Flood zoning of Shahristanak drainage basin using WMS hydrological model and GIS integration

Document Type : Applied Article

Authors

1 Assistant Professor, Chalus Branch, Department of Geography, Islamic Azad University, Chalus, Iran

2 Assistant Professor, Mahshahr Branch, Department of Geography, Islamic Azad University, Mahshahr, Iran

10.22059/jhsci.2024.374472.824

Abstract

Flooding is one of the most important challenges facing human civilization and its impact is expected to increase due to climate change and massive urbanization. The purpose of this research is to zonate flood occurrence using WMS hydrological model with GIS integration in Shahristanak watershed, one of the sub-basins of Karaj dam in Alborz province. In this study, geological maps 1:100000, topography 1:2000 and 1:50000, soil 1:100000, and data from synoptic and rain gauge stations are considered as the most basic data of this research for flood zoning in Shahristanak catchment area. Also, ArcGIS 10.3, WMS software was used to analyze and prepare maps. A modified version of TOPAZ model along with WMS was used to calculate the flow direction from the DEM active area. The results show that in the upstream and downstream of the catchment basin, as we move away from the center of the river, the area of water depth increases. While in some places upstream of the river, the water depth is less. In other words, the lower the water depth, the greater its area, and the greater the water depth, the smaller its area. Most of the area is 4.03 meters deep, which is equal to 44.2 hectares. The area of the depth of 8.06 meters is equal to 14.5 hectares, the area of the depth of 12.09 meters is equal to 4.6 hectares, the area of the depth of 16.12 meters is equal to 1.4 hectares, and the area of the depth of 20.15 meters is equal to 1.1. It is hectares. In general, the WMS model and its integration with GIS is for flood zoning and determining risk ranges in the region.

Keywords


  • احمدی، حمزه؛ باعقیده، محمد؛ اسدی، سعید؛ و احمدی، فریبرز (1394). تحلیل رخداد بارشی شدید منجر به سیل 28 تیر1394 در استان البرز. مدیریت مخاطرات طبیعی، 2(4)، 451-469.
  • حائری، ساناز؛ و مثنوی، محمدرضا (1402). تحلیل راهبردهای بهسازی اکولوژیک منظر رودخانۀ خشک شیراز در چارچوب توسعۀ پایدار شهری با تأکید بر مدیریت مخاطرات سیلاب. مدیریت مخاطرات طبیعی، 10‌(1)، 71-90.
  • رحیم‌پور، توحید؛ رضائی مقدم، محمدحسین؛ حجازی، سید اسدالله؛ و خلیل ولی‌زاده، کامران (1402). تحلیل تغییرات فضایی حساسیت خطر وقوع سیل برپایۀ نوعی مدل ترکیبی نوین، مطالعۀ موردی: حوضۀ آبریز الندچای، شهرستان خوی. مدیریت مخاطرات طبیعی، 8 (4)، 371-393.
  • صفاری، امیر؛ احمدآبادی، علی؛ و صدیقی‌فر، زهرا (1399). تحلیل مخاطرۀ ناشی از سیلاب با تکیه بر مدل WMS در حوضه‌های آبریز شهری، مطالعۀ موردی: حوضه‌های دربند، گلابدره و سعدآباد کلانشهر تهران. تحقیقات کاربردی علوم جغرافیایی، سال بیستم، 2(57)، 317-334.
  • lexander, T., Hughes, M., Baldock, T., Greenwood, B., Kroon, A., & Power, H. (2012). Sediment transport processes and morphodynamics on a reflective beach under storm and non-storm conditions. Geol. 326-328 (1), 154–165. https://doi.org/ 10.1016/j.margeo.2012.09.004
  • Amobichukwu , H.U., Troy, C.D., Habib, A., & Manish, R. (2024). A simple, fully automated shoreline detection algorithm for high-resolution multi-spectral imagery. Remote Sens. (Basel). 14 (3). https://doi.org/10.3390/rs14030557.
  • Amanambo, P.N., Inman, D.L., & Lovering, J.L. (2024). Effects of climate change and wave direction on longshore sediment transport patterns in Southern California. Change. 109, 211–228.
  • Barbarossa, R., Barry, D.A., Li, L., Jeng, D.S., & Yeganeh-Bakhtiary, A. (2022). Modeling sediment transport in the swash zone: a review. Ocean Eng. 36, 767–783. https:// doi.org/10.1016/j.oceaneng.2009.03.003. tps://doi.org/10.1007/s10584-011-0317-0.
  • Barnard, P.L., Short, A.D., Harley, M.D., Splinter, K.D., Vitousek, S., Turner, I.L., Allan, J., Banno, M., Bryan, K.R., Doria, A., Hansen, J.E., Kato, S., Kuriyama, Y., Randall-Goodwin, E., Ruggiero, P., Walker, I.J., & Heathfield, D.K. (2018). Coastal vulnerability across the Pacific dominated by El Nino/southern oscillation. Geosci. 8, 801–807. https://doi.org/10.1038/ngeo2539. Battjes, J.A., 1974. Surf similarity. Coast. Eng. 466-480 https:/
  • Berkovich, R.J., Rodriquez-Delgado, C., & Ortega-Sanchez, M. (2017). Advances in management tools for modeling artificial nourishments in mixed beaches. J. Syst. 172, 1–13. https://doi.org/10.1016/j.jmarsys.2017.02.009.
  • Chen, C., Liang, J., Xie, F., Hu, Z., Sun, W., Yang, G., Yu, J., Chen, L., Wang, L.H., Wang, L.Y., Chen, H., He, X., & Zhang, Z. (2022) Temporal and spatial variation of coastline using remote sensing images for Zhoushan archipelago. China. J. Appl. Earth Obs. Geoinf. 107, #102711.
  • Chen, C., Liang, J., Yang, G., & Sun, W. (2023). Spatio-temporal distribution of harmful algal blooms and their correlations with marine hydrological elements in offshore areas. China. Ocean & Coastal Management. 238, #106554.
  • Chen, H., Chen, C., Zhang, Z., Lu, C., Wang, L., He, X., Chu, Y., & Chen, J. (2021). Changes of the spatial and temporal characteristics of land-use landscape patterns using multi-temporal Landsat satellite data: A case study of Zhoushan Island. China. Ocean Coastal Manage. 213, #105842.
  • Dabija, A., Kluczek, M., Zagajewski, B., Raczko, E., Kycko, M., Al-Sulttani, A.H., Anna, T., Pineda, L., & Corbera, J. (2021). Comparison of support vector machines and random forests for corine land cover mapping. Remote Sens. 13 (4), 777.
  • Du, Z., Yang, J., Ou, C., & Zhang, T. (2021). Agricultural Land Abandonment and Retirement سMapping in the Northern China Crop-Pasture Band Using Temporal Consistency Check and Trajectory-Based Change Detection Approach. IEEE Trans. Geosci. Remote Sens. 60, 1–12.
  • Echogdali, C., Hou, X., Zheng, Q., Xu, H., Li, D., Donnici, S., & Tang, C. (2022). Emerging signals of coastal system changes under rapid anthropogenic disturbance in Hangzhou Bay. Ecol. Indic. 146, 109816.
  • Gilani, H., Naz, H.I., Arshad, M., Nazim, K., Akram, U., Abrar, A., & Asif, M. (2021). Evaluating mangrove conservation and sustainability through spatiotemporal (1990–2020) mangrove cover change analysis in Pakistan. Estuarine Coastal Shelf Sci 249, 107128.
  • Gislason, P.O., Benediktsson, J.A., & Sveinsson, J.R. (2006). Random forests for land cover classification. Pattern Recognit. Lett. 27 (4), 294–300.
  • Goffin, B.D., Thakur, R., Carlos, S.D.C., Srsic, D., Williams, C., Ross, K., Neira-Rom´ an, F., Cort´es-Monroy, C.C., & Lakshmi, V. (2022). Leveraging remotely-sensed vegetation indices to evaluate crop coefficients and actual irrigation requirements in the waterstressed Maipo River Basin of Central Chile. Sustainable Horizons. 4, #100039.
  • Liu, Y., Hou, X., Li, X., Song, B., & Wang, C. (2020). Assessing and predicting changes in ecosystem service values based on land use/cover change in the Bohai Rim coastal zone. Indic. 111, #106004.
  • Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Homayouni, S., & Gill, E. (2018). The first wetland inventory map of newfoundland at a spatial resolution of 10 m using sentinel-1 and sentinel-2 data on the google earth engine cloud computing platform. Remote Sens. 11 (1), 43.
  • Nagib Hegazy, D., Wang, Z., Du, B., Li, L., Tian, Y., Jia, M., & Wang, Y. (2022). National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images. ISPRS J. Photogramm. Remote Sens. 164, 11–25.
  • Pratico, ` S., Solano, F., Di Fazio, S., & Modica, G. (2021). Machine learning classification of mediterranean forest habitats in google earth engine based on seasonal sentinel-2 time-series and input image composition optimisation. Remote Sens. 13 (4), 586.
  • Quang, D.N., Ngan, V.H., Tam, H.S., Viet, N.T., Tinh, N.X., & Tanaka, H. (2021). Long-term shoreline evolution using dsas technique: A case study of Quang Nam province. Vietnam. J. Sci. Eng. 9 (10), 1124.
  • Rawat, J.S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. J. Remote Sens. Space. Sci. 18 (1), 77–84.
  • Sidhu, N., Pebesma, E., & Camara, G. (2018). Using Google Earth Engine to detect land cover change: Singapore as a use case. J. Remote Sens. 51 (1), 486–500.
  • Tian, P., Li, J., Cao, L., Pu, R., Gong, H., Liu, Y., Zhang, H., & Chen, H. (2021). Impacts of reclamation derived land use changes on ecosystem services in a typical gulf of eastern China: A case study of Hangzhou Bay. Ecol. Indic. 132, 108259.
  • Ullah, N., Siddique, M.A., Ding, M., Grigoryan, S., Zhang, T., & Hu, Y. (2022). Spatiotemporal Impact of Urbanization on Urban Heat Island and Urban Thermal Field Variance Index of Tianjin City. Buildings. 12 (4), 399.
  • Wang, C., Jia, M., Chen, N., & Wang, W. (2018). Long-term surface water dynamics analysis based on Landsat imagery and the Google Earth Engine platform: A case study in the middle Yangtze River Basin. Remote Sens. 10 (10), 1635.
  • Wang, J., Li, C., Hu, L., Zhao, Y., Huang, H., & Gong, P. (2015). Seasonal land cover dynamics in Beijing derived from Landsat 8 data using a spatio-temporal contextual approach. Remote Sens 7 (1), 865–881.
  • Wang, L., Chen, C., Xie, F., Hu, Z., Zhang, Z., Chen, H., He, X., & Chu, Y. (2021). Estimation of the value of regional ecosystem services of an archipelago using satellite remote sensing technology: A case study of Zhoushan Archipelago. China. Int. J. Appl. Earth Obs. Geoinf. 105, #102616.