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Open AccessArticle

A Stochastic Simulation Model for Monthly River Flow in Dry Season

1
College of Water Resources and Hydrology, Hohai University, Nanjing 210098, China
2
School of Earth Sciences and Engineering, Hohai University, No.1 Xikang Road, Nanjing 210098, China
3
Shanghai Hydraulic Engineering Group Co., Ltd., Shanghai 201600, China
*
Author to whom correspondence should be addressed.
Water 2018, 10(11), 1654; https://doi.org/10.3390/w10111654
Received: 11 October 2018 / Revised: 5 November 2018 / Accepted: 12 November 2018 / Published: 14 November 2018
(This article belongs to the Special Issue Advances in Hydrogeology: Trend, Model, Methodology and Concepts)
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Abstract

Streamflow simulation gives the major information on water systems to water resources planning and management. The monthly river flows in dry season often exhibit high autocorrelation. The headwater catchment of the Yellow River basin monthly flow series in dry season exhibits this clearly. However, existing models usually fail to capture the high-dimensional, nonlinear dependence. To address this issue, a stochastic model is developed using canonical vine copulas in combination with nonlinear correlation coefficients. Kendall’s tau values of different pairs of river flows are calculated to measure the mutual correlations so as to select correlated streamflows for every month. Canonical vine copula is used to capture the temporal dependence of every month with its correlated streamflows. Finally, monthly river flow by the conditional joint distribution functions conditioned upon the corresponding river flow records was generated. The model was applied to the simulation of monthly river flows in dry season at Tangnaihai station, which controls the streamflow of headwater catchment of Yellow River basin in the north of China. The results of the proposed method possess a smaller mean absolute error (MAE) than the widely-used seasonal autoregressive integrated moving average model. The performance test on seasonal distribution further verifies the great capacity of the stochastic-statistical method. View Full-Text
Keywords: monthly river flow simulation; canonical vine copula; Kendall’s tau value; Akaike information criteria monthly river flow simulation; canonical vine copula; Kendall’s tau value; Akaike information criteria
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Wang, W.; Dong, Z.; Zhu, F.; Cao, Q.; Chen, J.; Yu, X. A Stochastic Simulation Model for Monthly River Flow in Dry Season. Water 2018, 10, 1654.

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