Analysis of the relationship between anthropogenic pressure intensity and erosion susceptibility in sub-basin scope: Uluabat Lake Basin example

Abstract

The increase in the intensity and impact of anthropogenic pressure in natural environmental conditions can lead to different dimensional changes in dynamic processes. Human activities have direct and indirect effects on erosion, which occurs with various interactions of geomorphological, climatological, hydrographic and floristic elements. These effects are among the first factors that should be analysed in upper and lower scale basins that are examined with holistic approaches and sustainable planning. In this study, anthropogenic pressure intensity and erosion susceptibility were revealed within the scope of Uluabat Lake drainage basin, and the interaction analysis of the two data was evaluated within the scope of 233 sub-basins. In the study, firstly, 10 different main criteria and 58 sub-criteria of the basin were considered and anthropogenic pressure intensity data were generated by Analytical Hierarchy Process (AHP). Then, the soil loss and erosion sensitivity of the basin was revealed by using the RUSLE method. The average of the obtained data was analysed within the scope of the sub-basin and each data was divided into 5 categorical classes by natural breaks method. Finally, the anthropogenic pressure intensity and erosion susceptibility data of the sub-basins were correlated. According to the findings, a high level of anthropogenic pressure intensity was found in 3% of the basin. The average soil loss of the basin is 4.7 t/ha-1/year-1. On a sub-basin basis, it was found that anthropogenic pressure intensity and erosion susceptibility were highly to very highly correlated in 54 basins. The amount of sub-basins where both data are highly correlated constitutes 23% of the total basins. Especially in the sub-basins west of Orhaneli, north of Tavşancıl and around Dursunbey where mining and agricultural activities are intensive, these anthropogenic activities are found to increase erosion.

Keywords

• Anthropogenic pressure
• Erosion susceptibility
• RUSLE
• AHS
• Uluabat Lake Basin

Antropojenik baskı yoğunluğu ile erozyon duyarlılığı ilişkisinin alt havza kapsamında analizi: Uluabat Gölü Havzası örneği

Öz

Doğal ortam koşullarındaki antropojenik baskı yoğunluğu ve etkisinin artması dinamik süreçlerde farklı boyutlu değişimlere yol açabilmektedir. Jeomorfolojik, klimatolojik, hidrografik ve floristik unsurların çeşitli etkileşimi ile meydana gelen erozyonda da insan faaliyetlerinin doğrudan ve dolaylı etkileri olmaktadır. Bu etkiler bütüncül yaklaşımlarla incelenen ve sürdürülebilir planlaması yapılan üst ve alt ölçekli havzalarda analizlerle incelenmesi gereken ilk unsurların başında gelmektedir. Bu çalışmada da Uluabat Gölü drenaj havzası kapsamında antropojenik baskı yoğunluğu ile erozyon duyarlılığı ortaya konmuş, iki verinin etkileşim analizi 233 alt havza kapsamında değerlendirilmiştir. Araştırmada ilk olarak havzanın 10 farklı ana kriteri ve 58 alt kriteri ele alınarak Analitik Hiyerarşi Süreci (AHS) ile antropojenik baskı yoğunluğu verisi üretilmiştir. Daha sonra RUSLE yöntemi kullanılarak havzanın toprak kaybı ve erozyon duyarlılığı ortaya konmuştur. Elde edilen verilerin ortalaması alt havza kapsamında analiz edilmiş, doğal kırılım yöntemi ile her bir veri 5 kategorik sınıfa ayrılmıştır. Son olarak alt havzaların antropojenik baskı yoğunluğu ve erozyon duyarlılığı verileri korelasyona tabi tutulmuştur. Elde edilen bulgulara göre havzanın %3’ünde yüksek düzeyde antropojenik baskı yoğunluğu saptanmıştır. Havzanın ortalama toprak kaybı miktarı 4,7 t/ha-1/yıl-1‘dir. Alt havza tabanlı olarak antropojenik baskı yoğunluğu ile erozyon duyarlılığının 54 havzada yüksek-çok yüksek düzeyde korelasyon olduğu saptanmıştır. Her iki verinin yüksek düzeyde ilişkili olduğu alt havza miktarı toplam havzaların %23’ünü oluşturmaktadır. Özellikle Orhaneli batısı, Tavşancıl kuzeyi ve Dursunbey çevresindeki madencilik ve tarım faaliyetlerinin yoğun olduğu alt havzalarda, bu antropojenik faaliyetlerin erozyonu arttırdığı tespit edilmiştir.

Anahtar Kelimeler

• AHS
• Antropojenik Baskı
• Erozyon Duyarlılığı
• RUSLE Yöntemi
• Ulubat Gölü Havzası

References

1. Amobichukwu, C. A. & Mossa, J. (2024). Machine learning insights of anthropogenic and natural influences on riverbed deformation in a large lowland river. Geomorphology, 446, 108986. https://doi.org/10.1016/j.geomorph.2023.108986
2. Arnolds, H.M.J. (1977). Methodology used to determine the maximum potential average annual soil loss due to sheet and rill erosion in Morocco. FAO (Food and Agriculture Organization of the United Nations) Soils Bulletin.
3. 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
4. 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
5. Bruno, L., Meli, M. & Garberi, M. L. (2024). Human-induced landscape modification in the in the last two centuries in the Po delta plain (Northern Italy). Anthropocene, 48, 100453. https://doi.org/10.1016/j.ancene.2024.100453
6. Chen, T., Niu, R. Q., Li, P., X., Zhang, L. P. & Du, B. (2010). Regional soil erosion risk mapping using RUSLE, GIS and remote sensing: A case study in Miyun Watershed, North China. Environmetal Earth Science, 63, 533–541 https://doi.org/10.1007/s12665.010.0715-z
7. Choi, C-H., You, J-H. & Jung, S-G. (2013). Estimation of danger zone by soil erosion using RUSLE model in Gyeongju National Park. Korean J. Environ. Ecol., 27(5), 614-624. https://doi.org/10.13047/KJEE.2013.27.5.614
8. Crutzen, P.J. & Stoermer, E.F. (2000) The anthropocene. Global Change Newsletter, 41, 17-18.

9. Danacıoğlu, Ş. & Tağıl, Ş. (2017). Bakırçay Havzası’nda rusle modeli kullanarak erozyon riskinin değerlendirmesi. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 20(37), 1-18.
10. Darghouth, S., Ward, C., Gambarelli, G., Styger, E. & Roux, J. (2008). Watershed management approaches, policies, and operations: Lessons for scaling up. The World Bank. https://hdl.handle.net/10986/17240
11. Demirağ Turan, İ., Özkan, B. & Dengiz, O. (2020). Bulanık mantık analitik hiyerarşik süreç (BAHS) ile Sinop ili erozyon duyarlılığının mekânsal dağılımının belirlenmesi. Türk Coğrafya Dergisi, (75), 57-70 https://doi.org/10.17211/tcd.716914
12. De Montis, A., Martín, B., Ortega, E., Ledda, A. & Serra, V. (2017). Landscape fragmentation in Mediterranean Europe: A comparative approach. Land Use Policy, 64, 83–94. https://doi.org/10.1016/j.landusepol.2017.02. 028
13. De Filippi, F.M. & Sappa, G. (2024). The simulation of Bracciano Lake (Central Italy) levels based on hydrogeological water budget: A tool for lake water management when climate change and anthropogenic impacts occur. Environmental Processes, 11, 8. https://doi.org/10.1007/s40710-024-00688-5
14. Ellis, E. C. (2017). Physical geography in the anthropocene. Progress in Physical Geography: Earth and Environment, 41(5), 525- 532. https://doi.org/10.1177/030.913.3317736424
15. Ertek, A. (2023). Antroposen, antroposfer: Antropojenik jeomorfoloji. Pegem Yayınevi.
16. 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
17. Garipağaoğlu, N. & Uzun, M. (2021). Development stages of basin management and different models. International Journal of Geography and Geography Education (IGGE), 43, 338-357. https://doi.org/10.32003/igge.816758
18. Gibbs, H. K. & Salmon, J. M. (2015). Mapping the world’s degraded lands. Applied Geography, 57, 12-21. https://doi.org/10.1016/j.apgeog.2014.11.024
19. 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
20. Güney, Y. & Turoğlu, H. (2018). Çok ölçütlü karar analizi ile erozyon duyarlılık çalışmalarında erozyon yüzeyleri envanter verisinin kullanımı: Selendi Çayı Havzası örneği. Coğrafi Bilimler Dergisi, 16(1), 105-119.
21. Haidara, I., Tahri, M., Maanan, M. & Hakdaoui, M. (2019). Efficiency of fuzzy analytic hierarchy process to detect soil erosion vulnerability. Geoderma, 354, 113853.
22. 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
23. Head, M.J., Zalasiewicz, J.A., Waters, C.N., Turner, S.D., Williams, M., Barnosky, A.D., Steffen, W., Wagreich, M., Haff, P.K., Syvitski, J. & Leinfelder, R. (2022). The proposed anthropocene epoch/series is underpinned by an extensive array of mid-20th century stratigraphic event signals. Journal of Quaternary Science., 37(7), 1181–1187. https://doi.org/10.1002/jqs.3467
24. İmamoğlu, A., Turan Demirağ, İ., Dengiz, O. & Saygın, F. (2014). Soil erosion risk evaluation: Application of corine methodology at Engiz Watershed, Samsun. Current Advances in Environmental Science, 2(1), 15-21.
25. Jenks, G. F. (1967). The data model concept in statistical mapping. International Yearbook of Cartography, 7, 186-190.
26. Karabulut, M. & Küçükönder, M. (2008). Kahramanmaraş ovası ve çevresinde CBS kullanılarak erozyon alanlarının tespiti. KSÜ Fen ve Mühendislik Dergisi, 11(2), 14-22.
27. Karakoca, E. (2025). CBS ve uzaktan algılama teknikleri ile ICONA modeli kullanılarak Katrancı Çayı Havzası’nda (Haymana, Ankara) toprak erozyonu duyarlılığı değerlendirmesi. Jeomorfolojik Araştırmalar Dergisi, (14), 126-145. https://doi.org/10.46453/jader.1653839
28. Katusiime, J. & Schütt, B. (2020). Linking land tenure and integrated watershed management-a review. Sustainability, 12(4), 1667- 1678. https://doi.org/10.3390/su12041667
29. Ke, C., He, S. & Qin, Y. (2023). Comparison of natural breaks method and frequency ratio dividing attribute intervals for landslide susceptibility mapping. Bulletin of Engineering Geology and the Environment, 82, 384. https://doi.org/10.1007/s10064-023-03392-0
30. Koontz, T. M. & Newig, J. (2014). From planning to implementation: Top-down and bottom-up approaches for collaborative watershed management. Policy Studies Journal, 42(3), 416-442. https://doi.org/10.1111/psj.12067
31. Luengo, M. S., D’Amico, G., Pommarés, N. & Fucks, E. (2025). Natural and anthropogenic processes and landforms in the eastern sector of the Buenos Aires province, Argentina (from pleistocene to anthropocene). Anthropocene, 49, 100457. https://doi.org/10.1016/j.ancene.2024.100457
32. Lu, D., Li, G., Valladares, G. & Batistella, M. (2004). Mapping soil erosion risk in Rondonia, Brazilian Amazonia: Using RUSLE, remote sensing and GIS. Land Degradation & Development, 15, 499–512.
33. Mallick, M., Krishnaiah, Y.V. & Panja, K. (2025). Assessment of the soil erosion susceptibility zones in tea plantation areas of Jalpaiguri district, India: An integrated approach of RUSLE and WLC model. Journal of the Indian Society of Remote Sensing, 53, 1855-1874. https://doi.org/10.1007/s12524-024-02078-8
34. Marouf, Z., Derdous, O., Benmamar, S. & Tachi, S. H. (2025). Water erosion susceptibility assessment using RUSLE, AHP and ANN: A comparative study in the Cheliff Basin—Algeria. Eurasian Soil Science, 58(39), 1-13. https://doi.org/10.1134/S1064229324602063
35. Montgomery, D.R., Grant, G.E. & Sullivan, K. (1995). Watershed analysis as a framework for implementing ecosystem management. Journal of the American Water Resources Association, 31(3), 369-386.
36. Moore, I. & Burch, G. (1986). Physical basis of the length-slope factor in the universal soil loss equation. Soil Society of America Journal, 50, 1294 – 1298.
37. 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
38. Myneni, R. B., Hall, F. G., Sellers, P. J. & Marshak, A. L. (1995). The interpretation of spectral vegetation indexes. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 481–486.
39. Özşahin, E. (2014). Tekirdağ ilinde CBS tabanlı RUSLE modeli kullanarak erozyon risk değerlendirmesi. Tekirdağ Ziraat Fakültesi Dergisi, 11(3), 45-56.
40. Panagos, P., Borrelli, P., Meusburger, K., Alewell, C., Lugato, E. & Montanarella, L. (2015). Estimating the soil erosion cover-management factor at the European scale. Land Use Policy, 48, 38-50.
41. Pande, C.B. (2020). Watershed management and development. In Sustainable Watershed Development: A case study of semi-arid region in Maharashtra state of India (pp. 13-26). Springer Cham. https://doi.org/10.1007/978-3-030-47244-3_2
42. Prodanovic, P. & Simonovic, S.P. (2010). An operational model for support of integrated watershed management. Water Resour Manage, 24, 1161–1194. https://doi.org/10.1007/s11269-009-9490-6
43. Renard, K.G., Laflen, J.M., Foster, G.R. & McCool, D.K. (1994). The revised universal soil loss equation. In R. Lal (Ed.), Soil erosion research methods (pp. 105-126). Soil and Water Conservation Society.
44. Renard, K.G., Yoder, D.C., Lightle, D.T. & Dabney, S.M. (2011). Universal soil loss equation and revised universal soil loss equation. In R.P.C. Morgan & M.A. Nearing (Eds.), Handbook of erosion modelling (pp. 137-167). Wiley-Blackwell.
45. Romanillos, G., Robazza G. & Lovato, F., (2024). A fragmented world: Mapping the global extent of anthropogenic landscape fragmentation. Journal of Maps, 20(1), 2307539, https://doi.org/10.1080/17445647.2024.2307539
46. Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill.
47. Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26. https://doi.org/10.1016/0377-2217(90)90057-I
48. 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. https://doi.org/10.1007/s11518.006.0151-5
49. Saaty, T. L. & Vargas, L.G. (2012). Model, methods concepts & aplications of the analytic hierarchy process. Springer.
50. Selçuk Biricik, A. (2009). Fiziki coğrafya – jeomorfoloji ile hidrolojinin temel prensipleri ve araştırma yöntemleri (Cilt 1). Gonca Yayınevi.
51. Sharma, Y., Sajjad, H. & Saha, T.K. (2025). Soil loss estimation and susceptibility analysis using RUSLE and random forest algorithm: A case study of Nainital district, India. Spatial Information Research, 33, 21. https://doi.org/10.1007/s41324-025-00620-5
52. Sofia, F., Marinello, F. & Tarolli, P. (2016). Metrics for quantifying anthropogenic impacts on geomorphology: Road networks. Earth Surface Processes and Landforms, 41, 240-255. https://doi.org/10.1002/esp.3842
53. Steffen, W., Grinevald, J. & Crutzen, P. (2011). The Anthropocene: Conceptual and historical perspectives. Philosophical Transactions of the Royal Society, 369, 842-867. https://doi.org/10.1098/rsta.2010.0327
54. Sud, A., Sajan, B., Kanga, S., Singh, S. K., Singh, S., Durin, B., Kumar, P., Meraj, G., Sahariah, D., Debnath, J. & Chand, K. (2024). Integrating RUSLE model with cloud-based geospatial analysis: A google earth engine approach for soil erosion assessment in the Satluj Watershed. Water, 16(8), 1073. https://doi.org/10.3390/w16081073
55. 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
56. Szabó, J., David, L. & Loczy, D. (2010). Anthropogenic geomorphology: A guide to man-made landforms. Springer.
57. Tağıl, Ş. (2007). Tuzla Çayı Havzası’nda (Biga Yarımadası) CBS-tabanlı RUSLE modeli kullanarak arazi degradasyonu risk değerlendirmesi. Ekoloji Dergisi, 17(65), 11-20.
58. Tarolli, P. & Sofia, G. (2016). Human topographic signatures and derived geomorphic processes across landscapes. Geomorphology, 255, 140-161. https://doi.org/10.1016/j.geomorph.2015.12.007
59. Tarolli, P., Cao, W., Sofia, G., Evans, D. & Ellis, E. (2019). From features to fingerprints: A general diagnostic framework for anthropogenic geomorphology. Progress in Physical Geography: Earth and Environment, 43(1), 95-128. https://doi.org/10.1177/0309133318825284
60. Taşoğlu, E., Öztürk, M. Z. & Yazıcı, Ö. (2024). High resolution Köppen-Geiger climate zones of Türkiye. International Journal of Climatology, 44(14), 5248-5265. https://doi.org/10.1002/joc.8635
61. Uzun, M. & Garipağaoğlu, N. (2022). Mekânsal otokorelasyon ve kümeleme analizi yaklaşımı ile Göksu Çayı Havzası’nın (Sakarya Nehri Havzası) bütünleşik ve sürdürülebilir havza yönetim modeli. Türk Coğrafya Dergisi, (81), 23-38. https://doi.org/10.17211/tcd.1173420
62. Uzun, M. (2024). Yenişehir (Bursa) Havzası’nın coğrafi karakterizasyonuna dayalı jeoekolojik risk duyarlılığı analizi. International Journal of Geography and Geography Education, (51), 85-114. https://doi.org/10.32003/igge.1326841
63. Uzun, M. (2024). Uluabat Gölü yüzey alanının zamansal değişim analizi üzerinden DSAS ve yapay sinir ağları modellerine göre gelecek tahminleri. Türk Coğrafya Dergisi, (86), 25-43. https://doi.org/10.17211/tcd.1481187
64. 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
65. 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
66. 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
67. Wischmeir, W.H. & Smith, D.D. (1978). Predicting rainfall erosion losses-a guide to conservation planning. Science and Education Administration.
68. Zhang, K., Feng, R., Han, J., Zhang, Z., Zhang, H. & Liu, K. (2023). Temporal and spatial differentiation characteristics of ecosystem service based on the ecogeographical division of China: A case study in the Yellow River Basin, China. Environmental Science and Pollution Research, 30(3), 8317–8337. https://doi.org/10.1007/s11356.022.22748-9