Identification of flash-floods-prone river reaches in Beas river basin using GIS-based multi-criteria technique: validation using field and satellite observations

Singh, Sachchidanand; Dhote, Pankaj R.; Thakur, Praveen K.; Chouksey, Arpit; Aggarwal, Swati

doi:10.1007/s11069-020-04406-w

Cited by 32 publications

(7 citation statements)

References 53 publications

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“…The AHP and soil conservation service curve number (CN) methods are commonly used methods for drawing flash flood hazard maps. The AHP is used to assign grades and weights and is usually used to assign weights to the causes of mountain torrents in the study of flash flood hazards [1,53,93]. The SCS model is commonly used in distributed hydrological models and research in arid and semiarid regions, which is a method developed by the U.S. Department of Agriculture (USDA) to estimate runoff and peak discharge [94].…”

Section: Study Analytical Methods Factorsmentioning

confidence: 99%

A Survey of Remote Sensing and Geographic Information System Applications for Flash Floods

Ding

et al. 2021

Remote Sensing

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Flash floods are among the most dangerous natural disasters. As climate change and urbanization advance, an increasing number of people are at risk of flash floods. The application of remote sensing and geographic information system (GIS) technologies in the study of flash floods has increased significantly over the last 20 years. In this paper, more than 200 articles published in the last 20 years are summarized and analyzed. First, a visualization analysis of the literature is performed, including a keyword co-occurrence analysis, time zone chart analysis, keyword burst analysis, and literature co-citation analysis. Then, the application of remote sensing and GIS technologies to flash flood disasters is analyzed in terms of aspects such as flash flood forecasting, flash flood disaster impact assessments, flash flood susceptibility analyses, flash flood risk assessments, and the identification of flash flood disaster risk areas. Finally, the current research status is summarized, and the orientation of future research is also discussed.

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Section: Study Analytical Methods Factorsmentioning

confidence: 99%

A Survey of Remote Sensing and Geographic Information System Applications for Flash Floods

Ding

et al. 2021

Remote Sensing

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“…After the AHP allocates weights and relative significance based on the PCM to all causative factors for flooding, the consistency and reliability of the AHP-PCM model needs to be checked (Das, 2020;Singh et al, 2021). Equation 5 used a consistency ratio to assess the consistency of the pairwise decision matrix based on the AHP model in section 2.6 (Saaty, 1980):…”

Section: Consistency Checkmentioning

confidence: 99%

“…It is used to determine the weights of factors based on the pairwise comparison matrix (PCM) in order to conclude the extent to which one alternative factor outweighs another (Mahmoud and Gan, 2018;Al-Abadi and Al-Qurnawy, 2021). The key advantage of the AHP approach is that it is understandable to decision-makers, simple to implement, and adaptable in terms of factor selection (Singh et al, 2021). Currently, the RS and GIS-based AHP modelling is widely and efficiently applied for detecting and mapping flood-susceptible zones.…”

Section: Introductionmentioning

confidence: 99%

Flood Susceptibility Mapping Using an Analytic Hierarchy Process Model Based on Remote Sensing and GIS Approaches in Akre District, Kurdistan Region, Iraq

Fatah

Mustafa

Hassan

2022

IGJ

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In recent decades, floods have been the most common, complex, and destructive natural calamities worldwide. Hence, for inclusive flood risk assessment, creating flood susceptibility mapping to demarcate flood-vulnerable zones is fundamental for decision makers. To assess flood-prone locations in the Akre, Iraqi Kurdistan Region, fundamental for susceptibility mapping was undertaken using geographic information systems, remote sensing, and an analytic hierarchy process model. To assess flood susceptibility, the geographic information systems framework used 15 ideal causative factors for flooding: altitude, slope, distance to streams, flow accumulation, drainage density, rainfall, soil type, lithology, curvature, topographic wetness index (TWI), topographic roughness index stream power index, stream transport index, land use/land cover, and normalized difference vegetation index. The factors contributing to flooding were optimally weighted with respect to the proposed model. The final flood susceptibility map was reclassified into five different classes of susceptibility to flooding: very low (16.64% of the study area); low (19.53%); moderate (38.92%); high (17.83%); and very high (7.08%). The area under curve values for the predicted rate and success rate of the AHP model were 0.956 and 0.971, respectively. Therefore, the results were accurate and reliable. The AHP model is a powerful method for fundamental for susceptibility mapping to mitigate the serious impacts of flooding and assist scholars, local governments and policymakers in future master planning.

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“…The current section emphasized the methods adopted for deciding the threshold VI score to classify the sub-basins between low and highly vulnerable. Present research has used three different scientific techniques for this purpose such as: 1) Mean method, 2) Natural breaks (Jenks) method, and 3) Standard deviation method, respectively to find the Optimal Threshold Value (OTV) (Chung and Lee 2009;Bhattacharya et al 2020;Singh et al 2020), a minimum value above which the maximum of given regions become vulnerable. In this case, VI score (0.457) is considered as OTV (derived from the standard deviation method), see Fig.…”

Section: Threshold Criteriamentioning

confidence: 99%

Investigation of Basin Characteristics: Implications for Sub-basins Level Flood Peak and Vulnerability Assessment

Ranjan

Dhote

Thakur

2021

Preprint

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Flood vulnerability is a significant component in assessing the probable degree of damage to various exposures in hazard conditions. In this study, a semi-distributed event-based hydrological model and indicator-based method were applied to evaluate the sub-basin level flood vulnerability using the Geographical Information System (GIS). The flood peak discharge of each sub-basin corresponding to the 2014 extreme flood of the Jhelum river was related with different sub-basins characteristics (terrain, hydrological, land use and soil) using a theoretical framework under an indicator-based method. The calibrated (2014) and validated (1992, 1997) hydrological model showed Nash-Sutcliffe Efficiency (NSE) of 0.98 and (0.99, 0.99) at relatively upstream gauging station Sangam against optimized Curve Number (CN) scaling factor of 0.98. The Anantnag and Kulgam districts, exhibiting multiple sub-basins contributing to the Sangam gauging station, are falling into a highly vulnerable category located in the Jhelum basin's southern part, Greater Himalayan Range. It was also revealed that sub-basins at the upstream of the Jhelum basin are more vulnerable compared to downstream area, where sub-basin W810 (Greater Himalayan), Anantnag district draining at Sangam gauging site is found as most vulnerable among the all other sub-basins. However, hydrological characteristics control the most vulnerable sub-basin peak discharge rather than other characteristics such as terrain, soil, or Land Use. Outcomes of the study will be helpful in prioritizing the flood mitigation planning not only with respect to the hydrological boundary (sub-basin level) but also with administrative district boundaries. The proposed method is generic and can be applied to any flood-prone river basin.

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Identification of flash-floods-prone river reaches in Beas river basin using GIS-based multi-criteria technique: validation using field and satellite observations

Cited by 32 publications

References 53 publications

A Survey of Remote Sensing and Geographic Information System Applications for Flash Floods

A Survey of Remote Sensing and Geographic Information System Applications for Flash Floods

Flood Susceptibility Mapping Using an Analytic Hierarchy Process Model Based on Remote Sensing and GIS Approaches in Akre District, Kurdistan Region, Iraq

Investigation of Basin Characteristics: Implications for Sub-basins Level Flood Peak and Vulnerability Assessment

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