News

a.z
Masoumi, H., Habibi, A., Gharibreza, M. (2021). Prediction of rivers and floods in Dashtyari region for 2030 horizon. , 13(3), 638-649. doi: 10.22092/ijwmse.2021.352118.1842
Hamidreza Masoumi; Alireza Habibi; Mohamadreza Gharibreza. "Prediction of rivers and floods in Dashtyari region for 2030 horizon". , 13, 3, 2021, 638-649. doi: 10.22092/ijwmse.2021.352118.1842
Masoumi, H., Habibi, A., Gharibreza, M. (2021). 'Prediction of rivers and floods in Dashtyari region for 2030 horizon', , 13(3), pp. 638-649. doi: 10.22092/ijwmse.2021.352118.1842
Masoumi, H., Habibi, A., Gharibreza, M. Prediction of rivers and floods in Dashtyari region for 2030 horizon. , 2021; 13(3): 638-649. doi: 10.22092/ijwmse.2021.352118.1842

Prediction of rivers and floods in Dashtyari region for 2030 horizon

مهندسی و مدیریت آبخیز
Article 10, Volume 13, Issue 3, October 2021, Pages 638-649    PDF (1.58 M)
Document Type: Research Paper
DOI: 10.22092/ijwmse.2021.352118.1842
Authors
Hamidreza Masoumi* 1; Alireza Habibi2; Mohamadreza Gharibreza3
1Assistant Professor, Department of Geology, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran
2Researcher of Soil Conservation and Watershed Management Research Institute, Tehran, Iran
3Associate Professor, Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization, Tehran, Iran
Abstract
In recent years, flooding of rivers has resulted in destructive implications, especially in the coastal areas. Dashtyari coastal plain is located in the southeastern of Iran. The occurrence of destructive floods has led to extensive damage to agricultural facilities, buildings, and residents of the region in recent years. Morphometric factors of the river and its surroundings land-uses and their changes in the future are effective and necessary factors in the planning of coastal plains. Remote sensing is an applicable tool to investigate the past, present, and postcondition of rivers. The GIS-Ready layers included satellite images (Landsat 5, 1987; 7, 2001; 8, 2019; Sentinel-2, 2020), and specific software (Envi 5.3, ArcGIS 10.4.1, and Idrisi TerrSet), as well as the existence and fieldwork documents, have been used to achieve the research aims. Probability values of land-use changes in 2019 were obtained based on Markov chains. Accordingly, the highest probability of changes 24.87% and 23.5% were obtained between the river and plain units, and between farms and river, respectively. Then, an automatic cell prediction map of 2019 is accomplished with the overall kappa coefficient of 95%. According to the accuracy of the output of the cellular Automata Markov model, forecasted Land use and river morphology maps for 2030 were developed. Further, possible changes in the river environment were obtained by fitting the two maps of 2019 and forecasting 2030. Moreover, bank erosion was identified in the 6 critical points along the Kajo, Dashtyari, and Bahu rivers. Finally, the destructive flood event in January 2020 in the Dashtyari region was investigated to match the results with natural events.
Keywords
Bahu River; CA Markov model; Dashtyari region; Forecast; Remote sensing studies; River channel
References
  1. Abdolahi Baghsiahi, A., R. Heshmatian and S. Mahdavi. 2018. The main aphorism of the Makran coast with the integration of the HEC-RAS and GIS model (Bahu Kalat River). 11th International River Engineering Seminar, Shahid Chamran University, Ahwaz, Iran (in Persian).
  2. Alizadeh Rabiei, H. 2001. Remote sensing (principles and applications). Publication of Study and Compilation of Humanities Books, Tehran, 292 pages (in Persian).
  3. Amini Parsa, V. and A. Nejadi. 2016. Application of CA-Markov model in predicting the dynamics structure of protected areas. Natural Geography Research, 48(4): 661-674 (in Persian).
  4. Aizi Ghalaty, S., K. Rangzan, J. Sadidy, P. Heydarian and A. Taghizadeh. 2016. Predicting locational trend of land use changes using CA-Markov model, case study: Kohmare Sorkhi, Fars Province. Journal of RS and GIS for Natural Resources, 7(1): 59-71 (in Persian).
  5. Banihabib, M.E., M. Ezati Aminib and M.H. Shabestari. 2019. Framework for determining a sustainable development strategy to rehabilitate a seasonal urban river. Watershed Engineering and Management. 11(2): 316-327 (in Persian).
  6. Bravard, J.P. and F. Petit. 2009. Geomorphology of streams and rivers. Encyclopedia of Inland Waters, Elsevier, 2009: 387-95. DOI: 10.1016/B978-012370626-3.00043-0.
  7. Darabi, H., K. Shahedi and M. Mardian. 2016. Preparation of flood hazard and susceptibility maps using frequency ratio method in Pol-e Doab Shazand Watershed. Watershed Engineering and Management. 8(1): 68-79 (in Persian).
  8. Fathizad, H., N. Rostami and M. Faramarzi. 2015. Detection and prediction of land cover changes using Markov chain model in semi-arid rangeland in western Iran. Journal of Environmental Monitoring and Assessment, 187(10): 62-79.
  9. Fayazi, M., A. Bagheri, H. Sedghi, K. Keyhan and F. Kaveh. 2009. Study of flood zoning using one and two-dimensional mathematical models (MIKE11 and MIKE FLOOD). 8th International River Engineering Seminar, Shahid Chamran University, Ahwaz, Iran (in Persian).
  10. Halalat Naserian, H., M. Sadeghi, H.A. Vaezipour and S. Seif. 2013. Comprehensive modeling of floods in Chabahar Dashtiari region by Mike flood model and presentation of flood cure plan. 12th Hydraulic Conference, University of Tehran (in Persian).
  11. Heidarizadeh, Z. and A. Mohamadi. 2016. Predicting the land use change using Markov-Cellular automata model in Mehran Plain. Journal of Desert Ecosystem Engineering, 5(10): 57-68 (in Persian).
  12. Javaheri Tehrani, M., F. Mousavi and Kh. Hosseini. 2016. Morphologic study of meandering rivers under controlled flow using RS and GIS techniques, case study: Zayandehrud River at the downstream of Zayandehrud Dam. Journal of Water and Soil Science, 20(77): 25-44 (in Persian).
  13. Kaveh, N. and A. Ebrahimi. 2013. A Markov chain model for simulating land use/cover change, case study: Aghbolagh River. Journal of Applied RS and GIS Techniques in Natural Resource Science, 4(2): 41-51 (in Persian).
  14. Kondolf, G.M., H. Piégay, R.M. David, L. Schmitt and R.M. David. 2016. Geomorphic classification of rivers and streams. Chapter 7: tools in fluvial geomorphology. 169-202. DOI: 1002/97811186 48551.ch7.
  15. Koneshlou, H. 1998. A look at the natural resources of the southern coast of Iran. Publications of Forests and Rangelands Research Institute, 188 pages (in Persian).
  16. Masoumi, H.R., M.R. Gharibreza and A. Motamed. 2011. Investigation of meandering and morphology pattern of Hendijan River in delta plain area. Watershed Engineering and Management, 3(2): 102-112 (in Persian).
  17. Mirzaeizadeh, V., M. Niknwzhad and J. Ouladi. 2015. Evaluating non-parametric supervised classification algorithms in land cover map using Landsat-8 images. Journal of RS and GIS for Natural Resources. 6(3): 29-44.
  18. Mondal, S., N. Sharma, M. Kappas and P.K. Garg. 2020. Cellular Automata (CA) contiguity filters impacts on CA Markov modelling of land use land cover change predictions results. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII, B3, 1585–1591.
  19. Muller, M.R. and J. Middleton. 1994. A Markov model of land-use change dynamics in the Niagara region, Ontario and Canada. Landscape Ecology, 9: 151-157.
  20. Naderi, M., A. Ilderomi, H. Nouri, S. Aghabeigi Amin and H. Zeinivand. 2018. Investigating the impact of land use and climate change on watershed runoff by SWAT model, case study: Grin Basin. Hydrogeomorphology, 16: 61-79 (in Persian).
  21. Pal, M. and P.M. Mather. 2005. Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26(5): 1007-1011.
  22. Parvaresh, E., R. Mahdavi, A. Malekian, Y. Esmaeilpour and A. Holisaz. 2019. Investigation of effective factors and prioritization of flood potential in watersheds using fuzzy methods of TOPSIS and Electre III, case study: Sarkhoun Watershed. Watershed Engineering and Management, 11(2): 493-507 (in Persian).
  23. Roghani, M., M. Ghafouri and M. Tabatabaei. 2007. An innovative methodology for the prioritization of sub-catchments for flood control. International Journal of Applied Earth Observation and Geoinformation, 9: 79-87.
  24. Saghafian, B., H. Farazjoo, H. Sepehri and E. Najafzade. 2007. Assessment of land use changes on flooding condition in Golestan Dam Watershed. Iran Water Resources Research, 2: 18-28 (in Persian).
  25. Subedi, P., K. Subedi and B. Thapa. 2013. Application of a hybrid cellular automaton Markov (CA-Markov) model in land-use change prediction: a case study of Saddle Creek Drainage Basin, Florida. Applied Ecology and Environmental Sciences, 16: 126-132.
  26. Verburg, P., H. Schot, P.P. Dijst and M.J. Veldkamp. 2004. Land use change modeling: current practice and research priorities. GeoJournal, 61(4): 309- 324.
  27. Zhang, F., T. Tiyip, Z.D. Feng, H.T. Kung, V.C. Johnson, J.L. Ding, N. Tashpolat, M. Sawut and D.W. Gui. 2015. Spatio-temporal patterns of land use/cover changes over the past 20 years in the middle reaches of the Tarim River, Xinjiang, China. Land Degradation and Development, 26: 284- 299.
Statistics
Article View: 812
PDF Download: 415


counter
Journal management system. designed by sinaweb