Modelling bivariate extreme precipitation distribution for data-scarce regions using Gumbel-Hougaard copula with maximum entropy estimation
Abstract
A new method of parameter estimation in data scarce regions is valuable for bivariate hydrological extreme frequency analysis. Here, this paper proposes a new method of parameter estimation (maximum entropy estimation, MEE) for both Gumbel and Gumbel–Hougaard copula in situations when insufficient data are available. MEE requires only the lower and upper bounds of two hydrological variables. To test our new method, two experiments to model the joint distribution of the maximum daily precipitation at two pairs of stations on the tributaries of Heihe and Jinghe River, respectively, were performed and compared with the method of moments, correlation index estimation, and maximum likelihood estimation, which require a large amount of data. Both experiments show that for the Ye Niugou and Qilian stations, the performance of MEE is nearly identical to those of the conventional methods. For the Xifeng and Huanxian stations, MEE can capture information indicating that the maximum daily precipitation at the Xifeng and Huanxian stations has an upper tail dependence, whereas the results generated by correlation index estimation and maximum likelihood estimation are unreasonable. Moreover, MEE is proved tobe generally reliable and robust by many simulations under three different situations. TheGumbel–Hougaard copula with MEE can also be appliedmore »
- Authors:
-
[2];
- National Univ. of Defense Technology, Nanjing (China). Research Center of Ocean Environment Numerical Simulation, Inst. of Meteorology and Oceanography
- Beijing Normal Univ., Beijing (China). Key Lab. for Water and Sediment Sciences, College of Water Sciences
- Yellow River Inst. of Hydraulic Research, Zhengzhou (China). Yellow River Conservancy Commission
- Argonne National Lab. (ANL), Argonne, IL (United States). Environmental Science Division
- China Inst. of water Resources and Hydropower Research, Beijing (China). State Key Lab. of Simulation and Regulation of Water Cycle in River Basin,
- Publication Date:
- Research Org.:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- National Natural Science Foundation of China (NSFC); USDOE
- OSTI Identifier:
- 1466300
- Grant/Contract Number:
- AC02-06CH11357; 2016YFC0402409; 2016YFC0401407
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Hydrological Processes
- Additional Journal Information:
- Journal Volume: 32; Journal Issue: 2; Journal ID: ISSN 0885-6087
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Gumbel distribution; Gumbel-Hougaard copula; extreme frequency analysis; insufficient data; maximum entropy estimation
Citation Formats
Qian, Longxia, Wang, Hongrui, Dang, Suzhen, Wang, Cheng, Jiao, Zhiqian, and Zhao, Yong. Modelling bivariate extreme precipitation distribution for data-scarce regions using Gumbel-Hougaard copula with maximum entropy estimation. United States: N. p., 2017.
Web. doi:10.1002/hyp.11406.
Qian, Longxia, Wang, Hongrui, Dang, Suzhen, Wang, Cheng, Jiao, Zhiqian, & Zhao, Yong. Modelling bivariate extreme precipitation distribution for data-scarce regions using Gumbel-Hougaard copula with maximum entropy estimation. United States. https://doi.org/10.1002/hyp.11406
Qian, Longxia, Wang, Hongrui, Dang, Suzhen, Wang, Cheng, Jiao, Zhiqian, and Zhao, Yong. Thu .
"Modelling bivariate extreme precipitation distribution for data-scarce regions using Gumbel-Hougaard copula with maximum entropy estimation". United States. https://doi.org/10.1002/hyp.11406. https://www.osti.gov/servlets/purl/1466300.
@article{osti_1466300,
title = {Modelling bivariate extreme precipitation distribution for data-scarce regions using Gumbel-Hougaard copula with maximum entropy estimation},
author = {Qian, Longxia and Wang, Hongrui and Dang, Suzhen and Wang, Cheng and Jiao, Zhiqian and Zhao, Yong},
abstractNote = {A new method of parameter estimation in data scarce regions is valuable for bivariate hydrological extreme frequency analysis. Here, this paper proposes a new method of parameter estimation (maximum entropy estimation, MEE) for both Gumbel and Gumbel–Hougaard copula in situations when insufficient data are available. MEE requires only the lower and upper bounds of two hydrological variables. To test our new method, two experiments to model the joint distribution of the maximum daily precipitation at two pairs of stations on the tributaries of Heihe and Jinghe River, respectively, were performed and compared with the method of moments, correlation index estimation, and maximum likelihood estimation, which require a large amount of data. Both experiments show that for the Ye Niugou and Qilian stations, the performance of MEE is nearly identical to those of the conventional methods. For the Xifeng and Huanxian stations, MEE can capture information indicating that the maximum daily precipitation at the Xifeng and Huanxian stations has an upper tail dependence, whereas the results generated by correlation index estimation and maximum likelihood estimation are unreasonable. Moreover, MEE is proved tobe generally reliable and robust by many simulations under three different situations. TheGumbel–Hougaard copula with MEE can also be applied to the bivariate frequency analysis ofother extreme events in data-scarce regions.},
doi = {10.1002/hyp.11406},
journal = {Hydrological Processes},
number = 2,
volume = 32,
place = {United States},
year = {Thu Nov 23 00:00:00 EST 2017},
month = {Thu Nov 23 00:00:00 EST 2017}
}
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