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Literatura académica sobre el tema "Bivariate Failure Return Period"

Autor: Grafiati

Publicado: 10 de marzo de 2023

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Artículos de revistas sobre el tema "Bivariate Failure Return Period"

1

Kang, Ling, Shangwen Jiang, Xiaoyong Hu, and Changwen Li. "Evaluation of Return Period and Risk in Bivariate Non-Stationary Flood Frequency Analysis." Water 11, no. 1 (2019): 79. http://dx.doi.org/10.3390/w11010079.

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The concept of a traditional return period has long been questioned in non-stationary studies, and the risk of failure was recommended to evaluate the design events in flood modeling. However, few studies have been done in terms of multivariate cases. To investigate the impact of non-stationarity on the streamflow series, the Yichang station in the Yangtze River was taken as a case study. A time varying copula model was constructed for bivariate modeling of flood peak and 7-day flood volume, and the non-stationary return period and risk of failure were applied to compare the results between st

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2

Latif, Shahid, and Firuza Mustafa. "Bivariate Hydrologic Risk Assessment of Flood Episodes using the Notation of Failure Probability." Civil Engineering Journal 6, no. 10 (2020): 2002–23. http://dx.doi.org/10.28991/cej-2020-03091599.

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Floods are becoming the most severe and challenging hydrologic issue at the Kelantan River basin in Malaysia. Flood episodes are usually thoroughly characterized by flood peak discharge flow, volume and duration series. This study incorporated the copula-based methodology in deriving the joint distribution analysis of the annual flood characteristics and the failure probability for assessing the bivariate hydrologic risk. Both the Archimedean and Gaussian copula family were introduced and tested as possible candidate functions. The copula dependence parameters are estimated using the method-of

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3

Tosunoğlu, Fatih, Gianfausto Salvadori, and Muhammet Yilmaz. "Multivariate Assessment of Low-Flow Hazards via Copulas: The Case Study of the Çoruh Basin (Turkey)." Water 12, no. 10 (2020): 2848. http://dx.doi.org/10.3390/w12102848.

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Bivariate modeling and hazard assessment of low flows are performed exploiting copulas. 7-day low flows observed, respectively, in the upper, middle and lower parts of the Çoruh basin (Turkey) are examined, considering three pairs of certified stations located in different sub-basins. A thorough statistical analysis indicates that the GEV distribution can be used to model the marginal behavior of the low-flow. The joint distributions at each part are modeled via a dozen of copula families. As a result, the Husler–Reiss copula adequately fits the joint low flows in the upper part, while the t-S

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4

Gu, Lei, Jie Chen, Jiabo Yin, et al. "Projected increases in magnitude and socioeconomic exposure of global droughts in 1.5 and 2 °C warmer climates." Hydrology and Earth System Sciences 24, no. 1 (2020): 451–72. http://dx.doi.org/10.5194/hess-24-451-2020.

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Abstract. The Paris Agreement sets a long-term temperature goal to hold global warming to well below 2.0 ∘C and strives to limit it to 1.5 ∘C above preindustrial levels. Droughts with either intense severity or a long persistence could both lead to substantial impacts such as infrastructure failure and ecosystem vulnerability, and they are projected to occur more frequently and trigger intensified socioeconomic consequences with global warming. However, existing assessments targeting global droughts under 1.5 and 2.0 ∘C warming levels usually neglect the multifaceted nature of droughts and mig

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5

Latif, Shahid, and Slobodan P. Simonovic. "Parametric Vine Copula Framework in the Trivariate Probability Analysis of Compound Flooding Events." Water 14, no. 14 (2022): 2214. http://dx.doi.org/10.3390/w14142214.

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The interaction between oceanographic, meteorological, and hydrological factors can result in an extreme flooding scenario in the low-lying coastal area, called compound flooding (CF) events. For instance, rainfall and storm surge (or high river discharge) can be driven by the same meteorological forcing mechanisms, tropical or extra-tropical cyclones, resulting in a CF phenomenon. The trivariate distributional framework can significantly explain compound events’ statistical behaviour reducing the associated high-impact flood risk. Resolving heterogenous dependency of the multidimensional CF e

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6

Shiau, J. T. "Return period of bivariate distributed extreme hydrological events." Stochastic Environmental Research and Risk Assessment (SERRA) 17, no. 1-2 (2003): 42–57. http://dx.doi.org/10.1007/s00477-003-0125-9.

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7

Li, Qian, Liutong Chen, Zhengtao Yan, and Yingjun Xu. "Exploration of Copula Models Use in Risk Assessment for Freezing and Snow Events: A Case Study in Southern China." Sustainability 14, no. 5 (2022): 2568. http://dx.doi.org/10.3390/su14052568.

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Due to cold waves, low and extremely low temperatures occur every winter. Sudden cooling can cause freezing and snow disasters, which seriously affect transportation, power, safety, and other activities, resulting in serious economic losses. Based on precipitation and average temperature data from 258 national meteorological stations over the past 70 years, this study established a historical freezing and snow event data set, extracting the accumulated precipitation intensity (API) and accumulated temperature intensity (ATI). We selected the optimal distribution function and joint distribution

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8

Stamatatou, Nikoletta, Lampros Vasiliades, and Athanasios Loukas. "Bivariate Flood Frequency Analysis Using Copulas." Proceedings 2, no. 11 (2018): 635. http://dx.doi.org/10.3390/proceedings2110635.

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Flood frequency estimation for the design of hydraulic structures is usually performed as a univariate analysis of flood event magnitudes. However, recent studies show that for accurate return period estimation of the flood events, the dependence and the correlation pattern among flood attribute characteristics, such as peak discharge, volume and duration should be taken into account in a multivariate framework. The primary goal of this study is to compare univariate and joint bivariate return periods of floods that all rely on different probability concepts in Yermasoyia watershed, Cyprus. Pa

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9

Vandenberghe, S., M. J. van den Berg, B. Gräler, et al. "Joint return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation." Hydrology and Earth System Sciences Discussions 9, no. 5 (2012): 6781–828. http://dx.doi.org/10.5194/hessd-9-6781-2012.

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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, gi

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10

Requena, A. I., L. Mediero, and L. Garrote. "A bivariate return period based on copulas for hydrologic dam design: accounting for reservoir routing in risk estimation." Hydrology and Earth System Sciences 17, no. 8 (2013): 3023–38. http://dx.doi.org/10.5194/hess-17-3023-2013.

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Abstract. A multivariate analysis on flood variables is needed to design some hydraulic structures like dams, as the complexity of the routing process in a reservoir requires a representation of the full hydrograph. In this work, a bivariate copula model was used to obtain the bivariate joint distribution of flood peak and volume, in order to know the probability of occurrence of a given inflow hydrograph. However, the risk of dam overtopping is given by the maximum water elevation reached during the routing process, which depends on the hydrograph variables, the reservoir volume and the spill

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