Using geomorphologic indicators in preparation for flood zoning and flood risk maps in the Kashafroud basin, Iran

被引:0
作者
Panahi, Ghasem [1 ]
Khodashenas, Saeed Reza [1 ]
Faridhosseini, Alireza [1 ]
机构
[1] Ferdowsi Univ Mashhad FUM, Agr Fac, Water Sci & Engn Dept, Mashhad, Iran
来源
JOURNAL OF FLOOD RISK MANAGEMENT | 2024年 / 17卷 / 02期
关键词
flood risk map; flood zoning; geomorphic flood area (GFA); geomorphologic index (GI); SRTM DEM; standard flood map; DELINEATION; MORPHOLOGY; RESOLUTION;
D O I
10.1111/jfr3.12981
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The risk of flooding has become more significant in many parts of the world due to climate change and increased urbanization. Flood has devastating effects on infrastructure, and communities, causing damage to property and loss of life. Simulation of flood extent in a particular area is done by using various mathematical models, hydrologic-hydraulic models, and datasets. Flood modeling using hydraulic-hydrological models has many errors due to the lack of hydraulic-hydrologic data and insufficient statistical period length. This study demonstrates the fact that the geomorphological index (GI) method, which is based on the digital elevation model and requires little hydraulic-hydrologic data, is an effective method for flood modeling. Flood zoning based on GI was performed within the Kashafroud basin with 25, 100, and 200-year return periods by using geomorphic flood area (GFA) plugin in QGIS software. The true positive rates were 0.985, 0.989, and 0.992, respectively, which showed the high accuracy of flood zoning based on the GI method. Here proposed method showed that using the GFA plugin offers a good way for the flood risk assessment in a basin with the lack of measured data as an alternative to the hydraulic-hydrological methods.
引用
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页数:18
相关论文
共 69 条
  • [1] Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees
    Abedi, Rahebeh
    Costache, Romulus
    Shafizadeh-Moghadam, Hossein
    Pham, Quoc Bao
    [J]. GEOCARTO INTERNATIONAL, 2022, 37 (19) : 5479 - 5496
  • [2] Random forest and naïve Bayes approaches as tools for flash flood hazard susceptibility prediction, South Ras El-Zait, Gulf of Suez Coast, Egypt
    Sherif Ahmed Abu El-Magd
    [J]. Arabian Journal of Geosciences, 2022, 15 (3)
  • [3] Ahmad M., 2022, Liquids, V2, P147, DOI DOI 10.3390/LIQUIDS2030010
  • [4] Large Scale Flood Risk Mapping in Data Scarce Environments: An Application for Romania
    Albano, Raffaele
    Samela, Caterina
    Craciun, Iulia
    Manfreda, Salvatore
    Adamowski, Jan
    Sole, Aurelia
    Sivertun, Ake
    Ozunu, Alexandru
    [J]. WATER, 2020, 12 (06)
  • [5] Altaf S., 2022, GEOCARTO INT, V1-22, P37
  • [6] Investigating hydrogeomorphic floodplain mapping performance with varying DTM resolution and stream order
    Annis, A.
    Nardi, F.
    Morrison, R. R.
    Castelli, F.
    [J]. HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, 2019, 64 (05): : 525 - 538
  • [7] [Anonymous], Floods
  • [8] Arnaud-Fassetta G, 2009, GEOMORPHOLOGIE, P109
  • [9] Evaluation of Geomorphic Descriptors Thresholds for Flood Prone Areas Detection on Ephemeral Streams in the Metropolitan Area of Bari (Italy)
    Balacco, Gabriella
    Totaro, Vincenzo
    Gioia, Andrea
    Piccinni, Alberto Ferruccio
    [J]. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2019, PT IV, 2019, 11622 : 239 - 254
  • [10] Bosello F.I., 2018, adapting to climate change in Europe, P245