Modelling bivariate extreme precipitation distribution for data‐scarce regions using Gumbel–Hougaard copula with maximum entropy estimation

Qian, Longxia; Wang, Hongrui; Dang, Suzhen; Wang, Cheng; Jiao, Zhiqian; Zhao, Yong

doi:10.1002/hyp.11406

Cited by 36 publications

(16 citation statements)

References 31 publications

(49 reference statements)

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“…The joint behavior of different risk indicators has been paid widespread attention and studied in various fields, especially in hydrology [13][14][15][16]. As a powerful multidimensional statistical analysis method, copulas have a wide range of applications in various fields and are commonly used to assess the joint probabilistic behaviors of hydrological or meteorological features in hydrometeorological studies [17][18][19]. The copula function permits modeling the individual behaviors and dependence structures separately and the marginal distributions of individual indicators do not need to be unified [12].…”

Section: Introductionmentioning

confidence: 99%

Multi-Indicator Evaluation for Extreme Precipitation Events in the Past 60 Years over the Loess Plateau

Sun

Huang

Fan

2020

Water

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The unique characteristics of topography, landforms, and climate in the Loess Plateau make it especially important to investigate its extreme precipitation characteristics. Daily precipitation data of Loess Plateau covering a period of 1959–2017 are applied to evaluate the probability features of five precipitation indicators: the amount of extreme heavy precipitation (P95), the days with extreme heavy precipitation, the intensity of extreme heavy precipitation (I95), the continuous dry days, and the annual total precipitation. In addition, the joint risk of different combinations of precipitation indices is quantitatively evaluated based on the copula method. Moreover, the risk and severity of each extreme heavy precipitation factor corresponding to 50-year joint return period are achieved through inverse derivation process. Results show that the precipitation amount and intensity of the Loess Plateau vary greatly in spatial distribution. The annual precipitation in the northwest region may be too concentrated in several rainstorms, which makes the region in a serious drought state for most of the year. At the level of 10-year return period, more than five months with no precipitation events would occur in the Northwest Loess Plateau. While, P95 or I95 events of 100-year level may be encountered in a 50-year return period and in the southeastern region, which means there are foreseeable long-term extreme heavy precipitation events.

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Section: Introductionmentioning

confidence: 99%

Multi-Indicator Evaluation for Extreme Precipitation Events in the Past 60 Years over the Loess Plateau

Sun

Huang

Fan

2020

Water

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“…In this paper, Gumbel, Frank and Clayton copulas, all belong to Archimedean copulas, were chosen to match the two variables (SPEI and WEI+). The cumulative distribution functions (CDF) of the Gumbel, Frank and Clayton copulas are described in Equations (12) to (14) [27]:…”

Section: Copula Modelmentioning

confidence: 99%

Quantifying the Relationship between Drought and Water Scarcity Using Copulas: Case Study of Beijing–Tianjin–Hebei Metropolitan Areas in China

Fan

Wang

Liu

et al. 2018

Water

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Making the distinction between drought and water scarcity is not trivial, because they often occur simultaneously. In this study, we used Copulas to quantify the relationship between drought and water scarcity. Beijing–Tianjin–Hebei Metropolitan Areas (BTHMA) was chosen as the study area. Standard Precipitation and Evapotranspiration Index (SPEI) and water exploitation index plus (WEI+) was chosen to represent metrological drought and water scarcity. Inverse Distance Weighted method was used for spatial analysis of SPEI and WEI+, and Archimedean Copula was used to establish two-dimensional joint probability distribution of SPEI and WEI+. The results are as follows: (1) The southern part of the study area was wetter. The middle part was drier, with moderate drought happened for most times. (2) WEI+ of Beijing and Tianjin showed significant decreasing trends from 2000 to 2015, while WEI+ of Hebei Province did not, which indicated that Hebei Province is facing much severer water scarcity situation than Beijing and Tianjin. (3) Gumbel copula was the best-fitting model to establish the joint probability distribution of SPEI and WEI+. The condition probability provided a probability distribution of water scarcity under different drought conditions, which can provide technical support for government managers during policy making.

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“…For drought events that include multiple related variables, univariate analysis is limited in its ability to reflect the joint distribution of multiple variables. In general, dynamical forecasts based on the copula model are a promising tool and have been increasingly used for drought forecasting globally because they can flexibly perform joint probability analysis of multiple variables (Qian et al 2018a).…”

Section: Introductionmentioning

confidence: 99%

Study on drought events in China based on time-varying nested Archimedean-copula function

et al. 2021

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Drought forecasting, which can enable contingency actions to be implemented in advance of a drought, plays a significant role in reducing the risks and impacts of drought. In this study, a simulation framework of the occurrence probability of drought events based on nested Copula function and Gibbs sampling is proposed to effectively compensate for the high-dimensional problems and lack of initial data in traditional methods. And the precipitation data of 718 meteorological stations from 1961 to 2018 in China was analyzed. The results showed that the occurrence location of drought events was mainly concentrated from 35° to 42° north latitude and 105° to 120° east longitude, with the occurrence time mainly concentrated from September to November. The Archimedean-copula function, constructed based on latitude, longitude, and occurrence time, could precisely determine the spatiotemporal joint probability distribution of drought events (RMSE:0.01). The optimal time-varying nested Archimedean-copula functions were obtained from February to May (Spring), June to September (Summer) and October to January (Autumn and Winter). Compared to the nested Archimedean-copula function, the accuracy of Gibbs sampling and simulation based on time-varying nested Archimedean-copula function was increased by 84.05% latitude and 69.76% longitude. The results provide an effective means for scientific drought forecasting, and water resource management departments can take preventive measures at an early stage.

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Modelling bivariate extreme precipitation distribution for data‐scarce regions using Gumbel–Hougaard copula with maximum entropy estimation

Cited by 36 publications

References 31 publications

Multi-Indicator Evaluation for Extreme Precipitation Events in the Past 60 Years over the Loess Plateau

Multi-Indicator Evaluation for Extreme Precipitation Events in the Past 60 Years over the Loess Plateau

Quantifying the Relationship between Drought and Water Scarcity Using Copulas: Case Study of Beijing–Tianjin–Hebei Metropolitan Areas in China

Study on drought events in China based on time-varying nested Archimedean-copula function

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