scholarly journals Coastal Flooding Risk Assessment Using a GIS-Based Spatial Multi-Criteria Decision Analysis Approach

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2379
Author(s):  
Vahid Hadipour ◽  
Freydoon Vafaie ◽  
Kaveh Deilami
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Decision Analysis ◽  
Sea Level ◽  
Flood Risk ◽  
Social Vulnerability ◽  
Quantitative Methods ◽  
Coastal Flooding ◽  
Rcp Scenarios ◽  
Multi Criteria Decision Analysis

Coastal areas are expected to be at a higher risk of flooding when climate change-induced sea-level rise (SLR) is combined with episodic rises in sea level. Flood susceptibility mapping (FSM), mostly based on statistical and machine learning methods, has been widely employed to mitigate flood risk; however, they neglect exposure and vulnerability assessment as the key components of flood risk. Flood risk assessment is often conducted by quantitative methods (e.g., probabilistic). Such assessment uses analytical and empirical techniques to construct the physical vulnerability curves of elements at risk, but the role of people’s capacity, depending on social vulnerability, remains limited. To address this gap, this study developed a semiquantitative method, based on the spatial multi-criteria decision analysis (SMCDA). The model combines two representative concentration pathway (RCP) scenarios: RCP 2.6 and RCP 8.5, and factors triggering coastal flooding in Bandar Abbas, Iran. It also employs an analytical hierarchy process (AHP) model to weight indicators of hazard, exposure, and social vulnerability components. Under the most extreme flooding scenario, 14.8% of flooded areas were identified as high and very high risk, mostly located in eastern, western, and partly in the middle of the City. The results of this study can be employed by decision-makers to apply appropriate risk reduction strategies in high-risk flooding zones.

Multi-Criteria Decision Analysis of Coastal Inundation at Regional scale

2021 ◽  
Author(s):  
Vinay Shivamurthy ◽  
Bharath Aithal
Keyword(s):  
High Risk ◽  
Decision Analysis ◽  
Sea Level Rise ◽  
Sea Level ◽  
Natural Hazards ◽  
Flood Risk ◽  
Sea Levels ◽  
Risk Levels ◽  
Multi Criteria Decision Analysis ◽  
Rising Sea Levels

<p>Coastal flooding are natural processes that are both i) essential (providing nutrients to the coastal vegetation, habitats) and ii) hazardous (negatively impact human activities, livelihood, assets, livestock and so on). Climate changes have induced higher frequency of floods, rising sea levels, high amplitude tides and other climatic extremes at regional to global scales. The increasing intensity, duration of floods is proportionately increasing the risks associated with coastal human habitations. The regional risks are defined based on the physical, demographic, socio-economic vulnerability of the habitants. Sea level rise would further enhance the coastal inundations permanently breaching these productive, densely populated regions. This necessitates the need for spatially assessing the relative hazard, vulnerability and risks at regional scales to reduce/mitigate risks.</p><p>Indian subcontinent supports the second largest global population, with numerous megacities, towns and villages along the coast and mainland. This study's main objective is to quantify the risk associated with inundations caused by rising sea levels, tidal surge at the regional level. As a case study, Sagar Island located in the verge of Sundarbans, south of West Bengal is considered. Flood risk assessment in the island has been carried out using Multi-Criteria Decision Analysis (MCDA) framework based on 23 spatial parameters.</p><p>Results indicate, within a century (1922 – 2020), the island has lost most of its natural vegetation (mangroves - Sundarbans) (47% to 3%), with increasing cultivated (agriculture, horticulture) spaces (77.4 %) and built-up environs (8.2%). Sea level rise varies from 4.4 mm/year (South) to 5.25 mm/year (North) and in the last century has breached over 2824 hectares of mainland. The study's findings reveal 19.8% of horticulture and 33.3% of agriculture assets are highly exposed to natural hazards. 1.34% population are at relatively very high-risk levels, 17.81% at high-risk levels. The study's findings reveal the variable importance of socio-economic, demographic, topographic and proximity to public service, in defining the flood vulnerability and risk towards the habitants. The approach and findings of paves the way for planning authorities to prioritise risk mitigation strategies that are region-specific to reduce the impact of inundation due to natural hazards</p><p><em>Keywords: Sea level rise, Flood risk, MCDA, Vulnerability, flood hazard</em></p>

Flood risk assessment using deep learning integrated with multi-criteria decision analysis

2021 ◽  
Vol 219 ◽  
pp. 106899 ◽  
Author(s):  
Binh Thai Pham ◽  
Chinh Luu ◽  
Dong Van Dao ◽  
Tran Van Phong ◽  
Huu Duy Nguyen ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Deep Learning ◽  
Decision Analysis ◽  
Flood Risk ◽  
Flood Risk Assessment ◽  
Multi Criteria Decision Analysis

scholarly journals An Integrated Approach for the Simulation Modeling and Risk Assessment of Coastal Flooding

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2076
Author(s):  
Yazhi Zheng ◽  
Hai Sun
Keyword(s):  
Risk Assessment ◽  
Storm Surge ◽  
Flood Risk ◽  
Simulation Modeling ◽  
River Estuary ◽  
Coastal Flooding ◽  
Coastal Areas ◽  
Economic Losses ◽  
Actual Case ◽  
Ca Model

The evaluation of storm surge flood risk is vital to disaster management and planning at national, regional and local levels, particularly in coastal areas that are affected more severely by storm surges. The purpose of this paper is to propose a new method that includes two modules for the simulation modeling and risk assessment of coastal flooding. One is a hydrodynamic module for simulating the process of the flood inundation coastal inundation arising from storm surge, which is based on a cellular automata (CA) model. The other is a risk assessment module for quantitatively estimating the economic loss by using the inundation data and land use data. The coastal areas of Pearl River estuary in China were taken as a case study. Simulation results are compared to experimental results from MIKE 21 and depth data from a social-media-based dataset, which demonstrates the effectiveness of the CA model. By analyzing flood risk, the flood area and the direct economic losses predicted are close to the actual case incurred, further demonstrating the computational reliability of the proposed method. Additionally, an automatic risk assessment platform is designed by integrating the two modules in a Geographic Information System (GIS) framework, facilitating a more efficient and faster simulation of coastal flooding. The platform can provide the governments as well as citizens of coastal areas with user-friendly, real-time graphics for coastal flood disaster preparation, warning, response and recovery.

Multi‐criteria decision analysis for flood risk on the Chenab River

Weather ◽  
2019 ◽  
Vol 74 (S1) ◽  
Author(s):  
Sher Shah Hassan ◽  
Muhammad Ajmal ◽  
Aftab Ahmad Khan ◽  
Muhammad Arif Goheer ◽  
Muhammad Shahzad Khattak ◽  
...  
Keyword(s):  
Decision Analysis ◽  
Flood Risk ◽  
Multi Criteria Decision Analysis ◽  
Chenab River

Interactions of extreme river flows and sea levels for coastal flooding

2020 ◽  
Author(s):  
Peter Robins ◽  
Lisa Harrison ◽  
Mariam Elnahrawi ◽  
Matt Lewis ◽  
Tom Coulthard ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
West Coast ◽  
Flood Risk ◽  
Coastal Flooding ◽  
Relative Timing ◽  
The West ◽  
Sea Levels ◽  
Flooding Hazards ◽  
Fluvial Response

<p>Coastal flooding worldwide causes the vast majority of natural disasters; for the UK costing £2.2 billion/year. Fluvial and surge-tide extremes can occur synchronously resulting in combination flooding hazards in estuaries, intensifying the flood risk beyond fluvial-only or surge-only events. Worse, this flood risk has the potential to increase further in the future as the frequency and/or intensity of these drivers change, combined with projected sea-level rise. Yet, the sensitivity of contrasting estuaries to combination and compound flooding hazards at sub-daily scales – now and in the future – is unclear. Here, we investigate the dependence between fluvial and surge interactions at sub-daily scales for contrasting catchment and estuary types (Humber vs. Dyfi, UK), using 50+ years of data: 15-min fluvial flows and hourly sea levels. Additionally, we simulate intra-estuary (<50 m resolution) sensitivities to combination flooding hazards based on: (1) realistic extreme events (worst-on-record); (2) realistic events with shifted timings of the drivers to maximise flooding; and (3) modified drivers representing projected climate change.</p><p>For well-documented flooding events, we show significant correlation between skew surge and peak fluvial flow, for the Dyfi (small catchment and estuary with a fast fluvial response on the west coast of Britain), with a higher dependence during autumn/winter months. In contrast, we show no dependence for the Humber (large catchment and estuary with a slow fluvial response on the east coast of Britain). Cross-correlation results, however, did show correlation with a time lag (~10 hours). For the Dyfi, flood extent was sensitive to the relative timing of the fluvial and surge-tide drivers. In contrast, the relative timing of these drivers did not affect flooding in the Humber. However, extreme fluvial flows in the Humber actually reduced water levels in the outer estuary, compared with a surge-only event. Projected future changes in these drivers by 2100 are likely to increase combination flooding hazards: sea-level rise scenarios predicted substantial and widespread flooding in both estuaries. However, similar increases in storm surge resulted in a greater seawater influx, altering the character of the flooding. Projected changes in fluvial volumes were the weakest driver of estuarine flooding. On the west coast of Britain containing many small/steep catchments, combination flooding hazards from fluvial and surges extremes occurring together is likely. Moreover, high-resolution data and hydrodynamic modelling are necessary to resolve the impact and inform flood mitigation methodology.</p>

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