Multivariate Flood Risk Analysis at a Watershed Scale Considering Climatic Factors

Gao, Yuqin; Guo, Zichen; Wang, Dongdong; Zhang, Zhenxing; Liu, Yunping

doi:10.3390/w10121821

Cited by 6 publications

(8 citation statements)

References 20 publications

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“…It is applied in water resource engineering in analyzing flood frequency, precipitation, storm, and drought characteristics [29]. The joint probabilities of multiple attributes provide a wider span in analysis, thus improving the accuracy of the estimated risk [12].…”

Section: Flood Risk Assessment Through the Copula Functionmentioning

confidence: 99%

“…This is because of their capability of being easily generated and encapsulating a wide range of dependence structures with several desirable properties, such as symmetry and associativity [31]. Furthermore, it has no limitation on the marginal distribution for constructing the joint distribution, making it a perfect fit in the analysis of the joint probability distribution in the flood analysis [12].…”

Section: Types Of Copulasmentioning

confidence: 99%

“…More clarity is seen by considering more than one variable in analyses through a multivariate approach [10]. This kind of approach has been adopted in many research works [11][12][13] due to its proficiency in delivering a full screen for a flood event. For example, Karatzetzou [14] developed a unified seismic-flood-hazard model for the risk assessment of roadway networks in Greece, which supports the risk assessment of critical elements of transportation infrastructure in a multi-hazard environment.…”

Section: Introductionmentioning

confidence: 99%

“…However, flood factors are dependent on each other, which limits directly combining their marginal distributions. Recent studies employed copulas in modeling such multivariate hydrologic events [1,2,[10][11][12][15][16][17][18][19][20]. The copula function models the marginal distribution and multivariate dependency separately, offering flexibility in making the needful adjustment of the marginal and joint probability functions.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Hydrologic Risk Analysis for River Thames

Gabriel

Fan

2022

Water

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This study analyzed the multivariate flood risk for the river Thames at Kingston based on historical flood data from the National River Flow Archive (NRFA) website. The bivariate risk analysis framework was prepared from the joint return periods of the peak flow (m3/s) and 3-day annual maximum flow (m3/s) flood pair. A total of 137 samples of flood pairs from 1883 to 2019 were adopted for risk analysis. The multivariate return periods were characterized depending on the quantification of the bivariate flood frequency analysis of the pair through copulas methods. The unknown parameter of each copula was estimated using the method-of-moment (MOM) estimator based on Kendall’s tau inversion, in which the Clayton copula performed best to model the dependence of the two flood variables. Then, the bivariate hydrologic risk was characterized based on the joint return period in AND, established from the Clayton copula method. The results reveal that the flood pair would keep a constant hydrologic risk value for some time then moderately decrease as the 3-day AMAX flow increases from 700 m3/s. This hydrologic risk indicator was analyzed under four service time scenarios and three peak flows whose return periods were positioned at 50, 100, and 150 years. The outcomes from the bivariate risk analysis of the flood pairs can be used as decision support during the design of flood defenses and hydraulic facilities.

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Section: Flood Risk Assessment Through the Copula Functionmentioning

confidence: 99%

Section: Types Of Copulasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multivariate Hydrologic Risk Analysis for River Thames

Gabriel

Fan

2022

Water

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“…The main landscapes include dryland, paddy field, and urban land. Owing to the climate and terrain characteristics, the flood disaster occurs frequently in Qinhuai River basin [19], mainly in the rainy season (April to September). Table 1 shows the data used in this study.…”

Section: Introductionmentioning

confidence: 99%

Flood-Landscape Ecological Risk Assessment under the Background of Urbanization

Fang

Yan

et al. 2019

Water

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The Hydrologic Modeling System (HEC-HMS) and statistical analysis method were used to analyze the relationship between flood eigenvalues (i.e., flood volume and peak flow) and landscape pattern metrics. Then, the flood-landscape ecological risk index (ERI_FL) was proposed and constructed to quantitatively assess the flood-landscape ecological risk (FLER). The semivariogram method was used to spatialize the ERI_FL values. Lastly, this study analyzed the spatial–temporal change of FLER at watershed scale and at sub-basin scale, respectively. Two historical landscape distributions (i.e., 2003 and 2017) of Qinhuai River basin were used to perform this study. The results showed that there were certain relationships between landscape pattern and flood eigenvalues, and for different landscapes, the response metrics and degrees were different. FLER increased as urbanization increased. FLER change magnitude had a positive relationship with urban land percentage change magnitude. The distribution of FLER and the distribution of FLER change both showed spatial differences at watershed scale. The structural features of landscape pattern had significant effects on regional floods. In the urbanization process, avoiding forming large-scale landscape patches, improving landscape abundance, and increasing contact area between different types of landscape patches were helpful to reduce the negative effects caused by the increase of urban landscape area on flood.

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