A new bivariate risk classifier for flood management considering hazard and socio-economic dimensions

Mohanty, Mohit Prakash; Hari, Vittal; Yadav, Vinay; Ghosh, Subimal; Rao, G. Srinivasa; Karmakar, Subhankar

doi:10.1016/j.jenvman.2019.109733

Cited by 64 publications

(21 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Apart from representing the risk as product of hazard and vulnerability, it can also be represented using bivariate choropleth (Mohanty et al, 2020). Colorscale of these bivariate choropleth is characterized by all possible combinations of DHI and DVI classes.…”

Section: Methodsmentioning

confidence: 99%

Evolution of multivariate drought hazard, vulnerability and risk in India under climate change

Sahana

Mondal

2022

Preprint

View full text Add to dashboard Cite

Abstract. Changes in climate and socio-economic conditions pose major threat to water security, particularly in the densely-populated, agriculture-dependent and rapidly developing country of India. Therefore, for cogent mitigation and adaptation planning, it is important to assess the future evolution of drought hazard, vulnerability and risk. Earlier studies demonstrate projected drought risk over India on the basis of frequency analysis and/or hazard assessment alone. This study investigates and evaluates the change in projected drought risk combining vulnerability and hazard information at a country-wide scale for future climatic and socio-economic conditions. A multivariate standardized drought index (MSDI) accounting for concurrent deficits in precipitation and soil moisture is chosen to quantify droughts. Drought vulnerability assessment is carried out combining exposure, adaptive capacity and sensitivity indicators, using a robust multi-criteria decision-making method called the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In the worst-case scenario, though there is a projected decrease in the area under high or very high drought hazard classes in the country by approximately 7 %, presumably due to projected rise in precipitation, a 33 % rise in the areal extent of high or very high drought vulnerability classes is depicted. Regions of West Uttar Pradesh, Haryana, West Rajasthan and Odisha are found to be high risk under all scenarios. Bivariate choropleth analysis shows that the projected drought risk is majorly driven by change in drought vulnerability attributable to societal developments, rather than changes in drought hazard resulting from climatic conditions. The present study can aid policy makers, administrators and drought managers in developing decision support systems for efficient drought management.

show abstract

Section: Methodsmentioning

confidence: 99%

Evolution of multivariate drought hazard, vulnerability and risk in India under climate change

Sahana

Mondal

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To evaluate the severity of flood risk, the concept of risk matrix expressed by the composition of hazard and vulnerability levels is widely used (Mohanty et al, 2020;Hsiao et al, 2021). In this study, the four population-based indexes were separately divided into five levels in ascending order, whereas the income index was divided into five levels in descending order, according to the rules in Table 2.…”

Section: Flood Risk Analysismentioning

confidence: 99%

Improvement of Flood Risk Analysis Via Downscaling of Hazard and Vulnerability Maps

Jang

Vohnicky

Kuo

2021

Water Resour Manage

View full text Add to dashboard Cite

Flood risk maps are important references for disaster prevention and emergency response. The accuracy of risk maps is greatly affected by the resolutions of hazard and vulnerability maps. To determine the impact of map resolutions on flood risk analysis, a total of 12 risk maps were generated for Shanhua District, Taiwan, via the integration of hazard and vulnerability maps under two resolutions and three return periods. The hazard, vulnerability, and risk maps were classified into five levels according to flood depth, socio-economic indicators, and their products, respectively. The results show that the downscaling of hazard maps greatly increases the hit rate by 28% and decreases the false alarm rate by 53% in the flood risk analyses of households. In contrast, the downscaling of vulnerability maps only slightly increases the hit rate without an obvious decrease in the false alarm rate. To improve flood risk analysis under time and budget limitations, numerical downscaling of hazard maps should be given higher priority because it reduces the structural errors in hydraulic simulations that cannot be compensated for by the statistical downscaling of vulnerability maps.

show abstract

“…MIKE 11 is an engineering software model which has been developed by the Danish Hydraulic Institute (DHI) to simulate the 1D fully dynamic flows in rivers, irrigation systems, channels, estuaries, and other water bodies [12]. It has various editors like river cross-section editor, river network editor, boundary editor, and hydrodynamic editor.…”

Section: Mike 11mentioning

confidence: 99%

Flood Modelling for an Urban Indian Catchment: Challenges and Way Forward

Ghosh

Karmakar

Ghosh

2021

Lecture Notes in Civil Engineering

Self Cite

View full text Add to dashboard Cite

A new bivariate risk classifier for flood management considering hazard and socio-economic dimensions

Cited by 64 publications

References 76 publications

Evolution of multivariate drought hazard, vulnerability and risk in India under climate change

Evolution of multivariate drought hazard, vulnerability and risk in India under climate change

Improvement of Flood Risk Analysis Via Downscaling of Hazard and Vulnerability Maps

Flood Modelling for an Urban Indian Catchment: Challenges and Way Forward

Contact Info

Product

Resources

About