An assembled extreme value statistical model of karst spring discharge

Yonghui Fan, Xueli Huo, Yonghong Hao, Yan Liu, Tongke Wang, Youcun Liu, Tian-Chyi J Yeh

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Karst spring discharge processes are complicated and nonstationary, and can be expressed as long-term trends with periodic variation and random fluctuation. Based on the conceptual model, we propose an assembled extreme value statistical model (AEVSM) for obtaining the extreme distribution of spring discharge depletion under effects of extreme climate variability and intense groundwater development. We eliminated the trend and periodicity of spring discharge to acquire the residuals. Using the quantile plot and Kolmogorov-Smirnov methods, it can be demonstrated that the residuals are stationary. The m period return level of the residuals of spring discharge is obtained by using a generalized Pareto distribution (GPD). We thus acquired the spring discharge distribution of extreme values by combining the trend, periodicity and the return level of residuals. We applied an AEVSM to the monthly spring discharge records for Niangziguan Springs in China, from January 1959 to December 2009, and subsequently acquired the spring discharge distribution of extreme values. Results indicate that after November 2014, the depletion rate of Niangziguan Springs discharge will accelerate, and the spring discharge has the risk of flow cessation with probability of 0.01 from December 2021 to October 2023. A 1% probability is admittedly small, but the probability will increase with time. The AEVSM is a robust method for analyzing the distribution of extreme karst spring discharge.

Original languageEnglish (US)
Pages (from-to)57-68
Number of pages12
JournalJournal of Hydrology
Volume504
DOIs
StatePublished - Nov 11 2013

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karst
periodicity
return period
distribution
groundwater
climate

Keywords

  • Assembled extreme value statistical model
  • Flow cessation
  • Generalized Pareto distribution
  • Karst spring
  • Niangziguan Springs

ASJC Scopus subject areas

  • Water Science and Technology

Cite this

An assembled extreme value statistical model of karst spring discharge. / Fan, Yonghui; Huo, Xueli; Hao, Yonghong; Liu, Yan; Wang, Tongke; Liu, Youcun; Yeh, Tian-Chyi J.

In: Journal of Hydrology, Vol. 504, 11.11.2013, p. 57-68.

Research output: Contribution to journalArticle

Fan, Yonghui ; Huo, Xueli ; Hao, Yonghong ; Liu, Yan ; Wang, Tongke ; Liu, Youcun ; Yeh, Tian-Chyi J. / An assembled extreme value statistical model of karst spring discharge. In: Journal of Hydrology. 2013 ; Vol. 504. pp. 57-68.
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AU - Fan, Yonghui

AU - Huo, Xueli

AU - Hao, Yonghong

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AU - Wang, Tongke

AU - Liu, Youcun

AU - Yeh, Tian-Chyi J

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AB - Karst spring discharge processes are complicated and nonstationary, and can be expressed as long-term trends with periodic variation and random fluctuation. Based on the conceptual model, we propose an assembled extreme value statistical model (AEVSM) for obtaining the extreme distribution of spring discharge depletion under effects of extreme climate variability and intense groundwater development. We eliminated the trend and periodicity of spring discharge to acquire the residuals. Using the quantile plot and Kolmogorov-Smirnov methods, it can be demonstrated that the residuals are stationary. The m period return level of the residuals of spring discharge is obtained by using a generalized Pareto distribution (GPD). We thus acquired the spring discharge distribution of extreme values by combining the trend, periodicity and the return level of residuals. We applied an AEVSM to the monthly spring discharge records for Niangziguan Springs in China, from January 1959 to December 2009, and subsequently acquired the spring discharge distribution of extreme values. Results indicate that after November 2014, the depletion rate of Niangziguan Springs discharge will accelerate, and the spring discharge has the risk of flow cessation with probability of 0.01 from December 2021 to October 2023. A 1% probability is admittedly small, but the probability will increase with time. The AEVSM is a robust method for analyzing the distribution of extreme karst spring discharge.

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