیزداری. شماره 1. 1-11.

حافظی‌نسب، جواد؛ 1383. بررسی علل وقوع سیلاب گلستان و روش‌های جلوگیری از آن. پایان‌نامه کارشناسی ارشد مهندسی عمران. استاد راهنما: یوسف حسن‌زاده. دانشگاه تبریز.

رضوی زاده، سمانه؛ شاهدی، کاکا؛ 1395. اولویت‌بندی سیل‌خیزی زیرحوزه‌های حوزه آبخیز طالقان با استفاده از تلفیق AHP و TOPSIS. فصل‌نامه اکوسیستم‌های طبیعی ایران. دوره 7. شماره 4. زمستان 1395. 33-46.

سلاجقه، علی؛ رضوی‌زاده، سمانه؛ سلاجقه، سوسن؛ 1389. اولویت بندی سیل‌خیزی در زیر حوزه‌های آبخیز تنکاب استان فارس، با استفاده از مدل اسکالوگرام. اولین کنفرانس بین المللی مدلسازی گیاه، آب، خاک و هوا. مرکز بین المللی علوم و تکنولوژی پیشرفته و علوم محیطی. کرمان.

سلیمانی، کریم؛ گنبد، محمد بشیر؛ موسوی، سید رمضان؛ خلیفی، شهرام؛ 1387. پتانسیل تولید سیل در حوضه‌های آبخیز با استفاده از مدل HEC-HMS در محیط سامانه اطلاعات جغرافیایی (مطالعه موردی: حوضه معرف کسیلیان). پژوهش‌های جغرافیای طبیعی. شماره 65. 51-60.

سلیمانی‌ساردو، فرشاد؛ سلطانی کوپایی، سعید؛ سلاجقه، علی؛ 1392. انتخاب شاخص سیل‌خیزی مناسب با استفاده از مدل بارش- رواناب HEC-HMS و تکنیک‌های GIS و RS (مطالعه موردی: حوزه سد جیرفت). پژوهشنامه مدیریت حوزه آبخیز. شماره 8. 90-108.

عباسی‌کجانی، عبدالحسین؛ 1395. بررسی امکان اولویت بندی زیرحوضه‌ها ازنظر سیل‌خیزی با استفاده از مدل HEC-HMS(مطالعه موردی: حوزه آبخیز اسکندری- زاینده رود). پایان‌نامه کارشناسی ارشد. استاد راهنما: علی طالبی. دانشکده کشاورزی و منابع طبیعی. دانشگاه یزد.

فلاح، مقدسه؛ محمدی، مازیار؛ کاویان، عطااله؛ 1394. اولویت‌بندی زیرحوضه‌ها با استفاده از آنالیز مورفومتری و تغییرات کاربری اراضی. اکوهیدرولوژی. شماره 3. 261-274.

قائمی، محمود؛ 1373. مطالعات شناسایی تکمیلی پروژه آبخیزداری در حوضه آبخیز کرخه. مدیریت آبخیزداری وزارت جهاد کشاورزی.

قنواتی، عزت‌ا...؛ کرم، امیر؛ آقا علیخانی، مرضیه؛ 1390. کارآیی روش تحلیل سلسله مراتبی در مطالعات سیل‌خیزی. فصل‌نامه جغرافیا (انجمن جغرافیایی ایران). سال 9. شماره 31. 255-275.

کاظمی، محمد؛ نوحه‌گر، احمد؛ قصر دشتی روشن، محمد؛ رضائی، پیمان؛ 1391. بررسی تأثیر تغییر کاربری اراضی بر پتانسیل سیل‌خیزی (مطالعه موردی: حوزه آبخیز تنگ بستانک شیراز). پژوهش‌های فرسایش محیطی. شماره5. 28-41.

کریمی زاده، کیوان؛ سلاجقه، علی؛ محسنی ساروی، محسن؛ خلیقی، شهرام؛ 1388. بررسی نقش عملیات آبخیزداری اجرا شده در حوزه آبخیز سیرا - کلوان بر زمان تمرکز و زمان تأخیر حوضه. همایش ملی بحران آب در کشاورزی و منابع طبیعی. شهرری.

محمدی‌مطلق، رضا؛ جلال کمالی، نوید؛ جلال کمالی، امیر؛ 1392. بررسی نقش مشارکت زیر حوضه‌های آبریز در شدت سیل‌خیزی (مطالعه موردی حوضه آبریز دالکی). مهندسی آبیاری و آب ایران. شماره 13. 31-44.

محمودی، فرج‌ا...؛ یمانی، مجتبی؛ بهرامی، شهرام؛ 1386. ارزیابی مدل هیدروگراف واحد لحظه‌ای ژئومورفولوژیکی (GIUH) در حوزه آبخیز کنگیر (ایوان غرب). پژوهش‌های جغرافیایی. شماره 60. 1-14.

ملکیان، آرش؛ افتادگان خوزانی، اصغر؛ عشورنژاد، غدیر؛ 1391. پهنه‌بندی پتانسیل سیل‌خیزی حوزه آبخیز اخترآباد با استفاده از روش تحلیل سلسله مراتبی فازی. پژوهش‌های جغرافیای طبیعی. دوره 44. شماره 4. 131-152.

مهدوی، محمد؛ 1390. هیدرولوژی کاربردی. انتشارات دانشگاه تهران.

یوسفوند، مهدی؛ شعبانلو، سعید؛ هدایت، نجف؛ 1391. تعیین پهنه‌بندی سیل‌گیر با استفاده از نرم افزار WMS در محیط GIS (مطالعه موردی: حوضه آبریز گاماسیاب استان کرمانشاه). همایش ملی بهره برداری بهینه از منابع آب. دزفول.

Ameri AA, Pourghasemi HR, Cerda A., 2018. Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models. Science of The Total Environment, 613: 1385-1400.

Amiri M, Pourghasemi HR, Arabameri A, Vazirzadeh A, Yousefi H, Kafaei S., 2019. Prioritization of Flood Inundation of Maharloo Watershed in Iran Using Morphometric Parameters Analysis and TOPSIS MCDM Model. In Spatial Modeling in GIS and R for Earth and Environmental Sciences (pp. 371-390). Elsevier.

Biswas S, Sudhakar S, Desai VR., 2002. Remote sensing and geographic information system based approach for watershed conservation. Journal of Surveying Engineering. 128(3): 108-24.

Chen LY, Wang TC., 2009. Optimizing partners’ choice in IS/IT outsourcing projects: The strategic decision of fuzzy VIKOR. International Journal of Production Economics. 120(1): 233-42.

Ellram LM., 1990. The supplier selection decision in strategic partnerships. Journal of Purchasing and materials Management. 26(4): 8-14.

Hlaing KT, Haruyama S, Aye MM., 2008. Using GIS-based distributed soil loss modeling and morphometric analysis to prioritize watershed for soil conservation in Bago river basin of Lower Myanmar. Frontiers of Earth Science in China. 2(4): 465-78.

Jun KS, Chung ES, Kim YG, Kim Y., 2013. A fuzzy multi-criteria approach to flood risk vulnerability in South Korea by considering climate change impacts. Expert Systems with Applications. 40(4):1003-13.

Lee G, Jun KS, Chung ES., 2013. Integrated multi-criteria flood vulnerability approach using fuzzy TOPSIS and Delphi technique. Natural Hazards and Earth System Sciences. 13(5): 1293-312.

Lee G, Jun KS, Chung ES., 2015. Group decision-making approach for flood vulnerability identification using the fuzzy VIKOR method. Natural Hazards and Earth System Sciences. 15(4): 863-74.

Levy JK., 2005. Multiple criteria decision making and decision support systems for flood risk management. Stochastic Environmental Research and Risk Assessment. 19(6): 438-47.

Melton MA., 1958. Correlation structure of morphometric properties of drainage systems and their controlling agents. The Journal of Geology. 66(4): 442-60.

Opricovic S, Tzeng GH., 2007. Extended VIKOR method in comparison with outranking methods. European journal of operational research. 178(2): 514-29.

Rahmati O, Samadi M, Shahabi H, Azareh A, Rafiei-Sardooi E, Alilou H, Shirzadi A., 2019. SWPT: An automated GIS-based tool for prioritization of sub-watersheds based on morphometric and topo-hydrological factors. Geoscience Frontiers.

Sharma SK, Tignath S, Mishra SK., 2008. Morphometric analysis of drainage basin using GIS approach. JNKVV Res Jour. 42(1):91-5.

Sinha R, Bapalu GV, Singh LK, Rath B., 2008. Flood risk analysis in the Kosi river basin, north Bihar using multi-parametric approach of analytical hierarchy process (AHP). Journal of the Indian Society of Remote Sensing. 36(4):335-49.

Walczykiewicz T., 2015. Multi-criteria analysis for selection of activity options limiting flood risk. Water resources. 42(1):124-32.