Prioritization of Talar watershed flood risk potential in GIS environment

Document Type : Research Article

Authors

1 University Academic member

2 Ph.D student of Sari Agricultural and natural resources university

3 PhD student of Sari Agricultural Sciences and Natural Resources university

Abstract

Talar watershed with various land use, hydrological and vegetation characteristics is located in Mazandaran province In recent years, numerous devastating floods have occurred in this basin.In this study, flooding prioritization of Talar sub-basins under different hydrological and physiographic conditions were compared with an area of 189613.6/ha in agricultural, residential, rangeland and forest land-uses.First, the basin is divided into six sub-basins, including Shirgah, Karmozd, Drasleh, Polusfid, Arjangrudbar and Chashem and for each sub-basin, geometric, climatic, permeability and physiographic parameters such as; area, perimeter, length of main channel, length and slope of basin, time of concentration, CN, discharge, etc.have been calculated using GIS software.Time of concentration with methods of Branci Bay Williams, Johnson, Pilgrim-McDremat, Kirpich, California, Chow, Spey, Ventura was evaluated based on the characteristics of the basin but the Bransi-Williams method with 19.2 hours TC, showed the most suitable method for the all basin. Based on SCS method, the share of each sub-basin was determined in the flood of output from the whole basin. The results of the maximum stannous flood peak also showed that Polsefid with a flow of 380 cubic meters per second had the highest flow during the statistical period (1986-2019). Sub-basins are divided into three groups with high, medium and low flood potential in terms of similarity of flood potential, erosion, vegetation, and effects of human impact. The results showed that the highest amount of flooding in each of the sub-basins includes the sub-basin of Chashm with 29.19%, in Draseleh with 23.25% and finally shirgah with 16.76%

Keywords

Main Subjects


  • Asgari Sh, Safari A, Fathi H. Investigation of flooding in Jafarabad catchment using factor analysis. J of Applied Res in Geographical Sciences. 2018;18(50): 77-90. [Persian].
  • Azamirad M, Ghahraman B, Esmaili K. Investigation Flooding Potential in The Kashafrud watershed, Mashhad The Method SCS and GIS. J of Watershed Management. 2018;9(17):38-26. [Persian].
  • Bellos V, Papageorgaki I, Kourtis I, Vangelis H, Kalogiros I, Tsakiris G. Reconstruction of a flash flood event using a 2D hydrodynamic model under spatial and temporal variability of storm. Nat. Hazards. 2020;101: 711–726.
  • Bracken LJ, Oughton EA, Donaldson A, Cook B, Forrester J, Spray C, Cinderby S, Passmore D, Bissett N. Flood risk management, an approach to managing cross-border hazards. Nat. Hazards. 2016;82: 217–240.
  • Cristofor S, Vadineanu A, Ignat G. Importance of flood zones for nitrogen and phosphorus dynamics in the Danube Delta. Hydrobiologia. 1993;251(1–3): 143–148.
  • Elsadek WM, Ibrahim MG, Mahmod WE, Kanae S. Developing an overall assessment map for flood hazard on large area watershed using multi-method approach: case study of Wadi Qena watershed, Egypt. Natural Hazards. 2019;95(3): 739-767.
  • Eslami Z, Shojaei S, Hakimzadeh MA. Exploring prioritized sub-basins in terms of flooding risk using HEC_HMS model in Eskandari catchment, Iran. Spatial Information Res. 2017;25(5): 677-684.
  • Feiznia S, Sharifi F, Zare M. Sensetivity of geologic formations to erosion in Chandabe basin of Varamin. Res & construction. 2004;61: 33- 38. [Persian].
  • Francesch-Huidobro M, Dabrowski M, Tai Y, Chan F, Stead D. Governance challenges of flood-prone delta cities: Integrating flood risk management and climate change in spatial planning. Prog. Plann. 2017;114: 1–27.
  • Garosi Y, Sheklabadi M, Conoscenti C, Pourghasemi HR, Van Oost K. Assessing the performance of GIS-based machine learning models with different accuracy measures for determining susceptibility to gully erosion. Sci. Total Environ. 2019;664: 1117–1132.
  • Gharib M, Motamedvaziri B, Ahmadi H. Providing a method for determining the flood producing regions based on the relationship between flooding index and morphometric. J of Soil and Water Resour Protection. 2018;7(4): 87-101. [Persian].
  • Guerriero L, Ruzza G, Guadagno FM, Revellino P. Flood hazard mapping incorporating multiple probability models. J. Hydrol. 2020;587: 125020.
  • Hemati M, SHahnazi M, Ahmadi H, Salarijazi M. Flood Peak Flow Simulation and Determination of Flood Source Area in the QARANQU Watershed Using Hydrological Mod-Clark Model and GIS. Iranian J of Irrigation and Water Engineering. 2017;7(4); 65-80. [Persian].
  • Hoseinzadeh MM, Nosrati K, Imeni S. Determining the number of curves and estimating the runoff production potential of Hesarak watershed. J of Geographical Sciences. 2019;18(51): 133-150. [Persian].
  • Jongman B, Hochrainer-Stigler S, Feyen L, Aerts JC, Mechler R, Botzen WW, Bouwer LM, Pflug G, Rojas R, Ward PJ. Increasing stress on disaster-risk finance due to large floods. Nat. Clim. Change, 2014;4(4):264–268.
  • Khodayar P. Checking the country's flood situation and problems. Proceedings. 2012. [Persian].
  • Khosravi K, Panahi M, Golkarian A, Keesstra SD, Saco PM, Bui DT, Lee S. Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran. J of Hydrology. 2020;591: 125552.
  • Klaus S, Kreibich H, Merz B, Kuhlmann B, Schröter K. Large-scale, seasonal flood risk analysis for agricultural crops in Germany. Environ. Earth Sci. 2016;75 (18): 1289.
  • Lyubimova T, Lepikhin A, Parshakova Y, Tiunov A. The risk of river pollution due to washout from contaminated floodplain water bodies during periods of high magnitude floods. J. Hydrol. 2016;534: 579–589.
  • Mahato S, Pal S, Talukdar S, Saha TK, Mandal P. Field based index of flood vulnerability (IFV): A new validation technique for flood susceptible models. Geoscience Frontiers. 2021;12(5): 101175.
  • Milanesi L, Pilotti M, Bacchi B. Using web-based observations to identify thresholds of a person’s stability in a flow. Water Resour. Res. 2016;52: 7793–7805.
  • Mishra SK, Tyagi JV, Singh VP, Singh R. SCS-CN-based modeling of sediment yield. J of Hydrology. 2006;324: 301–322.
  • Nadiri M. Flood risk zoning using AHP-TOPSIS fuzzy logic in GIS environment (Case study of Aidoghmush watershed). J of Geography (Regional Planning). 2019;9(3): 293-306. [Persian].
  • Nguyen HD, Fox D, Dang DK, Pham LT, Viet Du QV, Nguyen THT, Petrisor AI. Predicting Future Urban Flood Risk Using Land Change and Hydraulic Modeling in a River Watershed in the Central Province of Vietnam. Remote Sensing. 2021;13(2): 262.
  • Percival S, Teeuw R. A methodology for urban micro-scale coastal flood vulnerability and risk assessment and mapping. Nat. Hazards. 2019;97: 355–377.
  • Prasad RN, Pani P. Geo-hydrological analysis and sub watershed prioritization for flash flood risk using weighted sum model and Snyder’s synthetic unit hydrograph. Modeling Earth Systems and 2017;3(4): 1491-1502.
  • Qodsian M. Flood Control and Drainage Engineering, (Translated) Tarbiat Modares University Press, Office of Science Publication, First Edition. 1998. [Persian].
  • Saberi A, Delfan H, Rangzan K, Kabolizadeh M. Estimation of runoff height and volume of watershed upstream of Kahnak Dezful river using remote sensing and ArcCN-Runoff tools in GIS environment. Nat Geomatics Conference. 2016;23: 25. [Persian].
  • Saghafian B, Ghermezcheshmeh B, Kheirkhah MM. Iso-Flood severity mapping: a New Tools for Distributed Flood Source Identification. Natural Hazards. 2010;55(2): 557-570.
  • Saremi N, Bazrafshan O, Esmaelpour Y, Souri M. Flood Zoning and Assessment of Surface Runoff Channels Effcientcy in Bandar- Abbas Urban Watershed. J of Iran-Watershed Management Science & Engineering. 2018;12(42): 42-51. [Persian].
  • Sepehr A, Abdollahi A, Mohammadian A, Nejad MP. Prioritization of Kashafrud sub-basins in terms of flooding sensitivity based on ELECTRE-TRI Algorithm. Universal J of Geoscience. 2017;5(4): 83-90.
  • Servati MR, Ahmadi M, Nosrati K, Mazbani M. Zoning potential of flooding Sarab Darrehshahr watershed. J of Geography. 2013;11(36): 56-76. [Persian].
  • Shaabani Bazneshin A, Emadi A, Fazloula R. Investigation the Flooding Potential of Basins and Determination Flood Producing Areas (Case Study: NEKA Basin). J of Watershed Management Res. 2016;7(14): 20-28. [Persian].
  • Svetlana D, Radovan D, Jan D. The economic impact of floods and their importance in different regions of the World with emphasis on Europe. Procedia Econ. Financ. 2015;34: 649–655.
  • Taghavi Moghadam A, Bahrami SH. Zangane Asadi MA, Mokhtari L. Quantitative Analysis of the Basin Components and its Role in the Rate of Annual Sediment Yield (17 Basins in North East Iran). J of Geography & Environmental Planning. 2018;29(3):147-172. [Persian].
  • Wang Y, Chen AS, Fu G, Djordjevi´c S, Zhang C, Savi´c DA. An integrated framework for high-resolution urban flood modelling considering multiple information sources and urban features. Environ. Model. Softw. 2018;107; 85–95.
  • Wu Q, Zhao Z, Liu L, Granger DE, Wang H, Cohen DJ, Wu X, Ye M, Bar-Yosef O, Lu B, Zhang J. Response to Comments on “Outburst flood at 1920 BCE supports historicity of China’s Great Flood and the Xia dynasty”. Science. 2017;355(6332): 1382.
  • Yamani M, Abbasi M. Evaluation of Flooding below Gadar Catchments based on Morphometric Parameters and Statistical Correlation. J of Land Management. 2020;12(1): 205-224. [Persian].
  • Zareian Sh, Esmali ouri A, Ahmadzade Gh, Mesri T. Zoning the flood potential of Namin watershed using a hierarchical preparation process. International Development Conf focusing on agriculture, environment & tourism. Tabriz. 2016. [Persian].
  • Zehtabian Gh, Ghodosi J, Ahmadi H, Khalilizadeh M. Investigating the priority of flood potential in sub-watersheds and determining flood-producing areas in it (Case study: Ma-Rameh watershed - Fars province). J of Natural Geography. 2010;2(6):1-13. [Persian].
  • Zhang G, Feng G, Li X, Xie C, Pi X. Flood effect on groundwater recharge on a typical silt loam soil. Water. 2017;9 (7): 523.