Quantitative multi-hazard risk assessment with vulnerability surface and hazard joint return period

2014 ◽  
Vol 29 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Xiaodong Ming ◽  
Wei Xu ◽  
Ying Li ◽  
Juan Du ◽  
Baoyin Liu ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Return Period ◽  
Hazard Risk ◽  
Joint Return ◽  
Joint Return Period ◽  
Hazard Risk Assessment

scholarly journals Quantitative Multi-Hazard Risk Assessment of Crop Loss in the Yangtze River Delta Region of China

2019 ◽  
Vol 11 (3) ◽  
pp. 922
Author(s):  
Wei Xu ◽  
Xiaodong Ming ◽  
Yunjia Ma ◽  
Xinhang Zhang ◽  
Peijun Shi ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
Crop Loss ◽  
The Yangtze River ◽  
Hazard Risk ◽  
Joint Return ◽  
The Yangtze River Delta ◽  
Hazard Risk Assessment

Due to their complexity, hazard interactions are often neglected in current studies of multi-hazard risk assessment. As a result, the assessment results are qualitative or semi-quantitative and are difficult to use in regional risk management. In this paper, the crop loss risk due to heavy rain and strong wind in the Yangtze River Delta (YRD) region of China was quantitatively assessed, based on the joint return periods of these hazards and a vulnerability surface. The joint return period is obtained with a copula function based on the marginal distribution of each hazard. The vulnerability is fitted by considering the joint hazard intensity, the sown area of crops, elevation, and GDP per capita. The results show that counties with a high value of joint hazard probability are clustered in the southeast coastal area and that the value gradually decreases from south to north and from east to west. The multi-hazard risk has a similar pattern, with a large value in the southeast coastal area and a low value in the northwest. The proposed method can be used for quantitative assessment of multi-hazard risk, and the results can be used for regional disaster risk management and planning.

scholarly journals Multi-Hazard Risk Assessment of Kathmandu Valley, Nepal

2021 ◽  
Vol 13 (10) ◽  
pp. 5369
Author(s):  
Rajesh Khatakho ◽  
Dipendra Gautam ◽  
Komal Raj Aryal ◽  
Vishnu Prasad Pandey ◽  
Rajesh Rupakhety ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Natural Hazards ◽  
Fire Hazard ◽  
Central Valley ◽  
Geographical Information ◽  
Kathmandu Valley ◽  
Fire Hazards ◽  
Hazard Risk ◽  
Urban Fire ◽  
Hazard Risk Assessment

Natural hazards are complex phenomena that can occur independently, simultaneously, or in a series as cascading events. For any particular region, numerous single hazard maps may not necessarily provide all information regarding impending hazards to the stakeholders for preparedness and planning. A multi-hazard map furnishes composite illustration of the natural hazards of varying magnitude, frequency, and spatial distribution. Thus, multi-hazard risk assessment is performed to depict the holistic natural hazards scenario of any particular region. To the best of the authors’ knowledge, multi-hazard risk assessments are rarely conducted in Nepal although multiple natural hazards strike the country almost every year. In this study, floods, landslides, earthquakes, and urban fire hazards are used to assess multi-hazard risk in Kathmandu Valley, Nepal, using the Analytical Hierarchy Process (AHP), which is then integrated with the Geographical Information System (GIS). First, flood, landslide, earthquake, and urban fire hazard assessments are performed individually and then superimposed to obtain multi-hazard risk. Multi-hazard risk assessment of Kathmandu Valley is performed by pair-wise comparison of the four natural hazards. The sum of observations concludes that densely populated areas, old settlements, and the central valley have high to very high level of multi-hazard risk.

Health And Hazard Risk Assessment

2017 ◽  
pp. 23-42
Author(s):  
Scott Lowe ◽  
David James
Keyword(s):  
Risk Assessment ◽  
Hazard Risk ◽  
Hazard Risk Assessment

scholarly journals JOINT RETURN PERIOD ESTIMATION OF DAILY MAXIMUM AND MINIMUM TEMPERATURES USING COPULA METHOD

2021 ◽  
Vol 66 (2) ◽  
pp. 175-190
Author(s):  
M. A. M. Abraj ◽  
A. P. Hewaarachchi
Keyword(s):  
Return Period ◽  
Daily Maximum ◽  
Copula Method ◽  
Period Estimation ◽  
Joint Return ◽  
Joint Return Period

scholarly journals Joint return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation

2012 ◽  
Vol 9 (5) ◽  
pp. 6781-6828 ◽  
Author(s):  
S. Vandenberghe ◽  
M. J. van den Berg ◽  
B. Gräler ◽  
A. Petroselli ◽  
S. Grimaldi ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Three Dimensional ◽  
Distribution Functions ◽  
Return Periods ◽  
Design Variables ◽  
Joint Return ◽  
Design Values ◽  
Multivariate Frequency Analysis ◽  
Joint Return Period

Abstract. Most of the hydrological and hydraulic studies refer to the notion of a return period to quantify design variables. When dealing with multiple design variables, the well-known univariate statistical analysis is no longer satisfactory and several issues challenge the practitioner. How should one incorporate the dependence between variables? How should the joint return period be defined and applied? In this study, an overview of the state-of-the-art for defining joint return periods is given. The construction of multivariate distribution functions is done through the use of copulas, given their practicality in multivariate frequency analysis and their ability to model numerous types of dependence structures in a flexible way. A case study focusing on the selection of design hydrograph characteristics is presented and the design values of a three-dimensional phenomenon composed of peak discharge, volume and duration are derived. Joint return period methods based on regression analysis, bivariate conditional distributions, bivariate joint distributions, and Kendal distribution functions are investigated and compared highlighting theoretical and practical issues of multivariate frequency analysis. Also an ensemble-based method is introduced. For a given design return period, the method chosen clearly affects the calculated design event. Eventually, light is shed on the practical implications of a chosen method.

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