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Journals / Türk Coğrafya Dergisi / 2022 / Sayı: 81

Integrated and sustainable watershed management model of Göksu River Basin (Sakarya River Basin) with spatial autocorrelation and cluster analysis approach

Mekânsal otokorelasyon ve kümeleme analizi yaklaşımı ile Göksu Çayı Havzası’nın (Sakarya Nehri Havzası) bütünleşik ve sürdürülebilir havza yönetim modeli

Murat Uzun(MARMARA ÜNİVERSİTESİ)0000-0003-2191-3936
Nuriye Garipağaoğlu(MARMARA ÜNİVERSİTESİ)0000-0003-4967-8536
Pages
23–38
Publication Date
2022-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In order to ensure sustainability in the basin, it is necessary to protect ecological areas, increase flood, overflow, erosion, landslide measures, control water quality in streams and direct anthropogenic pressure concentration to more suitable areas.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In the basins where natural and human environmental conditions are in intense interaction, management studies are carried out with various models in many contexts.
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Key Findings & Data:The data, in which 5 different clusters were identified, were also analyzed in terms of problem-risk potential and sustainable-appropriate use potential.
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Conclusion & Impact:In order to ensure sustainability in the basin, it is necessary to protect ecological areas, increase flood, overflow, erosion, landslide measures, control water quality in streams and direct anthropogenic pressure concentration to more suitable areas.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Havza Yönetim Modeli · Kümeleme Analizi · Sürdürülebilirlik

Abstract

In the basins where natural and human environmental conditions are in intense interaction, management studies are carried out with various models in many contexts. In this study, it is aimed to create a watershed management model based on spatial auto correlation and clustering analysis over different variables of the Göksu River Basin, which has geographical diversity, interactions and potential risks. In the study, in which Geographical Information Systems (GIS) was used effectively, a multi-step system was created with deterministic, quantitative, correlation and distribution analysis methods. Four main variables (sub-models) were produced by analyzing many parameters in order to reveal all the geographical elements, interactions, natural dynamic functioning of the basin and to establish the cluster distribution as relation. The main variables consist of the geomorphological suitability-availability model, the precipitation runoff model, the multi-risk model, and the land use model. Each model was subjected to spatial correlation and cluster analysis distribution data of the basin were produced. The data, in which 5 different clusters were identified, were also analyzed in terms of problem-risk potential and sustainable-appropriate use potential. Then, the distribution data was tested for significance and clustering with the Local Moran’s I-Anselin test and the Getis- Ord GI statistic. From the analyzes, it has been determined that the areas forming the high frame of the basin show a cluster with sustainable-appropriate use potential, and there is a cluster with high problem-risk potential in İnegöl Plain, north of Yenişehir and Göksu Valley. In order to ensure sustainability in the basin, it is necessary to protect ecological areas, increase flood, overflow, erosion, landslide measures, control water quality in streams and direct anthropogenic pressure concentration to more suitable areas.

Özet

Doğal ve beşeri ortam koşullarının yoğun etkileşim halinde olduğu havzalarda birçok kapsamda çeşitli modellerle yönetim çalışmaları uygulanmaktadır. Bu araştırmanın amacı, coğrafi çeşitliliği, etkileşimleri ve potansiyel riskleri barındıran Göksu Çayı Havzası’nın farklı değişkenler üzerinden mekânsal otokorelasyon ve kümeleme analizine dayalı havza yönetim modelinin oluşturulmasıdır. Coğrafi Bilgi Sistemlerinin (CBS) etkin kullanıldığı çalışmada, deterministik, kantitatif, korelasyon ve dağılış analizi yöntemleriyle çok basamaklı sistematik oluşturulmuştur. Havzanın bütün coğrafi unsurlarını, etkileşimleri, doğal dinamik işleyiş yapısını ortaya koymak ve ilişkisel olarak kümelenme dağılışını oluşturmak için birçok parametrenin analizleri ile dört ana değişken (alt model) üretilmiştir. Ana değişkenler, jeomorfolojik uygunluk-elverişlilik, yağış akış, çoklu-risk ve arazi kullanım modellerinden oluşur. Her bir model karşılıklı olarak mekansal korelasyona tabi tutulmuş ve havzanın kümeleme analizi dağılış verisi üretilmiştir. Beş farklı kümenin tespit edildiği veri, sorun-risk potansiyeli ve sürdürülebilir-uygun kullanım potansiyeli açısından da analiz edilmiştir. Daha sonra dağılış verisi, Lokal Moran’s I-Anselin testi ve Getis-Ord Gİ istatistiği ile anlamlılık ve kümelenme açısından test edilmiştir. Analizlerden, havzanın yüksek çerçevesini oluşturan sahaların sürdürülebilir-uygun kullanım potansiyeline sahip kümelenme gösterdiği, İnegöl Ovası, Yenişehir kuzeyi ve Göksu Vadisi’nde sorun-risk potansiyeli yüksek kümelenmenin olduğu tespit edilmiştir. Havzada sürdürülebilirliğin sağlanması için, ekolojik sahaların korunması, sel, taşkın, erozyon, heyelan tedbirlerin arttırılması, akarsulardaki su kalitesinin kontrol edilmesi ve antropojenik baskı yoğunlaşmasının daha uygun alanlara yönlendirilmesi gerekmektedir.

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Cite This Article

Uzun, M., Garipağaoğlu, N. (2022). Integrated and sustainable watershed management model of Göksu River Basin (Sakarya River Basin) with spatial autocorrelation and cluster analysis approach. Türk Coğrafya Dergisi, Sayı: 81, 23–38. https://doi.org/10.17211/tcd.1173420

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References (44)

  1. Adeli, Z., & Khorshiddoust, A. (2011). Application of geomorphology in urban planning: case study in landfill site selection. Procedia, Social and Behavioral Sciences, 19, 662-667. https://doi. org/10.1016/j.sbspro.2011.05.183
  2. Anselin, L. (1995). Local indicators of spatial association LISA. Geographical Analysis, 27(2), 93-115. https://doi.org/ 10.1111/j.1538-4632.1995.tb00338.x
  3. Anselin, L. (2019). A local indicator of multivariate spatial association: Extending Geary’s C. Geographical Analysis, 51(2), 133-150. https://doi.org/10.1111/gean.12164
  4. Asgari, M. A. (2021). Critical review on scale concept in GIS-based watershed management studies. Spatial Information Research, 29, 417–425. https://doi.org/10.1007/s41324-020-00361-7
  5. Atasoy, F., & Sarış, F. (2021). Kümeleme analizi ile Türkiye’nin biyoiklim bölgelerinin sınıflandırılması. Türk Coğrafya Dergisi, 77, 67- 76. https://doi.org/10.17211/tcd.835964
  6. Bremer, L. L., Hamel, P., Ponette‐González, A. G., Pompeu, P. V., Saad, S.I., & Brauman, K. A. (2020). Who arewe measuring and modeling for? Supporting multilevel decision‐makingin watershed management. Water Resources Research, 56, 1-18. https://doi.org/10.1029/2019WR026011
  7. Cooper, A., H. Farrant, A. R., & Price S. (2011). The use of Kkarst geomorphology for planning, hazard avoidance and development in Great Britain. Geomorphology, Elsevier, 134(1- 2), 118-131. https://doi.org/10.1016/j.geomorph.2011.06.004
  8. Daeghouth, S., Ward, C., Gambarelli, G., Styger, E., & Roux, J. (2008). Havza Yönetim Yaklaşımları, Politikaları ve Faaliyetleri: Ölçek Büyütmeye Yönelik Dersler. Su Sektörü Kurulu Kararı Belge Serisi Belge No.11, Dünya Bankası, Washington, DC.
  9. Evans, I. S. (1980). An integrated system of terrain analysis and slope mapping. Zeitschrift für Geomorphologie, Supplementband, 36, 274-295.
  10. Garcia, P. M. B., Augustin, C. H. R. R., & Casagrande, P. R. (2020). Geomorphological index as support to urban planning, Mercator, Fortaleza, 19, 1-25 https://doi.org/10.4215/rm2020.e19003
  11. Garipağaoğlu N., & Uzun, M. (2019). İznik Gölü Havzası’nda doğal ortam koşulları, değişimler ve muhtemel risklerin havza yönetimi ve planlamasına etkisi. Doğu Coğrafya Dergisi, 24(42), 1-15. https://doi.org/10.17295/ataunidcd.621776
  12. Gareth, S., & Wheeler, D. (1998). Statistical methods in geographical analysis, David Fulton Publishers Ltd, London.
  13. Grigg, N.S. (1999). Integrated water resources management: Who should lead, who should pay? Journal of the American Water Resources Association, 35(3), 527-534. https://doi. org/10.1111/j.1752-1688.1999.tb03609.x
  14. Gupta, Α., & Ahmad, R. (1999). Geomorphology and the urban tropics: building an interface between research and usage. Geomorphology, 31, 133-149. https://doi.org/10.1016/S0169- 555X(99)00076-8
  15. Han, J., Lee, J.G., & Kamber, M., (2009). An overview of clustering methods in geographic data analysis, In Miller H.J., Han H. (Eds.) Geographic Data Mining and Knowledge Discovery, Taylor & Francis Group, LLC.
  16. He, C., (2003). Integration of geographic information systems and simulation model for watershed management. Environmental Modelling & Software, 18(8-9), 809-813. https://doi. org/10.1016/S1364-8152(03)00080-X
  17. Karataş, A. (2017). Karasu Çayı Havzasının Hidrografik Planlaması, Çantay Kitabevi, İstanbul.
  18. Karataş, A. (2018). Identifying surface runoff distribution and amount in stream basins: Ergene River Basin. Turkish Journal of Water Science & Management, 2(2), 40-83. https://doi.org/10.31807/ tjwsm.364011
  19. Katusiime J, & Schütt B. (2020). Linking land tenure and ıntegrated watershed management-a review. Sustainability, 12(4), 1667- 1678. https://doi.org/10.3390/su12041667
  20. Koontz, T. M., & Newig, J. (2014). From planning to implementation: Top-Down and Bottom-Up approaches for collaborative watershed Mmanagement. Policy Studies Journal, 42(3), 416- 442. https://doi.org/10.1111/psj.12067
  21. Lee, J., & Wong, D.W. (2001). Statistical analysis with ArcView GIS. John Wiley & Sons.
  22. Liu, Z., Nadim, F., Garcia-Aristizabal, A., Mignan, A., Fleming, K., & Luna, B. Q. (2015) A Three-level framework for multi-risk assessment. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 9(2), 59-74, https://doi.or g/10.1080/17499518.2015.1041989
  23. Mark, D. M. (1975). Geomorphometric Parameters: A Review And Evaluation. Geographical Annals, 57(1), 165-177.
  24. Montgomery, D.R., Grant, G.E., & Sullivan, K. (1995). Watershed analysis as a framework for implementing ecosystem management. Water Resources Bulletin, 31, 369-85.
  25. Mudliar, P., & Koontz, T. M. (2021). Locating power in Ostrom’s design principles: watershed management in India and the United States, Society & Natural Resources, 34(5), 35-45 https:// doi.org/10.1080/08941920.2020.1864535
  26. Neil T.H. (2002). Applied Multivariate Analysis. Secaucus, Springer- Verlag New York.
  27. Nir, D. (1957). The ratio of relative and absolute altitude of Mt. Carmel. Geographical Review, 27, 564–569.
  28. Ord, J.K., & Getis, A. (1995). Local spatial autocorrelation statistics: distributional ıssues and an application. Geographical Analysis, 27, 286-306. https://doi.org/10.1111/j.1538-4632.1995. tb00912.x
  29. Özdemir, H. (2007). SCS-CN yağış-akış modelinin cbs ve uzaktan algılama yöntemleriyle uygulanması: Havran Çayı Havzası örneği (Balıkesir). Coğrafi Bilimler Dergisi, 5(2), 1-12. https://doi. org/10.1501/Cogbil_0000000078
  30. Pande, C.B. (2020). Watershed Management and Development. In: Sustainable Watershed Development. SpringerBriefs in Water Science and Technology. Springer, Cham. https://doi. org/10.1007/978-3-030-47244-3_2
  31. Prodanovic, P., & Simonovic, S.P. (2010). An operational model for support of ıntegrated watershed management. Water Resour Manage 24, 1161–1194. https://doi.org/10.1007/s11269-009- 9490-6
  32. Requia, W., & Roig, H., (2015). Analyzing spatial patterns of cardiorespiratory diseases in The federal district, Brazil, Health, 7(10), 1283-1290. https://doi.org/10.4236/health.2015.710143
  33. Riley, S. J., DeGloria S. D., & Elliot R. (1999). A terrain ruggedness ındex that quantifies topographic heterogeneity. Intermountain Journal of Sciences, 5, 1-4.
  34. Saaty, T. L. (2004). Decision making - the Analytic Hierarchy and Network Processes (AHP/ANP). Journal of Systems Science and Systems Engineering, 13(1), 1-35.
  35. Selva, J. (2013). Long-term multi-risk assessment: statistical treatment of interaction among risks. Natural Hazards 67, 701–722. https://doi.org/10.1007/s11069-013-0599-9
  36. Swain, S.S., Mishra, A., Sahoo, B., & Chatterjee, C. (2020). Water scarcity-risk assessment in data-scarce river basins under decadal climate change using a hydrological modelling approach. Journal of Hydrology, 590, 1-53. https://doi.org/10.1016/j.jhydrol. 2020.125260
  37. Tağıl, Ş. (2007). Balıkesir’de hava kirliliğinin solunum yolu hastalıklarının mekânsal dağılışı üzerine etkisini anlamada jeo-istatistik teknikler. Coğrafi Bilimler Dergisi, 5(1), 37-56. https://doi. org/10.1501/Cogbil_0000000070
  38. Tobler, W.R.A. (1970). Computer model simulating urban growth in the Detroit region, Economic Geography, 46, 234-240. Türkeş, M. (2018), Genel klimatoloji: Atmosfer, hava ve iklimin temelleri, (3. baskı) Kriter yayınevi.
  39. Vojtek, M., & Vojtekova, J. (2016). GIS-Based approach to estimate surface runoff in small catchments: a case study. Quaestiones Geographicae 35(3), 97-116. https://doi.org/10.1515/quageo- 2016-0030
  40. Vulević, T., & Dragović, N. (2017). Multi-criteria decision analysis for sub-watersheds ranking via the PROMETHEE method. International Soil and Water Conservation Research, 5, 50–55. https:// doi.org/10.1016/j.iswcr.2017.01.003
  41. Wang, L., Meng, W., Guo, H., Zhang, Z., Liu, Y,. & Fan, Y. (2006). An interval fuzzy multiobjective watershed management model for the Lake Qionghai Watershed, China. Water Resour Manage 20, 701–721. https://doi.org/10.1007/s11269-005-9003-1
  42. Youssef, A.M., Pradhan, B., Sefry, S.A., & Abdullah, M. M. (2015). Use of geological and geomorphological parameters in potential suitability assessment for urban planning development At Wadi Al-Asla. Arab Journal Geoscience 8, 5617-5630. https:// doi.org/10.1007/s12517-014-1663-9
  43. Zhang, C., Luo, L., Xu, W., & Ledwith, V., (2008). Use of Local Moran’s I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Science of the total environment, 398(1-3), 212- 221. https://doi.org/10.1016/j.scitotenv.2008.03.011
  44. Zhang, H., & Liu, D. (2006). Fuzzy modeling and fuzzy control. Springer Science & Business Media. Boston.