نوع مقاله : مقاله پژوهشی

نویسندگان

• بهرام چوبین ¹
• کریم سلیمانی ²
• محمود حبیب نژاد ³
• آرش ملکیان ⁴

¹ پژوهشگر پسادکتری/علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی،دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری،

² استاد گروه علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی،دانشگاه علوم کشاورزی و منابع طبیعی ساری

³ استاد گروه علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری

⁴ دانشیارگروه احیاء مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران

چکیده

مدیریت آبخیزها نیازمند درک شرایط آبخیزها در حوضه های دارای آمار و فاقد آمار است. شناسایی زیرحوضه های همگن ب همنظور اجرای هماهنگ عملیات آبخیزداری و کنترل سیلاب و نیز اولویت دادن به زیرحوضه ها از اهمیت بسزایی برخوردار است. در این پژوهش به منظور خوشه بندی زیرحوضه های آبخیز کرخه از شاخص های مکانی و فیزیکی (شامل خصوصیات توپوگرافی، مورفولوژیکی، خاک و کاربری اراضی) استفاده شد و تعداد 53 شاخص برای زیرحوضه های کرخه استخراج گردید. برای کاهش تعداد متغیرها تحلیل عاملی به طور جداگانه برای هر گروه از شاخص ها انجام شد. نتایج تحلیل عاملی نشان داد که از بین 53 شاخص فیزیکی- مکانی، 9 شاخص (4 شاخص مورفولوژیکی، 3 شاخص کاربری اراضی و  2 پارامترخاک) دارای بار عاملی بیشتر نسبت به سایر شاخص ها هستند. بنابراین، از بین شاخص مورفولوژیکی، شاخص های سطح حوضه، کشیدگی حوضه، میانگین طول زهکش ها و کل پستی و بلندی؛ از بین شاخص کاربری اراضی، شاخص  های درصد سطح مراتع، درصد سطح اراضی کشاورزی و درصد سطح اراضی بایر و از بین پارامترهای خاک، شاخص ظرفیت آب موجود در لایه خاک و شاخص هدایت هیدرولیکی اشباع شده به عنوان شاخص های نهایی جهت گروه بندی زیرحوضه ها انتخاب شدند. بااستفادهازروشفازی (FCM)^[1] 38 زیرحوضه مطالعاتی در سه گروه همگن قرار گرفتند. تعداد خوشه‌های بهینه از طریق سعی و خطا و توابع ارزیابی ضریب افزار و آنتروپی افزار تعیین شدند. نتایج نشان داد که گروه های سه گانه شامل زیرحوضه های مناطق شمال شرقی و بخ شهایی از مناطق مرکزی حوضه کرخه (گروه 1)،مناطق شمال غربی- جنوب شرقی به همراه مناطق جنوبی حوضه کرخه (گروه 2) و مناطق مرکزی و بخش‎هایی از مناطق جنوب غربی حوضه کرخه (گروه 3) رادربرمی‎گیرند. تفکیک یک حوضه به زیرحوض هها و گروه بندی آنها در دسته های مشابه از نظر خصوصیات مشابه می تواند به عنوان روشی در جهت اجرای عملیات آبخیزداری، کنترل سیلاب و اولویت قائل شدن برای زیرحوضه های بحرانی به کار گرفته شود.



⁵- Fuzzy C-Mean
 

کلیدواژه‌ها

• حوضه آبخیز کرخه
• خوشه بندی فازی
• زیرحوضه های همگن
• متغیرهای فیزیکی- مکانی

20.1001.1.25883860.1397.27.107.6.2

عنوان مقاله [English]

Classification of Karkheh watershed based on spatio-physical indices using fuzzy approach

نویسندگان [English]

• Bahram Choubin ¹
• karim solaimani ²
• Mahmoud Habibnejad Roshan ³
• Arash Malekian ⁴

¹ Postdoc researcher

² Professor, Faculty of natural resources, Sari University of agriculture sciences and natural resources, Sari, Iran

³ Professor, Faculty of natural resources, Sari University of agriculture sciences and natural resources, Sari, Iran

⁴ Associated professor, Faculty of natural resources, University of Tehran, Tehran, Iran.

چکیده [English]

Extended abstract
Introduction
Management of watersheds requires understanding of watershed conditions both in gauged and ungaugedbasins. The classification of watersheds by similarcharacteristics for the implementation of coordinated watershed operations and flood control as well as giving priority to sub-basinsis of great importance. The need for a classification framework in hydrology is not an entirely new subject. In fact, this subject has long been discussed and several studies have also attempted to advance this idea. So far, no acceptedcomprehensive protocol has been presented for the classification of watersheds,and questions can be raised regarding why this has not happened. More efforts must be made in order to develop such a classification.Previous studies have used hard clustering methods more, for the classification of watersheds but, the present study used fuzzy approach as asoft method. In general, the purpose of this research is to focus on the characteristics of the watersheds including morphological characteristics, soil and land use for the identification of similar watersheds. These parameters can facilitate the watershed classification scheme and our understanding ofthe watershed conditions.
 
Materials & Methods
The dataset for this study includes is base maps (sub-watersheds boundary, streams and rivers, digital elevation model (DEM), soil and landuse which have been collected from Iran Water Resources Management Company. To classify the Karkheh watershed, 35 spatio-physical indices including topographic, morphological, landuse characteristics and soil parameters were considered. These indices have been calculated for each watershed. The dimension reduction of the variables was an important part, because 35 indices were quite large for the classification of 38 watersheds. Therefore, factor analysis for each group of indices wasusedseparately to reduce the number of variables.
After reducing the variables and selecting the final indices, the fuzzy clustering approach was conducted to classify the watersheds into homogenous groups. The number of optimal clusterswas determined through trial and error and the functions of partition coefficient and partition entropy evaluation.
 
Results & Discussion
Kaiser-Meyer-Olkin (KMO) test statistics for each group of the morphological, landuse and soil indices were 0.71, 0.69 and 0.76 respectively, indicating that the data was suitable for factor analysis. Factor analysis was conducted using Principle Component Analysis (PCA) method and the results revealed that among 35 spatio-physical indices, 9 indices (4 morphological indices, 3 land use indices and 2 soil parameters) had a higher load factor than other indices. Therefore, indices of the watershed surface, basin elongation, average length of drainage network and total topographic indexamong the morphological indices;percentage indices of rangelands, agricultural lands and wastelandsamong the land use indices; and indices of water holding capacity in the soil layer and saturated hydraulic conductivity among the soil parameters were selected as the ultimate criteria for grouping the watersheds.
Theselectedfactors were normalized between zero and one before the classification. Then, sub-watersheds were classified using fuzzy C-mean (FCM) approach. The trial and error method was used to find thenumber of optimum clusters. The maximum amount of evaluation function of partition coefficient equal to 0.76 and the minimum amount of partition entropy function equal to 0.49 occurred in three clusterstherefore,the number of optimum clusters equal to 3 clusters was determined through trial and error.The results of classification indicated that the triple groups included the sub-watersheds of the northeastern regions and parts of central regions of the Karkheh watershed (group 1), the northwestern- southeasternalong with the southern regions of Karkheh watershed (group 2) and the central regions and parts of southwestern regions  of Karkheh watershed (group 3).
 
Conclusion
Watershed classification with similar characteristics can be used as a method for watershed management, flood control and giving the priority to critical sub-basins. However, watershed classification is only completedwhenit is understood why some catchments belong to certain groups of hydrological behavior, so as to be possible to classify gauged and ungaugedwatersheds through it.
Finally, it is important to remember that classification of watersheds is not the “be-all and end-all” of research on watersheds, but rather only a means towards achieving broader aims of planning and management of our ecosystems, environment, water resources, and other relevant earth systems and resources. However, watershed classification certainly allows us to study catchments more effectively and efficiently and develop more appropriate strategies in terms of simplification in models/model development, generalization in our modeling approach, and improvement in communication both within the hydrologic community and across disciplines, as much as possible.

کلیدواژه‌ها [English]

• Karkheh watershed
• Fuzzy clustering
• Homogeneous sub-basins
• Spatio-physical indices