پژوهشهای ژئومورفولوژی کمّی

پژوهشهای ژئومورفولوژی کمّی

ارزیابی ریسک زمین لغزش با رویکرد ژئومورفولوژی در حوضه کالپوش

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانش آموخته دکتری ژئومورفولوژی، دانشگاه فردوسی مشهد.
2 استاد ژئومورفولوژی، گروه جغرافیا، دانشگاه فردوسی مشهد.
3 استاد زمین شناسی مهندسی، گروه زمین شناسی، دانشگاه فردوسی مشهد.
4 دانشیار علوم اطلاعات جغرافیایی، گروه جغرافیا، دانشگاه فردوسی مشهد.
5 استادیار گروه آمار، دانشگاه بجنورد.
10.22034/gmpj.2024.473093.1515
چکیده
زمین لغزش ها تأثیرات منفی زیادی بر زندگی اجتماعی و اقتصادی مردم جهان دارند. هر ساله در بسیاری از کشورهای جهان، زمین لغزش ها خسارات زیادی را به روستاها و شهرهای کوهستانی و سازه های انسانی مانند ساختمان ها، جاده ها، خطوط انتقال نیرو و ... وارد می نمایند. در این پژوهش، ریسک زمین لغزش با رویکرد ژئومورفولوژی پیشنهادی در جهت کاهش آسیب پذیری عناصر در معرض خطر برای حوضه بحرانی کالپوش سمنان مورد ارزیابی قرار گرفت. با استفاده تلفیقی از داده های عکس های هوایی قدیمی، تصاویر ماهواره ای و نقشه برداری میدانی، زمین لغزش های گذشته و حال و تغییرات مورفولوژیکی آنها در یک دوره زمانی 54 ساله شناسایی شد و در نهایت نقشه موجودی زمین لغزش چندزمانه تهیه گردید. سپس ویژگی های مورفومتریک، نوع، سرعت، شدت، فراوانی، مناطق خطر، عناصر در معرض آسیب پذیری و ریسک زمین لغزش، شناسایی و مورد تحلیل قرار گرفت. نتایج این روش نشان می دهد که 109 زمین لغزش با زمان وقوع نسبی متفاوت (قبل از سال 1347 تا 1401) در حوضه کالپوش وجود دارد. قدیمی‌ترین آنها(قبل از سال 1347) دارای مساحت، عمق، حجم و شدت بیشتری بوده، بنابراین در صورت فعالیت مجدد خطر زیادی برای منطقه خواهند داشت. 9 منطقه با ریسک لغزشی بالا به صورت متمرکز در جنوب و غرب حوضه کالپوش شناسایی گردید. روستای پرجمعیت حسین آباد نیز به طور کامل در پهنه ریسک لغزش بالا با شدت و فراوانی زیاد و احتمال آسیب پذیری ساختاری و عملکردی زیاد سازه ها(ساختمان ها و جاده) قرار دارد. بنابراین توسعه و ساخت و ساز مجدد روی این پهنه، با احتمال فعالیت مجدد آن در آینده، این منطقه را مخاطره آمیز می کند.
کلیدواژه‌ها

عنوان مقاله English

Assessment of Landslide Risk with a Geomorphological Approach in the Kalpush Catchment

نویسندگان English

Mahdieh Ghayoor Bolorfroshan 1
Seyed Reza Hosseinzadeh 2
Gholam Reza Lashkaripour 3
Masoud Minaei 4
Hakimeh Morabbi Heravi 5
1 Mashhad university
2 Dep. of Geography, Faculty of Letters and Humanities, Ferdowsi University of Mashhad, Mashhad, Iran
3 Faculty of Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
4 Faculty of Letters and Humanities, Ferdowsi University of Mashhad, Mashhad, Iran
5 University of Bojnord, Bojnord, Iran
چکیده English

Introduction

In recent years, due to heavy rainfall affected by climate change, global warming, and human activities, the occurrence of landslides and the reactivation of old landslides have increased, and every year in many countries of the world, landslides cause great damage to human structures such as buildings, roads, power lines, etc.

There is no systematic information on the age, type, frequency, and distribution of landslides in the world, so the lack of this knowledge will have negative consequences. Therefore, preparing an inventory map of landslides, assessing the risk and hazard of landslides for areas with a high concentration of landslides is important for predicting and preventing future hazards.

One of the applications of landslide inventory maps is in landslide hazard and risk studies. Landslide hazard and risk assessment is a complex operation that requires the combination of different geomorphological and geological techniques

This research, by combining historical geomorphological data, remote sensing, and field studies with the proposed method of Cardinali et al. (2002), studies the hazard and risk of landslides in the Kalpush catchment and predicts the vulnerability of elements in order to plan and reduce future damage.

Methodology

The Kalpush Catchment is located in the north of Semnan province and adjacent to Golestan province. This Catchment is located in the east of the forested heights of the cities of Galikash and Minoodasht in Golestan province.

The research method used in this applied research and based on a systems approach using library, field survey and remote sensing methods.

First, an inventory map of landslides was prepared from historical images and field survey. Then, the relative age of the landslide event, geometric and morphological characteristics, type of landslide, etc. were calculated. After collecting the above documents, the landslide hazard and risk assessment was carried out in 5 stages using the geomorphological method proposed by Cardinali et al. (2002).

Results and Discussion

109 landslides with an approximate area of 11 square kilometers were identified, which constitute 10 percent of the total basin. These landslides are concentrated in the south and southwest of the Kalpush basin. The landslides were divided into 4 classes based on the relative time of occurrence (54-year period), image dates, rainfall events of 2018-2019, and the decrease in the water level of the Kalpush dam lake in 2021: before 1968, 1968-2019, 2019-2021, and 2021-2022. 66 percent of the landslides in 2019-2021 and 55 percent of the landslides in 1968-2019 occurred on landslides older than 1968. All new landslides in 2021-2022 were also formed on the shores of the dam lake.

According to the type of landslides in the Kalpush Catchment and based on the classification of Cruden and Varnes (1996), 98 percent of the landslides (107 cases), which include creep, rotational, translational, and lateral spreading landslides, have slow movement, and only 2 debris flows in the group of landslides in 2019-2021 have rapid movement. Therefore, almost all landslides in the Kalpush catchment have occurred with slow speed and movement from the past to the present.

Deep landslides older than 1968, which were probably affected by different geomorphological, climatic, and earthquake conditions at the time of occurrence, had high intensity and relatively low frequency of occurrence at the time of formation and formed 8 landslide hazard zones. These zones have been reduced to 5 zones for deep landslides in 1968-2019 and 2019-2021, and the reactivation of landslides has less intensity and more frequency, so that they showed a variable landslide hazard and the landslide of Hossein Abad village recorded the maximum landslide hazard.

The elements at risk of Kalpush and the location of landslides, the villages of Hossein Abad, Gushhe Degarman, and Korang, due to their high population and density and the roads leading to them, have a higher potential vulnerability hazard (V).

The results of the landslide risk zoning map show that there is no area with very high landslide risk in the Kalpush basin due to the absence of rapid landslides (collapse, etc.). Also, Hossein Abad village is completely located in the high landslide risk zone. High landslide risk (R3) refers to areas with slow landslides with high intensity and frequency, high probability of structural and functional damage to structures and infrastructure, and less risk of death. Part of Karang village is located in the medium landslide risk zone (R2). Medium landslide risk (R2) refers to areas with slow or rapid landslides with mild intensity and superficial vulnerability. The results of the above discussions show that in the Kalpush basin, 9 landslide hazard zones (LHZ) with an area of 17 square kilometers have been identified in a concentrated manner in the south and west of the basin

Conclusion

The proposed geomorphological method is a specialized, accurate, and efficient method that has different responses in different locations. This method is based on the geomorphological observations of experts and the preparation of a multi-temporal landslide inventory map. If the older landslides, their morphological pattern in the region, and aerial photographs are evident, this method can be used. Also, this method is reliable and cost-effective for different scales of watershed, provincial, city, and village. As in this research, it provided the correct answer for the Kalpush basin and its villages.

Therefore, it is suggested that given the complete presence of Hossein Abad village in the high landslide risk zone, the village be completely relocated to reduce the probability of vulnerability of elements at risk of landslides in the future. Also, this method is introduced and proposed as a reliable and compatible method for geomorphologists, engineers, and crisis management in the Alborz and Zagros slopes of Iran.

کلیدواژه‌ها English

Landslide
Landslide Inventory Map
Risk
Kalpush
انتظام، ا.، رضایی، ع.، وکیل­زاده، ی.، محمدی، ی. و آقابابازاده، ن.(1398). بررسی و تحلیل مقدماتی زمین لغزش حسین آباد کالپوش. سازمان زمین شناسی و اکتشافات معدنی کشور.
بروغنی، م.، پورهاشمی، س.، و زنگنه اسدی، م.، (1397). ارزﯾﺎﺑﯽ ﺧﻄﺮ و ﺧﺴﺎرت زﻣﯿﻦﻟﻐﺰش در ﺣﻮﺿﻪ آﺑﺨﯿﺰ بقیع به روش‌های فاکتور قطعیت و رگرسیون لجستیک. مجله آمایش جغرافیایی فضا, 8(29), 1-18.‎
تیموری یانسری، ز.،(1397). مطالعه حساسیت به وقوع زمین لغزش در حوزه آبخیز چهاردانگه با تأکید بر مقایسه تطبیقی روش­های ارزیابی. رساله دکتری، دانشگاه فردوسی مشهد، دانشکده ادبیات و علوم انسانی، گروه جغرافیا.
ذاکری نژاد، ر.، و عموشاهی، ن.، (1401). ارزیابی خطر زمین لغزش با استفاده از داده های سنجش از دور و مدل حداکثر آنتروپی (منطقه مورد مطالعه: حوضه آبخیز کمه، جنوب استان اصفهان). پژوهشهای ژئومورفولوژی کمّی, 11(2), 128-149.‎
غیور بلورفروشان، م.، حسین زاده، س.، لشکری پور، غ.، مینائی، م.، و مربی هروی، ح. (1402). ارزیابی عملکرد بارش سنگین در فعال‌ شدن مجدد پالئولنداسلاید روستای حسین آباد کالپوش. پژوهشهای ژئومورفولوژی کمّی، 11(4)، 22-38.
کریمی­نژاد،ن.، پورقاسمی، ح.ر.، حسین علیزاده،م.، و شفاهی،و.(1403). تشخیص فروچاله­ها و زمین لغزش­ها با استفاده از روش­های یادگیری عمیق و تصاویر پهپادی. مهندسی و مدیریت آبخیز ، ِ https://doi.org 10.22092/ijwmse.2024.363888.2037
Alexander, D. (1989). Urban landslides. Progress in Physical Geography, 13(2), 157-189.
Alexander, D. (2000). Landslide risk estimation in Umbria Region. Unpublished technical report for the CNR-IRPI, 110.
Antonini, G., Arsixxone, F., Cardinalli, M., Galli, M., Guzzetti,F., & Reichenbach, P. (2001). Surface deposits and landslide inventory map of the area affected by the 1997 Umbria-Marche earthquakes. Bollettino della Società geologica italiana, 121(1), 843-853.
Berardino, P., Fornaro, G., Lanari, R., & Sansosti, E. (2002). A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on geoscience and remote sensing, 40(11), 2375-2383.
Brabb, E. E. (1984). Innovative approaches to landslide hazard and risk mapping.
Brardinoni, F., Slaymaker, O., Hassan, M.A.(2003). Landslide inventory in a rugged forested watershed: a comparison between air-photo and field survey data. Geomorphology 54,179 – 196.
Brunsden, D. (1985). Landslide types, mechanisms, recognition, identification. In landslides in the South Wales coalfield, edited by: Morgan, CS, Proceedings Symposium, April ,1-3.
Cardinali, M., Antonini, G., Reichenbach, P., & Guzzetti, F. (2001). Photo geological and landslide inventory map for the Upper Tiber River basin. CNR, Gruppo Nazionale per la Difesa dalle Catastrofi Idrogeologiche, Publication, 2154.
Cardinali, M., Reichenbach, P., Guzzetti, F., Ardizzone, F., Antonini, G., Galli, M. & Salvati, P. (2002). A geomorphological approach to the estimation of landslide hazards and risks in Umbria, Central Italy. Natural Hazards and Earth System Sciences, 2(1/2), 57-72.
Cascini, L., Fornaro, G., & Peduto, D. (2009). Analysis at medium scale of low-resolution DInSAR data in slow-moving landslide-affected areas. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6), 598-611.
Cigna, F., Del Ventisette, C., Liguori, V., & Casagli, N. (2011). Advanced radar-interpretation of InSAR time series for mapping and characterization of geological processes. Natural Hazards and Earth System Sciences, 11(3), 865-881.
Cruden, D. M. and Varnes, D. J.(1996). Landslide types and processes. Landslides, investigation and mitigation: special report 247, 36-75.
Dikshit, A., Sarkar, R., Pradhan, B., Jena, R., Drukpa, D., & Alamri, A. M. (2020). Temporal probability assessment and its use in landslide susceptibility mapping for eastern Bhutan. Water, 12(1), 267.
Einstein, H.(1988). Special lecture: Landslide risk assessment procedure. Proceedings 5th International Symposium on Landslides, Lausanne, 2, 1075–1090.
Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., & Savage, W. Z. (2008). Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Engineering geology, 102(3-4), 85-98.
Ferretti A, Prati C, Rocca F .(2000). Multibaseline InSAR DEM reconstruction: the wavelet approach. IEEE Trans Geosci Remote Sens 37(2):705–715.
Fiorucci, F., Cardinali, M., Carlà, R., Rossi, M., Mondini, A. C., Santurri, L., ... & Guzzetti, F. (2011). Seasonal landslide mapping and estimation of landslide mobilization rates using aerial and satellite images. Geomorphology, 129(1-2), 59-70.
Fookes, P. G., Lee, E. M., Milligan, G. C., & Press, C. R. C.(2005). Geomorphology for engineers . Caithness: Whittles Publishing.
Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., & Reichenbach, P. (2008). Comparing landslide inventory maps. Geomorphology, 94(3-4), 268-289.
Guzzetti, F., Cardinali, M., Reichenbach, P., & Carrara, A. (2001). Comparing Landslide Maps: A Case Study in the Upper Tiber River Basin, Central Italy. Environmental management, 25(3).
Guzzetti, F., Carrara, A., Cardinali, M., Reichenbach, P., (1999). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31, 181– 216.
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K. T. (2012). Landslide inventory maps: New tools for an old problem. Earth-Science Reviews, 112(1-2), 42-66.
Hansen, A. (1984). Engineering geomorphology: the application of an evolutionary model of Hong Kong's terrain. Zeitschrift für Geomorphologie. Supplementband, 51, 39-50.
Hooper, A., Segall, P., & Zebker, H. (2007). Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis. J. Geophys. Res, 112, 1-21.
Hungr, O. (2018). Some methods of landslide hazard intensity mapping. In Landslide risk assessment . Routledge.
Luo, S. L. & Huang, D. (2020). Deformation characteristics and reactivation mechanisms of the Outang ancient landslide in the Three Gorges Reservoir, China. Bulletin of Engineering Geology and the Environment, 79, 3943-3958.
Ma, S., Qiu, H., Hu, S., Yang, D. & Liu, Z. (2021a). Characteristics and geomorphology change detection analysis of the Jiangdingya landslide on July 12, 2018, China. Landslides, 18, 383-396.
Martin, Y., Rood, K., Schwab, J. W., & Church, M. (2002). Sediment transfer by shallow landsliding in the Queen Charlotte Islands, British Columbia. Canadian Journal of Earth Sciences, 39(2), 189-205.
Mather, A.E., Griffiths, J.S., Stokes, M., (2003). Anatomy of a fossil landslide from the Pleistocene of SE Spain. Geomorphology 50,135–149.
McCalpin, J. (1984). Preliminary age classification of landslides for inventory mapping. In Proceedings of the Annual Symposium on Engineering Geology and Soil Engineering ( 21), 99-120.
Meng, Q., Li, W., Raspini, F., Xu, Q., Peng, Y., Ju, Y. & Casagli, N. (2021). Time-series analysis of the evolution of large-scale loess landslides using InSAR and UAV photogrammetry techniques: A case study in Hongheyan, Gansu Province, Northwest China. Landslides, 18, 251-265.
Pereira, S., Santos, P. P., Zêzere, J. L., Tavares, A. O., Garcia, R. A. C., & Oliveira, S. C. (2020). A landslide risk index for municipal land use planning in Portugal. Science of the Total Environment, 735, 139463.
Qiao, P.D. & Li, Z.J.,(1990). Engineering Geology in Loess Covered Area. Water and Power Press.
Rajabi, A.M., Khamehchiyan, M., Mahdavifar, M. R. & Del Gaudio, V.(2010). Attenuation relation of Arias intensity for Zagros Mountains region (Iran). Soil Dynamics and Earthquake Engineering, 30(3), 110-118.
Rib, H. T., & Liang, T. (1978). Recognition and identification. Transportation Research Board Special Report, (176).
Roering, J. J., Schmidt, K. M., Stock, J. D., Dietrich, W. E., & Montgomery, D. R. (2003). Shallow landsliding, root reinforcement, and the spatial distribution of trees in the Oregon Coast Range. Canadian Geotechnical Journal, 40(2), 237-253.
Sassa, K., Tsuchiya, S., Fukuoka, H., Mikos, M. & Doan, L. (2015). Landslides: review of achievements in the second 5-year period (2009–2013). Landslides, 12, 213-223.
Shoaei, Z., Shoaei, G., & Shoaei, A. (2021). Deadly Landslide and Debris Avalanche in Abikar Village, Farsan City, Chaharmahal and Bakhtiari Province, Iran.
Sidle, R. C. & Ochiai, H. (2006). Landslides: Processes, Prediction, and Land Use, Water Resour. vol. 18.Monogr.Ser.
Van Westen, C.J., Seijmonsbergen, A.C., Mantovani, F.(1999).Comparing landslide hazard maps. Natural Hazards 20,137–158.
Varnes, D. J. & IAEG Commission on Landslides.(1984). Landslide hazard zonation–a review of principles and practice. UNESSO Paris.
Vassileva, M., Motagh, M., Roessner, S., & Xia, Z. (2023). Reactivation of an old landslide in north–central Iran following reservoir impoundment: Results from multisensor satellite time-series analysis. Engineering Geology, 327, 107337.
Wang, H.B., Zhou, B., Wu, S.R., Shi, J.S. & Li, B.(2011). Characteristic analysis of large-scale loess landslides: a case study in Baoji city of loess plateau of northwest China: Natural Hazards  and Earth System Sciences, 11, 1829 –1837.