گروه بندي حوضه آبخيز كرخه براساس شاخص هاي فيزيكي مكاني با استفاده از رويكرد فازي

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

مديريت آبخيزها نيازمند درك شرايط ابخيزها در حوضه هاي داراي آمار و فاقد آمار است. شناسايي زير حوضه هاي همگن به منظور اجراي هماهنگ عمليات آبخيزداري و كنترل سيلاب و نيز اولويت دادن به زير حوضه ها از اهميت بسزايي برخوردار است. در اين پژوهش به منظور خوشه بندي زير حوضه هاي آبخيز كرخه از شاخص هاي مكاني و فيزيكي (شامل خصوصيات توپوگرافي، مورفولوژيكي، خاك و كاربري اراضي) استفاده شد و تعداد 53 شاخص براي زير حوضه هاي كرخه استخراج گرديد. براي كاهش تعداد متغيرهاي تحليل عاملي به طور جداگانه براي هر گروه از شاخص هاي انجام شد. نتايج تحليل عاملي نشان داد كه از بين 53 شاخص فيزيكي مكاني 9 شاخص (4 شاخص مورفولوژيكي، 3 شاخص كاربري اراضي و 2 پارامتر خاك) داراي بار عاملي بيشتر نسبت به ساير شاخص ها هستند. بنابراين، از بين شاخص مورفولوژيكي شاخص هاي سطح حوضه كشيدگي حوضه، ميانگين طول زهكش ها و كل پستي و بلندي، از بين شاخص كاربري اراضي، شاخص هاي درسد سطح مراتع، درصد سطح اراضي كشاورزي و درصد سطح اراضي باير و از بين پارامترهاي خاك، شاخص ظرفيت آب موجود در لايه خاك و شاخص ه دايت هيدروليكي اشباع شده به عنوان شاخص هاي نهايي جهت گروه بندي زير حوضه ها انتخاب شدند. با استفاده از روش فازي fcm 38ͦc زير حوضه مطالعاتي در سه گروه همگن قرار گرفتند. تعداد خوشه هاي بهينه از طريق سعي و خطا و توابع ارزيابي ضريب افزار و آنتروپي افزار تعيين شدند. نتايج نشان داد كه گروه هاي سه گانه شامل زير حوضه هاي مناطق شمال شرقي و بخش هايي از مناطق مركزي حوضه كرخه (گروه 1)، مناطق شمال غربي جنوب شرقي به همراه مناطق جنوبي حوضه كرخه گروه 2 و مناطق مركزي و بخش هايي از مناطق جنوب غربي حوضه كرخه (گروه 3) را در بر مي گرند. تفكيك يك حوضه به زير حوضه ها و گروه بندي آنها در دسته هاي مشابه از نظر خصوصيات مشابه مي تواند به عنوان روشي در جهت اجراي عمليات آبخيزداري، كنترل سيال و اولويت قايل شدن براي زير حوضه هاي بحراني به كار گرفته شود.

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.