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Title: Stochastic Simulation of Daily Suspended Sediment Concentration Using Multivariate Copulas

Abstract

Estimation of daily suspended sediment concentration (SSC) is required for water resources and environment management. In this paper, a copula-based stochastic method was proposed for daily SSC simulation. Here, the multivariate copula function, constructed based on a bivariate copula and two bivariate conditional probability distributions, was used to model the temporal and cross dependence structures in daily SSCs. Then, the daily SSCs were generated by sampling from the multivariate conditional distribution. As a result, synthetic long-term SSCs data beyond the limited observation period can be provided for water resources managers, which plays a critical role in accurately estimating frequency and magnitude of extreme SSCs events. The proposed method was under rigorous examination by applying to a case study at Pingshan station in the Jinsha River Basin, China. Results showed that the generated daily SSC sequences not only had a high degree of accuracy in preserving the statistical characteristics of the daily SSC observations, but also captured both the temporal correlation and the cross-correlation between the daily streamflow and daily SSC. Specifically, the average daily relative error values corresponding to mean, standard deviation, skewness, lag-1 temporal correlation, and cross correlation were 0.87%, 4.24%, 7.52%, 0.51% and 2.02%, respectively. The multivariate copulamore » framework proposed here can accurately and efficiently generate long-term daily SSC data for water resources management such as frequency analysis and risk assessment of extreme SSC events.« less

Authors:
ORCiD logo [1];  [1]; ORCiD logo [2];  [1]
  1. North China Electric Power Univ., Beijing (China)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE; National Natural Science Foundation of China (NNSFC); National Key Research and Development Program of China
OSTI Identifier:
1668766
Report Number(s):
PNNL-SA-154307
Journal ID: ISSN 0920-4741
Grant/Contract Number:  
AC05-76RL01830; 51679088; 2016YFC0402309
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Water Resources Management
Additional Journal Information:
Journal Volume: 34; Journal Issue: 12; Journal ID: ISSN 0920-4741
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
Daily suspended sediment concentration; Stochastic simulation; Multivariate Copulas; Joint distribution; Temporal correlation; Cross correlation

Citation Formats

Peng, Yang, Yu, Xianliang, Yan, Hongxiang, and Zhang, Jipeng. Stochastic Simulation of Daily Suspended Sediment Concentration Using Multivariate Copulas. United States: N. p., 2020. Web. doi:10.1007/s11269-020-02652-y.
Peng, Yang, Yu, Xianliang, Yan, Hongxiang, & Zhang, Jipeng. Stochastic Simulation of Daily Suspended Sediment Concentration Using Multivariate Copulas. United States. doi:10.1007/s11269-020-02652-y.
Peng, Yang, Yu, Xianliang, Yan, Hongxiang, and Zhang, Jipeng. Tue . "Stochastic Simulation of Daily Suspended Sediment Concentration Using Multivariate Copulas". United States. doi:10.1007/s11269-020-02652-y.
@article{osti_1668766,
title = {Stochastic Simulation of Daily Suspended Sediment Concentration Using Multivariate Copulas},
author = {Peng, Yang and Yu, Xianliang and Yan, Hongxiang and Zhang, Jipeng},
abstractNote = {Estimation of daily suspended sediment concentration (SSC) is required for water resources and environment management. In this paper, a copula-based stochastic method was proposed for daily SSC simulation. Here, the multivariate copula function, constructed based on a bivariate copula and two bivariate conditional probability distributions, was used to model the temporal and cross dependence structures in daily SSCs. Then, the daily SSCs were generated by sampling from the multivariate conditional distribution. As a result, synthetic long-term SSCs data beyond the limited observation period can be provided for water resources managers, which plays a critical role in accurately estimating frequency and magnitude of extreme SSCs events. The proposed method was under rigorous examination by applying to a case study at Pingshan station in the Jinsha River Basin, China. Results showed that the generated daily SSC sequences not only had a high degree of accuracy in preserving the statistical characteristics of the daily SSC observations, but also captured both the temporal correlation and the cross-correlation between the daily streamflow and daily SSC. Specifically, the average daily relative error values corresponding to mean, standard deviation, skewness, lag-1 temporal correlation, and cross correlation were 0.87%, 4.24%, 7.52%, 0.51% and 2.02%, respectively. The multivariate copula framework proposed here can accurately and efficiently generate long-term daily SSC data for water resources management such as frequency analysis and risk assessment of extreme SSC events.},
doi = {10.1007/s11269-020-02652-y},
journal = {Water Resources Management},
issn = {0920-4741},
number = 12,
volume = 34,
place = {United States},
year = {2020},
month = {8}
}

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