Document Type : Research Paper


1 Postdoc researcher

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

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


Extended abstract
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).
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.


1- اسلامی،  پرهمت،  ابراهیمی؛ علیرضا، جهانگیر، نادرقلی؛ (1393). تحلیل منطقه ای آبدهی رودخانه های مرکزی ایران. نشریه علمی-پژوهشی مهندسی و مدیریت آبخیز، دوره 6،شماره 1،ص. 74-82.
2- بابایی روچی،  فیضی؛ غلامرضا، آوات؛ (1383). رده بندی استان های کشور بر مبنای شاخص های بهداشتی و جمعیتی به کمک تکنیک آماری خوشه بندی فازی. نشریه حکیم، دوره 7 ،شماره 4،ص. 1-6.
3- تلوری، اسلامی؛ عبدالحسین، علیرضا؛ (1382). روش‌های برآورد جریان حداکثر لحظه ای سیل در حوضه های شمال کشور. پژوهش و سازندگی،دوره 16،شماره 1،ص 2-13.
4- دارابی،  سلیمانی،  شاهدی، میریعقو بزاده؛ حمید، کریم، کاکا، میرحسن؛ (1391). طبقه بندی زیرحوضه ها بر اساس پارامترهای مورفومتریک با استفاده از تحلیل های خوشه ای در حوضه آبخیز پل دوآب شازند. نشریه دانش آب و خاک، دوره 22، شماره 4،ص. 199-211.
5- زارع چاهوکی، محمدعلی؛ تجزیه و تحلیل داده‎ها در پژوهش‎های منابع طبیعی با نرم ‎افزار SPSS. (1389). انتشارات جهاد دانشگاهی  واحد تهران،ص. 171-161.
6- عطایی، شیران؛ هوشمند، مهناز؛ (1390). شناسایی زیر حوضه ‌های هیدرولوژیکی همگن از نظر عوامل ژئومورفولوژیک مؤثر بر سیلاب با استفاده از تحلیل خوشه‌ای (مطالعه موردی: دشت کرون)،مجله جغرافیا و برنامه ‌ریزی محیطی،دوره 42، شماره 2،ص. 79-98.
7- وزارت نیرو، (1383). مطالعات پتانسیل ‌یابی و پایه طرح نیروگاه‌ های برق ابی متوسط در حوضه‌های آبریز دز، کارون و کرخه، گزارش مطالعات پایه حوضه کرخه: هواشناسی،جلد 2-5.
8- Bardossy, A. (2007). Calibration of hydrological model parameters for ungauged catchments. Hydrology and Earth System Sciences, 11(2), 703-710.
9- Beven, K. J. (2000). Uniqueness of place and process representations in hydrological modelling. Hydrology and Earth System Sciences, 4(2), 203-213.
10- Bezdek, J.C. (1974), Cluster validity with fuzzy sets. J. Cybernetics 3, 58–73.
11- Bloeschl, G. (2009, December). The PUB report-gauging the status of predictions in ungauged catchments. In AGU Fall Meeting Abstracts (Vol. 1, p. 10).
12- Castellarin, A., Burn, D.H., &Brath, A. (2008). Homogeneity testing: How homogeneous do heterogeneous cross-correlated regions seem? Journal of Hydrology, 360(1), 67-76.
13- Castiglioni, S., L. Lombardi, E. Toth, Castellarin, A., &Montanari, A. (2010). Calibration of rainfall-runoff models in ungauged basins: A regional maximum
likelihood approach, Adv. Water Resour., 33, 1235–1242. DOI: 10.1016/j.advwatres.2010.04.009.
14- Cavadias, G. S., Ouarda, T. B., Bobée, B., & Girard, C. (2001). A canonical correlation approach to the determination of homogeneous regions for regional flood estimation of ungauged basins. Hydrological sciences journal, 46 (4), 499-512.
15- Chiang, S.M., Tsay, T.K., Nix, S.J. (2002a). Hydrologic regionalization of watersheds I: methodology development. J. Water Resour. Plan. Manage. 128, 3–11.
16- Chiang, S.M., Tsay, T.K., Nix, S.J. (2002b), Hydrologic regionalization of watersheds II: applications. J. Water Resour. Plan.Manage. 128, 12–20.
17- Choubin, B., Khalighi-Sigaroodi, S., Malekian, A., Ahmad, S., Attarod, P. (2014). Drought forecasting in a semi-arid watershed using climate signals: a neuro-fuzzy modeling approach. Journal of Mountain Science 11 (6): 1593–1605. DOI: 10.1007/s11629-014-3020-6.
18- Coulibaly, P., & Burn, D. H. (2005). Spatial and temporal variability of Canadian seasonal streamflows. Journal of Climate, 18(1), 191-210.
19- Feinstein, D.T., Hunt, R.J., & Reeves, H.W., (2010). Regional groundwater-flow model of the Lake Michigan Basin in support of Great Lakes Basin water availability and use studies: U.S. Geological Survey Scientific Investigations Report 2010–5109, 379 p.
20- Graf, W. L. (1999). Dam nation: A geographic census of American dams and their large scale hydrologic impacts. Water resources research, 35(4), 1305-1311.
21- He, C. S., &DeMarchi, C. (2010). Modeling spatial distributions of point and nonpoint source pollution loadings in the Great Lakes Watersheds. International Journal of Environmental Science and Engineering, 2(1), 24-30.
22- Hundecha, Y., Ouarda, T. B., &Bárdossy, A. (2008). Regional estimation of parameters of a rainfall runoff model at ungauged watersheds using the “spatial” structures of the parameters within a canonical physiographic climatic space. Water Resources Research, 44(1).
23- Kahya, E., Kalaycı, S., &Piechota, T. C. (2008).
Streamflow regionalization: case study of Turkey. Journal of Hydrologic Engineering, 13(4), 205-214.
24- Kult, J. (2013). Regionalization of hydrologic response in the Great Lakes basin: Considerations of temporal variability (Doctoral dissertation, The University of Wisconsin-Milwaukee).
25- Latt, Z.Z., Wittenberg, H., & Urban, B. (2014). Clustering Hydrological Homogeneous Regions and Neural Network Based Index Flood Estimation for Ungauged Catchments: an Example of the Chindwin River in Myanmar. Water Resource Management.DOI 10.1007/s11269-014-0851-4.
26- McIntyre, N., Lee, H., Wheater, H., Young, A., & Wagener, T. (2005). Ensemble predictions of runoff in ungauged catchments.Water Resources Research, 41(12), W12434.          
27- Nathan, R. J., & McMahon, T. A. (1990). Evaluation of automated techniques for base flow and recession analyses. Water Resources Research, 26(7), 1465-1473.
28- Omernik, J. M., & Bailey, R. G. (1997). Distinguishing between watersheds and ecoregions. Journal of the American Water Resources Association, 33(5), 935-949.
Omernik, J. M., & Griffith, G. E. (1991). Ecological regions versus hydrologic units: frameworks for managing water quality. Journal of Soil and Water Conservation, 46(5), 334-340.
29- Rao, A. R., &Srinivas, V. V. (2006). Regionalization of watersheds by hybrid-cluster analysis. Journal of Hydrology, 318(1), 37-56.
30- Razavi, T., &Coulibaly, P. (2013). Classification of Ontario watersheds based on physical attributes and streamflow series. Journal of Hydrology, 493, 81-94.
31- Sawicz, K., Wagener, T., Sivapalan, M., Troch, P. A., & Carrillo, G. (2011). Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA. Hydrology and Earth System Sciences, 15(9), 2895-2911.
32- Sellami, H., Jeunesse, I.L., Benabdallah, S., Baghdadi, N., &Vanclooster, M. (2014). Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France). Hydrology and Earth System Sciences, Copernicus Publications, 2393-2413.
33- Sewell, M. (2007). Kernel methods. London: University of London, Department of Computer Science.
Sivakumar, B., & Singh, V. P. (2012).Hydrologic system complexity and nonlinear dynamic concepts for a catchment classification framework. Hydrology and Earth System Sciences, 16(11), 4119-4131.
34- Sivapalan, M., Takeuchi, K., Franks, S. W., Gupta, V. K., Karambiri, H., Lakshmi, V., ... & Oki, T. (2003). IAHS Decade on Predictions in Ungauged Basins (PUB), 2003–2012: Shaping an exciting future for the hydrological sciences. Hydrological sciences journal, 48(6), 857-880.
35- Ssegane, H., Tollner, E. W., Mohamoud, Y. M., Rasmussen, T. C., & Dowd, J. F. (2012). Advances in variable selection methods II: Effect of variable selection method on classification of hydrologically similar watersheds in three Mid-Atlantic ecoregions. Journal of Hydrology, 438, 26-38.
36- Wagener, T., Sivapalan, M., Troch, P., & Woods, R. (2007). Catchment classification and hydrologic similarity. Geography Compass, 1(4), 901-931.
37- Wardrop, D. H., Kentula, M. E., Stevens, D. L., Jensen, S. F., & Brooks, R. P. (2007). Assessment of wetland condition: an example from the Upper Juniata Watershed in Pennsylvania, USA. Wetlands, 27(3), 416-431.
39- Wolock, D. M., Winter, T. C., & McMahon, G. (2004). Delineation and evaluation of hydrologic-landscape regions in the United States using geographic information system tools and multivariate statistical analyses.Environmental Management, 34(1), S71-S88.
40- Yadav, M., Wagener, T., & Gupta, H. (2007). Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins. Advances in Water Resources, 30(8), 1756-1774.