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.

scholarly journals Estimations of statistical dependence as joint return period modulator of compound events. Part I: storm surge and wave height

2017 ◽  
Author(s):  
Thomas I. Petroliagkis
Keyword(s):  
Storm Surge ◽  
Wave Height ◽  
Return Period ◽  
Flood Hazard ◽  
Joint Probability ◽  
Irish Sea ◽  
Statistical Dependence ◽  
Joint Return ◽  
Compound Events ◽  
Joint Return Period

Abstract. The possibility of utilising statistical dependence methods in coastal flood hazard calculations is investigated, since flood risk is rarely a function of just one source variable but usually two or more. Source variables in most cases are not independent as they may be driven by the same weather event, so their dependence, which is capable of modulating their joint return period, has to be estimated before the calculation of their joint probability. Dependence and correlation may differ substantially from one another since dependence is focused heavily on tail (extreme) percentiles. The statistical analysis between surge and wave is performed over 32 river ending points along European coasts. Two sets of almost 35-year hindcasts of storm surge and wave height were adapted and results are presented by means of analytical tables and maps referring to both correlation and statistical dependence values. Further, the top 80 compound events were defined for each river ending point. Their frequency of occurrence was found to be distinctly higher during the cold months while their main low-level flow characteristics appear to be mainly in harmony with the transient nature of storms and their tracks. Overall, significantly strong values of positive correlations and dependencies were found over the Irish Sea, English Channel, south coasts of the North Sea, Norwegian Sea and Baltic Sea, with compound events taking place in a zero-lag mode. For the rest, mostly positive moderate dependence values were estimated even if a considerable number of them had correlations of almost zero or even negative value.

scholarly journals Multi-factor joint return period of rainstorms and its agricultural risk analysis in Liaoning Province, China

2019 ◽  
Vol 10 (1) ◽  
pp. 1988-2008 ◽  
Author(s):  
Yu Feng ◽  
Ying Li ◽  
Zhiru Zhang ◽  
Shiyu Gong ◽  
Meijiao Liu ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Return Period ◽  
Liaoning Province ◽  
Agricultural Risk ◽  
Joint Return ◽  
Joint Return Period

Assessment of overtopping reliability and benefits of a flood detention dam

2008 ◽  
Vol 35 (10) ◽  
pp. 1177-1182 ◽  
Author(s):  
A. Melih Yanmaz ◽  
M. Engin Gunindi
Keyword(s):  
Return Period ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Site Specific ◽  
Joint Return ◽  
Dam Site ◽  
Bivariate Gamma Distribution ◽  
Project Characteristics ◽  
Joint Return Period

There is a growing tendency to assess safety levels of existing dams and to design new dams using probabilistic approaches according to project characteristics and site-specific conditions. This study is a probabilistic assessment of the overtopping reliability of a dam, which will be designed for flood detention purpose, and will compute the benefits that can be gained as a result of the implementation of this dam. In a case study, a bivariate flood frequency analysis was carried out using a five-parameter bivariate gamma distribution. A family of joint return period curves relating the runoff peak discharges to the runoff volumes at the dam site was derived. A number of hydrographs were also obtained under a joint return period of 100 years to observe the variation of overtopping tendency. The maximum reservoir elevation and overtopping reliability were determined by performing a probabilistic reservoir routing based on Monte Carlo simulations.

scholarly journals A maximum entropy copula-based frequency analysis method for assessing bivariate drought risk: a case study of the Kaidu River Basin

2021 ◽  
Author(s):  
X. Yang ◽  
Y. P. Li ◽  
G. H. Huang
Keyword(s):  
Maximum Entropy ◽  
River Basin ◽  
Frequency Analysis ◽  
Return Period ◽  
Drought Risk ◽  
Return Periods ◽  
Drought Frequency ◽  
Joint Return ◽  
Joint Return Period ◽  
Kaidu River

Abstract In this study, a maximum entropy copula-based frequency analysis (MECFA) method is developed through integrating maximum entropy, copulas and frequency analysis into a general framework. The advantages of MECFA are that the marginal modeling requires no assumption and joint distribution preserves the dependence structure of drought variables. MECFA is applied to assessing bivariate drought frequency in the Kaidu River Basin, China. Results indicate that the Kaidu River Basin experienced 28 drought events during 1958–2011, and drought inter-arrival time is 10.8 months. The average duration is 6.2 months (severity 4.6), and the most severe drought event lasts for 35 months (severity 41.2) that occurred from June 1977 to March 1980. Results also disclose that hydrological drought index (HDI) 1 is suitable for drought frequency analysis in target year of return periods of 5 and 10, HDI 3, HDI 6 and HDI 12 are fit for the target year of return periods of 20, 50 and 100. The joint return period can be used as the upper bound of the target return period, and the joint return period that either duration or severity reaches the drought threshold can be used as the lower bound of the target return period.

scholarly journals Swell wave progression in the English Channel: implications for coastal monitoring

2021 ◽  
Vol 4 (1) ◽  
pp. 281-305
Author(s):  
Thomas Dhoop ◽  
Charlie Thompson
Keyword(s):  
Water Level ◽  
Return Period ◽  
High Water ◽  
English Channel ◽  
Coastal Monitoring ◽  
Period Analysis ◽  
Significant Wave ◽  
Joint Return ◽  
Joint Return Period ◽  
Joint Consideration

Energetic swell waves, particularly when they coincide with high water levels, can present significant coastal hazards. To better understand and predict these risks, analysis of the sea levels and waves that generate these events and the resulting coastal impacts is essential. Two energetic swell events, neither of which were predicted by modelled flood forecasts, occurred in quick succession in the English Channel. The first event, on 30 January 2021, produced moderate significant wave heights at or just below the 0.25 year return period along the southwest English coast, but combined with significant swell caused overtopping at East Beach in West Bay and at Chesil Beach. The second event, on 1 February 2021, generated the highest wave energy periods measured at many locations along the southern English coastline and, at high water, caused waves to run up over the promenades at Poole Bay and Christchurch Bay and caused overtopping at Hayling Island. Both events are described in detail, and their spatial footprints are mapped through a joint return period analysis using a copula function. It is found that typical joint return period analysis of water level and significant wave height underestimates potential impacts, while a joint consideration of water level and wave power (P) describes the 31 January event better and a joint consideration of water level and energy period (Te) best describes the 1 February event. Therefore, it is recommended that Te and P are adopted for coastal monitoring purposes, and that future studies further explore the use of both parameters for swell monitoring.

scholarly journals Time-varying copula and design life level-based nonstationary risk analysis of extreme rainfall events

2019 ◽  
Author(s):  
Pengcheng Xu ◽  
Dong Wang ◽  
Vijay P. Singh ◽  
Yuankun Wang ◽  
Jichun Wu ◽  
...  
Keyword(s):  
Climate Change ◽  
Risk Analysis ◽  
Return Period ◽  
Extreme Rainfall ◽  
Time Dependent ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Design Life ◽  
Joint Return ◽  
Joint Return Period

Abstract. Due to global climate change and urbanization, more attention has been paid to decipher the nonstationary multivariate risk analysis from the perspective of probability distribution establishment. Because of the climate change, the exceedance probability belonging to a certain extreme rainfall event would not be time invariant any more, which impedes the widely-used return period method for the usual hydrological and hydraulic engineering practice, hence calling for a time dependent method. In this study, a multivariate nonstationary risk analysis of annual extreme rainfall events, extracted from daily precipitation data observed at six meteorological stations in Haihe River basin, China, was done in three phases: (1) Several statistical tests, such as Ljung-Box test, and univariate and multivariate Mann-Kendall and Pettist tests were applied to both the marginal distributions and the dependence structures to decipher different forms of nonstationarity; (2) Time-dependent Archimedean and elliptical copulas combined with the Generalized Extreme Value (GEV) distribution were adopted to model the distribution structure from marginal and dependence angles; (3) A design life level-based (DLL-based) risk analysis associated with Kendall's joint return period (JRPken)and AND's joint return period (JRPand) methods was done to compare stationary and nonstationary models. Results showed DLL-based risk analysis through the JRPken method exhibited more sensitivity to the nonstationarity of marginal and bivariate distribution models than that through the JRPand method.

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